System

Abstract

The system provided by the embodiment of the invention aims to optimize the learning environment and improve the learning efficiency. A system according to an embodiment includes an authentication unit, a calling unit, a monitoring unit, an assistance unit, a selection unit, a temperature adjustment unit, a shield unit, a communication unit, and a display unit. The authentication unit recognizes a user. The calling unit calls the learning data of the user recognized by the authentication unit. The monitoring unit monitors an action of a user. The auxiliary unit gives a prompt when the operation monitored by the monitoring unit is stopped. The selection unit selects a question that has been mistakenly answered in the past and sets the question again. The temperature adjusting part performs automatic temperature adjustment through the AI. The shielding part shields radio waves from the outside. The communication unit provides Wi-Fi. The display unit displays information on the display.

Description

TECHNICAL FIELD

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

[0002] Patent Literature 1 discloses a personalized chatbot control method executed by at least one processor, the method including: a step of receiving a user utterance; a step of adding the user utterance to a prompt containing an instruction of a description related to a chatbot character; a step of encoding the prompt; and a step of inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

[0003] Patent Literature 1: Japanese Patent Application Publication No. 2022-180282. SUMMARY

[0004] In the prior art, there is a lack of a multifunctional system for optimizing a learning environment, and there is room for improvement in improving learning efficiency.

[0005] The system according to the present technology is intended to optimize a learning environment and improve learning efficiency.

[0006] The system according to the present technology includes an authentication unit, a calling unit, a monitoring unit, an assisting unit, a selecting unit, a temperature adjusting unit, a shielding unit, a communication unit, and a display unit. The authentication unit is configured to identify a user. The calling unit is configured to call learning data of the user identified by the authentication unit. The monitoring unit is configured to monitor a motion of the user. The assisting unit is configured to give a prompt when the motion monitored by the monitoring unit stops. The selecting unit is configured to select a past question answered incorrectly and ask the question again. The temperature adjusting unit is configured to automatically adjust a temperature by AI. The shielding unit is configured to shield an electric wave from an outside. The communication unit is configured to provide Wi-Fi. The display unit is configured to display information on a display.

[0007] The system according to the present technology is capable of optimizing a learning environment and improving learning efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1 is a conceptual diagram showing an example of a configuration of a data processing system according to the first embodiment.

[0009] Figure 2 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to the first embodiment.

[0010] Figure 3 is a conceptual diagram showing an example of a configuration of a data processing system according to the second embodiment.

[0011] Figure 4 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to the second embodiment.

[0012] Figure 5 is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment.

[0013] Figure 6 is a conceptual diagram showing an example of the main functions of a data processing device and a head-mounted terminal according to the third embodiment.

[0014] Figure 7 is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment.

[0015] Figure 8 is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment.

[0016] Figure 9 is an emotion map mapping a plurality of emotions.

[0017] Figure 10 is an emotion map mapping a plurality of emotions.

[0018] Explanation of Reference Numerals

[0019] 10, 210, 310, 410 data processing system

[0020] 12 data processing device

[0021] 14 intelligent device

[0022] 214 intelligent glasses

[0023] 314 head-mounted terminal

[0024] 414 robot DETAILED DESCRIPTION

[0025] Hereinafter, an example of an embodiment of a system to which the present technology is applied will be described with reference to the accompanying drawings.

[0026] First, words used in the following description will be explained.

[0027] In the following embodiments, the processor (hereinafter, referred to as "processor") denoted by the reference sign can be one arithmetic device or a combination of a plurality of arithmetic devices. In addition, the processor can be one arithmetic device or a combination of a plurality of arithmetic devices. An example of the arithmetic device includes 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), and the like.

[0028] In the following embodiments, the RAM (Random Access Memory) denoted by the reference sign is a memory that temporarily stores information, and is used as a work memory by the processor.

[0029] In the following embodiments, the memory denoted by the reference sign is one or a plurality of nonvolatile storage devices for storing various programs and various parameters, and the like. An example of the nonvolatile storage device includes a flash memory (SSD (Solid State Drive)), a magnetic disk (for example, a hard disk), a magnetic tape, and the like.

[0030] In the following embodiments, the communication I / F (Interface) denoted by the reference sign is an interface including a communication processor and an antenna, and the like. The communication I / F is responsible for communication between a plurality of computers. An example of the communication standard suitable for the communication I / F includes a wireless communication standard such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0031] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it can be only A, it can be only B, or it can be a combination of A and B. In addition, in the present specification, when three or more items are described using "and / or", the same idea as "A and / or B" is also applied.

[0032] First Embodiment

[0033] Figure 1 An example of the configuration of the data processing system 10 of the first embodiment is shown.

[0034] like Figure 1 As shown, the data processing system 10 includes a data processing device 12 and an intelligent device 14. An example of the data processing device 12 is a server.

[0035] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, 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. An example of the network 54 includes a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0036] The smart device 14 includes a computer 36, a receiver 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the receiver 38, output device 40, and camera 42 are also connected to the bus 52.

[0037] The receiving device 38 includes a touchscreen 38A and a microphone 38B, etc., for receiving user input. The touchscreen 38A receives user input generated by contact with an indicator (e.g., a pen or finger). The microphone 38B receives user input generated by sound by detecting the user's voice. The control unit 46A sends data representing user input received via the touchscreen 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, a specific processing unit 290 (see reference...) Figure 2 Get the data that represents the user input.

[0038] The output device 40 includes a display 40A and a speaker 40B, which present data to the user by outputting data in a user-perceptible format (e.g., sound and / or text). The display 40A displays visual information such as text and images according to instructions from the processor 46. The speaker 40B outputs sound according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0039] The communication I / F 44 is connected to the network 54. The communication I / F 44 and 26 are responsible for the transmission and reception of various information between the processor 46 and the processor 28 via the network 54.

[0040] Figure 2 The main functions of the data processing apparatus 12 and the smart device 14 are shown as an example.

[0041] As shown in Figure 2 In the data processing apparatus 12, specific processing is performed by the processor 28. The specific processing program 56 is stored in the memory 32. The specific processing program 56 is an example of the "program" related to the present technology. The processor 28 reads out the specific processing program 56 from the memory 32 and executes the read-out specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as the specific processing section 290 according to the specific processing program 56 executed on the RAM 30.

[0042] The data generation model 58 and the emotion specific model 59 are stored in the memory 32. The data generation model 58 and the emotion specific model 59 are used by the specific processing section 290. The specific processing section 290 can use the emotion specific model 59 to infer the emotion of the user and perform specific processing using the emotion of the user. The emotion inference function (emotion specific function) using the emotion specific model 59 includes emotion inference and prediction of the user, and various inferences and predictions related to the emotion of the user, but is not limited to this example. In addition, the inference and prediction of the emotion include, for example, analysis (resolution) of the emotion, and the like.

[0043] In the smart device 14, specific processing is performed by the processor 46. The specific processing program 60 is stored in the memory 50. The specific processing program 60 is used by the data processing system 10 in common with the specific processing program 56. The processor 46 reads out the specific processing program 60 from the memory 50 and executes the read-out specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as the control section 46A according to the specific processing program 60 executed on the RAM 48. In addition, the smart device 14 can have the same data generation model and emotion specific model as the data generation model 58 and the emotion specific model 59, and perform the same processing as the specific processing section 290 using these models.

[0044] Further, a device other than the data processing device 12 can have the data generation model 58. For example, a server device (for example, a generation server) can have the data generation model 58. In this case, the data processing device 12 obtains a processing result (a prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Further, the data processing device 12 can be a server device, or can be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) possessed by a user. Next, an example of the processing performed by the data processing system 10 of the first embodiment will be described.

[0045] Embodiment 1

[0046] The learning support system according to the embodiment is a system that provides an environment in which a user can concentrate on learning for a short time. The learning support system is equipped with a box like a telephone booth, and has a face authentication system, an AI camera, an AI automatic temperature adjustment function, an electric wave shielding box, Wi-Fi, and a display. The learning support system creates an environment in which a user can concentrate on learning for a short time. First, when a user enters the telephone booth, the face authentication system recognizes the user and calls up personalized learning data. Next, the AI camera monitors the user's actions and grasps the learning progress in real time. For example, when the user stops moving in response to a question, the AI generates a prompt a few seconds later. In addition, the system has a function of selecting a question that the user answered incorrectly in the past and asking the question again. Further, the system has an AI automatic temperature adjustment function that maintains a comfortable learning environment for the user. The electric wave shielding box prevents external interference and improves concentration. The Wi-Fi and the display support online learning and remote meetings. With this system, a user can efficiently advance learning for a short time, and can maintain high productivity even in a modern society in which a deadline is chased. For example, when a user enters the telephone booth, the face authentication system recognizes the user and calls up personalized learning data. Next, the AI camera monitors the user's actions and grasps the learning progress in real time. For example, when the user stops moving in response to a question, the AI generates a prompt a few seconds later. In addition, the system has a function of selecting a question that the user answered incorrectly in the past and asking the question again. Further, the system has an AI automatic temperature adjustment function that maintains a comfortable learning environment for the user. The electric wave shielding box prevents external interference and improves concentration. The Wi-Fi and the display support online learning and remote meetings. With this system, a user can efficiently advance learning for a short time, and can maintain high productivity even in a modern society in which a deadline is chased. Thus, the learning support system can optimize the learning environment for a user and support efficient learning.

[0047] The learning support system according to the present embodiment includes an authentication unit, a calling unit, a monitoring unit, an assisting unit, a selecting unit, a temperature adjusting unit, a shielding unit, a communication unit, and a display unit. The authentication unit is configured to identify a user. For example, the user can be identified by face authentication, fingerprint authentication, voice authentication, or the like. The calling unit is configured to call learning data of the user identified by the authentication unit. For example, the learning data can include text data, image data, voice data, or the like. The monitoring unit is configured to monitor the user's action. For example, the user's action can be monitored by camera action detection, sensor action detection, or the like. The assisting unit is configured to give a prompt when the action monitored by the monitoring unit stops. For example, the prompt can be given by a text message, a voice message, a visual prompt, or the like. The selecting unit is configured to select a past question that was answered incorrectly and ask the question again. For example, the past question that was answered incorrectly can be selected based on the number of incorrect answers, the content of the incorrect answers, or the like. The temperature adjusting unit is configured to automatically adjust the temperature by AI. For example, the temperature can be automatically adjusted by the use of a temperature sensor, the type of learning algorithm, or the like. The shielding unit is configured to shield external radio waves. For example, the external radio waves can be shielded by the use of a radio wave shielding material, a shielding technique, or the like. The communication unit is configured to provide Wi-Fi. For example, the Wi-Fi can be provided by the type of router, the communication protocol, or the like. The display unit is configured to display information on a display. For example, the information can be displayed by the type of display, the display format, or the like. Thus, the learning support system according to the present embodiment can optimize the user's learning environment and support efficient learning.

[0048] The monitoring unit can monitor the user's action in real time. For example, the monitoring unit can monitor the user's action in real time by a camera. For example, the monitoring unit can capture the user's action by a high-resolution camera and analyze the action in real time. In addition, the monitoring unit can monitor the user's action in real time by a sensor. For example, the monitoring unit can detect the user's action by a motion sensor and analyze the action in real time. In addition, the monitoring unit can monitor the user's action in real time by AI. For example, the monitoring unit can analyze the user's action by an AI camera and monitor the action in real time. Thus, the monitoring unit can accurately grasp the learning progress by monitoring the user's action in real time.

[0049] The assisting unit can give a prompt after a certain period of time when the user's action stops. For example, the assisting unit can give a prompt a few seconds after the user's action stops. For example, the assisting unit can give a prompt by a text message a few seconds after the user's action stops. In addition, the assisting unit can give a prompt by a voice message when the user's action stops. For example, the assisting unit can give a prompt by a voice message a few seconds after the user's action stops. In addition, the assisting unit can give a prompt by a visual prompt when the user's action stops. For example, the assisting unit can give a prompt by a visual prompt a few seconds after the user's action stops. Thus, the assisting unit can provide a prompt in a timely manner when the user is stuck on a question.

[0050] The selection department can select questions that were previously answered incorrectly and generate new questions. For example, the selection department can retrieve previously incorrectly answered questions from the database and generate new questions. Furthermore, the selection department can also select questions based on the number of times a question was answered incorrectly in the past. For example, the selection department can select questions to be re-assigned based on the number of times a user answered a question incorrectly in the past. Additionally, the selection department can also select questions based on the content of the incorrect answers to previously answered questions. For example, the selection department can select questions to be re-assigned based on the content of the incorrect answers to previously answered questions. Thus, the selection department can effectively review based on the user's past learning history.

[0051] The temperature control unit can automatically adjust the temperature using AI. For example, it measures the room temperature using a temperature sensor, calculates the optimal temperature using AI, and automatically adjusts it. Furthermore, it can measure the user's body temperature, calculate the optimal room temperature using AI, and automatically adjust it. For example, it can monitor the user's movements using a camera, calculate the optimal temperature using AI, and automatically adjust it. Thus, the temperature control unit can maintain a comfortable learning environment for the user.

[0052] The shielding component can block external radio waves. For example, it can use a radio wave shielding material to shield external radio waves. Alternatively, it can use shielding technology to shield external radio waves. Furthermore, it can use a radio wave shielding film to shield external radio waves. Thus, the shielding component can prevent external interference and improve concentration.

[0053] The communications department can provide Wi-Fi. For example, the communications department provides Wi-Fi via a router. Alternatively, the communications department can provide Wi-Fi via a communication protocol. Furthermore, the communications department can provide Wi-Fi via a Wi-Fi access point. Thus, the communications department can support online learning and remote conferencing.

[0054] The display unit is capable of displaying information on a monitor. For example, the display unit displays information using a monitor. Furthermore, the display unit can also display information using a specific display format. For example, the display unit uses a specific display format. Additionally, the display unit can also display information using a projector. For example, the display unit uses a projector to display information. Thus, the display unit can intuitively view learning content and meeting materials.

[0055] The authentication department can improve accuracy by referencing a user's past authentication history during the authentication process. For example, it can prioritize authentication methods previously used by the user (such as facial recognition or fingerprint authentication). Furthermore, the authentication department can predict authentication time based on the user's past authentication history and propose the optimal authentication method. Additionally, the authentication department can analyze the user's past authentication history to identify patterns that improve authentication accuracy. Therefore, the authentication department can improve authentication accuracy based on past authentication history.

[0056] The authentication department can additionally acquire users' biometric information during authentication to improve authentication reliability. For example, the authentication department can acquire additional biometric information (such as heart rate or body temperature) during authentication to enhance reliability. For instance, the authentication department measures the user's heart rate to confirm if it is within the normal range. Furthermore, the authentication department can measure the user's body temperature to confirm if it is abnormal. In addition, the authentication department can comprehensively analyze the user's biometric information to improve authentication reliability. Therefore, the authentication department can improve authentication reliability based on biometric information.

[0057] The authentication department can consider the user's geographical location information to customize the authentication process. For example, it can provide a simplified authentication process when the user is at home, a detailed process when the user is in a public place, and a location-specific authentication method when the user is in a particular location. Thus, the authentication department can optimize the authentication process based on geographical location information.

[0058] The authentication department can analyze users' social media activity during the authentication process to improve authentication reliability. For example, by analyzing users' social media activity, the department can understand the current situation and adjust the authentication process accordingly. Furthermore, the department can analyze users' social media activity to identify patterns that improve authentication reliability. Additionally, the department can select authentication methods based on users' social media activity. Therefore, the authentication department can improve authentication reliability by leveraging social media activity.

[0059] The calling unit can select the optimal learning data by referring to the user's past learning history during the calling process. For example, the calling unit might prioritize calling questions the user previously answered incorrectly. Furthermore, the calling unit can select the most effective learning data based on the user's past learning history. Additionally, the calling unit can analyze the user's past learning history to propose optimal learning data. Thus, the calling unit can provide optimal learning data based on past learning history.

[0060] The invocation unit can customize data based on the user's current learning progress at the time of invocation. For example, the invocation unit can suggest the next learning data based on the user's current learning progress. Furthermore, the invocation unit can monitor the user's learning progress in real time and provide optimal learning data. Additionally, the invocation unit can analyze the user's learning progress and select effective learning data. Therefore, the invocation unit can provide optimal learning data based on the current learning progress.

[0061] The invocation unit can consider the user's geographical location information during the invocation process, prioritizing the retrieval of highly relevant learning data. For example, when the user is located in a specific location, the invocation unit will prioritize the retrieval of learning data related to that location. Furthermore, the invocation unit can also propose optimal learning data based on the user's geographical location information. Thus, the invocation unit can provide highly relevant learning data based on geographical location information.

[0062] The invocation unit can analyze a user's social media activity and retrieve relevant learning data during the invocation process. For example, the invocation unit can analyze a user's social media activity to determine their current interests and provide relevant learning data. Furthermore, the invocation unit can analyze a user's social media activity to propose optimal learning data. Additionally, the invocation unit can retrieve highly relevant learning data based on the user's social media activity. Thus, the invocation unit can provide highly relevant learning data based on social media activity.

[0063] The monitoring department can improve monitoring accuracy by referencing users' past behavior patterns during monitoring. For example, the monitoring department can propose the optimal monitoring method based on users' past behavior patterns. Furthermore, the monitoring department can analyze users' past behavior patterns to discover patterns that improve monitoring accuracy. Additionally, the monitoring department can adjust the timing of monitoring based on users' past behavior patterns. Therefore, the monitoring department can improve monitoring accuracy based on past behavior patterns.

[0064] The monitoring department can additionally acquire users' biometric information during monitoring to improve monitoring reliability. For example, the monitoring department can acquire additional user biometric information (such as heart rate or body temperature) during monitoring to enhance reliability. For instance, the monitoring department measures the user's heart rate to confirm if it is within the normal range. Furthermore, the monitoring department can measure the user's body temperature to confirm if there are any abnormalities. In addition, the monitoring department can comprehensively analyze the user's biometric information to improve monitoring reliability. Therefore, the monitoring department can improve monitoring reliability based on biometric information.

[0065] The monitoring department can customize the monitoring range by considering the user's geographical location information during monitoring. For example, when a user is located in a specific location, the monitoring department can set a monitoring range suitable for that location. Furthermore, the monitoring department can propose an optimal monitoring range based on the user's geographical location information. Additionally, the monitoring department can adjust the monitoring range based on the user's geographical location information. Thus, the monitoring department can provide an optimal monitoring range based on geographical location information.

[0066] The monitoring department can analyze users' social media activities during monitoring to improve monitoring accuracy. For example, by analyzing users' social media activities, the monitoring department can grasp the current situation and improve monitoring accuracy. Furthermore, the monitoring department can analyze users' social media activities to propose optimal monitoring methods. Additionally, the monitoring department can adjust the monitoring scope based on users' social media activities. Therefore, the monitoring department can improve monitoring accuracy by leveraging social media activity.

[0067] The support department can refer to the user's past learning history to provide optimal suggestions during assistance. For example, the support department can provide relevant suggestions for questions the user answered incorrectly in the past. Furthermore, the support department can select the most effective suggestions based on the user's past learning history. Additionally, the support department can analyze the user's past learning history to propose optimal suggestions. Therefore, the support department can provide optimal suggestions based on past learning history.

[0068] The support department can customize prompts based on the user's current learning progress during assistance. For example, it can provide prompts related to the next learning content based on the user's current progress. Furthermore, the support department can monitor the user's learning progress in real time and provide optimal prompts. Additionally, it can analyze the user's learning progress and select effective prompts. Therefore, the support department can provide optimal prompts based on the current learning progress.

[0069] The support unit can consider the user's geographical location information when providing assistance, prioritizing highly relevant prompts. For example, when the user is in a specific location, the support unit will prioritize prompts related to that location. Furthermore, the support unit can also provide optimal prompts based on the user's geographical location information. Thus, the support unit is able to provide highly relevant prompts based on geographical location information.

[0070] The support department can analyze users' social media activity and provide relevant prompts during assistance. For example, the support department can analyze users' social media activity to understand their current interests and provide relevant prompts. Furthermore, the support department can analyze users' social media activity to propose optimal prompts. Additionally, the support department can provide highly relevant prompts based on users' social media activity. Thus, the support department is able to provide highly relevant prompts based on social media activity.

[0071] The selection unit can refer to the user's past learning history to select the optimal question during the selection process. For example, the selection unit may prioritize selecting questions the user previously answered incorrectly. Furthermore, the selection unit can also select the most effective questions based on the user's past learning history. Additionally, the selection unit can analyze the user's past learning history to propose the optimal question. Thus, the selection unit can provide the optimal question based on past learning history.

[0072] The selection unit can customize questions based on the user's current learning progress at the time of selection. For example, the selection unit can select the next question to be learned based on the user's current learning progress. Furthermore, the selection unit can monitor the user's learning progress in real time and provide optimal questions. Additionally, the selection unit can analyze the user's learning progress and select effective questions. Therefore, the selection unit can provide optimal questions based on the current learning progress.

[0073] The selection unit can consider the user's geographical location information during selection, prioritizing highly relevant questions. For example, when the user is located in a specific location, the selection unit prioritizes questions related to that location. Furthermore, the selection unit can also propose optimal questions based on the user's geographical location information. Thus, the selection unit can provide highly relevant questions based on geographical location information.

[0074] The selection department can analyze a user's social media activity during the selection process to select relevant questions. For example, the selection department can analyze a user's social media activity to determine their current interests and provide relevant questions. Furthermore, the selection department can analyze a user's social media activity to propose optimal questions. Additionally, the selection department can select highly relevant questions based on the user's social media activity. Thus, the selection department can provide highly relevant questions based on social media activity.

[0075] The temperature control unit can refer to the user's past temperature setting history to set the optimal temperature during temperature adjustment. For example, the temperature control unit can suggest an optimal temperature based on the user's past temperature settings. Furthermore, the temperature control unit can select the most comfortable temperature based on the user's past temperature setting history. Additionally, the temperature control unit can analyze the user's past temperature setting history to set the optimal temperature. Therefore, the temperature control unit can provide the optimal temperature based on past temperature setting history.

[0076] The temperature control unit can additionally acquire the user's biometric information during temperature adjustment to improve temperature reliability. For example, the temperature control unit can measure the user's heart rate to confirm whether it is within the normal range. Furthermore, the temperature control unit can also measure the user's body temperature to confirm whether it is abnormal. In addition, the temperature control unit can comprehensively analyze the user's biometric information to set the optimal temperature. Therefore, the temperature control unit can provide the optimal temperature based on biometric information.

[0077] The temperature control unit can take into account the user's geographical location information to set the optimal temperature during temperature adjustment. For example, when the user is in a specific location, the temperature control unit sets an optimal temperature suitable for that location. Furthermore, the temperature control unit can also propose an optimal temperature based on the user's geographical location information. Thus, the temperature control unit can provide an optimal temperature based on geographical location information.

[0078] The temperature control unit can analyze the user's social media activity during temperature adjustment to provide relevant temperature settings. For example, the temperature control unit can understand the current situation through the user's social media activity and set an optimal temperature. Furthermore, the temperature control unit can also analyze the user's social media activity to suggest an optimal temperature. In addition, the temperature control unit can provide highly relevant temperature settings based on the user's social media activity. Therefore, the temperature control unit can provide an optimal temperature based on social media activity.

[0079] The blocking unit can select the optimal blocking method by referring to a user's past blocking history during the blocking process. For example, the blocking unit may prioritize blocking methods previously used by the user. Furthermore, the blocking unit can select the most effective blocking method based on the user's past blocking history. Additionally, the blocking unit can analyze the user's past blocking history to propose the optimal blocking method. Thus, the blocking unit can provide the optimal blocking method based on past blocking history.

[0080] The shielding unit can select the optimal shielding method by considering the user's geographical location information during shielding. For example, when the user is located in a specific location, the shielding unit selects the optimal shielding method suitable for that location. Furthermore, the shielding unit can also propose an optimal shielding method based on the user's geographical location information. Thus, the shielding unit can provide an optimal shielding method based on geographical location information.

[0081] The communications department can select the optimal connection method by referring to the user's past communication history during communication. For example, the communications department may prioritize connection methods previously used by the user. Furthermore, the communications department can select the most efficient connection method based on the user's past communication history. Additionally, the communications department can analyze the user's past communication history to propose the optimal connection method. Thus, the communications department can provide the optimal connection method based on past communication history.

[0082] The communications department can select the optimal connection method by considering the user's geographical location information during communication. For example, when the user is located in a specific location, the communications department selects the optimal connection method suitable for that location. Furthermore, the communications department can also propose an optimal connection method based on the user's geographical location information. Thus, the communications department can provide an optimal connection method based on geographical location information.

[0083] The display unit can select the optimal display method by referring to the user's past display history during display. For example, the display unit may prioritize display methods previously used by the user. Furthermore, the display unit can select the most effective display method based on the user's past display history. Additionally, the display unit can analyze the user's past display history to propose the optimal display method. Thus, the display unit can provide the optimal display method based on past display history.

[0084] The display unit can consider the user's geographical location information to select the optimal display method during display. For example, when the user is in a specific location, the display unit selects the optimal display method suitable for that location. Furthermore, the display unit can also propose an optimal display method based on the user's geographical location information. Thus, the display unit can provide an optimal display method based on geographical location information.

[0085] The system involved in this embodiment is not limited to the examples described above. For example, various modifications can be made as shown below.

[0086] The authentication department can additionally obtain users' biometric information to improve authentication reliability. For example, the authentication department measures a user's heart rate or body temperature to confirm whether it is within the normal range. Furthermore, the authentication department can comprehensively analyze users' biometric information to improve authentication reliability. Therefore, the authentication department can improve authentication reliability based on biometric information.

[0087] The monitoring department can learn users' action patterns and detect abnormal actions. For example, it can issue an alert when it detects actions that users wouldn't normally perform. Furthermore, the monitoring department can analyze user action patterns and propose efficient learning methods. Thus, the monitoring department can improve its learning efficiency based on user action patterns.

[0088] The support department can customize prompts based on users' learning styles. For example, it can provide visual prompts to users who prefer visual learning, and voice prompts to users who prefer auditory learning. Thus, the support department can provide optimal prompts based on each user's learning style.

[0089] The selection department can choose questions based on the user's learning goals. For example, if the user aims to pass a specific exam, the selection department will prioritize questions related to that exam. Furthermore, the selection department can also propose long-term learning plans based on the user's learning goals. Thus, the selection department is able to provide optimal questions based on the user's learning objectives.

[0090] The temperature control unit can adjust the temperature according to the user's activity level. For example, when the user is actively moving, the temperature control unit can lower the room temperature to provide a comfortable environment. Conversely, when the user is sitting quietly, the room temperature can be raised to provide a comfortable environment. Thus, the temperature control unit can provide the optimal temperature based on the user's activity level.

[0091] The following is a brief description of the processing flow of Implementation Method 1.

[0092] Step 1: The authentication department identifies the user. For example, users can be identified through facial recognition, fingerprint authentication, voice authentication, etc.

[0093] Step 2: The calling department retrieves the user's learning data identified by the authentication department. For example, it can retrieve learning data such as text data, image data, and voice data.

[0094] Step 3: The monitoring department monitors the user's actions. For example, user actions can be monitored through methods such as camera motion detection and sensor motion detection.

[0095] Step 4: The support department provides a prompt when the action monitored by the monitoring department stops. For example, the prompt can be given through text messages, voice messages, visual cues, etc.

[0096] Step 5: Select previously incorrectly answered questions and create new ones. For example, you can select previously incorrectly answered questions based on criteria such as the number of times the question was answered incorrectly or the content of the incorrect answer.

[0097] Step 6: The temperature control unit performs automatic temperature adjustment using AI. For example, automatic temperature adjustment can be achieved through the use of temperature sensors or different learning algorithms.

[0098] Step 7: Shielding the part to block radio waves from the outside. For example, external radio waves can be shielded by using radio wave shielding materials, shielding technologies, etc.

[0099] Step 8: The communications department provides Wi-Fi. For example, Wi-Fi can be provided through the type of router, communication protocol, etc.

[0100] Step 9: The display unit displays the information on the monitor. For example, the information can be displayed by the type of monitor, display format, etc.

[0101] Implementation Method 2

[0102] The learning support system described in this invention provides users with an environment conducive to focused learning within a short period. This system is equipped with a box about the size of a telephone booth, housing facial recognition, an AI camera, AI automatic temperature control, a radio wave shielding box, Wi-Fi, and a display, creating an environment conducive to concentrated learning. First, upon entering the telephone booth, the facial recognition system identifies the user and accesses personalized learning data. Next, the AI ​​camera monitors the user's actions, tracking their learning progress in real time. For example, if the user pauses while answering a question, the AI ​​will provide a prompt after a few seconds. Furthermore, it has the function of selecting previously answered incorrect questions and re-asking them. Further, it features AI automatic temperature control to maintain a comfortable learning environment. The radio wave shielding box prevents external interference, improving concentration. Equipped with Wi-Fi and a display, it supports online learning and remote conferencing. Through this system, users can efficiently advance their learning in a short time, maintaining high productivity even in today's deadline-driven society. For example, upon entering the telephone booth, the facial recognition system identifies the user and accesses personalized learning data. Next, the AI ​​camera monitors the user's actions, tracking their learning progress in real time. For example, if a user pauses their action on a question, the generated AI will provide a prompt after a few seconds. It also has the function of selecting previously answered questions and generating new ones. Furthermore, it features AI-powered automatic temperature control to maintain a comfortable learning environment. An electromagnetic shielding box prevents external interference, enhancing focus. Equipped with Wi-Fi and a monitor, it supports online learning and remote meetings. Through this system, users can efficiently advance their learning in a short time, maintaining high productivity even in today's deadline-driven society. Thus, the learning support system optimizes the user's learning environment and supports efficient learning.

[0103] The learning support system described in this embodiment includes an authentication unit, a calling unit, a monitoring unit, an auxiliary unit, a selection unit, a temperature regulation unit, a shielding unit, a communication unit, and a display unit. The authentication unit identifies the user. For example, it can identify the user through facial recognition, fingerprint authentication, or voice authentication. The calling unit calls the learning data of the user identified by the authentication unit. For example, it can call learning data such as text data, image data, and voice data. The monitoring unit monitors the user's actions. For example, it can monitor the user's actions through camera motion detection or sensor motion detection. The auxiliary unit provides a prompt when the action monitored by the monitoring unit stops. For example, it can provide a prompt through text messages, voice messages, or visual cues. The selection unit selects previously incorrectly answered questions and re-answers them. For example, it can select previously incorrectly answered questions based on criteria such as the number of incorrect answers or the content of the incorrect answers. The temperature regulation unit performs automatic temperature regulation using AI. For example, it can perform automatic temperature regulation through the use of temperature sensors or the type of learning algorithm. The shielding unit shields external radio waves. For example, it can shield external radio waves by using radio wave shielding materials or shielding technologies. The communication unit provides Wi-Fi. For example, Wi-Fi can be provided through the type of router, communication protocol, etc. The display unit shows information on the monitor. For example, information can be displayed through the type of monitor, display format, etc. Therefore, the learning support system according to this embodiment can optimize the user's learning environment and support efficient learning.

[0104] The monitoring department can monitor user actions in real time. For example, it can use cameras to capture and analyze user movements in real time. Alternatively, it can use sensors to detect and analyze user movements. Furthermore, it can utilize AI to analyze and monitor user actions in real time. Thus, by monitoring user actions in real time, the monitoring department can accurately grasp the learning progress.

[0105] The support unit can provide a prompt after a certain period of time when the user stops performing an action. For example, the support unit might provide a prompt a few seconds after the user stops performing an action related to a problem via a text message. Alternatively, the support unit can provide a prompt via voice message after the user stops performing an action related to a problem via a voice message after a few seconds. Furthermore, the support unit can provide a prompt via visual cues after the user stops performing an action related to a problem via a visual cues after a few seconds. Thus, the support unit can provide timely prompts when the user is stuck on a problem.

[0106] The selection department can select questions that were previously answered incorrectly and generate new questions. For example, the selection department can retrieve previously incorrectly answered questions from the database and generate new questions. Furthermore, the selection department can also select questions based on the number of times a question was answered incorrectly in the past. For example, the selection department can select questions to be re-assigned based on the number of times a user answered a question incorrectly in the past. Additionally, the selection department can also select questions based on the content of the incorrect answers to previously answered questions. For example, the selection department can select questions to be re-assigned based on the content of the incorrect answers to previously answered questions. Thus, the selection department can effectively review based on the user's past learning history.

[0107] The temperature control unit can automatically adjust the temperature using AI. For example, it measures the room temperature using a temperature sensor, calculates the optimal temperature using AI, and automatically adjusts it. Furthermore, it can measure the user's body temperature, calculate the optimal room temperature using AI, and automatically adjust it. For example, it can monitor the user's movements using a camera, calculate the optimal temperature using AI, and automatically adjust it. Thus, the temperature control unit can maintain a comfortable learning environment for the user.

[0108] The shielding component can block external radio waves. For example, it can use a radio wave shielding material to shield external radio waves. Alternatively, it can use shielding technology to shield external radio waves. Furthermore, it can use a radio wave shielding film to shield external radio waves. Thus, the shielding component can prevent external interference and improve concentration.

[0109] The communications department can provide Wi-Fi. For example, the communications department provides Wi-Fi via a router. Alternatively, the communications department can provide Wi-Fi via a communication protocol. Furthermore, the communications department can provide Wi-Fi via a Wi-Fi access point. Thus, the communications department can support online learning and remote conferencing.

[0110] The display unit is capable of displaying information on a monitor. For example, the display unit displays information using a monitor. Furthermore, the display unit can also display information using a specific display format. For example, the display unit uses a specific display format. Additionally, the display unit can also display information using a projector. For example, the display unit uses a projector to display information. Thus, the display unit can intuitively view learning content and meeting materials.

[0111] The authentication department can infer a user's emotional state and adjust the speed of the authentication process accordingly. For example, when a user is stressed, the department can expedite the authentication process to reduce their burden. Conversely, when a user is relaxed, the department can proceed at its normal pace for thorough verification. Furthermore, when a user is anxious, the department can speed up the authentication process by quickly accessing learning data. Thus, the authentication department can tailor the authentication process to the user's emotional state, thereby reducing their workload.

[0112] The authentication department can improve accuracy by referencing a user's past authentication history during the authentication process. For example, it can prioritize authentication methods previously used by the user (such as facial recognition or fingerprint authentication). Furthermore, the authentication department can predict authentication time based on the user's past authentication history and propose the optimal authentication method. Additionally, the authentication department can analyze the user's past authentication history to identify patterns that improve authentication accuracy. Therefore, the authentication department can improve authentication accuracy based on past authentication history.

[0113] The authentication department can additionally acquire users' biometric information during authentication to improve authentication reliability. For example, the authentication department can acquire additional biometric information (such as heart rate or body temperature) during authentication to enhance reliability. For instance, the authentication department measures the user's heart rate to confirm if it is within the normal range. Furthermore, the authentication department can measure the user's body temperature to confirm if it is abnormal. In addition, the authentication department can comprehensively analyze the user's biometric information to improve authentication reliability. Therefore, the authentication department can improve authentication reliability based on biometric information.

[0114] The authentication department can infer a user's emotional state and select the appropriate authentication method based on this inference. For example, when a user is nervous, facial recognition is prioritized for quick authentication. Conversely, when a user is relaxed, fingerprint authentication can be used for more detailed verification. Furthermore, when a user is anxious, voice authentication can be used for rapid completion. Thus, the authentication department can provide the optimal authentication method based on the user's emotional state.

[0115] The authentication department can consider the user's geographical location information to customize the authentication process. For example, it can provide a simplified authentication process when the user is at home, a detailed process when the user is in a public place, and a location-specific authentication method when the user is in a particular location. Thus, the authentication department can optimize the authentication process based on geographical location information.

[0116] The authentication department can analyze users' social media activity during the authentication process to improve authentication reliability. For example, by analyzing users' social media activity, the department can understand the current situation and adjust the authentication process accordingly. Furthermore, the department can analyze users' social media activity to identify patterns that improve authentication reliability. Additionally, the department can select authentication methods based on users' social media activity. Therefore, the authentication department can improve authentication reliability by leveraging social media activity.

[0117] The retrieval unit can infer the user's emotions and adjust the order in which learning data is retrieved based on these inferences. For example, when the user is stressed, the retrieval unit might start with easier questions and retrieve them sequentially. Conversely, when the user is relaxed, it might start with more difficult questions and retrieve them sequentially. Furthermore, when the user is anxious, it might start with more important questions and retrieve them sequentially. Thus, the retrieval unit can provide the optimal order for retrieving learning data based on the user's emotions.

[0118] The calling unit can select the optimal learning data by referring to the user's past learning history during the calling process. For example, the calling unit might prioritize calling questions the user previously answered incorrectly. Furthermore, the calling unit can select the most effective learning data based on the user's past learning history. Additionally, the calling unit can analyze the user's past learning history to propose optimal learning data. Thus, the calling unit can provide optimal learning data based on past learning history.

[0119] The invocation unit can customize data based on the user's current learning progress at the time of invocation. For example, the invocation unit can suggest the next learning data based on the user's current learning progress. Furthermore, the invocation unit can monitor the user's learning progress in real time and provide optimal learning data. Additionally, the invocation unit can analyze the user's learning progress and select effective learning data. Therefore, the invocation unit can provide optimal learning data based on the current learning progress.

[0120] The calling unit can infer the user's emotions and adjust the display of learning data accordingly. For example, when the user is nervous, the calling unit provides a simple and easily identifiable display. Conversely, when the user is relaxed, it can provide a display with detailed information. Furthermore, when the user is anxious, it can provide a display highlighting key points. Thus, the calling unit can provide the optimal display based on the user's emotions.

[0121] The invocation unit can consider the user's geographical location information during the invocation process, prioritizing the retrieval of highly relevant learning data. For example, when the user is located in a specific location, the invocation unit will prioritize the retrieval of learning data related to that location. Furthermore, the invocation unit can also propose optimal learning data based on the user's geographical location information. Thus, the invocation unit can provide highly relevant learning data based on geographical location information.

[0122] The invocation unit can analyze a user's social media activity and retrieve relevant learning data during the invocation process. For example, the invocation unit can analyze a user's social media activity to determine their current interests and provide relevant learning data. Furthermore, the invocation unit can analyze a user's social media activity to propose optimal learning data. Additionally, the invocation unit can retrieve highly relevant learning data based on the user's social media activity. Thus, the invocation unit can provide highly relevant learning data based on social media activity.

[0123] The monitoring department can infer users' emotions and adjust the monitoring frequency accordingly. For example, when a user is stressed, the monitoring department can reduce the monitoring frequency to alleviate the user's burden. Conversely, when a user is relaxed, the monitoring department can maintain its normal frequency. Furthermore, when a user is anxious, the monitoring department can increase the monitoring frequency to respond quickly. Thus, the monitoring department can provide an optimal monitoring frequency based on the user's emotions.

[0124] The monitoring department can improve monitoring accuracy by referencing users' past behavior patterns during monitoring. For example, the monitoring department can propose the optimal monitoring method based on users' past behavior patterns. Furthermore, the monitoring department can analyze users' past behavior patterns to discover patterns that improve monitoring accuracy. Additionally, the monitoring department can adjust the timing of monitoring based on users' past behavior patterns. Therefore, the monitoring department can improve monitoring accuracy based on past behavior patterns.

[0125] The monitoring department can additionally acquire users' biometric information during monitoring to improve monitoring reliability. For example, the monitoring department can acquire additional user biometric information (such as heart rate or body temperature) during monitoring to enhance reliability. For instance, the monitoring department measures the user's heart rate to confirm if it is within the normal range. Furthermore, the monitoring department can measure the user's body temperature to confirm if there are any abnormalities. In addition, the monitoring department can comprehensively analyze the user's biometric information to improve monitoring reliability. Therefore, the monitoring department can improve monitoring reliability based on biometric information.

[0126] The monitoring department can infer a user's emotions and adjust the display of monitoring results accordingly. For example, when a user is tense, the monitoring department provides a simple and easily identifiable display. Conversely, when a user is relaxed, it can provide a display containing detailed information. Furthermore, when a user is anxious, it can provide a display highlighting key points. Thus, the monitoring department can provide the optimal display based on the user's emotions.

[0127] The monitoring department can customize the monitoring range by considering the user's geographical location information during monitoring. For example, when a user is located in a specific location, the monitoring department can set a monitoring range suitable for that location. Furthermore, the monitoring department can propose an optimal monitoring range based on the user's geographical location information. Additionally, the monitoring department can adjust the monitoring range based on the user's geographical location information. Thus, the monitoring department can provide an optimal monitoring range based on geographical location information.

[0128] The monitoring department can analyze users' social media activities during monitoring to improve monitoring accuracy. For example, by analyzing users' social media activities, the monitoring department can grasp the current situation and improve monitoring accuracy. Furthermore, the monitoring department can analyze users' social media activities to propose optimal monitoring methods. Additionally, the monitoring department can adjust the monitoring scope based on users' social media activities. Therefore, the monitoring department can improve monitoring accuracy by leveraging social media activity.

[0129] The support unit can infer the user's emotions and adjust the way prompts are made based on the inferred emotions. For example, when the user is stressed, the support unit can provide simple prompts to reduce the user's burden. Furthermore, when the user is relaxed, the support unit can provide detailed prompts to support learning. Additionally, when the user is anxious, the support unit can quickly provide prompts to improve learning efficiency. Thus, the support unit can provide optimal prompts based on the user's emotions.

[0130] The support department can refer to the user's past learning history to provide optimal suggestions during assistance. For example, the support department can provide relevant suggestions for questions the user answered incorrectly in the past. Furthermore, the support department can select the most effective suggestions based on the user's past learning history. Additionally, the support department can analyze the user's past learning history to propose optimal suggestions. Therefore, the support department can provide optimal suggestions based on past learning history.

[0131] The support department can customize prompts based on the user's current learning progress during assistance. For example, it can provide prompts related to the next learning content based on the user's current progress. Furthermore, the support department can monitor the user's learning progress in real time and provide optimal prompts. Additionally, it can analyze the user's learning progress and select effective prompts. Therefore, the support department can provide optimal prompts based on the current learning progress.

[0132] The support unit can infer the user's emotions and adjust the display of prompts accordingly. For example, when the user is tense, the support unit provides a simple and easily identifiable display. Conversely, when the user is relaxed, it can provide a display with detailed information. Furthermore, when the user is anxious, it can provide a display highlighting key points. Thus, the support unit can provide the optimal display based on the user's emotions.

[0133] The support unit can consider the user's geographical location information when providing assistance, prioritizing highly relevant prompts. For example, when the user is in a specific location, the support unit will prioritize prompts related to that location. Furthermore, the support unit can also provide optimal prompts based on the user's geographical location information. Thus, the support unit is able to provide highly relevant prompts based on geographical location information.

[0134] The support department can analyze users' social media activity and provide relevant prompts during assistance. For example, the support department can analyze users' social media activity to understand their current interests and provide relevant prompts. Furthermore, the support department can analyze users' social media activity to propose optimal prompts. Additionally, the support department can provide highly relevant prompts based on users' social media activity. Thus, the support department is able to provide highly relevant prompts based on social media activity.

[0135] The selection unit can infer the user's emotions and adjust the timing of re-generating questions based on these inferences. For example, if the user is under pressure, the selection unit might postpone re-generating questions. Conversely, it could re-generate questions at a normal time if the user is relaxed. Furthermore, it could re-generate questions earlier if the user is anxious. Thus, the selection unit can provide the optimal timing for re-generating questions based on the user's emotions.

[0136] The selection unit can refer to the user's past learning history to select the optimal question during the selection process. For example, the selection unit may prioritize selecting questions the user previously answered incorrectly. Furthermore, the selection unit can also select the most effective questions based on the user's past learning history. Additionally, the selection unit can analyze the user's past learning history to propose the optimal question. Thus, the selection unit can provide the optimal question based on past learning history.

[0137] The selection unit can customize questions based on the user's current learning progress at the time of selection. For example, the selection unit can select the next question to be learned based on the user's current learning progress. Furthermore, the selection unit can monitor the user's learning progress in real time and provide optimal questions. Additionally, the selection unit can analyze the user's learning progress and select effective questions. Therefore, the selection unit can provide optimal questions based on the current learning progress.

[0138] The selection unit can infer the user's mood and adjust the display of subsequent questions based on this inference. For example, when the user is nervous, the selection unit provides a concise and easily recognizable display. Conversely, when the user is relaxed, it can provide a display with detailed information. Furthermore, when the user is anxious, it can provide a display highlighting key points. Thus, the selection unit can provide the optimal display based on the user's mood.

[0139] The selection unit can consider the user's geographical location information during selection, prioritizing highly relevant questions. For example, when the user is located in a specific location, the selection unit prioritizes questions related to that location. Furthermore, the selection unit can also propose optimal questions based on the user's geographical location information. Thus, the selection unit can provide highly relevant questions based on geographical location information.

[0140] The selection department can analyze a user's social media activity during the selection process to select relevant questions. For example, the selection department can analyze a user's social media activity to determine their current interests and provide relevant questions. Furthermore, the selection department can analyze a user's social media activity to propose optimal questions. Additionally, the selection department can select highly relevant questions based on the user's social media activity. Thus, the selection department can provide highly relevant questions based on social media activity.

[0141] The temperature control unit can anticipate the user's emotions and adjust the frequency of temperature regulation accordingly. For example, when the user is stressed, the unit increases the temperature regulation frequency to maintain a comfortable environment. Conversely, when the user is relaxed, the unit can adjust the temperature at the normal frequency. Furthermore, when the user is anxious, the unit can decrease the temperature regulation frequency to maintain focus. Thus, the temperature control unit can provide an optimal temperature regulation frequency based on the user's emotions.

[0142] The temperature control unit can refer to the user's past temperature setting history to set the optimal temperature during temperature adjustment. For example, the temperature control unit can suggest an optimal temperature based on the user's past temperature settings. Furthermore, the temperature control unit can select the most comfortable temperature based on the user's past temperature setting history. Additionally, the temperature control unit can analyze the user's past temperature setting history to set the optimal temperature. Therefore, the temperature control unit can provide the optimal temperature based on past temperature setting history.

[0143] The temperature control unit can additionally acquire the user's biometric information during temperature adjustment to improve temperature reliability. For example, the temperature control unit can measure the user's heart rate to confirm whether it is within the normal range. Furthermore, the temperature control unit can also measure the user's body temperature to confirm whether it is abnormal. In addition, the temperature control unit can comprehensively analyze the user's biometric information to set the optimal temperature. Therefore, the temperature control unit can provide the optimal temperature based on biometric information.

[0144] The temperature control unit can anticipate the user's emotions and select the appropriate temperature control method based on those anticipations. For example, when the user is tense, the unit prioritizes cooling to provide a comfortable environment. Conversely, when the user is relaxed, the unit can perform normal temperature adjustments. Furthermore, when the user is anxious, the unit can quickly adjust the temperature to maintain focus. Thus, the temperature control unit can provide the optimal temperature control method based on the user's emotions.

[0145] The temperature control unit can take into account the user's geographical location information to set the optimal temperature during temperature adjustment. For example, when the user is in a specific location, the temperature control unit sets an optimal temperature suitable for that location. Furthermore, the temperature control unit can also propose an optimal temperature based on the user's geographical location information. Thus, the temperature control unit can provide an optimal temperature based on geographical location information.

[0146] The temperature control unit can analyze the user's social media activity during temperature adjustment to provide relevant temperature settings. For example, the temperature control unit can understand the current situation through the user's social media activity and set an optimal temperature. Furthermore, the temperature control unit can also analyze the user's social media activity to suggest an optimal temperature. In addition, the temperature control unit can provide highly relevant temperature settings based on the user's social media activity. Therefore, the temperature control unit can provide an optimal temperature based on social media activity.

[0147] The shielding unit can anticipate a user's emotions and adjust the timing of radio wave shielding accordingly. For example, it can perform radio wave shielding earlier when the user is under stress. Conversely, it can perform shielding at the normal time when the user is relaxed. Furthermore, it can postpone shielding when the user is anxious. Thus, the shielding unit can provide optimal radio wave shielding timing based on the user's emotions.

[0148] The blocking unit can select the optimal blocking method by referring to a user's past blocking history during the blocking process. For example, the blocking unit may prioritize blocking methods previously used by the user. Furthermore, the blocking unit can select the most effective blocking method based on the user's past blocking history. Additionally, the blocking unit can analyze the user's past blocking history to propose the optimal blocking method. Thus, the blocking unit can provide the optimal blocking method based on past blocking history.

[0149] The shielding unit can anticipate the user's emotions and select the appropriate shielding method based on these anticipations. For example, when the user is tense, the shielding unit can perform complete radio wave shielding to improve concentration. Conversely, when the user is relaxed, the shielding unit can perform partial radio wave shielding. Furthermore, when the user is anxious, the shielding unit can choose not to perform radio wave shielding, allowing the task to proceed quickly. Thus, the shielding unit can provide the optimal shielding method based on the user's emotions.

[0150] The shielding unit can select the optimal shielding method by considering the user's geographical location information during shielding. For example, when the user is located in a specific location, the shielding unit selects the optimal shielding method suitable for that location. Furthermore, the shielding unit can also propose an optimal shielding method based on the user's geographical location information. Thus, the shielding unit can provide an optimal shielding method based on geographical location information.

[0151] The communication unit can infer a user's emotions and adjust the Wi-Fi connection speed accordingly. For example, when a user is stressed, the communication unit can increase the Wi-Fi connection speed to provide a comfortable communication environment. Conversely, when a user is relaxed, the communication unit can maintain the normal connection speed. Furthermore, when a user is in a hurry, the communication unit can maximize the Wi-Fi connection speed for rapid communication. Thus, the communication unit can provide the optimal Wi-Fi connection speed based on the user's emotions.

[0152] The communications department can select the optimal connection method by referring to the user's past communication history during communication. For example, the communications department may prioritize connection methods previously used by the user. Furthermore, the communications department can select the most efficient connection method based on the user's past communication history. Additionally, the communications department can analyze the user's past communication history to propose the optimal connection method. Thus, the communications department can provide the optimal connection method based on past communication history.

[0153] The communications department can infer a user's emotional state and select the appropriate Wi-Fi connection method based on this inference. For example, when a user is stressed, the communications department prioritizes a stable connection method. Conversely, when a user is relaxed, the communications department can use a standard connection method. Furthermore, when a user is anxious, the communications department can select the fastest connection method. Thus, the communications department can provide the optimal Wi-Fi connection method based on the user's emotional state.

[0154] The communications department can select the optimal connection method by considering the user's geographical location information during communication. For example, when the user is located in a specific location, the communications department selects the optimal connection method suitable for that location. Furthermore, the communications department can also propose an optimal connection method based on the user's geographical location information. Thus, the communications department can provide an optimal connection method based on geographical location information.

[0155] The display unit can infer the user's emotions and adjust the displayed content accordingly. For example, when the user is stressed, the display unit provides concise and easily identifiable content. Conversely, when the user is relaxed, it can provide content containing detailed information. Furthermore, when the user is anxious, it can provide content highlighting key points. Thus, the display unit can provide optimal display content based on the user's emotions.

[0156] The display unit can select the optimal display method by referring to the user's past display history during display. For example, the display unit may prioritize display methods previously used by the user. Furthermore, the display unit can select the most effective display method based on the user's past display history. Additionally, the display unit can analyze the user's past display history to propose the optimal display method. Thus, the display unit can provide the optimal display method based on past display history.

[0157] The display unit can infer the user's emotions and select a display method based on those inferences. For example, when the user is tense, the display unit provides a simple and easily identifiable display. Conversely, when the user is relaxed, it can provide a display containing detailed information. Furthermore, when the user is anxious, it can provide a display highlighting key points. Thus, the display unit can provide the optimal display method based on the user's emotions.

[0158] The display unit can consider the user's geographical location information to select the optimal display method during display. For example, when the user is in a specific location, the display unit selects the optimal display method suitable for that location. Furthermore, the display unit can also propose an optimal display method based on the user's geographical location information. Thus, the display unit can provide an optimal display method based on geographical location information.

[0159] The system involved in this embodiment is not limited to the examples described above. For example, various modifications can be made as shown below.

[0160] The authentication department can additionally obtain users' biometric information to improve authentication reliability. For example, the authentication department measures a user's heart rate or body temperature to confirm whether it is within the normal range. Furthermore, the authentication department can comprehensively analyze users' biometric information to improve authentication reliability. Therefore, the authentication department can improve authentication reliability based on biometric information.

[0161] The monitoring department can learn users' action patterns and detect abnormal actions. For example, it can issue an alert when it detects actions that users wouldn't normally perform. Furthermore, the monitoring department can analyze user action patterns and propose efficient learning methods. Thus, the monitoring department can improve its learning efficiency based on user action patterns.

[0162] The support department can customize prompts based on users' learning styles. For example, it can provide visual prompts to users who prefer visual learning, and voice prompts to users who prefer auditory learning. Thus, the support department can provide optimal prompts based on each user's learning style.

[0163] The selection department can choose questions based on the user's learning goals. For example, if the user aims to pass a specific exam, the selection department will prioritize questions related to that exam. Furthermore, the selection department can also propose long-term learning plans based on the user's learning goals. Thus, the selection department is able to provide optimal questions based on the user's learning objectives.

[0164] The temperature control unit can adjust the temperature according to the user's activity level. For example, when the user is actively moving, the temperature control unit can lower the room temperature to provide a comfortable environment. Conversely, when the user is sitting quietly, the room temperature can be raised to provide a comfortable environment. Thus, the temperature control unit can provide the optimal temperature based on the user's activity level.

[0165] The authentication department can infer a user's emotional state and adjust the speed of the authentication process accordingly. For example, when a user is under pressure, the department can expedite the authentication process to reduce the user's burden. Conversely, when a user is relaxed, the department can proceed with the authentication process at its normal pace, providing thorough verification. Thus, the authentication department can tailor the authentication process to the user's emotional state, thereby reducing their workload.

[0166] The data retrieval unit can infer the user's emotions and adjust the order in which learning data is retrieved based on these inferences. For example, when the user is under stress, the unit will retrieve data starting with easier questions. Conversely, when the user is relaxed, the unit can retrieve data starting with more challenging questions. Thus, the unit can provide the optimal order for retrieving learning data based on the user's emotional state.

[0167] The monitoring department can infer users' emotions and adjust the monitoring frequency accordingly. For example, when a user is feeling stressed, the monitoring department can reduce the monitoring frequency to alleviate the user's burden. Conversely, when a user is relaxed, the monitoring department can monitor at the normal frequency. Thus, the monitoring department can provide the optimal monitoring frequency based on the user's emotional state.

[0168] The support department can infer the user's emotions and adjust the way prompts are made based on the inferred emotions. For example, when the user is feeling stressed, the support department provides simple prompts to reduce the user's burden. Furthermore, when the user is relaxing, the support department can provide detailed prompts to support learning. Thus, the support department can provide optimal prompts based on the user's emotions.

[0169] The selection unit can infer the user's emotions and adjust the timing of re-generating questions based on these inferences. For example, the selection unit can postpone re-generating questions when the user is feeling stressed. Conversely, it can re-generate questions at a normal time when the user is relaxed. Thus, the selection unit can provide the optimal timing for re-generating questions based on the user's emotions.

[0170] The following is a brief description of the processing flow of Implementation Method 2.

[0171] Step 1: The authentication department identifies the user. For example, users can be identified through facial recognition, fingerprint authentication, voice authentication, etc.

[0172] Step 2: The calling department retrieves the user's learning data identified by the authentication department. For example, it can retrieve learning data such as text data, image data, and voice data.

[0173] Step 3: The monitoring department monitors the user's actions. For example, user actions can be monitored through methods such as camera motion detection and sensor motion detection.

[0174] Step 4: The support department provides a prompt when the action monitored by the monitoring department stops. For example, the prompt can be given through text messages, voice messages, visual cues, etc.

[0175] Step 5: Select previously incorrectly answered questions and create new ones. For example, you can select previously incorrectly answered questions based on criteria such as the number of times the question was answered incorrectly or the content of the incorrect answer.

[0176] Step 6: The temperature control unit performs automatic temperature adjustment using AI. For example, automatic temperature adjustment can be achieved through the use of temperature sensors or different learning algorithms.

[0177] Step 7: Shielding the part to block radio waves from the outside. For example, external radio waves can be shielded by using radio wave shielding materials, shielding technologies, etc.

[0178] Step 8: The communications department provides Wi-Fi. For example, Wi-Fi can be provided through the type of router, communication protocol, etc.

[0179] Step 9: The display unit displays the information on the monitor. For example, the information can be displayed by the type of monitor, display format, etc.

[0180] The specific processing unit 290 sends 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 voice representing the user's input to the result of the specific processing. The control unit 46A sends the voice data representing the user's 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.

[0181] Data generation model 58 is what is known as generative AI (Artificial Intelligence). An example of data generation model 58 includes ChatGPT (registered trademark) (Internet search).<URL:https: / / openai.com / blog / chatgpt> Generative AI, such as data generation model 58, is obtained by deep learning through a neural network. The data generation model 58 is input with a prompt containing instructions, and with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data of still images or data of moving images). The data generation model 58 infers the input inference data according to the instructions shown in the prompt, and outputs the inference result in one or more data forms such as speech data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the above-described specific processing while using the data generation model 58. The data generation model 58 can also be a fine-tuned model to output inference results from prompts without instructions; in this case, the data generation model 58 can output inference results from prompts without instructions. The data processing device 12, etc., includes multiple data generation models 58, including AI other than generative AI. AI other than generative AI includes, but is not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or Naive Bayes. Furthermore, AI can also act as an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to this example. Moreover, processing performed by AI, including generative AI, can be replaced by rule-based processing, and vice versa.

[0182] Furthermore, the processing performed by the aforementioned 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 it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Additionally, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or external devices, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or external devices.

[0183] Each of the aforementioned elements—authentication unit, recall unit, monitoring unit, auxiliary unit, selection unit, temperature control unit, shielding unit, communication unit, and display unit—is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the authentication unit uses the camera 42 or microphone 38B of the smart device 14 to identify the user, and the authentication process is executed by the control unit 46A. The recall unit recalls the user's learning data through the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the user's actions using the camera 42 of the smart device 14, and the monitoring data is processed by the control unit 46A. The auxiliary unit generates prompts through the specific processing unit 290 of the data processing device 12 and displays the prompts through the output device 40 of the smart device 14. The selection unit selects previously incorrectly answered questions and re-assigns them through the specific processing unit 290 of the data processing device 12. The temperature control unit automatically adjusts the temperature using the control unit 46A of the smart device 14. The shielding unit uses the shielding technology of the smart device 14 to shield external radio waves. The communication unit provides Wi-Fi using the communication I / F 44 of the smart device 14. The display unit uses the display 40A of the smart device 14 to display information. The correspondence between each unit and the device or control unit is not limited to the example above and can be changed in various ways.

[0184] Second Implementation Method

[0185] Figure 3 An example of the configuration of the data processing system 210 according to the second embodiment is shown.

[0186] like Figure 3 As shown, the data processing system 210 includes a data processing device 12 and smart glasses 214. One example of the data processing device 12 is a server.

[0187] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, 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. An example of the network 54 includes a WAN and / or LAN, etc.

[0188] 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, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0189] Microphone 238 receives user commands by receiving the user's voice. Microphone 238 captures the user's voice, converts the captured sound into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to commands from processor 46.

[0190] The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, used to photograph the user's surroundings (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0191] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0192] Figure 4 An example of the main functions of the data processing device 12 and the smart glasses 214 is shown. Figure 4 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0193] The processor 28 reads a specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0194] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function (emotion-specific function) using the emotion-specific model 59 includes various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0195] In the smart glasses 214, specific processing is performed by the processor 46. A specific processing program 60 is stored in the memory 50. The processor 46 reads the specific processing program 60 from the memory 50 and executes the read specific processing program 60 on the RAM 48. Specific processing is implemented by the processor 46 operating as a control unit 46A based on the specific processing program 60 executed on the RAM 48. Furthermore, the smart glasses 214 may also have the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and use these models to perform the same processing as the specific processing unit 290.

[0196] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. In addition, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.).

[0197] The specific processing unit 290 sends 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 voice input representing the user's input to the specific processing result. The control unit 46A sends the voice data representing the user's 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.

[0198] Data generation model 58 is a so-called generative AI. An example of data generation model 58 includes generative AIs such as ChatGPT. Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing instructions is input to data generation model 58, along with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data from still images or moving images). Data generation model 58 infers the inference data based on the instructions shown in the prompt and outputs the inference result in one or more data forms such as speech data, text data, and image data. Data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. Specific processing unit 290 performs the aforementioned specific processing while using data generation model 58. Data generation model 58 can also be a fine-tuned model to output inference results from prompts without instructions; in this case, data generation model 58 can output inference results from prompts without instructions. The data processing device 12, etc., includes various data generation models 58, which include AI other than generative AI. These AIs include, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or Naive Bayes, and are capable of various processing methods, but are not limited to these examples. Furthermore, AI can also be an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to these examples. Moreover, processing performed by AI including generative AI can be replaced by rule-based processing, and vice versa.

[0199] The data processing system 210 of the second embodiment performs the same processing as the data processing system 10 of the first embodiment. The processing performed 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 it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or external devices, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or external devices.

[0200] Each of the aforementioned elements—authentication unit, recall unit, monitoring unit, assistance unit, selection unit, temperature control unit, shielding unit, communication unit, and display unit—is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the authentication unit uses the camera 42 or microphone 238 of the smart glasses 214 to identify the user, and the control unit 46A executes the authentication process. The recall unit recalls the user's learning data through the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the user's actions using the camera 42 of the smart glasses 214, and the control unit 46A processes the monitoring data. The assistance unit generates prompts through the specific processing unit 290 of the data processing device 12 and delivers these prompts through the speaker 240 of the smart glasses 214. The selection unit selects previously incorrectly answered questions and presents them again through the specific processing unit 290 of the data processing device 12. The temperature control unit automatically adjusts the temperature using the control unit 46A of the smart glasses 214. The shielding unit uses the shielding technology of the smart glasses 214 to shield external radio waves. The communication unit provides Wi-Fi via the communication I / F44 of the smart glasses 214. The display unit displays information via the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above example and can be modified in various ways.

[0201] Third Implementation Method

[0202] Figure 5 An example of the configuration of the data processing system 310 according to the third embodiment is shown.

[0203] like Figure 5 As shown, the data processing system 310 includes a data processing device 12 and a head-mounted terminal 314. An example of the data processing device 12 is a server.

[0204] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, 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. An example of the network 54 includes a WAN and / or LAN, etc.

[0205] The head-mounted 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, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0206] Microphone 238 receives user commands by receiving the user's voice. Microphone 238 captures the user's voice, converts the captured sound into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to commands from processor 46.

[0207] The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, used to photograph the user's surroundings (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0208] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0209] Figure 6 An example of the main functions of the data processing device 12 and the head-mounted terminal 314 is shown. Figure 6 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0210] The processor 28 reads a specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0211] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function (emotion-specific function) using the emotion-specific model 59 includes various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0212] In the head-mounted terminal 314, specific processing is performed by the processor 46. A specific program 60 is stored in the memory 50. The processor 46 reads the specific program 60 from the memory 50 and executes the read specific program 60 on the RAM 48. Specific processing is implemented by the processor 46 operating as a control unit 46A based on the specific program 60 executed on the RAM 48. Furthermore, the head-mounted terminal 314 may also have the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and use these models to perform the same processing as the specific processing unit 290.

[0213] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. In addition, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.).

[0214] The specific processing unit 290 sends the result of the specific processing to the head-mounted terminal 314. In the head-mounted terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires voice representing the user's input to the specific processing result. The control unit 46A sends the voice data representing the user's 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.

[0215] Data generation model 58 is a so-called generative AI. An example of data generation model 58 includes generative AIs such as ChatGPT. Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing instructions is input to data generation model 58, along with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data from still images or moving images). Data generation model 58 infers the inference data based on the instructions shown in the prompt and outputs the inference result in one or more data forms such as speech data, text data, and image data. Data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. Specific processing unit 290 performs the aforementioned specific processing while using data generation model 58. Data generation model 58 can also be a fine-tuned model to output inference results from prompts without instructions; in this case, data generation model 58 can output inference results from prompts without instructions. The data processing device 12, etc., includes various data generation models 58, which include AI other than generative AI. These AIs include, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or Naive Bayes, and are capable of various processing methods, but are not limited to these examples. Furthermore, AI can also be an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to these examples. Moreover, processing performed by AI including generative AI can be replaced by rule-based processing, and vice versa.

[0216] The data processing system 310 of the third embodiment performs the same processing as the data processing system 10 of the first embodiment. The processing performed 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 head-mounted terminal 314, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the head-mounted terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the head-mounted terminal 314 or external devices, and the head-mounted terminal 314 acquires or collects information required for processing from the data processing device 12 or external devices.

[0217] Each of the aforementioned elements—authentication unit, recall unit, monitoring unit, assistance unit, selection unit, temperature control unit, shielding unit, communication unit, and display unit—is implemented, for example, by at least one of the head-mounted terminal 314 and the data processing device 12. For example, the authentication unit identifies the user using the camera 42 or microphone 238 of the head-mounted terminal 314, and the authentication process is executed by the control unit 46A. The recall unit recalls the user's learning data via the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the user's actions using the camera 42 of the head-mounted terminal 314, and the monitoring data is processed by the control unit 46A. The assistance unit generates prompts via the specific processing unit 290 of the data processing device 12 and provides these prompts via the speaker 240 of the head-mounted terminal 314. The selection unit selects previously incorrectly answered questions and presents them again via the specific processing unit 290 of the data processing device 12. The temperature control unit automatically adjusts the temperature using the control unit 46A of the head-mounted terminal 314. The shielding unit uses the shielding technology of the head-mounted terminal 314 to shield external radio waves. The communication unit provides Wi-Fi via the communication I / F44 of the head-mounted terminal 314. The display unit displays information via the display 343 of the head-mounted terminal 314. The correspondence between each unit and the device or control unit is not limited to the above example and can be modified in various ways.

[0218] Fourth Implementation Method

[0219] Figure 7 An example of the configuration of the data processing system 410 according to the fourth embodiment is shown.

[0220] like Figure 7 As shown, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0221] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, 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. An example of the network 54 includes a WAN and / or LAN, etc.

[0222] Robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control object 443. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, and control object 443 are also connected to the bus 52.

[0223] Microphone 238 receives user commands by receiving the user's voice. Microphone 238 captures the user's voice, converts the captured sound into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to commands from processor 46.

[0224] The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, used to photograph the user's surroundings (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0225] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0226] The controlled object 443 includes a display device, LEDs for the eyes, and motors for driving the arms, hands, and feet. The posture and movements of the robot 414 are controlled by controlling the motors for the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. In addition, facial expressions of the robot 414 can also be expressed by controlling the illumination state of the LEDs for the robot 414's eyes.

[0227] Figure 8 An example of the main functions of the data processing device 12 and the robot 414 is shown. For example... Figure 8 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0228] The processor 28 reads a specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0229] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function (emotion-specific function) using the emotion-specific model 59 includes various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0230] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in memory 50. Processor 46 reads the specific program 60 from memory 50 and executes the read specific program 60 on RAM 48. Specific processing is achieved by processor 46 acting as control unit 46A based on the specific program 60 executed on RAM 48. Furthermore, robot 414 may also have the same data generation model and emotion-specific model as data generation model 58 and emotion-specific model 59, and use these models to perform the same processing as specific processing unit 290.

[0231] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. In addition, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.).

[0232] The specific processing unit 290 sends the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires voice representing the user's input regarding the result of the specific processing. The control unit 46A sends the voice data representing 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.

[0233] Data generation model 58 is a so-called generative AI. An example of data generation model 58 includes generative AIs such as ChatGPT. Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing instructions is input to data generation model 58, along with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data from still images or moving images). Data generation model 58 infers the inference data based on the instructions shown in the prompt and outputs the inference result in one or more data forms such as speech data, text data, and image data. Data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. Specific processing unit 290 performs the aforementioned specific processing while using data generation model 58. Data generation model 58 can also be a fine-tuned model to output inference results from prompts without instructions; in this case, data generation model 58 can output inference results from prompts without instructions. The data processing device 12, etc., includes various data generation models 58, which include AI other than generative AI. These AIs include, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or Naive Bayes, and are capable of various processing methods, but are not limited to these examples. Furthermore, AI can also be an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to these examples. Moreover, processing performed by AI including generative AI can be replaced by rule-based processing, and vice versa.

[0234] The data processing system 410 of the fourth embodiment performs the same processing as the data processing system 10 of the first embodiment. The processing performed 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 it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or external devices, and the robot 414 acquires or collects information required for processing from the data processing device 12 or external devices.

[0235] Each of the aforementioned elements—authentication unit, recall unit, monitoring unit, auxiliary unit, selection unit, temperature control unit, shielding unit, communication unit, and display unit—is implemented, for example, by at least one of robot 414 and data processing device 12. For example, the authentication unit uses the robot 414's camera 42 or microphone 238 to identify the user, and the control unit 46A executes the authentication process. The recall unit recalls the user's learning data via the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the user's actions using the robot 414's camera 42, and the control unit 46A processes the monitoring data. The auxiliary unit generates prompts via the specific processing unit 290 of the data processing device 12 and delivers these prompts via the robot 414's speaker 240. The selection unit selects previously incorrectly answered questions and presents them again via the specific processing unit 290 of the data processing device 12. The temperature control unit automatically adjusts the temperature using the robot 414's control unit 46A. The shielding unit uses the robot 414's shielding technology to shield external radio waves. The communication unit provides Wi-Fi using the robot 414's communication I / F 44. The display unit uses the display screen of robot 414 to display information. The correspondence between each unit and the device or control unit is not limited to the above example and can be modified in various ways.

[0236] Furthermore, the emotion-specific model 59, serving as an emotion engine, can determine the user's emotion based on a specific mapping. Specifically, the emotion-specific model 59 can determine the user's emotion based on an emotion graph that serves as a specific mapping (see...). Figure 9 The robot's emotions can be determined by the emotion-specific model 59. In addition, the emotion-specific model 59 can also determine the robot's emotions in the same way, and the specific processing unit 290 can also perform specific processing using the robot's emotions.

[0237] Figure 9 This is a diagram representing an emotion map 400 that maps various emotions. In the emotion map 400, emotions are arranged radially from the center in concentric circles. The closer to the center of the concentric circles, the more primitive the emotion is. Further out on the concentric circles, emotions are arranged representing states or actions arising from mood. Emotion is a concept that includes both feelings and mental states. To the left of the concentric circles, emotions generated by reactions occurring in the brain are arranged roughly. To the right of the concentric circles, emotions guided by situational judgments are arranged roughly. Above and below the concentric circles, emotions generated by reactions occurring in the brain and guided by situational judgments are arranged roughly. Furthermore, the emotion of "pleasure" is arranged above the concentric circles, and the emotion of "unpleasantness" is arranged below. Thus, in the emotion map 400, various emotions are mapped according to the structure of emotion generation, while easily generated emotions are mapped nearby.

[0238] These emotions are distributed at the 3 o'clock position on the Emotion Chart 400, and usually fluctuate between peace and unease. In the right half of the Emotion Chart 400, because situational awareness is more dominant than internal feelings, it gives a sense of calm.

[0239] The inner side of the emotion diagram 400 represents the mind, and the outer side of the emotion diagram 400 represents actions. Therefore, the further you go to the outer side of the emotion diagram 400, the more the emotion can be seen (manifested in actions).

[0240] Here, human emotions are based on a balance of various factors such as posture and blood sugar levels. When these balances deviate from the ideal, it indicates unhappiness; when they approach the ideal, it indicates pleasure. In robots, cars, and motorcycles, emotions can also be created based on a balance of factors such as posture and remaining battery power. When these balances deviate from the ideal, it indicates unhappiness; when they approach the ideal, it indicates pleasure. Emotion maps can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a Brain Physiological Signal Analysis System for Speech Emotion Recognition and Emotion, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). In the left half of the emotion map, emotions belonging to the "response" domain, where sensation is dominant, are arranged. Furthermore, in the right half of the emotion map, emotions belonging to the "situation" domain, where situational cognition is dominant, are arranged.

[0241] The emotion map defines two types of emotions that promote learning. One is a negative emotion located near the middle of "repentance" or "reflection" on the situation side. That is, when the robot experiences negative emotions such as "I never want to feel this way again" or "I never want to be scolded again." The other is a positive emotion located near "desire" on the response side. That is, when the robot experiences positive feelings such as "wanting more" or "wanting to know more."

[0242] The emotion-specific model 59 feeds user input into a pre-learned neural network to obtain emotion values ​​representing each emotion shown in the emotion graph 400, and determines the user's emotion. This neural network is pre-learned based on multiple learning data sets that combine user input with emotion values ​​representing each emotion shown in the emotion graph 400. Furthermore, this neural network is learned to... Figure 10 As shown in sentiment graph 900, sentiment values ​​in nearby configurations are similar to each other. Figure 10 Examples show that multiple emotions such as "peace of mind", "stability", and "reassurance" have similar emotional values.

[0243] In the above embodiments, a specific processing is described by a single computer 22, but the technology disclosed herein is not limited to this, and distributed processing by multiple computers, including computer 22, is also possible.

[0244] In the above embodiments, an example of storing a specific processing program 56 in memory 32 is illustrated, but the technology disclosed herein is not limited thereto. For example, the specific processing program 56 may also be stored in a portable computer-readable non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed into the computer 22 of the data processing device 12. The processor 28 performs specific processing according to the specific processing program 56.

[0245] Alternatively, the specific processing program 56 can be stored in a storage device such as a server connected to the data processing device 12 via a network 54, and the specific processing program 56 can be downloaded and installed into the computer 22 upon request from the data processing device 12.

[0246] Furthermore, it is not necessary to store the entire specific process 56 in a storage device such as a server connected to the data processing device 12 via the network 54, nor is it necessary to store the entire specific process 56 in the memory 32; a portion of the specific process 56 may also be stored.

[0247] As a hardware resource for performing specific processing, various processors can be used. For example, a CPU is a general-purpose processor that functions as a hardware resource for performing specific processing by executing software, i.e., programs. Additionally, a dedicated circuit can be listed as a processor; it is a processor with a circuit structure specifically designed for performing specific processing, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit). Every processor has built-in or connected memory, and every processor executes specific processing by using memory.

[0248] The hardware resources for performing a specific process can consist of one of these various processors, or a combination of two or more processors of the same or different types (e.g., a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Furthermore, the hardware resources for performing a specific process can also be a single processor.

[0249] As an example of a single processor, the first type consists of a combination of one or more CPUs and software, which functions as a hardware resource to perform specific processing. The second type uses a processor, such as a System-on-a-chip (SoC), which implements the entire system functionality, including multiple hardware resources for performing specific processing, using a single IC chip. In this case, the specific processing is implemented using one or more of the aforementioned processors as hardware resources.

[0250] Furthermore, as the hardware architecture of these various processors, more specifically, circuits combining semiconductor elements and other circuit components can be used. Moreover, the specific process described above is merely an example. Therefore, it goes without saying that, without departing from the main point, unnecessary steps can be removed, new steps can be added, or the processing order can be changed.

[0251] Furthermore, although the above examples have been described in terms of first to fourth embodiments, some or all of these embodiments can be combined. Additionally, the smart device 14, smart glasses 214, head-mounted terminal 314, and robot 414 are just examples and can be combined separately, or other devices may be used. Furthermore, although the above examples have been described in terms of morphological example 1 and morphological example 2, these can also be combined.

[0252] The foregoing descriptions and illustrations are detailed explanations of the parts covered by this disclosure and are merely one example of this disclosure. For instance, the descriptions of the above-described structure, function, role, and effect are just one example of the structure, function, role, and effect of the parts covered by this disclosure. Therefore, it goes without saying that, without departing from the spirit of this disclosure, unnecessary parts can be deleted, new elements can be added, or replacements can be made to the foregoing descriptions and illustrations. Furthermore, to avoid confusion and facilitate understanding of the parts covered by this disclosure, explanations of technical common sense that does not require special explanation for implementing this disclosure have been omitted from the foregoing descriptions and illustrations.

[0253] All documents, patent applications and technical standards described in this specification are incorporated herein by reference as if they were specifically and individually described as incorporated by reference.

[0254] [Postscript 1]

[0255] A system characterized by having:

[0256] The authentication department is used to identify users;

[0257] The calling unit is used to call the learning data of the user identified by the authentication unit;

[0258] The monitoring department is used to monitor user actions;

[0259] The auxiliary unit is used to provide a prompt when the action monitored by the monitoring unit stops;

[0260] The selection section is used to select previously incorrectly answered questions and generate new ones.

[0261] Temperature regulation unit, used for automatic temperature regulation via AI;

[0262] Shielding section, used to shield radio waves from the outside;

[0263] The communications department is responsible for providing Wi-Fi.

[0264] The display unit is used to display information on a monitor.

[0265] [Postscript 2]

[0266] The system as described in Appendix 1 is characterized in that,

[0267] The monitoring unit monitors the user's actions in real time.

[0268] [Postscript 3]

[0269] The system as described in Appendix 1 is characterized in that,

[0270] The auxiliary unit provides a prompt after a certain period of time when the user stops performing an action.

[0271] [Postscript 4]

[0272] The system as described in Appendix 1 is characterized in that,

[0273] The selection process involves selecting previously incorrectly answered questions and generating new ones.

[0274] [Postscript 5]

[0275] The system as described in Appendix 1 is characterized in that the temperature regulation unit performs automatic temperature regulation via AI.

[0276] [Postscript 6]

[0277] The system as described in Appendix 1 is characterized in that,

[0278] The shielding part blocks radio waves from the outside.

[0279] [Postscript 7]

[0280] The system as described in Appendix 1 is characterized in that,

[0281] The communications department provides Wi-Fi.

[0282] [Postscript 8]

[0283] The system as described in Appendix 1 is characterized in that,

[0284] The display unit displays information on the monitor.

[0285] [Postscript 9]

[0286] The system as described in Appendix 1 is characterized in that,

[0287] The authentication department estimates the user's emotions and adjusts the speed of the authentication process based on the estimated user emotions.

[0288] [Postscript 10]

[0289] The system as described in Appendix 1 is characterized in that,

[0290] The authentication department refers to the user's past authentication history during the authentication process to improve authentication accuracy.

[0291] [Postscript 11]

[0292] The system as described in Appendix 1 is characterized in that,

[0293] The authentication department additionally obtains the user's biometric information during authentication to improve the reliability of the authentication.

[0294] [Postscript 12]

[0295] The system as described in Appendix 1 is characterized in that,

[0296] The authentication department presupposes the user's emotions and selects an authentication method based on the presumed user emotions.

[0297] [Postscript 13]

[0298] The system as described in Appendix 1 is characterized in that,

[0299] The authentication department considers the user's geographical location information to customize the authentication process during the authentication process.

[0300] [Postscript 14]

[0301] The system as described in Appendix 1 is characterized in that,

[0302] The authentication department analyzes users' social media activity during the authentication process to improve the reliability of the authentication.

[0303] [Postscript 15]

[0304] The system as described in Appendix 1 is characterized in that,

[0305] The calling unit infers the user's emotions and adjusts the order of calling learning data based on the inferred user emotions.

[0306] [Postscript 16]

[0307] The system as described in Appendix 1 is characterized in that,

[0308] When making a call, the calling unit refers to the user's past learning history to select the optimal learning data.

[0309] [Postscript 17]

[0310] The system as described in Appendix 1 is characterized in that,

[0311] When the calling unit is invoked, it customizes the data according to the user's current learning progress.

[0312] [Postscript 18]

[0313] The system as described in Appendix 1 is characterized in that,

[0314] The calling unit infers the user's emotions and adjusts the display method of the learning data according to the inferred user emotions.

[0315] [Postscript 19]

[0316] The system as described in Appendix 1 is characterized in that,

[0317] When making a call, the calling unit considers the user's geographical location information and prioritizes calling learning data with high relevance.

[0318] [Postscript 20]

[0319] The system as described in Appendix 1 is characterized in that,

[0320] When making a call, the calling unit analyzes the user's social media activities and calls relevant learning data.

[0321] [Postscript 21]

[0322] The system as described in Appendix 1 is characterized in that,

[0323] The monitoring unit estimates the user's emotions and adjusts the monitoring frequency based on the estimated user emotions.

[0324] [Postscript 22]

[0325] The system as described in Appendix 1 is characterized in that,

[0326] The monitoring unit refers to the user's past action patterns during monitoring to improve monitoring accuracy.

[0327] [Postscript 23]

[0328] The system as described in Appendix 1 is characterized in that,

[0329] The monitoring unit additionally acquires the user's biometric information during monitoring to improve the reliability of the monitoring.

[0330] [Postscript 24]

[0331] The system as described in Appendix 1 is characterized in that,

[0332] The monitoring department infers the user's emotions and adjusts the display method of the monitoring results based on the inferred user emotions.

[0333] [Postscript 25]

[0334] The system as described in Appendix 1 is characterized in that,

[0335] When monitoring, the monitoring unit considers the user's geographical location information to customize the monitoring range.

[0336] [Postscript 26]

[0337] The system as described in Appendix 1 is characterized in that,

[0338] The monitoring unit analyzes users' social media activities during monitoring to improve monitoring accuracy.

[0339] [Postscript 27]

[0340] The system as described in Appendix 1 is characterized in that,

[0341] The auxiliary unit estimates the user's emotions and adjusts the prompts accordingly.

[0342] [Postscript 28]

[0343] The system as described in Appendix 1 is characterized in that,

[0344] When providing assistance, the auxiliary unit refers to the user's past learning history to provide optimal prompts.

[0345] [Postscript 29]

[0346] The system as described in Appendix 1 is characterized in that,

[0347] When providing assistance, the auxiliary unit provides customized prompts based on the user's current learning progress.

[0348] [Postscript 30]

[0349] The system as described in Appendix 1 is characterized in that,

[0350] The auxiliary unit estimates the user's emotions and adjusts the display method of the prompts according to the estimated user emotions.

[0351] [Postscript 31]

[0352] The system as described in Appendix 1 is characterized in that,

[0353] When providing assistance, the auxiliary unit considers the user's geographical location information and prioritizes providing highly relevant prompts.

[0354] [Postscript 32]

[0355] The system as described in Appendix 1 is characterized in that,

[0356] When providing assistance, the auxiliary unit analyzes the user's social media activity and provides relevant prompts.

[0357] [Postscript 33]

[0358] The system as described in Appendix 1 is characterized in that,

[0359] The selected part infers the user's emotions and adjusts the timing of the next question based on the inferred user emotions.

[0360] [Postscript 34]

[0361] The system as described in Appendix 1 is characterized in that,

[0362] When making a selection, the selection process refers to the user's past learning experience to select the optimal problem.

[0363] [Postscript 35]

[0364] The system as described in Appendix 1 is characterized in that,

[0365] When a selection is made, the selected section customizes the questions based on the user's current learning progress.

[0366] [Postscript 36]

[0367] The system as described in Appendix 1 is characterized in that,

[0368] The selected part presumes the user's emotions and adjusts the display method of the questions when they are presented again based on the presumed user emotions.

[0369] [Postscript 37]

[0370] The system as described in Appendix 1 is characterized in that,

[0371] When making a selection, the selection unit considers the user's geographical location information and prioritizes selecting issues with high relevance.

[0372] [Postscript 38]

[0373] The system as described in Appendix 1 is characterized in that,

[0374] When selecting a question, the selection process analyzes the user's social media activity and selects relevant questions.

[0375] [Postscript 39]

[0376] The system as described in Appendix 1 is characterized in that,

[0377] The temperature control unit estimates the user's emotions and adjusts the frequency of temperature control based on the estimated user emotions.

[0378] [Postscript 40]

[0379] The system as described in Appendix 1 is characterized in that,

[0380] When adjusting the temperature, the temperature control unit refers to the user's past temperature setting history to set the optimal temperature.

[0381] [Postscript 41]

[0382] The system as described in Appendix 1 is characterized in that,

[0383] The temperature regulation unit additionally acquires the user's biological information during temperature regulation to improve temperature reliability.

[0384] [Postscript 42]

[0385] The system as described in Appendix 1 is characterized in that,

[0386] The temperature control unit estimates the user's emotions and selects a temperature control method based on the estimated user emotions.

[0387] [Postscript 43]

[0388] The system as described in Appendix 1 is characterized in that,

[0389] When adjusting the temperature, the temperature control unit takes into account the user's geographical location information to set the optimal temperature.

[0390] [Postscript 44]

[0391] The system as described in Appendix 1 is characterized in that,

[0392] When adjusting the temperature, the temperature control unit analyzes the user's social media activity and provides relevant temperature settings.

[0393] [Postscript 45]

[0394] The system as described in Appendix 1 is characterized in that,

[0395] The shielding unit presupposes the user's emotions and adjusts the timing of radio wave shielding based on the presumed user emotions.

[0396] [Postscript 46]

[0397] The system as described in Appendix 1 is characterized in that,

[0398] When shielding, the shielding unit refers to the user's past shielding history to select the optimal shielding method.

[0399] [Postscript 47]

[0400] The system as described in Appendix 1 is characterized in that the shielding unit presupposes the user's emotions and selects a shielding method based on the presumed user emotions.

[0401] [Postscript 48]

[0402] The system as described in Appendix 1 is characterized in that,

[0403] When shielding, the shielding unit considers the user's geographical location information to select the optimal shielding method.

[0404] [Postscript 49]

[0405] The system as described in Appendix 1 is characterized in that,

[0406] The communication unit estimates the user's emotions and adjusts the Wi-Fi connection speed based on the estimated user emotions.

[0407] [Postscript 50]

[0408] The system as described in Appendix 1 is characterized in that,

[0409] When communicating, the communication unit refers to the user's past communication history to select the optimal connection method.

[0410] [Postscript 51]

[0411] The system as described in Appendix 1 is characterized in that,

[0412] The communication unit estimates the user's emotions and selects a Wi-Fi connection method based on the estimated user emotions.

[0413] [Postscript 52]

[0414] The system as described in Appendix 1 is characterized in that,

[0415] When communicating, the communication unit considers the user's geographical location information to select the optimal connection method.

[0416] [Postscript 53]

[0417] The system as described in Appendix 1 is characterized in that,

[0418] The display unit estimates the user's emotions and adjusts the displayed content based on the estimated user emotions.

[0419] [Postscript 54]

[0420] The system as described in Appendix 1 is characterized in that,

[0421] When displaying data, the display unit refers to the user's past display history to select the optimal display method.

[0422] [Postscript 55]

[0423] The system as described in Appendix 1 is characterized in that,

[0424] The display unit estimates the user's emotions and selects a display method based on the estimated user emotions.

[0425] [Postscript 56]

[0426] The system as described in Appendix 1 is characterized in that,

[0427] When displaying data, the display unit takes into account the user's geographical location information to select the optimal display method.

Claims

1. A system, characterized by comprising: an authentication unit for identifying a user; a calling unit for calling learning data of the user identified by the authentication unit; a monitoring unit for monitoring the user's action; an assisting unit for giving a hint when the action monitored by the monitoring unit stops; a selecting unit for selecting a past wrong question and asking it again; a temperature adjusting unit for automatically adjusting temperature by AI; a shielding unit for shielding an electric wave from the outside; a communication unit for providing Wi-Fi; a display unit for displaying information on a display.

2. The system according to claim 1, wherein the monitoring unit monitors the user's action in real time.

3. The system according to claim 1, wherein the assisting unit gives a hint after a certain time when the user's action stops.

4. The system according to claim 1, wherein the selecting unit selects a past wrong question and asks it again.

5. The system according to claim 1, wherein the temperature adjusting unit automatically adjusts temperature by AI.

6. The system according to claim 1, wherein the shielding unit shields an electric wave from the outside.

7. The system according to claim 1, wherein the communication unit provides Wi-Fi.

8. The system according to claim 1, wherein the display unit displays information on a display.

Images