System

Abstract

To provide a system capable of allowing people with physical disabilities to reflect their own will in physical movements.SOLUTION: A system in an embodiment includes a device, an analysis unit, and an apparatus. The device acquires an electroencephalogram. The analysis unit analyzes the electroencephalogram data acquired by the device and identifies the action that the person wants to take. The apparatus performs physical exercises based on the behavior identified by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have the problem that it is difficult for physically disabled people to reflect their own will in their physical movements.

[0005] The system according to the embodiment aims to enable physically disabled people to reflect their own will in their physical movements. [Means for solving the problem]

[0006] A system according to an embodiment includes a device, an analysis unit, and another device. The device acquires electroencephalograms. The analysis unit analyzes the electroencephalogram data acquired by the device and identifies an action that the person wants to take. The device executes physical exercise based on the action identified by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment enables physically disabled people to reflect their own will in their physical movements. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

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

[0028] (Example 1) The AI ​​system according to the embodiment of the present invention is a system that allows a person with a physical disability to express his / her true self to the fullest. This system reads the brainwaves of the person and acquires data, analyzes big data of brainwaves collected from healthy people and people with disabilities by AI, identifies the behavior that the person wants to do, and supports physical movement in conjunction with robot technology. For example, the person wears a device for reading the brainwaves of the person. This device can read brainwaves in real time and acquire data. For example, an brainwave sensor is worn on the head to continuously monitor the brainwaves of the person. This data is sent to the AI ​​system. Next, the AI ​​system analyzes big data of brainwaves collected from healthy people and people with disabilities. The AI ​​identifies the behavior that the person wants to do based on these data. For example, if the person has the intention to move his / her hands, the AI ​​can read and identify that intention from the brainwave data. This makes it possible to accurately grasp the intention of the person. Finally, the system supports physical movement in conjunction with robot technology. The robot executes physical movement based on the behavior that the person wants to do identified by AI. For example, if the person has the intention to move his / her hands, the robot moves his / her hands based on that intention. In this way, the affected person can move their body according to their intention. This system enables people with physical disabilities to move their body in a way that is comparable to that of able-bodied people. The affected person can express their intentions accurately, improving the quality of their daily life. For example, the affected person will be able to feed themselves and enjoy hobbies. In this way, the affected person's independence can be supported and their participation in society can be promoted. In this way, the AI ​​system can enable people with physical disabilities to express themselves to the fullest extent, improving the quality of their daily life.

[0029] The AI ​​system according to the embodiment includes a device for acquiring brain waves, an analysis unit, and a device for executing physical movement. The device for acquiring brain waves reads the brain waves of the person. The brain waves of the person include, for example, EEG (Electroencephalography) data and specific brain wave patterns, but are not limited to such examples. The device for acquiring brain waves, for example, wears an brain wave sensor on the head and acquires brain wave data in real time. For example, the brain wave sensor can continuously monitor the brain waves of the person and acquire data. In addition, the device for acquiring brain waves can transmit the acquired brain wave data to the AI ​​system. The analysis unit analyzes the brain wave data using a large amount of data collected from healthy people and people with disabilities. The large amount of data includes, for example, brain wave data of healthy people and people with disabilities, but are not limited to such examples. The analysis unit identifies the behavior that the person wants to perform based on these data. For example, if the person has an intention to move his / her hands, the analysis unit can read and identify the intention from the brain wave data. The analysis unit can accurately grasp the intention of the person. The device for executing physical movement executes physical movement based on the behavior identified by the analysis unit. For example, if the person has the intention to move his / her hands, the device that executes the physical movement moves his / her hands based on the intention. This allows the person to move his / her body as intended. This allows the AI ​​system according to the embodiment to analyze the brainwave data of the person, identify the behavior that the person wants to do, and execute the physical movement. For example, the output unit executes the movement as intended by the person, improving the quality of life of the person. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can execute the physical movement using an AI model that inputs the behavior identified by the analysis unit and outputs the physical movement.

[0030] The device for acquiring brainwaves reads the brainwaves of the relevant person. The brainwaves of the relevant person include, but are not limited to, EEG (Electroencephalography) data and specific brainwave patterns. The device for acquiring brainwaves, for example, wears an EEG sensor on the head and acquires brainwave data in real time. Specifically, the EEG sensor has a plurality of electrodes that contact the scalp, and detects electrical activity of the brain through these electrodes. As a result, the EEG sensor can continuously monitor the brainwaves of the relevant person and acquire data. The EEG sensor is designed to be lightweight, comfortable to wear, and durable for long-term use. In addition, the EEG sensor has a wireless communication function and can transmit the acquired brainwave data to the AI ​​system in real time. As a result, the brainwave data is sent to the analysis unit without delay, enabling rapid processing. In addition, the EEG sensor has a noise removal function and can minimize interference from the external environment. As a result, the accuracy of the acquired brainwave data is improved, and the reliability of the analysis is increased. The device for acquiring brainwaves acquires the brainwave data of the relevant person with high accuracy and transmits it to the AI ​​system, thereby providing a foundation for accurately understanding the intentions of the relevant person.

[0031] The analysis unit analyzes the electroencephalogram data using a large amount of data collected from healthy people and people with disabilities. The large amount of data includes, for example, electroencephalogram data from healthy people and people with disabilities, but is not limited to such examples. The analysis unit identifies the behavior that the person wants to do based on these data. Specifically, the analysis unit analyzes the electroencephalogram data using a machine learning algorithm to identify the intention of the person. For example, if the person has the intention to move his / her hands, the analysis unit can read and identify the intention from the electroencephalogram data. The analysis unit can accurately grasp the intention of the person by extracting features of the electroencephalogram data and comparing it with past data. Furthermore, the analysis unit uses deep learning technology to analyze complex patterns of the electroencephalogram data and realizes highly accurate intention identification. The analysis unit can analyze the electroencephalogram data in real time and quickly identify the intention of the person. Furthermore, the analysis unit can improve the accuracy of the analysis by accumulating past data and continuously learning. As a result, the analysis unit can accurately and quickly identify the intention of the person and provide information for performing physical movement.

[0032] The device that executes the physical movement executes the physical movement based on the behavior specified by the analysis unit. Specifically, if the person has the intention to move his / her hand, the device that executes the physical movement moves the hand based on that intention. The device that executes the physical movement includes, for example, a robot arm and an exoskeleton device. The robot arm is equipped with a high-precision motor and a sensor, and realizes smooth and accurate movement based on the intention of the person. The exoskeleton device is worn on the body of the person and supports the movement of the body based on the intention of the person. This allows the person to move his / her body according to his / her intention. Furthermore, the device that executes the physical movement can control the movement using an AI model. For example, the device can execute the physical movement using an AI model that inputs the behavior specified by the analysis unit and outputs the physical movement. The AI ​​model can learn based on past data and generate optimal movements. This allows the device that executes the physical movement to realize highly accurate movements based on the intention of the person, improving the quality of life of the person.

[0033] The device for acquiring brainwaves can acquire brainwave data in real time. Real time includes, for example, a delay time in acquiring data and an update frequency, but is not limited to such examples. The device for acquiring brainwaves, for example, wears an brainwave sensor on the head and acquires brainwave data in real time. For example, the brainwave sensor can continuously monitor the brainwaves of the person and acquire data. In addition, the device for acquiring brainwaves can transmit the acquired brainwave data to an AI system. As a result, by acquiring brainwave data in real time, immediate analysis and response are possible. A part or all of the above-mentioned processing in the device for acquiring brainwaves may be performed, for example, using AI, or may be performed without using AI. For example, the device for acquiring brainwaves can input the brainwave data acquired in real time to a generation AI and have the generation AI execute analysis of the data.

[0034] The analysis unit can analyze the electroencephalogram data using a large amount of data collected from healthy people and people with disabilities. The large amount of data includes, for example, electroencephalogram data from healthy people and people with disabilities, but is not limited to such examples. The analysis unit identifies the behavior that the person wants to do based on these data. For example, if the person has an intention to move his / her hands, the analysis unit can read and identify the intention from the electroencephalogram data. The analysis unit can accurately grasp the intention of the person. As a result, the accuracy of the analysis is improved by using big data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input a large amount of electroencephalogram data collected from healthy people and people with disabilities to the generation AI and have the generation AI perform analysis of the data.

[0035] The analysis unit can identify the intention of the relevant person. The intention includes, for example, the purpose and motivation of the action, but is not limited to such examples. The analysis unit analyzes the brainwave data of the relevant person to identify the intention of the relevant person. For example, if the relevant person has the intention to move his / her hands, the analysis unit can read and identify the intention from the brainwave data. The analysis unit can accurately grasp the intention of the relevant person. As a result, the intention of the relevant person can be accurately identified, and appropriate physical movement can be performed. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the brainwave data of the relevant person to the generation AI and cause the generation AI to identify the intention of the relevant person.

[0036] The device that executes the physical exercise can execute the physical exercise based on the intention of the person. Based on the intention includes, for example, a method of detecting the intention and an action corresponding to the intention, but is not limited to such examples. The device that executes the physical exercise executes the physical exercise based on the action specified by the analysis unit. For example, if the person has the intention to move his / her hand, the device that executes the physical exercise moves his / her hand based on the intention. This allows the person to move his / her body according to his / her intention. This allows the person to move his / her body according to his / her intention by executing the physical exercise based on the intention of the person. A part or all of the above-mentioned processing in the device that executes the physical exercise may be performed using, for example, AI, or may be performed without using AI. For example, the device that executes the physical exercise can input the action specified by the analysis unit to the generation AI and have the generation AI execute the execution of the physical exercise.

[0037] The device for acquiring brain waves can select the optimal acquisition method by referring to the user's past brain wave data when acquiring brain waves. The past brain wave data includes, for example, the period of past data and the method of storing data, but is not limited to such examples. The device for acquiring brain waves selects the optimal acquisition method by referring to the user's past brain wave data when acquiring brain waves. For example, the device analyzes the user's past brain wave data and selects the most stable acquisition method. In addition, the device can select the optimal acquisition method for a specific time period from the user's past brain wave data. In addition, the device can select the optimal acquisition method under specific environmental conditions based on the user's past brain wave data. In this way, the optimal acquisition method can be selected by referring to the past brain wave data. Part or all of the above-mentioned processing in the device for acquiring brain waves may be performed, for example, using AI, or may be performed without using AI. For example, the device for acquiring brain waves can input past brain wave data to a generation AI and cause the generation AI to select the optimal acquisition method.

[0038] The device for acquiring electroencephalograms can perform filtering based on the user's current activity status and environment when acquiring electroencephalograms. The activity status includes, for example, a situation classification such as exercising or resting, but is not limited to such examples. The environment includes, for example, indoors, outdoors, noise level, etc., but is not limited to such examples. The device for acquiring electroencephalograms performs filtering based on the user's current activity status and environment when acquiring electroencephalograms. For example, when the user is in a quiet environment, noise filtering is minimized to acquire electroencephalogram data. Also, when the user is in a noisy environment, noise filtering is strengthened to acquire electroencephalogram data. Also, when the user is performing a specific activity, electroencephalogram data related to the activity is preferentially acquired. As a result, by performing filtering based on the current activity status and environment, data with less noise can be acquired. Part or all of the above-mentioned processing in the device for acquiring electroencephalograms may be performed using, for example, AI, or may be performed without using AI. For example, the device for acquiring electroencephalograms can input the current activity status and environmental data to the generation AI and cause the generation AI to execute filtering.

[0039] The device for acquiring electroencephalograms can preferentially acquire highly relevant data based on the geographical location information of the user when acquiring electroencephalograms. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. The highly relevant data includes, for example, data that is geographically close and data in the same environment, but is not limited to such examples. The device for acquiring electroencephalograms preferentially acquires highly relevant data based on the geographical location information of the user when acquiring electroencephalograms. For example, when the user is at home, the device preferentially acquires electroencephalogram data at home. Also, when the user is at work, the device preferentially acquires electroencephalogram data at the work place. Also, when the user is out, the device preferentially acquires electroencephalogram data at the out-of-home location. As a result, more useful data can be collected by preferentially acquiring highly relevant data based on the geographical location information. Part or all of the above-mentioned processing in the device for acquiring electroencephalograms may be performed, for example, using AI, or may be performed without using AI. For example, the device for acquiring electroencephalograms can input geographical location information to a generation AI and cause the generation AI to execute preferential acquisition of highly relevant data.

[0040] The device for acquiring brainwaves can analyze the social media activity of the user at the time of acquiring brainwaves and acquire related brainwave data. The social media activity includes, for example, the content of posts, the number of likes, and comments, but is not limited to such examples. The related brainwave data includes, for example, the brainwave pattern related to a specific activity, but is not limited to such examples. The device for acquiring brainwaves can analyze the social media activity of the user at the time of acquiring brainwaves and acquire related brainwave data. For example, when the user is relaxed on social media, the device preferentially acquires brainwave data at that time. Also, when the user is nervous on social media, the device preferentially acquires brainwave data at that time. Also, when the user is concentrating on social media, the device preferentially acquires brainwave data at that time. In this way, the related brainwave data can be acquired by analyzing the social media activity. Part or all of the above-mentioned processing in the device for acquiring brainwaves may be performed, for example, using AI, or may be performed without using AI. For example, the device for acquiring brainwaves can input social media activity data to a generation AI and cause the generation AI to acquire related brainwave data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the electroencephalogram data during the analysis. The level of importance includes, for example, the reliability and the degree of influence of the data, but is not limited to such examples. The level of detail includes, for example, the depth of the analysis and the number of analysis items, but is not limited to such examples. The analysis unit adjusts the level of detail of the analysis based on the importance of the electroencephalogram data during the analysis. For example, a detailed analysis is performed on electroencephalogram data with high importance. Also, a simple analysis is performed on electroencephalogram data with low importance. Also, an analysis with a moderate level of detail is performed on electroencephalogram data with medium importance. This allows efficient analysis by adjusting the level of detail of the analysis based on the importance of the electroencephalogram data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the electroencephalogram data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis methods depending on the category of the electroencephalogram data during analysis. The categories include, for example, electroencephalogram frequency bands and specific electroencephalogram patterns, but are not limited to such examples. The analysis methods include, for example, spectrum analysis and time domain analysis, but are not limited to such examples. The analysis unit applies different analysis methods depending on the category of the electroencephalogram data during analysis. For example, an analysis method dedicated to relaxation is applied to electroencephalogram data when relaxed. Also, an analysis method dedicated to tension is applied to electroencephalogram data when tense. Also, an analysis method dedicated to concentration is applied to electroencephalogram data when concentrating. In this way, appropriate analysis is possible by applying different analysis methods depending on the category of the electroencephalogram data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the category of the electroencephalogram data to the generation AI and cause the generation AI to apply the analysis method.

[0043] The analysis unit can determine the priority of the analysis based on the collection time of the electroencephalogram data during the analysis. The collection time includes, for example, the freshness of the data and the collection date and time, but is not limited to such examples. The priority includes, for example, the importance of the data and the recentness of the collection time, but is not limited to such examples. The analysis unit determines the priority of the analysis based on the collection time of the electroencephalogram data during the analysis. For example, the latest electroencephalogram data is analyzed preferentially. Also, the latest data is analyzed while referring to past electroencephalogram data. Also, the electroencephalogram data collected during a specific period is analyzed preferentially. As a result, the latest data can be analyzed preferentially by determining the priority of the analysis based on the collection time of the electroencephalogram data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collection time data to the generation AI and cause the generation AI to execute the analysis priority determination.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the electroencephalogram data during analysis. The relevance includes, for example, correlation and common features of the data, but is not limited to such examples. The order includes, for example, analysis priority and processing order, but is not limited to such examples. The analysis unit adjusts the order of analysis based on the relevance of the electroencephalogram data during analysis. For example, electroencephalogram data with high relevance is analyzed preferentially. Also, electroencephalogram data with medium relevance is analyzed next. Also, electroencephalogram data with low relevance is analyzed last. By this, the order of analysis is adjusted based on the relevance of the electroencephalogram data, and efficient analysis is possible. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the electroencephalogram data to the generation AI and cause the generation AI to adjust the order of analysis.

[0045] The device for performing physical exercise can select an optimal exercise method by referring to the user's past exercise history when performing physical exercise. The past exercise history includes, for example, the frequency of exercise and the type of exercise, but is not limited to such examples. The appropriate exercise method includes, for example, the effect of exercise and the safety of exercise, but is not limited to such examples. The device for performing physical exercise selects an optimal exercise method by referring to the user's past exercise history when performing physical exercise. For example, the device analyzes the user's past exercise history and selects the most effective exercise method. In addition, a specific exercise method is preferentially selected from the user's past exercise history. In addition, an optimal exercise method under specific environmental conditions is selected based on the user's past exercise history. In this way, the optimal exercise method can be selected by referring to the past exercise history. Part or all of the above-mentioned processing in the device for performing physical exercise may be performed, for example, using AI, or may be performed without using AI. For example, the device for performing physical exercise can input past exercise history data to a generation AI and cause the generation AI to select an optimal exercise method.

[0046] The device for performing physical exercise can customize the means of exercise based on the user's current physical condition when performing physical exercise. The physical condition includes, for example, a physical strength level and a health condition, but is not limited to such examples. The customization includes, for example, adjustment of the intensity of exercise and selection of the type of exercise, but is not limited to such examples. The device for performing physical exercise customizes the means of exercise based on the user's current physical condition when performing physical exercise. For example, when the user is tired, light exercise is performed. Also, when the user is energetic, high-intensity exercise is performed. Also, when the user is in a specific physical condition, exercise according to the condition is performed. In this way, by customizing the means of exercise based on the current physical condition, more appropriate exercise is possible. Part or all of the above-mentioned processing in the device for performing physical exercise may be performed using, for example, AI, or may be performed without using AI. For example, the device for performing physical exercise can input current physical condition data to a generation AI and have the generation AI execute customization of the exercise means.

[0047] The device that performs physical exercise can select an appropriate exercise method based on the geographical location information of the user when performing the physical exercise. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. The appropriate exercise method includes, for example, the effect of exercise and the safety of exercise, but is not limited to such examples. The device that performs physical exercise selects an appropriate exercise method based on the geographical location information of the user when performing the physical exercise. For example, when the user is at home, an exercise method that can be performed at home is selected. Also, when the user is at work, an exercise method that can be performed at the work place is selected. Also, when the user is out, an exercise method that can be performed at the out-of-home location is selected. As a result, more appropriate exercise is possible by selecting an appropriate exercise method based on the geographical location information. Part or all of the above-mentioned processing in the device that performs physical exercise may be performed using, for example, AI, or may be performed without using AI. For example, the device that performs physical exercise can input geographical location information to a generation AI and cause the generation AI to select an exercise method.

[0048] The device for performing physical exercise can analyze the social media activity of the user when performing the physical exercise and suggest a means of exercise. The social media activity includes, for example, the content of posts, the number of likes, and comments, but is not limited to such examples. The means of exercise includes, for example, the type of exercise and the intensity of exercise, but is not limited to such examples. The device for performing physical exercise analyzes the social media activity of the user when performing the physical exercise and suggests a means of exercise. For example, if the user is relaxing on social media, the device suggests a method of exercise at that time. Also, if the user is nervous on social media, the device suggests a method of exercise at that time. Also, if the user is concentrating on social media, the device suggests a method of exercise at that time. In this way, by analyzing the social media activity, it is possible to suggest a related means of exercise. Part or all of the above-mentioned processing in the device for performing physical exercise may be performed, for example, using AI, or may be performed without using AI. For example, the device for performing physical exercise can input social media activity data to a generation AI and have the generation AI execute a suggestion of a means of exercise.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] When analyzing the user's electroencephalogram data, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral history. For example, if the user frequently performed a specific behavior in the past, the analysis unit preferentially analyzes the electroencephalogram pattern related to that behavior. In addition, the analysis unit can analyze behavioral patterns during a specific time period from the user's past behavioral history. Furthermore, the analysis unit can analyze behavioral patterns under specific environmental conditions based on the user's past behavioral history. In this way, the analysis accuracy can be improved by referring to the past behavioral history.

[0051] When acquiring a user's brainwave data, the device for acquiring brainwaves can monitor the user's current health condition and adjust the frequency and method of data acquisition according to the health condition. For example, if the user is tired, the frequency of data acquisition can be set low and data can be acquired for a long period of time. Also, if the user is in good health, the frequency of data acquisition can be set high and detailed data can be acquired in a short period of time. Also, if the user is in a particular health condition, a data acquisition method according to that condition can be selected. In this way, more appropriate data can be acquired by adjusting the frequency and method of data acquisition according to the health condition.

[0052] When analyzing the user's electroencephalogram data, the analysis unit can adjust the analysis algorithm taking into account the user's current activity state. For example, if the user is relaxed, an analysis algorithm specialized for electroencephalogram data when relaxed is applied. If the user is tense, an analysis algorithm specialized for electroencephalogram data when tense is applied. If the user is concentrating, an analysis algorithm specialized for electroencephalogram data when concentrating is applied. In this way, by adjusting the analysis algorithm according to the user's activity state, more accurate analysis is possible.

[0053] The device for acquiring electroencephalograms can preferentially acquire electroencephalogram data at a specific location based on the geographical location information of the user. For example, when the user is at home, the device preferentially acquires electroencephalogram data at home. When the user is at work, the device preferentially acquires electroencephalogram data at work. When the user is out, the device preferentially acquires electroencephalogram data at the outside location. This allows more useful data to be collected by preferentially acquiring data that is highly relevant based on the geographical location information.

[0054] When analyzing the user's electroencephalogram data, the analysis unit can analyze the user's social media activities and prioritize analysis of related electroencephalogram data. For example, if the user is relaxed on social media, the analysis unit prioritizes analysis of the electroencephalogram data at that time. Also, if the user is nervous on social media, the analysis unit prioritizes analysis of the electroencephalogram data at that time. Also, if the user is concentrating on social media, the analysis unit prioritizes analysis of the electroencephalogram data at that time. In this way, by analyzing social media activities, it is possible to prioritize analysis of related electroencephalogram data.

[0055] A device for performing physical exercise can select an optimal exercise method by referring to a user's past exercise history. For example, the device analyzes the user's past exercise history and selects the most effective exercise method. Also, a specific exercise method is preferentially selected from the user's past exercise history. Also, the device can select an optimal exercise method under specific environmental conditions based on the user's past exercise history. In this way, the device can select an optimal exercise method by referring to the past exercise history.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The device for acquiring brainwaves reads the brainwaves of the person in question. For example, an EEG sensor is attached to the head and acquires brainwave data in real time. The EEG sensor can continuously monitor the brainwaves of the person in question and acquire data. The acquired brainwave data can also be transmitted to the AI ​​system. Step 2: The analysis unit uses large amounts of data collected from able-bodied and disabled people to analyze the EEG data and identify the action the person wants to take. For example, if the person wants to move their hands, the analysis unit can read and identify that intention from the EEG data. Step 3: The device for executing the physical movement executes the physical movement based on the action identified by the analysis unit. For example, if the person has an intention to move his / her hand, the device for executing the physical movement moves his / her hand based on the intention.

[0058] (Example 2) The AI ​​system according to the embodiment of the present invention is a system that allows a person with a physical disability to express his / her true self to the fullest. This system reads the brainwaves of the person and acquires data, analyzes big data of brainwaves collected from healthy people and people with disabilities by AI, identifies the behavior that the person wants to do, and supports physical movement in conjunction with robot technology. For example, the person wears a device for reading the brainwaves of the person. This device can read brainwaves in real time and acquire data. For example, an brainwave sensor is worn on the head to continuously monitor the brainwaves of the person. This data is sent to the AI ​​system. Next, the AI ​​system analyzes big data of brainwaves collected from healthy people and people with disabilities. The AI ​​identifies the behavior that the person wants to do based on these data. For example, if the person has the intention to move his / her hands, the AI ​​can read and identify that intention from the brainwave data. This makes it possible to accurately grasp the intention of the person. Finally, the system supports physical movement in conjunction with robot technology. The robot executes physical movement based on the behavior that the person wants to do identified by AI. For example, if the person has the intention to move his / her hands, the robot moves his / her hands based on that intention. In this way, the affected person can move their body according to their intention. This system enables people with physical disabilities to move their body in a way that is comparable to that of able-bodied people. The affected person can express their intentions accurately, improving the quality of their daily life. For example, the affected person will be able to feed themselves and enjoy hobbies. In this way, the affected person's independence can be supported and their participation in society can be promoted. In this way, the AI ​​system can enable people with physical disabilities to express themselves to the fullest extent, improving the quality of their daily life.

[0059] The AI ​​system according to the embodiment includes a device for acquiring brain waves, an analysis unit, and a device for executing physical movement. The device for acquiring brain waves reads the brain waves of the person. The brain waves of the person include, for example, EEG (Electroencephalography) data and specific brain wave patterns, but are not limited to such examples. The device for acquiring brain waves, for example, wears an brain wave sensor on the head and acquires brain wave data in real time. For example, the brain wave sensor can continuously monitor the brain waves of the person and acquire data. In addition, the device for acquiring brain waves can transmit the acquired brain wave data to the AI ​​system. The analysis unit analyzes the brain wave data using a large amount of data collected from healthy people and people with disabilities. The large amount of data includes, for example, brain wave data of healthy people and people with disabilities, but are not limited to such examples. The analysis unit identifies the behavior that the person wants to perform based on these data. For example, if the person has an intention to move his / her hands, the analysis unit can read and identify the intention from the brain wave data. The analysis unit can accurately grasp the intention of the person. The device for executing physical movement executes physical movement based on the behavior identified by the analysis unit. For example, if the person has the intention to move his / her hands, the device that executes the physical movement moves his / her hands based on the intention. This allows the person to move his / her body as intended. This allows the AI ​​system according to the embodiment to analyze the brainwave data of the person, identify the behavior that the person wants to do, and execute the physical movement. For example, the output unit executes the movement as intended by the person, improving the quality of life of the person. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can execute the physical movement using an AI model that inputs the behavior identified by the analysis unit and outputs the physical movement.

[0060] The device for acquiring brainwaves reads the brainwaves of the relevant person. The brainwaves of the relevant person include, but are not limited to, EEG (Electroencephalography) data and specific brainwave patterns. The device for acquiring brainwaves, for example, wears an EEG sensor on the head and acquires brainwave data in real time. Specifically, the EEG sensor has a plurality of electrodes that contact the scalp, and detects electrical activity of the brain through these electrodes. As a result, the EEG sensor can continuously monitor the brainwaves of the relevant person and acquire data. The EEG sensor is designed to be lightweight, comfortable to wear, and durable for long-term use. In addition, the EEG sensor has a wireless communication function and can transmit the acquired brainwave data to the AI ​​system in real time. As a result, the brainwave data is sent to the analysis unit without delay, enabling rapid processing. In addition, the EEG sensor has a noise removal function and can minimize interference from the external environment. As a result, the accuracy of the acquired brainwave data is improved, and the reliability of the analysis is increased. The device for acquiring brainwaves acquires the brainwave data of the relevant person with high accuracy and transmits it to the AI ​​system, thereby providing a foundation for accurately understanding the intentions of the relevant person.

[0061] The analysis unit analyzes the electroencephalogram data using a large amount of data collected from healthy people and people with disabilities. The large amount of data includes, for example, electroencephalogram data from healthy people and people with disabilities, but is not limited to such examples. The analysis unit identifies the behavior that the person wants to do based on these data. Specifically, the analysis unit analyzes the electroencephalogram data using a machine learning algorithm to identify the intention of the person. For example, if the person has the intention to move his / her hands, the analysis unit can read and identify the intention from the electroencephalogram data. The analysis unit can accurately grasp the intention of the person by extracting features of the electroencephalogram data and comparing it with past data. Furthermore, the analysis unit uses deep learning technology to analyze complex patterns of the electroencephalogram data and realizes highly accurate intention identification. The analysis unit can analyze the electroencephalogram data in real time and quickly identify the intention of the person. Furthermore, the analysis unit can improve the accuracy of the analysis by accumulating past data and continuously learning. As a result, the analysis unit can accurately and quickly identify the intention of the person and provide information for performing physical movement.

[0062] The device that executes the physical movement executes the physical movement based on the behavior specified by the analysis unit. Specifically, if the person has the intention to move his / her hand, the device that executes the physical movement moves the hand based on that intention. The device that executes the physical movement includes, for example, a robot arm and an exoskeleton device. The robot arm is equipped with a high-precision motor and a sensor, and realizes smooth and accurate movement based on the intention of the person. The exoskeleton device is worn on the body of the person and supports the movement of the body based on the intention of the person. This allows the person to move his / her body according to his / her intention. Furthermore, the device that executes the physical movement can control the movement using an AI model. For example, the device can execute the physical movement using an AI model that inputs the behavior specified by the analysis unit and outputs the physical movement. The AI ​​model can learn based on past data and generate optimal movements. This allows the device that executes the physical movement to realize highly accurate movements based on the intention of the person, improving the quality of life of the person.

[0063] The device for acquiring brainwaves can acquire brainwave data in real time. Real time includes, for example, a delay time in acquiring data and an update frequency, but is not limited to such examples. The device for acquiring brainwaves, for example, wears an brainwave sensor on the head and acquires brainwave data in real time. For example, the brainwave sensor can continuously monitor the brainwaves of the person and acquire data. In addition, the device for acquiring brainwaves can transmit the acquired brainwave data to an AI system. As a result, by acquiring brainwave data in real time, immediate analysis and response are possible. A part or all of the above-mentioned processing in the device for acquiring brainwaves may be performed, for example, using AI, or may be performed without using AI. For example, the device for acquiring brainwaves can input the brainwave data acquired in real time to a generation AI and have the generation AI execute analysis of the data.

[0064] The analysis unit can analyze the electroencephalogram data using a large amount of data collected from healthy people and people with disabilities. The large amount of data includes, for example, electroencephalogram data from healthy people and people with disabilities, but is not limited to such examples. The analysis unit identifies the behavior that the person wants to do based on these data. For example, if the person has an intention to move his / her hands, the analysis unit can read and identify the intention from the electroencephalogram data. The analysis unit can accurately grasp the intention of the person. As a result, the accuracy of the analysis is improved by using big data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input a large amount of electroencephalogram data collected from healthy people and people with disabilities to the generation AI and have the generation AI perform analysis of the data.

[0065] The analysis unit can identify the intention of the relevant person. The intention includes, for example, the purpose and motivation of the action, but is not limited to such examples. The analysis unit analyzes the brainwave data of the relevant person to identify the intention of the relevant person. For example, if the relevant person has the intention to move his / her hands, the analysis unit can read and identify the intention from the brainwave data. The analysis unit can accurately grasp the intention of the relevant person. As a result, the intention of the relevant person can be accurately identified, and appropriate physical movement can be performed. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the brainwave data of the relevant person to the generation AI and cause the generation AI to identify the intention of the relevant person.

[0066] The device that executes the physical exercise can execute the physical exercise based on the intention of the person. Based on the intention includes, for example, a method of detecting the intention and an action corresponding to the intention, but is not limited to such examples. The device that executes the physical exercise executes the physical exercise based on the action specified by the analysis unit. For example, if the person has the intention to move his / her hand, the device that executes the physical exercise moves his / her hand based on the intention. This allows the person to move his / her body according to his / her intention. This allows the person to move his / her body according to his / her intention by executing the physical exercise based on the intention of the person. A part or all of the above-mentioned processing in the device that executes the physical exercise may be performed using, for example, AI, or may be performed without using AI. For example, the device that executes the physical exercise can input the action specified by the analysis unit to the generation AI and have the generation AI execute the execution of the physical exercise.

[0067] The device for acquiring electroencephalograms can estimate the emotion of a user and adjust the timing of acquiring electroencephalogram data based on the estimated emotion of the user. The emotion includes, for example, emotion classifications such as joy, sadness, and anger, but is not limited to such examples. The device for acquiring electroencephalograms can estimate the emotion of a user and adjust the timing of acquiring electroencephalogram data based on the estimated emotion of the user. For example, when a user is relaxed, the frequency of acquiring electroencephalogram data is set low and data is acquired for a long period of time. When a user is tense, the frequency of acquiring electroencephalogram data is set high and detailed data is acquired in a short period of time. When a user is concentrating, electroencephalogram data is acquired during a specific task and data related to the task is preferentially collected. In this way, by adjusting the timing of acquiring electroencephalogram data according to the emotion of the user, more appropriate data can be acquired. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the device for acquiring electroencephalograms may be performed using, for example, an AI, or may be performed without using an AI. For example, a device that acquires brainwaves can input a user's emotional data into a generation AI, causing the generation AI to estimate emotions.

[0068] The device for acquiring brain waves can select the optimal acquisition method by referring to the user's past brain wave data when acquiring brain waves. The past brain wave data includes, for example, the period of past data and the method of storing data, but is not limited to such examples. The device for acquiring brain waves selects the optimal acquisition method by referring to the user's past brain wave data when acquiring brain waves. For example, the device analyzes the user's past brain wave data and selects the most stable acquisition method. In addition, the device can select the optimal acquisition method for a specific time period from the user's past brain wave data. In addition, the device can select the optimal acquisition method under specific environmental conditions based on the user's past brain wave data. In this way, the optimal acquisition method can be selected by referring to the past brain wave data. Part or all of the above-mentioned processing in the device for acquiring brain waves may be performed, for example, using AI, or may be performed without using AI. For example, the device for acquiring brain waves can input past brain wave data to a generation AI and cause the generation AI to select the optimal acquisition method.

[0069] The device for acquiring electroencephalograms can perform filtering based on the user's current activity status and environment when acquiring electroencephalograms. The activity status includes, for example, a situation classification such as exercising or resting, but is not limited to such examples. The environment includes, for example, indoors, outdoors, noise level, etc., but is not limited to such examples. The device for acquiring electroencephalograms performs filtering based on the user's current activity status and environment when acquiring electroencephalograms. For example, when the user is in a quiet environment, noise filtering is minimized to acquire electroencephalogram data. Also, when the user is in a noisy environment, noise filtering is strengthened to acquire electroencephalogram data. Also, when the user is performing a specific activity, electroencephalogram data related to the activity is preferentially acquired. As a result, by performing filtering based on the current activity status and environment, data with less noise can be acquired. Part or all of the above-mentioned processing in the device for acquiring electroencephalograms may be performed using, for example, AI, or may be performed without using AI. For example, the device for acquiring electroencephalograms can input the current activity status and environmental data to the generation AI and cause the generation AI to execute filtering.

[0070] The device for acquiring electroencephalograms can estimate the emotion of a user and determine the priority of electroencephalogram data to be acquired based on the estimated emotion of the user. The emotion includes, for example, emotion classifications such as joy, sadness, and anger, but is not limited to such examples. The device for acquiring electroencephalograms can estimate the emotion of a user and determine the priority of electroencephalogram data to be acquired based on the estimated emotion of the user. For example, when a user is relaxed, the device preferentially acquires electroencephalogram data at the time of relaxation. Also, when a user is tense, the device preferentially acquires electroencephalogram data at the time of tension. Also, when a user is concentrating, the device preferentially acquires electroencephalogram data at the time of concentration. In this way, important data can be preferentially acquired by determining the priority of electroencephalogram data based on the emotion of the user. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the device for acquiring electroencephalograms may be performed using, for example, an AI, or may be performed without using an AI. For example, a device that acquires brainwaves can input a user's emotional data into a generation AI, allowing the generation AI to prioritize data based on emotions.

[0071] The device for acquiring electroencephalograms can preferentially acquire highly relevant data based on the geographical location information of the user when acquiring electroencephalograms. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. The highly relevant data includes, for example, data that is geographically close and data in the same environment, but is not limited to such examples. The device for acquiring electroencephalograms preferentially acquires highly relevant data based on the geographical location information of the user when acquiring electroencephalograms. For example, when the user is at home, the device preferentially acquires electroencephalogram data at home. Also, when the user is at work, the device preferentially acquires electroencephalogram data at the work place. Also, when the user is out, the device preferentially acquires electroencephalogram data at the out-of-home location. As a result, more useful data can be collected by preferentially acquiring highly relevant data based on the geographical location information. Part or all of the above-mentioned processing in the device for acquiring electroencephalograms may be performed, for example, using AI, or may be performed without using AI. For example, the device for acquiring electroencephalograms can input geographical location information to a generation AI and cause the generation AI to execute preferential acquisition of highly relevant data.

[0072] The device for acquiring brainwaves can analyze the social media activity of the user at the time of acquiring brainwaves and acquire related brainwave data. The social media activity includes, for example, the content of posts, the number of likes, and comments, but is not limited to such examples. The related brainwave data includes, for example, the brainwave pattern related to a specific activity, but is not limited to such examples. The device for acquiring brainwaves can analyze the social media activity of the user at the time of acquiring brainwaves and acquire related brainwave data. For example, when the user is relaxed on social media, the device preferentially acquires brainwave data at that time. Also, when the user is nervous on social media, the device preferentially acquires brainwave data at that time. Also, when the user is concentrating on social media, the device preferentially acquires brainwave data at that time. In this way, the related brainwave data can be acquired by analyzing the social media activity. Part or all of the above-mentioned processing in the device for acquiring brainwaves may be performed, for example, using AI, or may be performed without using AI. For example, the device for acquiring brainwaves can input social media activity data to a generation AI and cause the generation AI to acquire related brainwave data.

[0073] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. The emotion includes, for example, emotion classifications such as joy, sadness, and anger, but is not limited to such examples. The analysis algorithm includes, for example, a machine learning algorithm and a parameter adjustment method, but is not limited to such examples. The analysis unit estimates the user's emotion and adjusts the analysis algorithm based on the estimated user's emotion. For example, when the user is relaxed, an analysis algorithm specialized for EEG data at the time of relaxation is applied. Also, when the user is tense, an analysis algorithm specialized for EEG data at the time of tension is applied. Also, when the user is concentrating, an analysis algorithm specialized for EEG data at the time of concentration is applied. This allows for more accurate analysis by adjusting the analysis algorithm based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotional data into the generation AI and cause the generation AI to adjust the algorithm based on the emotion.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the electroencephalogram data during the analysis. The level of importance includes, for example, the reliability and the degree of influence of the data, but is not limited to such examples. The level of detail includes, for example, the depth of the analysis and the number of analysis items, but is not limited to such examples. The analysis unit adjusts the level of detail of the analysis based on the importance of the electroencephalogram data during the analysis. For example, a detailed analysis is performed on electroencephalogram data with high importance. Also, a simple analysis is performed on electroencephalogram data with low importance. Also, an analysis with a moderate level of detail is performed on electroencephalogram data with medium importance. This allows efficient analysis by adjusting the level of detail of the analysis based on the importance of the electroencephalogram data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the electroencephalogram data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0075] The analysis unit can apply different analysis methods depending on the category of the electroencephalogram data during analysis. The categories include, for example, electroencephalogram frequency bands and specific electroencephalogram patterns, but are not limited to such examples. The analysis methods include, for example, spectrum analysis and time domain analysis, but are not limited to such examples. The analysis unit applies different analysis methods depending on the category of the electroencephalogram data during analysis. For example, an analysis method dedicated to relaxation is applied to electroencephalogram data in a relaxed state. Also, an analysis method dedicated to tension is applied to electroencephalogram data in a tense state. Also, an analysis method dedicated to concentration is applied to electroencephalogram data in a concentrated state. In this way, by applying different analysis methods depending on the category of the electroencephalogram data, appropriate analysis is possible. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the category of the electroencephalogram data to the generation AI and cause the generation AI to apply the analysis method.

[0076] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The emotion includes, for example, emotion classification such as joy, sadness, and anger, but is not limited to such examples. The display method includes, for example, graph display, text display, and interactive display, but is not limited to such examples. The analysis unit estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. For example, when the user is relaxed, a detailed analysis result is displayed. Also, when the user is nervous, a concise analysis result is displayed. Also, when the user is concentrating, a visually easy-to-understand analysis result is displayed. In this way, by adjusting the display method of the analysis result based on the user's emotion, it is possible to display it in a way that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotional data into the generation AI and have the generation AI adjust the display method.

[0077] The analysis unit can determine the priority of the analysis based on the collection time of the electroencephalogram data during the analysis. The collection time includes, for example, the freshness of the data and the collection date and time, but is not limited to such examples. The priority includes, for example, the importance of the data and the recentness of the collection time, but is not limited to such examples. The analysis unit determines the priority of the analysis based on the collection time of the electroencephalogram data during the analysis. For example, the latest electroencephalogram data is analyzed preferentially. Also, the latest data is analyzed while referring to past electroencephalogram data. Also, the electroencephalogram data collected during a specific period is analyzed preferentially. As a result, the latest data can be analyzed preferentially by determining the priority of the analysis based on the collection time of the electroencephalogram data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collection time data to the generation AI and cause the generation AI to execute the analysis priority determination.

[0078] The analysis unit can adjust the order of analysis based on the relevance of the electroencephalogram data during analysis. The relevance includes, for example, correlation and common features of the data, but is not limited to such examples. The order includes, for example, analysis priority and processing order, but is not limited to such examples. The analysis unit adjusts the order of analysis based on the relevance of the electroencephalogram data during analysis. For example, electroencephalogram data with high relevance is analyzed preferentially. Also, electroencephalogram data with medium relevance is analyzed next. Also, electroencephalogram data with low relevance is analyzed last. By this, the order of analysis is adjusted based on the relevance of the electroencephalogram data, and efficient analysis is possible. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the electroencephalogram data to the generation AI and cause the generation AI to adjust the order of analysis.

[0079] The device for performing physical exercise can estimate the user's emotion and adjust the execution method of the physical exercise based on the estimated user's emotion. The emotion includes, for example, emotion classifications such as joy, sadness, and anger, but is not limited to such examples. The execution method includes, for example, the intensity of the exercise and the type of the exercise, but is not limited to such examples. The device for performing physical exercise can estimate the user's emotion and adjust the execution method of the physical exercise based on the estimated user's emotion. For example, when the user is relaxed, a relaxed movement is performed. Also, when the user is tense, a quick and accurate movement is performed. Also, when the user is concentrating, a fine movement is performed. As a result, by adjusting the execution method of the physical exercise based on the user's emotion, more appropriate exercise is possible. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the device for performing physical exercise may be performed using, for example, an AI, or may be performed without using an AI. For example, a device that performs physical exercise can input a user's emotional data into a generation AI and have the generation AI adjust the execution method.

[0080] The device for performing physical exercise can select an optimal exercise method by referring to the user's past exercise history when performing physical exercise. The past exercise history includes, for example, the frequency of exercise and the type of exercise, but is not limited to such examples. The appropriate exercise method includes, for example, the effect of exercise and the safety of exercise, but is not limited to such examples. The device for performing physical exercise selects an optimal exercise method by referring to the user's past exercise history when performing physical exercise. For example, the device analyzes the user's past exercise history and selects the most effective exercise method. In addition, a specific exercise method is preferentially selected from the user's past exercise history. In addition, an optimal exercise method under specific environmental conditions is selected based on the user's past exercise history. In this way, the optimal exercise method can be selected by referring to the past exercise history. Part or all of the above-mentioned processing in the device for performing physical exercise may be performed, for example, using AI, or may be performed without using AI. For example, the device for performing physical exercise can input past exercise history data to a generation AI and cause the generation AI to select an optimal exercise method.

[0081] The device for performing physical exercise can customize the means of exercise based on the user's current physical condition when performing physical exercise. The physical condition includes, for example, a physical strength level and a health condition, but is not limited to such examples. The customization includes, for example, adjustment of the intensity of exercise and selection of the type of exercise, but is not limited to such examples. The device for performing physical exercise customizes the means of exercise based on the user's current physical condition when performing physical exercise. For example, when the user is tired, light exercise is performed. Also, when the user is energetic, high-intensity exercise is performed. Also, when the user is in a specific physical condition, exercise according to the condition is performed. In this way, by customizing the means of exercise based on the current physical condition, more appropriate exercise is possible. Part or all of the above-mentioned processing in the device for performing physical exercise may be performed using, for example, AI, or may be performed without using AI. For example, the device for performing physical exercise can input current physical condition data to a generation AI and have the generation AI execute customization of the exercise means.

[0082] The device for performing physical exercise can estimate the emotion of the user and determine the priority of the physical exercise based on the estimated emotion of the user. The emotion includes, for example, emotion classification such as joy, sadness, and anger, but is not limited to such examples. The priority includes, for example, the intensity of the emotion and the importance of the exercise, but is not limited to such examples. The device for performing physical exercise estimates the emotion of the user and determines the priority of the physical exercise based on the estimated emotion of the user. For example, when the user is relaxed, the exercise performed when relaxed is performed preferentially. Also, when the user is tense, the exercise performed when tense is performed preferentially. Also, when the user is concentrating, the exercise performed when concentrating is performed preferentially. In this way, by determining the priority of the physical exercise based on the emotion of the user, more appropriate exercise is possible. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the device for performing physical exercise may be performed using, for example, an AI, or may be performed without using an AI. For example, a device that performs physical exercise can input a user's emotional data into a generation AI and have the generation AI determine priorities.

[0083] The device that performs physical exercise can select an appropriate exercise method based on the geographical location information of the user when performing the physical exercise. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. The appropriate exercise method includes, for example, the effect of exercise and the safety of exercise, but is not limited to such examples. The device that performs physical exercise selects an appropriate exercise method based on the geographical location information of the user when performing the physical exercise. For example, when the user is at home, an exercise method that can be performed at home is selected. Also, when the user is at work, an exercise method that can be performed at the work place is selected. Also, when the user is out, an exercise method that can be performed at the out-of-home location is selected. As a result, by selecting an appropriate exercise method based on the geographical location information, more appropriate exercise is possible. Part or all of the above-mentioned processing in the device that performs physical exercise may be performed using, for example, AI, or may be performed without using AI. For example, the device that performs physical exercise can input geographical location information to a generation AI and cause the generation AI to select an exercise method.

[0084] The device for performing physical exercise can analyze the social media activity of the user when performing the physical exercise and suggest a means of exercise. The social media activity includes, for example, the content of posts, the number of likes, and comments, but is not limited to such examples. The means of exercise includes, for example, the type of exercise and the intensity of exercise, but is not limited to such examples. The device for performing physical exercise analyzes the social media activity of the user when performing the physical exercise and suggests a means of exercise. For example, if the user is relaxing on social media, the device suggests a method of exercise at that time. Also, if the user is nervous on social media, the device suggests a method of exercise at that time. Also, if the user is concentrating on social media, the device suggests a method of exercise at that time. In this way, by analyzing the social media activity, it is possible to suggest a related means of exercise. Part or all of the above-mentioned processing in the device for performing physical exercise may be performed, for example, using AI, or may be performed without using AI. For example, the device for performing physical exercise can input social media activity data to a generation AI and have the generation AI execute a suggestion of a means of exercise.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] When analyzing the user's electroencephalogram data, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral history. For example, if the user frequently performed a specific behavior in the past, the analysis unit preferentially analyzes the electroencephalogram pattern related to that behavior. In addition, the analysis unit can analyze behavioral patterns during a specific time period from the user's past behavioral history. Furthermore, the analysis unit can analyze behavioral patterns under specific environmental conditions based on the user's past behavioral history. In this way, the analysis accuracy can be improved by referring to the past behavioral history.

[0087] When acquiring a user's brainwave data, the device for acquiring brainwaves can monitor the user's current health condition and adjust the frequency and method of data acquisition according to the health condition. For example, if the user is tired, the frequency of data acquisition can be set low and data can be acquired for a long period of time. Also, if the user is in good health, the frequency of data acquisition can be set high and detailed data can be acquired in a short period of time. Also, if the user is in a particular health condition, a data acquisition method according to that condition can be selected. In this way, more appropriate data can be acquired by adjusting the frequency and method of data acquisition according to the health condition.

[0088] When analyzing the user's electroencephalogram data, the analysis unit can adjust the analysis algorithm taking into account the user's current activity state. For example, if the user is relaxed, an analysis algorithm specialized for electroencephalogram data when relaxed is applied. If the user is tense, an analysis algorithm specialized for electroencephalogram data when tense is applied. If the user is concentrating, an analysis algorithm specialized for electroencephalogram data when concentrating is applied. In this way, by adjusting the analysis algorithm according to the user's activity state, more accurate analysis is possible.

[0089] When analyzing the user's electroencephalogram data, the analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is relaxed, the analysis of the electroencephalogram data during the relaxed state is prioritized. If the user is tense, the analysis of the electroencephalogram data during the tense state is prioritized. If the user is concentrating, the analysis of the electroencephalogram data during the concentrating state is prioritized. In this way, by determining the priority of analysis based on the user's emotions, important data can be analyzed with priority.

[0090] A device that performs physical exercise can estimate the user's emotions and adjust the intensity and type of exercise based on the estimated emotions. For example, when the user is relaxed, the device performs relaxed movements. When the user is tense, the device performs quick and accurate movements. When the user is concentrating, the device performs detailed movements. In this way, the device can perform more appropriate exercise by adjusting the intensity and type of exercise based on the user's emotions.

[0091] The device for acquiring electroencephalograms can preferentially acquire electroencephalogram data at a specific location based on the geographical location information of the user. For example, when the user is at home, the device preferentially acquires electroencephalogram data at home. When the user is at work, the device preferentially acquires electroencephalogram data at work. When the user is out, the device preferentially acquires electroencephalogram data at the outside location. This allows more useful data to be collected by preferentially acquiring data that is highly relevant based on the geographical location information.

[0092] When analyzing the user's electroencephalogram data, the analysis unit can analyze the user's social media activities and prioritize analysis of related electroencephalogram data. For example, if the user is relaxed on social media, the analysis unit prioritizes analysis of the electroencephalogram data at that time. Also, if the user is nervous on social media, the analysis unit prioritizes analysis of the electroencephalogram data at that time. Also, if the user is concentrating on social media, the analysis unit prioritizes analysis of the electroencephalogram data at that time. In this way, by analyzing social media activities, it is possible to prioritize analysis of related electroencephalogram data.

[0093] A device that performs physical exercise can estimate the user's emotions and determine the priority order of exercises based on the estimated emotions. For example, if the user is relaxed, exercises performed when relaxed are given priority. If the user is tense, exercises performed when tense are given priority. If the user is concentrating, exercises performed when concentrating are given priority. In this way, by determining the priority order of exercises based on the user's emotions, more appropriate exercises can be performed.

[0094] A device for performing physical exercise can select an optimal exercise method by referring to a user's past exercise history. For example, the device analyzes the user's past exercise history and selects the most effective exercise method. Also, a specific exercise method is preferentially selected from the user's past exercise history. Also, the device can select an optimal exercise method under specific environmental conditions based on the user's past exercise history. In this way, the device can select an optimal exercise method by referring to the past exercise history.

[0095] A device for performing physical exercise can estimate the user's emotions and adjust the method of performing the exercise based on the estimated emotions. For example, when the user is relaxed, the device performs relaxed movements. When the user is tense, the device performs quick and accurate movements. When the user is concentrating, the device performs fine movements. In this way, the device can perform more appropriate exercise by adjusting the method of performing the exercise based on the user's emotions.

[0096] The process flow of the second embodiment will be briefly described below.

[0097] Step 1: The device for acquiring brainwaves reads the brainwaves of the person in question. For example, an EEG sensor is attached to the head and acquires brainwave data in real time. The EEG sensor can continuously monitor the brainwaves of the person in question and acquire data. The acquired brainwave data can also be transmitted to the AI ​​system. Step 2: The analysis unit uses large amounts of data collected from able-bodied and disabled people to analyze the EEG data and identify the action the person wants to take. For example, if the person wants to move their hands, the analysis unit can read and identify that intention from the EEG data. Step 3: The device for executing the physical movement executes the physical movement based on the action identified by the analysis unit. For example, if the person has an intention to move his / her hand, the device for executing the physical movement moves his / her hand based on the intention.

[0098] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0100] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0101] Each of the above-mentioned elements including the device for acquiring brainwaves, the analysis unit, and the device for executing physical exercise is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the device for acquiring brainwaves uses the brainwave sensor of the smart device 14 to read the brainwaves of the person in real time and acquire data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes big data of brainwaves collected from healthy people and people with disabilities, and identifies the behavior that the person wants to do. The device for executing physical exercise is realized, for example, by the control unit 46A of the smart device 14, and executes physical exercise based on the intention of the person. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0109] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0112] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0114] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0117] Each of the above-mentioned elements including the device for acquiring brain waves, the analysis unit, and the device for performing physical exercise is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the device for acquiring brain waves uses the brain wave sensor of the smart glasses 214 to read the brain waves of the person in real time and acquire data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes big data of brain waves collected from healthy people and people with disabilities, and identifies the behavior that the person wants to do. The device for performing physical exercise is realized, for example, by the control unit 46A of the smart glasses 214, and performs physical exercise based on the intention of the person. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0128] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0133] Each of the above-mentioned elements including the device for acquiring brainwaves, the analysis unit, and the device for executing physical exercise is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the device for acquiring brainwaves uses the brainwave sensor of the headset type terminal 314 to read the brainwaves of the person in real time and acquire data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes big data of brainwaves collected from healthy people and people with disabilities, and identifies the behavior that the person wants to do. The device for executing physical exercise is realized, for example, by the control unit 46A of the headset type terminal 314, and executes physical exercise based on the intention of the person. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0141] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0145] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] Each of the above-mentioned elements including the device for acquiring brain waves, the analysis unit, and the device for executing physical exercise is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the device for acquiring brain waves uses the brain wave sensor of the robot 414 to read the brain waves of the person in real time and acquire data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes big data of brain waves collected from healthy people and people with disabilities, and identifies the behavior that the person wants to do. The device for executing physical exercise is realized, for example, by the control unit 46A of the robot 414, and executes physical exercise based on the intention of the person. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0151] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0152] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0155] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0156] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] In addition, 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 upon request from the data processing device 12.

[0161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0163] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0167] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0169] (Appendix 1) A device for acquiring brain waves; An analysis unit that analyzes the electroencephalogram data acquired by the device and identifies the behavior that the person in question wants to perform; and a device for performing physical exercise based on the behavior identified by the analysis unit. A system characterized by: (Appendix 2) The device for acquiring the electroencephalogram comprises: Acquiring EEG data in real time 2. The system of claim 1, (Appendix 3) The analysis unit is Analyzing EEG data using large amounts of data collected from healthy and disabled individuals 2. The system of claim 1, (Appendix 4) The analysis unit is Identify the person's intent 2. The system of claim 1, (Appendix 5) The device for performing the physical exercise comprises: Carry out physical movements based on the individual's intentions 2. The system of claim 1, (Appendix 6) The device for acquiring the electroencephalogram comprises: By estimating the user's emotion, the timing of acquiring electroencephalogram data is adjusted based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The device for acquiring the electroencephalogram comprises: When acquiring EEG, the appropriate acquisition method is selected by referring to the user's past EEG data. 2. The system of claim 1, (Appendix 8) The device for acquiring the electroencephalogram comprises: When acquiring EEG, filtering is performed based on the user's current activity and environment. 2. The system of claim 1, (Appendix 9) The device for acquiring the electroencephalogram comprises: By estimating the user's emotion, the priority of the electroencephalogram data to be acquired is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 10) The device for acquiring the electroencephalogram comprises: When acquiring EEG data, the system prioritizes acquisition of data that is highly relevant based on the user's geographic location information. 2. The system of claim 1, (Appendix 11) The device for acquiring the electroencephalogram comprises: When EEG is acquired, the user's social media activity is analyzed to obtain relevant EEG data. 2. The system of claim 1, (Appendix 12) The analysis unit is By estimating the user's emotions, the analysis algorithm is adjusted based on the estimated user's emotions. 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, adjust the level of detail of the analysis based on the importance of the EEG data 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, different analysis methods are applied depending on the category of EEG data. 2. The system of claim 1, (Appendix 15) The analysis unit is By estimating the user's emotions, the method of displaying the analysis results is adjusted based on the estimated user's emotions. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, prioritize analysis based on when the EEG data was collected. 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, adjust the order of analysis based on the relevance of the EEG data 2. The system of claim 1, (Appendix 18) The device for performing the physical exercise comprises: By estimating the user's emotion, the method of performing physical exercise is adjusted based on the estimated user's emotion. 2. The system of claim 1, (Appendix 19) The device for performing the physical exercise comprises: When performing physical exercise, an appropriate exercise method is selected by referring to the user's past exercise history. 2. The system of claim 1, (Appendix 20) The device for performing the physical exercise comprises: To customize a means of exercise based on a user's current physical condition when performing physical exercise 2. The system of claim 1, (Appendix 21) The device for performing the physical exercise comprises: By estimating the user's emotion, the priority of physical exercises is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 22) The device for performing the physical exercise comprises: When performing physical exercise, an appropriate exercise method is selected based on the user's geographical location information. 2. The system of claim 1, (Appendix 23) The device for performing the physical exercise comprises: When performing physical exercise, the system analyzes the user's social media activity and suggests ways to exercise. 2. The system of claim 1, [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A device for acquiring brain waves; An analysis unit that analyzes the electroencephalogram data acquired by the device and identifies the behavior that the person in question wants to perform; and a device for performing physical exercise based on the behavior identified by the analysis unit. A system characterized by:

2. The device for acquiring the electroencephalogram comprises: Acquiring EEG data in real time 2. The system of claim 1 .

3. The analysis unit is Analyzing EEG data using large amounts of data collected from healthy and disabled individuals 2. The system of claim 1 .

4. The analysis unit is Identify the person's intent 2. The system of claim 1 .

5. The device for performing the physical exercise comprises: Carry out physical movements based on the individual's intentions 2. The system of claim 1 .

6. The device for acquiring the electroencephalogram comprises: By estimating the user's emotion, the timing of acquiring electroencephalogram data is adjusted based on the estimated user's emotion.

2. The system of claim 1 .

7. The device for acquiring the electroencephalogram comprises: When acquiring EEG, the appropriate acquisition method is selected by referring to the user's past EEG data.

2. The system of claim 1 .

8. The device for acquiring the electroencephalogram comprises: When acquiring EEG, filtering is performed based on the user's current activity and environment.

2. The system of claim 1 .

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