system

Abstract

The system according to this embodiment aims to achieve more accurate and efficient communication by utilizing brainwaves and facial microexpressions. [Solution] The system according to the embodiment comprises a measurement unit, a conversion unit, an analysis unit, an auxiliary unit, and a training unit. The measurement unit measures brain waves. The conversion unit converts the brain waves measured by the measurement unit into objective words. The analysis unit analyzes facial microexpressions. The auxiliary unit provides assistance and guidance based on the facial microexpressions analyzed by the analysis unit. The training unit performs facial training.

Description

Technical Field

[0006] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to accurately convey emotions and the communication efficiency is low.

[0005] The system according to the embodiment aims to achieve more accurate and efficient communication by using electroencephalogram and micro-expressions of the face.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a measurement unit, a conversion unit, an analysis unit, an auxiliary unit, and a training unit. The measurement unit measures brain waves. The conversion unit converts the brain waves measured by the measurement unit into objective words. The analysis unit analyzes facial microexpressions. The auxiliary unit provides assistance and guidance based on the facial microexpressions analyzed by the analysis unit. The training unit performs facial training. [Effects of the Invention]

[0007] The system according to this embodiment can achieve more accurate and efficient communication by utilizing brainwaves and facial microexpressions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The communication support system according to an embodiment of the present invention is a system that effectively transmits the user's intentions and emotions using brainwaves and facial microexpressions to support communication. This system measures brainwaves when the user wears an earphone-type AI device and analyzes the user's intentions and emotions. Next, the system converts the measured brainwaves into objective words. Furthermore, the system analyzes facial microexpressions. Based on the analyzed brainwaves and facial microexpressions, the system automatically provides assistance and guidance. The system also contributes to improving the user's communication skills through facial training. For example, the system measures brainwaves when the user wears an earphone-type AI device and analyzes the user's intentions and emotions. Next, the system converts the measured brainwaves into objective words. Furthermore, the system analyzes facial microexpressions. Based on the analyzed brainwaves and facial microexpressions, the system automatically provides assistance and guidance. The system also contributes to improving the user's communication skills through facial training. This mechanism allows users to convey their intentions and emotions without using words. This facilitates smoother communication with partners and foreigners, improving the success rate of business negotiations and presentations. Furthermore, facial training can improve users' communication skills and help them build better relationships. This allows the communication support system to effectively convey users' intentions and emotions, thereby facilitating communication.

[0029] The communication support system according to this embodiment comprises a measurement unit, a conversion unit, an analysis unit, an auxiliary unit, and a training unit. The measurement unit measures brain waves. The measurement unit can measure brain waves using, for example, an earphone-type device. The earphone-type device measures brain waves when worn by the user. For example, the earphone-type device is equipped with a sensor that measures brain waves when worn in the ear. The earphone-type device is also equipped with a high-precision sensor for measuring brain waves. Furthermore, the earphone-type device is equipped with a noise-canceling function for measuring brain waves. The conversion unit converts the brain waves measured by the measurement unit into objective words. The conversion unit can convert brain waves into objective words using, for example, a generation AI. The generation AI analyzes brain waves and converts them into words that express the user's intentions and emotions. For example, the generation AI analyzes brain wave patterns and generates words that express the user's intentions and emotions. The generation AI can also analyze changes in brain waves and generate words that express the user's intentions and emotions. Furthermore, the generation AI can analyze the characteristics of brain waves and generate words that express the user's intentions and emotions. The analysis unit analyzes facial microexpressions. The analysis unit can analyze facial microexpressions using, for example, AI. The AI ​​analyzes facial microexpressions and estimates the user's emotions. For example, the AI ​​analyzes changes in facial microexpressions and estimates the user's emotions. The AI ​​can also analyze the characteristics of facial microexpressions and estimate the user's emotions. Furthermore, the AI ​​can analyze patterns of facial microexpressions and estimate the user's emotions. The support unit provides assistance and guidance based on the facial microexpressions analyzed by the analysis unit. The support unit can provide assistance and guidance using, for example, AI. The AI ​​provides appropriate assistance and guidance to the user based on the analyzed facial microexpressions. For example, the AI ​​provides voice guidance to the user based on the analyzed facial microexpressions. The AI ​​can also provide visual instructions to the user based on the analyzed facial microexpressions. Furthermore, the AI ​​can provide text messages to the user based on the analyzed facial microexpressions. The training unit performs facial training. The training department can, for example, use AI to perform facial training.The AI ​​analyzes the user's facial microexpressions and performs facial training. For example, the AI ​​analyzes changes in the user's facial microexpressions and performs facial training. The AI ​​can also analyze the characteristics of the user's facial microexpressions and perform facial training. Furthermore, the AI ​​can analyze patterns of the user's facial microexpressions and perform facial training. As a result, the communication support system according to this embodiment can effectively convey the user's intentions and emotions and support communication.

[0030] The measurement unit measures brainwaves (EEG). The measurement unit can measure EEG using, for example, an earphone-type device. The earphone-type device measures EEG when worn by the user. For example, the earphone-type device is equipped with a sensor that measures EEG when worn in the ear. Furthermore, the earphone-type device is equipped with a high-precision sensor for measuring EEG. In addition, the earphone-type device has a noise-canceling function for measuring EEG. The earphone-type device is designed for everyday use by the user and provides a comfortable fit. This allows the user to wear the device for extended periods, enabling continuous EEG measurement. The earphone-type device can transmit the measured EEG data in real time using wireless communication technologies such as Bluetooth® or Wi-Fi. This allows the measurement unit to quickly collect the user's EEG data and collaborate with other departments for analysis and conversion. Furthermore, the earphone-type device is customizable to the shape of the user's ear, providing an optimal fit for each individual user. This improves the accuracy of EEG measurement and allows for the acquisition of more accurate data. The measurement unit can adjust the frequency of EEG data collection and the accuracy of analysis, enabling flexible responses to specific situations and conditions. For example, the method of collecting EEG data can be optimized according to different situations, such as when the user is relaxed or focused. This allows the measurement unit to collect EEG data efficiently and effectively, improving the overall performance of the system.

[0031] The conversion unit converts brainwaves measured by the measurement unit into objective language. The conversion unit can convert brainwaves into objective language using, for example, a generative AI. The generative AI analyzes brainwaves and converts them into language that expresses the user's intentions and emotions. For example, the generative AI analyzes brainwave patterns and generates language that expresses the user's intentions and emotions. The generative AI can also analyze changes in brainwaves and generate language that expresses the user's intentions and emotions. Furthermore, the generative AI can analyze the characteristics of brainwaves and generate language that expresses the user's intentions and emotions. The generative AI learns using a vast dataset and models the relationship between brainwaves and corresponding language with high accuracy. As a result, the generative AI can generate language quickly and accurately from the user's brainwave data. The generative AI uses natural language processing technology to select language that appropriately expresses the user's intentions and emotions. For example, if the user is relaxed, it will generate language such as "calm," and if the user is tense, it will generate language such as "tense." The generative AI continuously learns from the user's brainwave data and can generate language that is optimal for each individual user. This allows the translation unit to accurately convey the user's intentions and emotions, thereby supporting communication. Furthermore, the generating AI uses filtering technology to eliminate noise and external influences when analyzing the user's brainwave data. As a result, the translation unit can generate words based on more accurate data, effectively conveying the user's intentions and emotions.

[0032] The analysis unit analyzes facial microexpressions. The analysis unit can, for example, use AI to analyze facial microexpressions. The AI ​​analyzes facial microexpressions and estimates the user's emotions. For example, the AI ​​analyzes changes in facial microexpressions and estimates the user's emotions. The AI ​​can also analyze the characteristics of facial microexpressions and estimate the user's emotions. Furthermore, the AI ​​can analyze patterns of facial microexpressions and estimate the user's emotions. The AI ​​uses deep learning technology to learn in order to analyze facial microexpressions with high accuracy. This allows the AI ​​to capture subtle changes in facial expressions and accurately estimate the user's emotions. The analysis unit acquires images of the user's face using cameras and sensors and performs real-time analysis. This allows the analysis unit to quickly grasp changes in the user's emotions and take appropriate action. When analyzing a user's facial microexpressions, the analysis unit considers the characteristics of each individual user. For example, it learns the shape of the user's face and their facial expression habits, and performs individually optimized analysis. This allows the analysis unit to achieve more accurate sentiment estimation. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term sentiment trends. This enables continuous monitoring of user sentiment changes and the provision of appropriate support. The analysis unit can use anomaly detection algorithms to detect unusual changes in facial expressions and issue early warnings. As a result, the analysis unit can handle not only real-time sentiment analysis but also long-term sentiment management and anomaly detection, improving the reliability and security of the entire system.

[0033] The support unit provides assistance and guidance based on the microexpressions of the face analyzed by the analysis unit. The support unit can, for example, use AI to provide assistance and guidance. The AI ​​provides appropriate assistance and guidance to the user based on the analyzed microexpressions. For example, the AI ​​provides voice guidance to the user based on the analyzed microexpressions. The AI ​​can also provide visual instructions to the user based on the analyzed microexpressions. Furthermore, the AI ​​can provide text messages to the user based on the analyzed microexpressions. The support unit uses multiple communication methods to provide appropriate support according to the user's emotions and situation. For example, voice guidance selects a tone and wording that matches the user's emotions, and visual instructions use graphics and animations that are easy for the user to understand. Text messages provide concise and clear instructions to enable the user to respond quickly. The support unit can collect user feedback and continuously improve the accuracy and effectiveness of the assistance and guidance it provides. For example, it records how the user reacted to the guidance provided and incorporates this into the next guidance. The support unit can also select the optimal communication method according to the user's situation and environment. For example, voice guidance can be used in quiet environments, while visual instructions are prioritized in noisy environments. This allows the assistance unit to provide users with quick and reliable assistance and guidance, effectively supporting communication. Furthermore, the assistance unit can provide personalized support tailored to the user's emotions and circumstances. This ensures that users receive the most appropriate support, improving the quality of communication.

[0034] The training unit performs facial training. The training unit can, for example, use AI to perform facial training. The AI ​​analyzes the user's microexpressions and performs facial training. For example, the AI ​​analyzes changes in the user's microexpressions and performs facial training. The AI ​​can also analyze the characteristics of the user's microexpressions and perform facial training. Furthermore, the AI ​​can analyze the patterns of the user's microexpressions and perform facial training. The training unit provides individually optimized training programs to effectively train the user's facial muscles. For example, repeatedly performing specific facial expressions strengthens facial muscles and improves facial expression control. The training unit can also monitor the user's progress and adjust the training program as needed. This allows the user to train at their own pace and effectively train their facial muscles. The training unit provides real-time feedback when analyzing the user's microexpressions. For example, it instantly evaluates whether the user is making the correct facial expressions and provides corrective instructions as needed. This allows the user to train effectively and control their facial expressions more naturally. Furthermore, the training department can utilize past data and statistical information when analyzing the user's facial microexpressions to evaluate the long-term effectiveness of training. This allows users to track their progress and maintain motivation. The training department can also detect abnormal patterns and problems early when analyzing the user's facial microexpressions and take appropriate measures. This allows the training department to not only effectively train the user's facial muscles and improve facial expression control, but also contribute to their overall health management.

[0035] The measurement unit can measure brain waves using an earphone-type device. The measurement unit measures brain waves using an earphone-type device. The earphone-type device measures brain waves when worn by the user. For example, the earphone-type device is equipped with a sensor that measures brain waves when worn in the ear. The earphone-type device is also equipped with a high-precision sensor for measuring brain waves. Furthermore, the earphone-type device is equipped with a noise-canceling function for measuring brain waves. This makes it easier to measure brain waves by using an earphone-type device. The earphone-type device is equipped with a sensor that measures brain waves when worn in the ear. The earphone-type device is equipped with a high-precision sensor for measuring brain waves. The earphone-type device is equipped with a noise-canceling function for measuring brain waves. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can measure brain waves using an earphone-type device, input the measured brain wave data into a generating AI, and the generating AI can analyze the brain wave data.

[0036] The conversion unit can convert measured brainwaves into objective language. For example, the conversion unit converts measured brainwaves into objective language. The conversion unit uses a generating AI to convert brainwaves into objective language. The generating AI analyzes brainwaves and converts them into language that expresses the user's intentions and emotions. For example, the generating AI analyzes brainwave patterns and generates language that expresses the user's intentions and emotions. The generating AI can also analyze changes in brainwaves and generate language that expresses the user's intentions and emotions. Furthermore, the generating AI can analyze the characteristics of brainwaves and generate language that expresses the user's intentions and emotions. In this way, intentions and emotions can be accurately conveyed by converting brainwaves into objective language. Objective language includes, for example, language that expresses emotions and language that expresses intentions. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the conversion unit can input measured brainwave data into a generating AI, which can analyze the brainwave data and generate objective language.

[0037] The analysis unit can analyze facial microexpressions. The analysis unit analyzes facial microexpressions, for example. The analysis unit uses AI to analyze facial microexpressions. The AI ​​analyzes facial microexpressions and estimates the user's emotions. For example, the AI ​​analyzes changes in facial microexpressions and estimates the user's emotions. The AI ​​can also analyze the characteristics of facial microexpressions and estimate the user's emotions. Furthermore, the AI ​​can analyze patterns of facial microexpressions and estimate the user's emotions. This allows for a more accurate understanding of the user's emotions by analyzing facial microexpressions. Facial microexpressions include, for example, smiles and eyebrow movements. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input facial microexpression data into a generating AI, which can analyze the facial microexpression data and estimate the user's emotions.

[0038] The support unit can provide assistance and guidance based on the analyzed facial microexpressions. For example, the support unit provides assistance and guidance based on the analyzed facial microexpressions. The support unit uses AI to provide assistance and guidance. The AI ​​provides appropriate assistance and guidance to the user based on the analyzed facial microexpressions. For example, the AI ​​provides voice guidance to the user based on the analyzed facial microexpressions. The AI ​​can also provide visual instructions to the user based on the analyzed facial microexpressions. Furthermore, the AI ​​can provide text messages to the user based on the analyzed facial microexpressions. This allows the user to receive appropriate support by providing assistance and guidance based on facial microexpressions. Assistance and guidance include, for example, voice guidance and visual instructions. Some or all of the above processing in the support unit may be performed using, for example, AI, or not using AI. For example, the support unit can input the analyzed facial microexpression data into a generating AI, which can then analyze the facial microexpression data and provide appropriate assistance and guidance to the user.

[0039] The training unit can perform facial training. The training unit can perform facial training, for example. The training unit can perform facial training using AI. The AI ​​analyzes the user's facial microexpressions and performs facial training. For example, the AI ​​analyzes changes in the user's facial microexpressions and performs facial training. The AI ​​can also analyze the characteristics of the user's facial microexpressions and perform facial training. Furthermore, the AI ​​can analyze the patterns of the user's facial microexpressions and perform facial training. In this way, facial training can improve the user's communication skills. Facial training includes, for example, facial muscle training and vocal exercises. Some or all of the above processes in the training unit may be performed using, for example, AI, or not using AI. For example, the training unit can input the user's facial microexpression data into a generating AI, which can then analyze the facial microexpression data and perform facial training.

[0040] The measurement unit can analyze the user's past brainwave data and select the optimal measurement method. For example, the measurement unit analyzes the user's past brainwave data and selects the optimal measurement method. The measurement unit uses AI to analyze the user's past brainwave data. The AI ​​analyzes the user's past brainwave data and selects the optimal measurement method. For example, the AI ​​selects the most stable measurement method from the user's past brainwave data. The AI ​​can also select the optimal measurement method for a specific time period based on the user's past brainwave data. Furthermore, the AI ​​can analyze the user's past brainwave data and propose an individually customized measurement method. This allows for the selection of the optimal measurement method by analyzing past brainwave data. The optimal measurement method includes, for example, measurement accuracy and user comfort. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's past brainwave data into a generating AI, which can analyze the brainwave data and select the optimal measurement method.

[0041] The measurement unit can filter the brainwave data based on the user's current activity level and environment. For example, the measurement unit filters the brainwave data based on the user's current activity level and environment. The measurement unit analyzes the user's current activity level and environment using AI. The AI ​​filters the data based on the user's current activity level and environment. For example, if the user is in a quiet environment, the AI ​​performs measurements with minimal noise. The AI ​​can also use noise cancellation technology to perform measurements if the user is in a noisy environment. Furthermore, if the user is exercising, the AI ​​can perform motion-dependent filtering to obtain accurate brainwave data. This allows for the acquisition of accurate brainwave data by filtering based on the user's activity level and environment. Filtering includes, for example, noise reduction and data selection. Some or all of the above processing in the measurement unit may be performed using, for example, AI, or without AI. For example, the measurement unit can input user activity level and environment data into a generating AI, which can analyze the data and perform filtering.

[0042] The measurement unit can prioritize measuring highly relevant data based on the user's geographical location information when measuring brainwaves. For example, the measurement unit prioritizes measuring highly relevant data based on the user's geographical location information when measuring brainwaves. The measurement unit analyzes the user's geographical location information using AI. The AI ​​prioritizes measuring highly relevant data based on the user's geographical location information. For example, if the user is at home, the AI ​​prioritizes measuring brainwaves in a relaxed state. The AI ​​can also prioritize measuring brainwaves in a focused state if the user is at work. Furthermore, if the user is traveling, the AI ​​can prioritize measuring brainwave responses to a new environment. This allows for the acquisition of more useful data by prioritizing the measurement of highly relevant data based on the user's geographical location information. Geographical location information includes, for example, GPS data and location services. Some or all of the above processing in the measurement unit may be performed using, for example, AI, or without AI. For example, the measurement unit inputs the user's geographic location data into a generating AI, which analyzes the data and prioritizes measuring the most relevant data.

[0043] The measurement unit can analyze the user's social media activity and measure relevant data while measuring brainwaves. For example, the measurement unit can analyze the user's social media activity and measure relevant data while measuring brainwaves. The measurement unit uses AI to analyze the user's social media activity. The AI ​​analyzes the user's social media activity and measures relevant data. For example, if the user is experiencing stress on social media, the AI ​​will prioritize measuring brainwaves associated with stress. The AI ​​can also prioritize measuring brainwaves associated with relaxation if the user is relaxed on social media. Furthermore, if the AI ​​is concentrating on social media, it can prioritize measuring brainwaves associated with concentration. This allows for the priority acquisition of relevant brainwave data by analyzing social media activity. Social media activity includes, for example, analysis of post content and analysis of activity frequency. Some or all of the above-described processes in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's social media activity data into a generating AI, which can analyze the data and prioritize measuring relevant brainwave data.

[0044] The conversion unit can adjust the level of detail of the conversion based on the importance of the brainwaves during the conversion process. For example, the conversion unit can adjust the level of detail of the conversion based on the importance of the brainwaves during the conversion process. The conversion unit uses a generating AI to evaluate the importance of the brainwaves. The generating AI evaluates the importance of the brainwaves and adjusts the level of detail of the conversion. For example, the generating AI converts important brainwave data into detailed language. The generating AI can also convert general brainwave data into concise language. Furthermore, if the brainwave data is important in a particular situation, the generating AI can convert it with a level of detail appropriate to the situation. This allows for conversion with an appropriate level of detail by adjusting the level of detail of the conversion based on the importance of the brainwaves. The importance of the brainwaves includes, for example, the importance of a particular brainwave pattern or the priority of the data. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the conversion unit can input brainwave data into a generating AI, which can analyze the brainwave data and adjust the level of detail of the conversion.

[0045] The conversion unit can apply different conversion algorithms depending on the category of the brainwaves during conversion. For example, the conversion unit applies different conversion algorithms depending on the category of the brainwaves during conversion. The conversion unit classifies the brainwave categories using a generating AI. The generating AI classifies the brainwave categories and applies different conversion algorithms. For example, if the brainwaves are alpha waves, the generating AI applies a conversion algorithm suitable for a relaxed state. The generating AI can also apply a conversion algorithm suitable for a stressed state if the brainwaves are beta waves. Furthermore, the generating AI can also apply a conversion algorithm suitable for a focused state if the brainwaves are gamma waves. This allows for more appropriate conversion by applying different conversion algorithms depending on the category of the brainwaves. Brainwave categories include, for example, relaxed states and focused states. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the conversion unit can input brainwave data into a generating AI, the generating AI can analyze the brainwave data, and apply a conversion algorithm according to the category.

[0046] The conversion unit can determine the conversion priority based on the timing of EEG measurements during conversion. For example, the conversion unit determines the conversion priority based on the timing of EEG measurements during conversion. The conversion unit uses a generating AI to evaluate the timing of EEG measurements. The generating AI evaluates the timing of EEG measurements and determines the conversion priority. For example, the generating AI prioritizes the conversion of recently measured EEG data. The generating AI can also prioritize the conversion of EEG data measured at specific events. Furthermore, the generating AI can also prioritize the conversion of EEG data related to the user's important appointments. This allows important data to be converted preferentially by determining the conversion priority based on the timing of EEG measurements. The timing of EEG measurements includes, for example, specific time periods or specific events. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the conversion unit can input EEG data into a generating AI, which can analyze the EEG data and determine the conversion priority.

[0047] The conversion unit can adjust the order of words based on the relevance of brainwaves during conversion. For example, the conversion unit adjusts the order of words based on the relevance of brainwaves during conversion. The conversion unit evaluates the relevance of brainwaves using a generation AI. The generation AI evaluates the relevance of brainwaves and adjusts the order of words. For example, the generation AI converts important brainwave data first and adjusts the order. The generation AI can also preferentially convert highly relevant brainwave data and adjust the order. Furthermore, the generation AI can also adjust the order of words based on the user's intent. This makes it possible to express things in a more appropriate order by adjusting the order of words based on the relevance of brainwaves. Brainwave relevance includes, for example, the correlation of brainwave patterns and the relevance of data. Some or all of the above processing in the conversion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the conversion unit can input brainwave data into a generation AI, which can analyze the brainwave data and adjust the order of words.

[0048] The analysis unit can improve the accuracy of the analysis by considering the interrelationships of facial microexpressions during the analysis. For example, the analysis unit can improve the accuracy of the analysis by considering the interrelationships of facial microexpressions during the analysis. The analysis unit uses AI to evaluate the interrelationships of facial microexpressions. The AI ​​evaluates the interrelationships of facial microexpressions and improves the accuracy of the analysis. For example, the AI ​​analyzes by linking the movement of the eyebrows and the movement of the mouth. The AI ​​can also analyze by linking the movement of the eyes and the movement of the cheeks. Furthermore, the AI ​​can also analyze by considering the interrelationships of microexpressions of the entire face. This improves the accuracy of the analysis by considering the interrelationships of facial microexpressions. The interrelationships of facial microexpressions include, for example, the linkage and correlation of expressions. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input facial microexpression data into a generating AI, the generating AI can analyze the data, and improve the accuracy of the analysis by considering the interrelationships.

[0049] The analysis unit can perform analysis while considering the user's attribute information. For example, the analysis unit can perform analysis while considering the user's attribute information. The analysis unit uses AI to evaluate the user's attribute information. The AI ​​evaluates the user's attribute information and performs analysis. For example, the AI ​​adjusts the micro-expression analysis criteria based on the user's age. The AI ​​can also adjust the micro-expression analysis criteria based on the user's gender. Furthermore, the AI ​​can also adjust the micro-expression analysis criteria based on the user's cultural background. This makes it possible to perform analysis that is more individually suited by considering the user's attribute information. User attribute information includes, for example, age, gender, and occupation. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input user attribute information data into a generating AI, the generating AI can analyze the data, and perform analysis while considering the attribute information.

[0050] The analysis unit can perform analysis while considering the geographical distribution of faces. For example, the analysis unit performs analysis while considering the geographical distribution of faces. The analysis unit uses AI to evaluate the geographical distribution of faces. The AI ​​evaluates the geographical distribution of faces and performs analysis. For example, if the user is in a specific region, the AI ​​will perform analysis while considering the facial characteristics of that region. Also, if the user is in a different region, the AI ​​can perform analysis while considering the facial characteristics of each region. Furthermore, if the user is traveling, the AI ​​can perform analysis while considering the facial characteristics of the region they are visiting. This makes it possible to perform analysis that reflects the characteristics of each region by considering the geographical distribution of faces. The geographical distribution of faces includes, for example, regional characteristics and cultural background. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input geographical distribution data of faces into a generating AI, the generating AI can analyze the data, and perform analysis while considering the geographical distribution.

[0051] The analysis unit can improve the accuracy of its analysis based on relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on relevant literature during the analysis process. The analysis unit uses AI to evaluate relevant literature. The AI ​​evaluates relevant literature and improves the accuracy of the analysis. For example, the AI ​​updates the analysis criteria by referring to the latest research papers. The AI ​​can also improve the accuracy of its analysis by referring to relevant academic literature. Furthermore, the AI ​​can also improve the accuracy of its analysis by referring to past research data. This makes it possible to perform more reliable analyses by improving the accuracy of the analysis based on relevant literature. Relevant literature includes, for example, highly reliable literature and the latest research results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI, the generating AI can analyze the data, and improve the accuracy of the analysis based on the relevant literature.

[0052] The support unit can select the optimal method based on past data when providing assistance or guidance. For example, the support unit selects the optimal method based on past data when providing assistance or guidance. The support unit uses AI to evaluate past data. The AI ​​evaluates past data and selects the optimal method. For example, the AI ​​selects the optimal method by referring to the user's past assistance history. The AI ​​can also select the optimal method by referring to the user's past guidance history. Furthermore, the AI ​​can analyze the user's past behavior patterns and select the optimal assistance or guidance method. This makes it possible to provide more effective assistance and guidance by selecting the optimal method based on past data. Past data includes, for example, past measurement results and historical data. Some or all of the above processing in the support unit may be performed using, for example, AI, or without AI. For example, the support unit can input past data into a generating AI, which can analyze the data and select the optimal method.

[0053] The support unit can customize the means of assistance and guidance based on the user's current situation. For example, the support unit customizes the means of assistance and guidance based on the user's current situation. The support unit uses AI to evaluate the user's current situation. The AI ​​evaluates the user's current situation and customizes the means. For example, if the user is at home, the AI ​​provides assistance and guidance appropriate for home. The AI ​​can also provide assistance and guidance appropriate for the workplace if the user is at work. Furthermore, if the user is traveling, the AI ​​can provide assistance and guidance appropriate for the travel destination. By customizing the means based on the user's current situation, more appropriate assistance and guidance become possible. Current situation includes, for example, current activities and environmental conditions. Some or all of the above processing in the support unit may be performed using, for example, AI, or without AI. For example, the support unit can input the user's current situation data into a generating AI, which can analyze the data and customize the means.

[0054] The support unit can select the optimal method based on the user's geographical location information when providing assistance or guidance. For example, the support unit selects the optimal method based on the user's geographical location information when providing assistance or guidance. The support unit uses AI to evaluate the user's geographical location information. The AI ​​evaluates the user's geographical location information and selects the optimal method. For example, if the user is at home, the AI ​​provides assistance and guidance appropriate for home. The AI ​​can also provide assistance and guidance appropriate for the workplace if the user is at work. Furthermore, if the user is traveling, the AI ​​can provide assistance and guidance appropriate for the travel destination. This makes it possible to provide more appropriate assistance and guidance by selecting the optimal method based on the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. Some or all of the above processing in the support unit may be performed using, for example, AI, or without AI. For example, the support unit can input the user's geographical location information data into a generating AI, which can analyze the data and select the optimal method.

[0055] The support unit can analyze the user's social media activity and suggest solutions when providing support or guidance. For example, the support unit can analyze the user's social media activity and suggest solutions when providing support or guidance. The support unit uses AI to evaluate the user's social media activity. The AI ​​evaluates the user's social media activity and suggests solutions. For example, if the user is feeling stressed on social media, the AI ​​can provide support and guidance to reduce stress. The AI ​​can also provide support and guidance to maintain relaxation if the user is feeling relaxed on social media. Furthermore, if the AI ​​is feeling excited on social media, it can provide support and guidance to alleviate excitement. By analyzing social media activity, more appropriate support and guidance become possible. Social media activity includes, for example, analysis of post content and analysis of activity frequency. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's social media activity data into a generating AI, which can analyze the data and suggest solutions.

[0056] The training unit can provide the optimal training method based on the user's past training history during training. For example, the training unit can provide the optimal training method based on the user's past training history during training. The training unit uses AI to evaluate past training history. The AI ​​evaluates past training history and provides the optimal method. For example, the AI ​​refers to the user's past training history and provides the optimal training method. The AI ​​can also provide the optimal training method for a specific time period based on the user's past training history. Furthermore, the AI ​​can analyze the user's past training history and provide individually customized training methods. This enables more effective training by providing the optimal method based on past training history. Past training history includes, for example, past training results and historical data. Some or all of the above processing in the training unit may be performed using, for example, AI, or without AI. For example, the training unit can input past training history data into a generating AI, which analyzes the data and provides the optimal method.

[0057] The training unit can customize training methods based on the user's current situation during training. For example, the training unit can customize training methods based on the user's current situation during training. The training unit uses AI to evaluate the user's current situation. The AI ​​evaluates the user's current situation and customizes the training methods. For example, if the user is at home, the AI ​​can provide training methods that can be done at home. The AI ​​can also provide training methods that can be done at work if the user is at work. Furthermore, if the user is traveling, the AI ​​can provide training methods that can be done at their travel destination. By customizing training methods based on the user's current situation, more effective training becomes possible. Current situation includes, for example, current activities and environmental conditions. Some or all of the above processing in the training unit may be performed using, for example, AI, or not using AI. For example, the training unit can input the user's current situation data into a generating AI, which can analyze the data and customize the training methods.

[0058] The training unit can provide the optimal training method based on the user's geographical location information during training. For example, the training unit can provide the optimal training method based on the user's geographical location information during training. The training unit uses AI to evaluate the user's geographical location information. The AI ​​evaluates the user's geographical location information and provides the optimal method. For example, if the user is at home, the AI ​​can provide a training method that can be done at home. The AI ​​can also provide a training method that can be done at work if the user is at work. Furthermore, if the user is traveling, the AI ​​can provide a training method that can be done at their travel destination. This enables more effective training by providing the optimal method based on the user's geographical location information. Geographical location information includes, for example, GPS data and location services. Some or all of the above processing in the training unit may be performed using, for example, AI, or not using AI. For example, the training unit can input the user's geographical location information data into a generating AI, which will analyze the data and provide the optimal method.

[0059] The training department can analyze a user's social media activity during training and propose training methods. For example, the training department can analyze a user's social media activity during training and propose training methods. The training department uses AI to evaluate a user's social media activity. The AI ​​evaluates the user's social media activity and proposes training methods. For example, if a user is experiencing stress on social media, the AI ​​can provide training methods to reduce stress. The AI ​​can also provide training methods to maintain relaxation if a user is feeling relaxed on social media. Furthermore, if a user is feeling excited on social media, the AI ​​can provide training methods to alleviate that excitement. This allows for more effective training by analyzing social media activity. Social media activity includes, for example, analysis of post content and analysis of activity frequency. Some or all of the above-described processes in the training department may be performed using AI, for example, or without AI. For example, the training department can input user social media activity data into a generating AI, which can analyze the data and propose training methods.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] A communication support system can analyze a user's past communication history and suggest the optimal communication method. For example, the system can analyze successful communication methods used by the user in the past and suggest them again in similar situations. It can also suggest avoiding communication methods that have failed in the past. Furthermore, it can suggest individually customized communication methods based on the user's past communication history. This allows for more effective communication by analyzing past communication history. Past communication history includes, for example, the content of past conversations and the results of those conversations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past communication history data into a generating AI, which can then analyze the data and suggest the optimal communication method.

[0062] A communication support system can analyze a user's current activity status and suggest the most appropriate communication method. For example, if the user is working, the system can suggest a communication method suitable for work. If the user is on a break, it can suggest a relaxed communication method. Furthermore, if the user is exercising, it can suggest a communication method suitable for exercise. This allows for more appropriate communication by analyzing the user's current activity status. Current activity status includes, for example, the current work content and environmental conditions. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's current activity status data into a generating AI, which can analyze the data and suggest the most appropriate communication method.

[0063] The communication support system can suggest the optimal communication method based on the user's geographical location information. For example, if the user is at home, the system can suggest a communication method suitable for home. It can also suggest a communication method suitable for the workplace if the user is at work. Furthermore, if the user is traveling, it can suggest a communication method suitable for their travel destination. This enables more appropriate communication by suggesting the optimal communication method based on the user's geographical location information. Geographical location information includes, for example, GPS data and location-based services. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's geographical location information data into a generating AI, which can then analyze the data and suggest the optimal communication method.

[0064] A communication support system can analyze a user's social media activity and suggest the most appropriate communication method. For example, if a user is experiencing stress on social media, the system can suggest communication methods to reduce that stress. It can also suggest communication methods to maintain relaxation if the user is feeling relaxed on social media. Furthermore, if the user is feeling excited on social media, the system can suggest communication methods to alleviate that excitement. This allows for more appropriate communication by analyzing social media activity. Social media activity includes, for example, analysis of post content and activity frequency. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input user social media activity data into a generating AI, which can then analyze the data and suggest the most appropriate communication method.

[0065] A communication support system can analyze a user's past emotional data and suggest the most suitable communication method. For example, the system can analyze communication methods used when the user was relaxed in the past and suggest them again in similar situations. It can also suggest avoiding communication methods used when the user was stressed in the past. Furthermore, it can suggest individually customized communication methods based on the user's past emotional data. This allows for more effective communication by analyzing past emotional data. Past emotional data includes, for example, past emotional states and changes in emotions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past emotional data into a generating AI, which can analyze the data and suggest the most suitable communication method.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The measurement unit measures brain waves. The measurement unit can measure brain waves using, for example, an earphone-type device. The earphone-type device measures brain waves when worn by the user. For example, the earphone-type device is equipped with a sensor that measures brain waves when worn in the ear. The earphone-type device is also equipped with a high-precision sensor for measuring brain waves. Furthermore, the earphone-type device is equipped with a noise-canceling function for measuring brain waves. Step 2: The conversion unit converts the brainwaves measured by the measurement unit into objective language. The conversion unit can convert brainwaves into objective language using, for example, a generating AI. The generating AI analyzes brainwaves and converts them into language that expresses the user's intentions and emotions. For example, the generating AI analyzes brainwave patterns and generates language that expresses the user's intentions and emotions. The generating AI can also analyze changes in brainwaves and generate language that expresses the user's intentions and emotions. Furthermore, the generating AI can analyze the characteristics of brainwaves and generate language that expresses the user's intentions and emotions. Step 3: The analysis unit analyzes the microexpressions of the face. The analysis unit can analyze the microexpressions of the face using, for example, AI. The AI ​​analyzes the microexpressions of the face and estimates the user's emotions. For example, the AI ​​analyzes changes in the microexpressions of the face and estimates the user's emotions. The AI ​​can also analyze the characteristics of the microexpressions of the face and estimate the user's emotions. Furthermore, the AI ​​can analyze patterns of the microexpressions of the face and estimate the user's emotions. Step 4: The support unit provides assistance and guidance based on the facial microexpressions analyzed by the analysis unit. The support unit can, for example, use AI to provide assistance and guidance. The AI ​​provides appropriate assistance and guidance to the user based on the analyzed facial microexpressions. For example, the AI ​​provides voice guidance to the user based on the analyzed facial microexpressions. The AI ​​can also provide visual instructions to the user based on the analyzed facial microexpressions. Furthermore, the AI ​​can provide text messages to the user based on the analyzed facial microexpressions. Step 5: The training unit performs facial training. The training unit can perform facial training using, for example, AI. The AI ​​analyzes the user's facial microexpressions and performs facial training. For example, the AI ​​analyzes changes in the user's facial microexpressions and performs facial training. The AI ​​can also analyze the characteristics of the user's facial microexpressions and perform facial training. Furthermore, the AI ​​can analyze patterns of the user's facial microexpressions and perform facial training.

[0068] (Example of form 2) The communication support system according to an embodiment of the present invention is a system that effectively transmits the user's intentions and emotions using brainwaves and facial microexpressions to support communication. This system measures brainwaves when the user wears an earphone-type AI device and analyzes the user's intentions and emotions. Next, the system converts the measured brainwaves into objective words. Furthermore, the system analyzes facial microexpressions. Based on the analyzed brainwaves and facial microexpressions, the system automatically provides assistance and guidance. The system also contributes to improving the user's communication skills through facial training. For example, the system measures brainwaves when the user wears an earphone-type AI device and analyzes the user's intentions and emotions. Next, the system converts the measured brainwaves into objective words. Furthermore, the system analyzes facial microexpressions. Based on the analyzed brainwaves and facial microexpressions, the system automatically provides assistance and guidance. The system also contributes to improving the user's communication skills through facial training. This mechanism allows users to convey their intentions and emotions without using words. This facilitates smoother communication with partners and foreigners, improving the success rate of business negotiations and presentations. Furthermore, facial training can improve users' communication skills and help them build better relationships. This allows the communication support system to effectively convey users' intentions and emotions, thereby facilitating communication.

[0069] The communication support system according to this embodiment comprises a measurement unit, a conversion unit, an analysis unit, an auxiliary unit, and a training unit. The measurement unit measures brain waves. The measurement unit can measure brain waves using, for example, an earphone-type device. The earphone-type device measures brain waves when worn by the user. For example, the earphone-type device is equipped with a sensor that measures brain waves when worn in the ear. The earphone-type device is also equipped with a high-precision sensor for measuring brain waves. Furthermore, the earphone-type device is equipped with a noise-canceling function for measuring brain waves. The conversion unit converts the brain waves measured by the measurement unit into objective words. The conversion unit can convert brain waves into objective words using, for example, a generation AI. The generation AI analyzes brain waves and converts them into words that express the user's intentions and emotions. For example, the generation AI analyzes brain wave patterns and generates words that express the user's intentions and emotions. The generation AI can also analyze changes in brain waves and generate words that express the user's intentions and emotions. Furthermore, the generation AI can analyze the characteristics of brain waves and generate words that express the user's intentions and emotions. The analysis unit analyzes facial microexpressions. The analysis unit can analyze facial microexpressions using, for example, AI. The AI ​​analyzes facial microexpressions and estimates the user's emotions. For example, the AI ​​analyzes changes in facial microexpressions and estimates the user's emotions. The AI ​​can also analyze the characteristics of facial microexpressions and estimate the user's emotions. Furthermore, the AI ​​can analyze patterns of facial microexpressions and estimate the user's emotions. The support unit provides assistance and guidance based on the facial microexpressions analyzed by the analysis unit. The support unit can provide assistance and guidance using, for example, AI. The AI ​​provides appropriate assistance and guidance to the user based on the analyzed facial microexpressions. For example, the AI ​​provides voice guidance to the user based on the analyzed facial microexpressions. The AI ​​can also provide visual instructions to the user based on the analyzed facial microexpressions. Furthermore, the AI ​​can provide text messages to the user based on the analyzed facial microexpressions. The training unit performs facial training. The training department can, for example, use AI to perform facial training.The AI ​​analyzes the user's facial microexpressions and performs facial training. For example, the AI ​​analyzes changes in the user's facial microexpressions and performs facial training. The AI ​​can also analyze the characteristics of the user's facial microexpressions and perform facial training. Furthermore, the AI ​​can analyze patterns of the user's facial microexpressions and perform facial training. As a result, the communication support system according to this embodiment can effectively convey the user's intentions and emotions and support communication.

[0070] The measurement unit measures brainwaves (EEG). The measurement unit can measure EEG using, for example, an earphone-type device. The earphone-type device measures EEG when worn by the user. For example, the earphone-type device is equipped with a sensor that measures EEG when worn in the ear. It also features a high-precision sensor for EEG measurement. Furthermore, the earphone-type device has a noise-canceling function for EEG measurement. The earphone-type device is designed for everyday use and provides a comfortable fit. This allows the user to wear the device for extended periods, enabling continuous EEG measurement. The earphone-type device can transmit measured EEG data in real time using wireless communication technologies such as Bluetooth or Wi-Fi. This allows the measurement unit to quickly collect user EEG data and collaborate with other departments for analysis and conversion. Furthermore, the earphone-type device is customizable to the shape of the user's ear, providing an optimal fit for each individual user. This improves the accuracy of EEG measurement and allows for the acquisition of more accurate data. The measurement unit can adjust the frequency of EEG data collection and the accuracy of analysis, enabling flexible responses to specific situations and conditions. For example, the method of collecting EEG data can be optimized according to different situations, such as when the user is relaxed or focused. This allows the measurement unit to collect EEG data efficiently and effectively, improving the overall performance of the system.

[0071] The conversion unit converts brainwaves measured by the measurement unit into objective language. The conversion unit can convert brainwaves into objective language using, for example, a generative AI. The generative AI analyzes brainwaves and converts them into language that expresses the user's intentions and emotions. For example, the generative AI analyzes brainwave patterns and generates language that expresses the user's intentions and emotions. The generative AI can also analyze changes in brainwaves and generate language that expresses the user's intentions and emotions. Furthermore, the generative AI can analyze the characteristics of brainwaves and generate language that expresses the user's intentions and emotions. The generative AI learns using a vast dataset and models the relationship between brainwaves and corresponding language with high accuracy. As a result, the generative AI can generate language quickly and accurately from the user's brainwave data. The generative AI uses natural language processing technology to select language that appropriately expresses the user's intentions and emotions. For example, if the user is relaxed, it will generate language such as "calm," and if the user is tense, it will generate language such as "tense." The generative AI continuously learns from the user's brainwave data and can generate language that is optimal for each individual user. This allows the translation unit to accurately convey the user's intentions and emotions, thereby supporting communication. Furthermore, the generating AI uses filtering technology to eliminate noise and external influences when analyzing the user's brainwave data. As a result, the translation unit can generate words based on more accurate data, effectively conveying the user's intentions and emotions.

[0072] The analysis unit analyzes facial microexpressions. The analysis unit can, for example, use AI to analyze facial microexpressions. The AI ​​analyzes facial microexpressions and estimates the user's emotions. For example, the AI ​​analyzes changes in facial microexpressions and estimates the user's emotions. The AI ​​can also analyze the characteristics of facial microexpressions and estimate the user's emotions. Furthermore, the AI ​​can analyze patterns of facial microexpressions and estimate the user's emotions. The AI ​​uses deep learning technology to learn in order to analyze facial microexpressions with high accuracy. This allows the AI ​​to capture subtle changes in facial expressions and accurately estimate the user's emotions. The analysis unit acquires images of the user's face using cameras and sensors and performs real-time analysis. This allows the analysis unit to quickly grasp changes in the user's emotions and take appropriate action. When analyzing a user's facial microexpressions, the analysis unit considers the characteristics of each individual user. For example, it learns the shape of the user's face and their facial expression habits, and performs individually optimized analysis. This allows the analysis unit to achieve more accurate sentiment estimation. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term sentiment trends. This enables continuous monitoring of user sentiment changes and the provision of appropriate support. The analysis unit can use anomaly detection algorithms to detect unusual changes in facial expressions and issue early warnings. As a result, the analysis unit can handle not only real-time sentiment analysis but also long-term sentiment management and anomaly detection, improving the reliability and security of the entire system.

[0073] The support unit provides assistance and guidance based on the microexpressions of the face analyzed by the analysis unit. The support unit can, for example, use AI to provide assistance and guidance. The AI ​​provides appropriate assistance and guidance to the user based on the analyzed microexpressions. For example, the AI ​​provides voice guidance to the user based on the analyzed microexpressions. The AI ​​can also provide visual instructions to the user based on the analyzed microexpressions. Furthermore, the AI ​​can provide text messages to the user based on the analyzed microexpressions. The support unit uses multiple communication methods to provide appropriate support according to the user's emotions and situation. For example, voice guidance selects a tone and wording that matches the user's emotions, and visual instructions use graphics and animations that are easy for the user to understand. Text messages provide concise and clear instructions to enable the user to respond quickly. The support unit can collect user feedback and continuously improve the accuracy and effectiveness of the assistance and guidance it provides. For example, it records how the user reacted to the guidance provided and incorporates this into the next guidance. The support unit can also select the optimal communication method according to the user's situation and environment. For example, voice guidance can be used in quiet environments, while visual instructions are prioritized in noisy environments. This allows the assistance unit to provide users with quick and reliable assistance and guidance, effectively supporting communication. Furthermore, the assistance unit can provide personalized support tailored to the user's emotions and circumstances. This ensures that users receive the most appropriate support, improving the quality of communication.

[0074] The training unit performs facial training. The training unit can, for example, use AI to perform facial training. The AI ​​analyzes the user's microexpressions and performs facial training. For example, the AI ​​analyzes changes in the user's microexpressions and performs facial training. The AI ​​can also analyze the characteristics of the user's microexpressions and perform facial training. Furthermore, the AI ​​can analyze the patterns of the user's microexpressions and perform facial training. The training unit provides individually optimized training programs to effectively train the user's facial muscles. For example, repeatedly performing specific facial expressions strengthens facial muscles and improves facial expression control. The training unit can also monitor the user's progress and adjust the training program as needed. This allows the user to train at their own pace and effectively train their facial muscles. The training unit provides real-time feedback when analyzing the user's microexpressions. For example, it instantly evaluates whether the user is making the correct facial expressions and provides corrective instructions as needed. This allows the user to train effectively and control their facial expressions more naturally. Furthermore, the training department can utilize past data and statistical information when analyzing the user's facial microexpressions to evaluate the long-term effectiveness of training. This allows users to track their progress and maintain motivation. The training department can also detect abnormal patterns and problems early when analyzing the user's facial microexpressions and take appropriate measures. This allows the training department to not only effectively train the user's facial muscles and improve facial expression control, but also contribute to their overall health management.

[0075] The measurement unit can measure brain waves using an earphone-type device. The measurement unit measures brain waves using an earphone-type device. The earphone-type device measures brain waves when worn by the user. For example, the earphone-type device is equipped with a sensor that measures brain waves when worn in the ear. The earphone-type device is also equipped with a high-precision sensor for measuring brain waves. Furthermore, the earphone-type device is equipped with a noise-canceling function for measuring brain waves. This makes it easier to measure brain waves by using an earphone-type device. The earphone-type device is equipped with a sensor that measures brain waves when worn in the ear. The earphone-type device is equipped with a high-precision sensor for measuring brain waves. The earphone-type device is equipped with a noise-canceling function for measuring brain waves. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can measure brain waves using an earphone-type device, input the measured brain wave data into a generating AI, and the generating AI can analyze the brain wave data.

[0076] The conversion unit can convert measured brainwaves into objective language. For example, the conversion unit converts measured brainwaves into objective language. The conversion unit uses a generating AI to convert brainwaves into objective language. The generating AI analyzes brainwaves and converts them into language that expresses the user's intentions and emotions. For example, the generating AI analyzes brainwave patterns and generates language that expresses the user's intentions and emotions. The generating AI can also analyze changes in brainwaves and generate language that expresses the user's intentions and emotions. Furthermore, the generating AI can analyze the characteristics of brainwaves and generate language that expresses the user's intentions and emotions. In this way, intentions and emotions can be accurately conveyed by converting brainwaves into objective language. Objective language includes, for example, language that expresses emotions and language that expresses intentions. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the conversion unit can input measured brainwave data into a generating AI, which can analyze the brainwave data and generate objective language.

[0077] The analysis unit can analyze facial microexpressions. The analysis unit analyzes facial microexpressions, for example. The analysis unit uses AI to analyze facial microexpressions. The AI ​​analyzes facial microexpressions and estimates the user's emotions. For example, the AI ​​analyzes changes in facial microexpressions and estimates the user's emotions. The AI ​​can also analyze the characteristics of facial microexpressions and estimate the user's emotions. Furthermore, the AI ​​can analyze patterns of facial microexpressions and estimate the user's emotions. This allows for a more accurate understanding of the user's emotions by analyzing facial microexpressions. Facial microexpressions include, for example, smiles and eyebrow movements. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input facial microexpression data into a generating AI, which can analyze the facial microexpression data and estimate the user's emotions.

[0078] The support unit can provide assistance and guidance based on the analyzed facial microexpressions. For example, the support unit provides assistance and guidance based on the analyzed facial microexpressions. The support unit uses AI to provide assistance and guidance. The AI ​​provides appropriate assistance and guidance to the user based on the analyzed facial microexpressions. For example, the AI ​​provides voice guidance to the user based on the analyzed facial microexpressions. The AI ​​can also provide visual instructions to the user based on the analyzed facial microexpressions. Furthermore, the AI ​​can provide text messages to the user based on the analyzed facial microexpressions. This allows the user to receive appropriate support by providing assistance and guidance based on facial microexpressions. Assistance and guidance include, for example, voice guidance and visual instructions. Some or all of the above processing in the support unit may be performed using, for example, AI, or not using AI. For example, the support unit can input the analyzed facial microexpression data into a generating AI, which can then analyze the facial microexpression data and provide appropriate assistance and guidance to the user.

[0079] The training unit can perform facial training. The training unit can perform facial training, for example. The training unit can perform facial training using AI. The AI ​​analyzes the user's facial microexpressions and performs facial training. For example, the AI ​​analyzes changes in the user's facial microexpressions and performs facial training. The AI ​​can also analyze the characteristics of the user's facial microexpressions and perform facial training. Furthermore, the AI ​​can analyze the patterns of the user's facial microexpressions and perform facial training. In this way, facial training can improve the user's communication skills. Facial training includes, for example, facial muscle training and vocal exercises. Some or all of the above processes in the training unit may be performed using, for example, AI, or not using AI. For example, the training unit can input the user's facial microexpression data into a generating AI, which can then analyze the facial microexpression data and perform facial training.

[0080] The measurement unit can estimate the user's emotions and adjust the timing of brainwave measurements based on the estimated emotions. The measurement unit estimates the user's emotions using AI. The AI ​​estimates the user's emotions and adjusts the timing of brainwave measurements based on the estimated emotions. For example, if the user is relaxed, the AI ​​can periodically measure brainwaves to collect stable data. The AI ​​can also frequently measure brainwaves if the user is stressed to capture fluctuations. Furthermore, if the user is focused, the AI ​​can adjust the measurement timing to acquire data at the peak of concentration. This allows for more accurate data to be obtained by adjusting the timing of brainwave measurements based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input user emotion data into a generating AI, which can analyze the emotion data and adjust the timing of brainwave measurement.

[0081] The measurement unit can analyze the user's past brainwave data and select the optimal measurement method. For example, the measurement unit analyzes the user's past brainwave data and selects the optimal measurement method. The measurement unit uses AI to analyze the user's past brainwave data. The AI ​​analyzes the user's past brainwave data and selects the optimal measurement method. For example, the AI ​​selects the most stable measurement method from the user's past brainwave data. The AI ​​can also select the optimal measurement method for a specific time period based on the user's past brainwave data. Furthermore, the AI ​​can analyze the user's past brainwave data and propose an individually customized measurement method. This allows for the selection of the optimal measurement method by analyzing past brainwave data. The optimal measurement method includes, for example, measurement accuracy and user comfort. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's past brainwave data into a generating AI, which can analyze the brainwave data and select the optimal measurement method.

[0082] The measurement unit can filter the brainwave data based on the user's current activity level and environment. For example, the measurement unit filters the brainwave data based on the user's current activity level and environment. The measurement unit analyzes the user's current activity level and environment using AI. The AI ​​filters the data based on the user's current activity level and environment. For example, if the user is in a quiet environment, the AI ​​performs measurements with minimal noise. The AI ​​can also use noise cancellation technology to perform measurements if the user is in a noisy environment. Furthermore, if the user is exercising, the AI ​​can perform motion-dependent filtering to obtain accurate brainwave data. This allows for the acquisition of accurate brainwave data by filtering based on the user's activity level and environment. Filtering includes, for example, noise reduction and data selection. Some or all of the above processing in the measurement unit may be performed using, for example, AI, or without AI. For example, the measurement unit can input user activity level and environment data into a generating AI, which can analyze the data and perform filtering.

[0083] The measurement unit can estimate the user's emotions and determine the priority of brainwave measurements based on the estimated emotions. The measurement unit estimates the user's emotions using AI. The AI ​​estimates the user's emotions and determines the priority of brainwave measurements based on the estimated emotions. For example, the AI ​​prioritizes alpha wave measurement when the user is relaxed. The AI ​​can also prioritize beta wave measurement when the user is stressed. Furthermore, the AI ​​can prioritize gamma wave measurement when the user is focused. This allows for the priority acquisition of important brainwave data by prioritizing brainwave measurements based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's emotional data into a generating AI, which can then analyze the emotional data and determine the priority of the brainwaves to be measured.

[0084] The measurement unit can prioritize measuring highly relevant data based on the user's geographical location information when measuring brainwaves. For example, the measurement unit prioritizes measuring highly relevant data based on the user's geographical location information when measuring brainwaves. The measurement unit analyzes the user's geographical location information using AI. The AI ​​prioritizes measuring highly relevant data based on the user's geographical location information. For example, if the user is at home, the AI ​​prioritizes measuring brainwaves in a relaxed state. The AI ​​can also prioritize measuring brainwaves in a focused state if the user is at work. Furthermore, if the user is traveling, the AI ​​can prioritize measuring brainwave responses to a new environment. This allows for the acquisition of more useful data by prioritizing the measurement of highly relevant data based on the user's geographical location information. Geographical location information includes, for example, GPS data and location services. Some or all of the above processing in the measurement unit may be performed using, for example, AI, or without AI. For example, the measurement unit inputs the user's geographic location data into a generating AI, which analyzes the data and prioritizes measuring the most relevant data.

[0085] The measurement unit can analyze the user's social media activity and measure relevant data while measuring brainwaves. For example, the measurement unit can analyze the user's social media activity and measure relevant data while measuring brainwaves. The measurement unit uses AI to analyze the user's social media activity. The AI ​​analyzes the user's social media activity and measures relevant data. For example, if the user is experiencing stress on social media, the AI ​​will prioritize measuring brainwaves associated with stress. The AI ​​can also prioritize measuring brainwaves associated with relaxation if the user is relaxed on social media. Furthermore, if the AI ​​is concentrating on social media, it can prioritize measuring brainwaves associated with concentration. This allows for the priority acquisition of relevant brainwave data by analyzing social media activity. Social media activity includes, for example, analysis of post content and analysis of activity frequency. Some or all of the above-described processes in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's social media activity data into a generating AI, which can analyze the data and prioritize measuring relevant brainwave data.

[0086] The translation unit can estimate the user's emotions and adjust the way words are expressed based on the estimated emotions. The translation unit estimates the user's emotions using a generative AI. The generative AI estimates the user's emotions and adjusts the way words are expressed based on the estimated emotions. For example, if the user is relaxed, the generative AI will use a gentle expression. The generative AI may also use a concise and clear expression if the user is stressed. Furthermore, the generative AI may use a lively expression if the user is excited. This allows for more appropriate expression by adjusting the way words are expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the translation unit may be performed using a generative AI, for example, or without a generative AI. For example, the conversion unit can input the user's emotional data into a generating AI, which can then analyze the emotional data and adjust the way the words are expressed.

[0087] The conversion unit can adjust the level of detail of the conversion based on the importance of the brainwaves during the conversion process. For example, the conversion unit can adjust the level of detail of the conversion based on the importance of the brainwaves during the conversion process. The conversion unit uses a generating AI to evaluate the importance of the brainwaves. The generating AI evaluates the importance of the brainwaves and adjusts the level of detail of the conversion. For example, the generating AI converts important brainwave data into detailed language. The generating AI can also convert general brainwave data into concise language. Furthermore, if the brainwave data is important in a particular situation, the generating AI can convert it with a level of detail appropriate to the situation. This allows for conversion with an appropriate level of detail by adjusting the level of detail of the conversion based on the importance of the brainwaves. The importance of the brainwaves includes, for example, the importance of a particular brainwave pattern or the priority of the data. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the conversion unit can input brainwave data into a generating AI, which can analyze the brainwave data and adjust the level of detail of the conversion.

[0088] The conversion unit can apply different conversion algorithms depending on the category of the brainwaves during conversion. For example, the conversion unit applies different conversion algorithms depending on the category of the brainwaves during conversion. The conversion unit classifies the brainwave categories using a generating AI. The generating AI classifies the brainwave categories and applies different conversion algorithms. For example, if the brainwaves are alpha waves, the generating AI applies a conversion algorithm suitable for a relaxed state. The generating AI can also apply a conversion algorithm suitable for a stressed state if the brainwaves are beta waves. Furthermore, the generating AI can also apply a conversion algorithm suitable for a focused state if the brainwaves are gamma waves. This allows for more appropriate conversion by applying different conversion algorithms depending on the category of the brainwaves. Brainwave categories include, for example, relaxed states and focused states. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the conversion unit can input brainwave data into a generating AI, the generating AI can analyze the brainwave data, and apply a conversion algorithm according to the category.

[0089] The translation unit can estimate the user's emotions and adjust the length of words based on the estimated emotions. The translation unit estimates the user's emotions using a generative AI. The generative AI estimates the user's emotions and adjusts the length of words based on the estimated emotions. For example, if the user is relaxed, the generative AI will provide a longer explanation. The generative AI can also provide a shorter, more concise explanation if the user is stressed. Furthermore, the generative AI can provide a more detailed explanation if the user is excited. By adjusting the length of words based on the user's emotions, it becomes possible to express emotions of a more appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the translation unit may be performed using a generative AI, for example, or without a generative AI. For example, the conversion unit can input the user's emotional data into a generating AI, which can then analyze the emotional data and adjust the length of the words.

[0090] The conversion unit can determine the conversion priority based on the timing of EEG measurements during conversion. For example, the conversion unit determines the conversion priority based on the timing of EEG measurements during conversion. The conversion unit uses a generating AI to evaluate the timing of EEG measurements. The generating AI evaluates the timing of EEG measurements and determines the conversion priority. For example, the generating AI prioritizes the conversion of recently measured EEG data. The generating AI can also prioritize the conversion of EEG data measured at specific events. Furthermore, the generating AI can also prioritize the conversion of EEG data related to the user's important appointments. This allows important data to be converted preferentially by determining the conversion priority based on the timing of EEG measurements. The timing of EEG measurements includes, for example, specific time periods or specific events. Some or all of the above processing in the conversion unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the conversion unit can input EEG data into a generating AI, which can analyze the EEG data and determine the conversion priority.

[0091] The conversion unit can adjust the order of words based on the relevance of brainwaves during conversion. For example, the conversion unit adjusts the order of words based on the relevance of brainwaves during conversion. The conversion unit evaluates the relevance of brainwaves using a generation AI. The generation AI evaluates the relevance of brainwaves and adjusts the order of words. For example, the generation AI converts important brainwave data first and adjusts the order. The generation AI can also preferentially convert highly relevant brainwave data and adjust the order. Furthermore, the generation AI can also adjust the order of words based on the user's intent. This makes it possible to express things in a more appropriate order by adjusting the order of words based on the relevance of brainwaves. Brainwave relevance includes, for example, the correlation of brainwave patterns and the relevance of data. Some or all of the above processing in the conversion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the conversion unit can input brainwave data into a generation AI, which can analyze the brainwave data and adjust the order of words.

[0092] The analysis unit can estimate the user's emotions and adjust the micro-expression analysis criteria based on the estimated user emotions. The analysis unit estimates the user's emotions using AI. The AI ​​estimates the user's emotions and adjusts the micro-expression analysis criteria based on the estimated user emotions. For example, if the user is relaxed, the AI ​​sets the micro-expression analysis criteria loosely. The AI ​​can also set the micro-expression analysis criteria strictly if the user is stressed. Furthermore, the AI ​​can dynamically adjust the micro-expression analysis criteria if the user is excited. This allows for more accurate analysis by adjusting the micro-expression analysis criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI, which can then analyze the emotion data and adjust the micro-expression analysis criteria.

[0093] The analysis unit can improve the accuracy of the analysis by considering the interrelationships of facial microexpressions during the analysis. For example, the analysis unit can improve the accuracy of the analysis by considering the interrelationships of facial microexpressions during the analysis. The analysis unit uses AI to evaluate the interrelationships of facial microexpressions. The AI ​​evaluates the interrelationships of facial microexpressions and improves the accuracy of the analysis. For example, the AI ​​analyzes by linking the movement of the eyebrows and the movement of the mouth. The AI ​​can also analyze by linking the movement of the eyes and the movement of the cheeks. Furthermore, the AI ​​can also analyze by considering the interrelationships of microexpressions of the entire face. This improves the accuracy of the analysis by considering the interrelationships of facial microexpressions. The interrelationships of facial microexpressions include, for example, the linkage and correlation of expressions. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input facial microexpression data into a generating AI, the generating AI can analyze the data, and improve the accuracy of the analysis by considering the interrelationships.

[0094] The analysis unit can perform analysis while considering the user's attribute information. For example, the analysis unit can perform analysis while considering the user's attribute information. The analysis unit uses AI to evaluate the user's attribute information. The AI ​​evaluates the user's attribute information and performs analysis. For example, the AI ​​adjusts the micro-expression analysis criteria based on the user's age. The AI ​​can also adjust the micro-expression analysis criteria based on the user's gender. Furthermore, the AI ​​can also adjust the micro-expression analysis criteria based on the user's cultural background. This makes it possible to perform analysis that is more individually suited by considering the user's attribute information. User attribute information includes, for example, age, gender, and occupation. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input user attribute information data into a generating AI, the generating AI can analyze the data, and perform analysis while considering the attribute information.

[0095] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. The analysis unit estimates the user's emotions using AI. The AI ​​estimates the user's emotions and adjusts the display order of the analysis results based on the estimated emotions. For example, if the user is relaxed, the AI ​​will display detailed analysis results first. The AI ​​can also display concise analysis results first if the user is stressed. Furthermore, if the user is excited, the AI ​​can display important analysis results first. By adjusting the display order of the analysis results based on the user's emotions, a more appropriate display order becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI, which then analyzes the emotion data and adjusts the display order of the analysis results.

[0096] The analysis unit can perform analysis while considering the geographical distribution of faces. For example, the analysis unit performs analysis while considering the geographical distribution of faces. The analysis unit uses AI to evaluate the geographical distribution of faces. The AI ​​evaluates the geographical distribution of faces and performs analysis. For example, if the user is in a specific region, the AI ​​will perform analysis while considering the facial characteristics of that region. Also, if the user is in a different region, the AI ​​can perform analysis while considering the facial characteristics of each region. Furthermore, if the user is traveling, the AI ​​can perform analysis while considering the facial characteristics of the region they are visiting. This makes it possible to perform analysis that reflects the characteristics of each region by considering the geographical distribution of faces. The geographical distribution of faces includes, for example, regional characteristics and cultural background. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input geographical distribution data of faces into a generating AI, the generating AI can analyze the data, and perform analysis while considering the geographical distribution.

[0097] The analysis unit can improve the accuracy of its analysis based on relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on relevant literature during the analysis process. The analysis unit uses AI to evaluate relevant literature. The AI ​​evaluates relevant literature and improves the accuracy of the analysis. For example, the AI ​​updates the analysis criteria by referring to the latest research papers. The AI ​​can also improve the accuracy of its analysis by referring to relevant academic literature. Furthermore, the AI ​​can also improve the accuracy of its analysis by referring to past research data. This makes it possible to perform more reliable analyses by improving the accuracy of the analysis based on relevant literature. Relevant literature includes, for example, highly reliable literature and the latest research results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI, the generating AI can analyze the data, and improve the accuracy of the analysis based on the relevant literature.

[0098] The support unit can estimate the user's emotions and adjust the methods of support and guidance based on the estimated emotions. For example, the support unit estimates the user's emotions and adjusts the methods of support and guidance based on the estimated emotions. The support unit estimates the user's emotions using AI. The AI ​​estimates the user's emotions and adjusts the methods of support and guidance based on the estimated emotions. For example, if the user is relaxed, the AI ​​will provide gentle support and guidance. The AI ​​can also provide concise and clear support and guidance if the user is stressed. Furthermore, the AI ​​can provide lively support and guidance if the user is excited. This allows for more appropriate support by adjusting the methods of support and guidance based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user emotion data into a generating AI, which can then analyze the emotion data and adjust the methods of support and guidance.

[0099] The support unit can select the optimal method based on past data when providing assistance or guidance. For example, the support unit selects the optimal method based on past data when providing assistance or guidance. The support unit uses AI to evaluate past data. The AI ​​evaluates past data and selects the optimal method. For example, the AI ​​selects the optimal method by referring to the user's past assistance history. The AI ​​can also select the optimal method by referring to the user's past guidance history. Furthermore, the AI ​​can analyze the user's past behavior patterns and select the optimal assistance or guidance method. This makes it possible to provide more effective assistance and guidance by selecting the optimal method based on past data. Past data includes, for example, past measurement results and historical data. Some or all of the above processing in the support unit may be performed using, for example, AI, or without AI. For example, the support unit can input past data into a generating AI, which can analyze the data and select the optimal method.

[0100] The support unit can customize the means of assistance and guidance based on the user's current situation. For example, the support unit customizes the means of assistance and guidance based on the user's current situation. The support unit uses AI to evaluate the user's current situation. The AI ​​evaluates the user's current situation and customizes the means. For example, if the user is at home, the AI ​​provides assistance and guidance appropriate for home. The AI ​​can also provide assistance and guidance appropriate for the workplace if the user is at work. Furthermore, if the user is traveling, the AI ​​can provide assistance and guidance appropriate for the travel destination. By customizing the means based on the user's current situation, more appropriate assistance and guidance become possible. Current situation includes, for example, current activities and environmental conditions. Some or all of the above processing in the support unit may be performed using, for example, AI, or without AI. For example, the support unit can input the user's current situation data into a generating AI, which can analyze the data and customize the means.

[0101] The support unit can estimate the user's emotions and determine the priority of support and guidance based on the estimated emotions. For example, the support unit estimates the user's emotions and determines the priority of support and guidance based on the estimated emotions. The support unit uses AI to estimate the user's emotions. The AI ​​estimates the user's emotions and determines the priority of support and guidance based on the estimated emotions. For example, if the user is relaxed, the AI ​​will set a lower priority for support and guidance. The AI ​​can also set a higher priority for support and guidance if the user is stressed. Furthermore, the AI ​​can dynamically adjust the priority of support and guidance if the user is agitated. This allows for the prioritization of more important support by determining the priority of support and guidance based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user emotion data into a generating AI, which can then analyze the emotion data and determine the priority of support and guidance.

[0102] The support unit can select the optimal method based on the user's geographical location information when providing assistance or guidance. For example, the support unit selects the optimal method based on the user's geographical location information when providing assistance or guidance. The support unit uses AI to evaluate the user's geographical location information. The AI ​​evaluates the user's geographical location information and selects the optimal method. For example, if the user is at home, the AI ​​provides assistance and guidance appropriate for home. The AI ​​can also provide assistance and guidance appropriate for the workplace if the user is at work. Furthermore, if the user is traveling, the AI ​​can provide assistance and guidance appropriate for the travel destination. This makes it possible to provide more appropriate assistance and guidance by selecting the optimal method based on the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. Some or all of the above processing in the support unit may be performed using, for example, AI, or without AI. For example, the support unit can input the user's geographical location information data into a generating AI, which can analyze the data and select the optimal method.

[0103] The support unit can analyze the user's social media activity and suggest solutions when providing support or guidance. For example, the support unit can analyze the user's social media activity and suggest solutions when providing support or guidance. The support unit uses AI to evaluate the user's social media activity. The AI ​​evaluates the user's social media activity and suggests solutions. For example, if the user is feeling stressed on social media, the AI ​​can provide support and guidance to reduce stress. The AI ​​can also provide support and guidance to maintain relaxation if the user is feeling relaxed on social media. Furthermore, if the AI ​​is feeling excited on social media, it can provide support and guidance to alleviate excitement. By analyzing social media activity, more appropriate support and guidance become possible. Social media activity includes, for example, analysis of post content and analysis of activity frequency. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's social media activity data into a generating AI, which can analyze the data and suggest solutions.

[0104] The training unit can estimate the user's emotions and adjust the training method based on the estimated emotions. The training unit estimates the user's emotions using AI. The AI ​​estimates the user's emotions and adjusts the training method based on the estimated emotions. For example, if the user is relaxed, the AI ​​provides a gentle training method. The AI ​​can also provide a stress-reducing training method if the user is stressed. Furthermore, if the user is excited, the AI ​​can provide a training method to calm the excitement. This allows for more effective training by adjusting the training method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input user emotional data into a generating AI, which then analyzes the emotional data and adjusts the training method accordingly.

[0105] The training unit can provide the optimal training method based on the user's past training history during training. For example, the training unit can provide the optimal training method based on the user's past training history during training. The training unit uses AI to evaluate past training history. The AI ​​evaluates past training history and provides the optimal method. For example, the AI ​​refers to the user's past training history and provides the optimal training method. The AI ​​can also provide the optimal training method for a specific time period based on the user's past training history. Furthermore, the AI ​​can analyze the user's past training history and provide individually customized training methods. This enables more effective training by providing the optimal method based on past training history. Past training history includes, for example, past training results and historical data. Some or all of the above processing in the training unit may be performed using, for example, AI, or without AI. For example, the training unit can input past training history data into a generating AI, which analyzes the data and provides the optimal method.

[0106] The training unit can customize training methods based on the user's current situation during training. For example, the training unit can customize training methods based on the user's current situation during training. The training unit uses AI to evaluate the user's current situation. The AI ​​evaluates the user's current situation and customizes the training methods. For example, if the user is at home, the AI ​​can provide training methods that can be done at home. The AI ​​can also provide training methods that can be done at work if the user is at work. Furthermore, if the user is traveling, the AI ​​can provide training methods that can be done at their travel destination. By customizing training methods based on the user's current situation, more effective training becomes possible. Current situation includes, for example, current activities and environmental conditions. Some or all of the above processing in the training unit may be performed using, for example, AI, or not using AI. For example, the training unit can input the user's current situation data into a generating AI, which can analyze the data and customize the training methods.

[0107] The training unit can estimate the user's emotions and determine training priorities based on the estimated emotions. The training unit estimates the user's emotions using AI. The AI ​​estimates the user's emotions and determines training priorities based on the estimated emotions. For example, the AI ​​may set a lower priority for training if the user is relaxed. The AI ​​may also set a higher priority for training if the user is stressed. Furthermore, the AI ​​may dynamically adjust training priorities if the user is excited. This allows for the prioritization of more important training based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input user emotional data into a generating AI, which then analyzes the emotional data and determines training priorities.

[0108] The training unit can provide the optimal training method based on the user's geographical location information during training. For example, the training unit can provide the optimal training method based on the user's geographical location information during training. The training unit uses AI to evaluate the user's geographical location information. The AI ​​evaluates the user's geographical location information and provides the optimal method. For example, if the user is at home, the AI ​​can provide a training method that can be done at home. The AI ​​can also provide a training method that can be done at work if the user is at work. Furthermore, if the user is traveling, the AI ​​can provide a training method that can be done at their travel destination. This enables more effective training by providing the optimal method based on the user's geographical location information. Geographical location information includes, for example, GPS data and location services. Some or all of the above processing in the training unit may be performed using, for example, AI, or not using AI. For example, the training unit can input the user's geographical location information data into a generating AI, which will analyze the data and provide the optimal method.

[0109] The training department can analyze a user's social media activity during training and propose training methods. For example, the training department can analyze a user's social media activity during training and propose training methods. The training department uses AI to evaluate a user's social media activity. The AI ​​evaluates the user's social media activity and proposes training methods. For example, if a user is experiencing stress on social media, the AI ​​can provide training methods to reduce stress. The AI ​​can also provide training methods to maintain relaxation if a user is feeling relaxed on social media. Furthermore, if a user is feeling excited on social media, the AI ​​can provide training methods to alleviate that excitement. This allows for more effective training by analyzing social media activity. Social media activity includes, for example, analysis of post content and analysis of activity frequency. Some or all of the above-described processes in the training department may be performed using AI, for example, or without AI. For example, the training department can input user social media activity data into a generating AI, which can analyze the data and propose training methods.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] A communication support system can analyze a user's voice tone and speaking style to estimate their emotions. For example, the system can estimate that a user is excited if their voice tone is high. It can also estimate that a user is relaxed if they speak slowly. Furthermore, it can analyze the volume of the user's voice to estimate whether they are feeling stressed. This allows for a more accurate understanding of a user's emotions by analyzing their voice tone and speaking style. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user voice tone and speaking style data into a generative AI, which can analyze the data and estimate the user's emotions.

[0112] The communication support system can measure the user's body temperature and heart rate and estimate the user's emotions. For example, the system can estimate that the user is excited if their body temperature is elevated. It can also estimate that the user is stressed if their heart rate is fast. Furthermore, it can analyze changes in the user's body temperature and heart rate to estimate whether they are relaxed. This allows for a more accurate understanding of the user's emotions by measuring their body temperature and heart rate. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the measurement unit may be performed using AI, or not using AI. For example, the measurement unit can input the user's body temperature and heart rate data into a generative AI, which can analyze the data and estimate the user's emotions.

[0113] The communication support system can analyze a user's walking pattern and estimate their emotions. For example, the system can estimate that a user is excited if they walk quickly, and relaxed if they walk slowly. Furthermore, it can analyze the rhythm of the user's walking and estimate whether they are feeling stressed. This allows for a more accurate understanding of the user's emotions by analyzing their walking pattern. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's walking pattern data into the generative AI, which can analyze the data and estimate the user's emotions.

[0114] The communication support system can analyze a user's eating patterns and estimate their emotions. For example, the system can estimate that a user is stressed if they eat frequently. It can also estimate that a user is relaxed if they eat slowly. Furthermore, it can analyze the type of food a user eats and estimate whether they are excited or not. This allows for a more accurate understanding of a user's emotions by analyzing their eating patterns. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user eating pattern data into a generative AI, which can analyze the data and estimate the user's emotions.

[0115] The communication support system can analyze a user's sleep patterns and estimate their emotions. For example, the system can estimate that a user is stressed if their sleep duration is short, and that they are relaxed if their sleep duration is long. Furthermore, it can analyze the quality of the user's sleep and estimate whether they are agitated. This allows for a more accurate understanding of the user's emotions by analyzing their sleep patterns. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's sleep pattern data into a generative AI, which can analyze the data and estimate the user's emotions.

[0116] A communication support system can analyze a user's past communication history and suggest the optimal communication method. For example, the system can analyze successful communication methods used by the user in the past and suggest them again in similar situations. It can also suggest avoiding communication methods that have failed in the past. Furthermore, it can suggest individually customized communication methods based on the user's past communication history. This allows for more effective communication by analyzing past communication history. Past communication history includes, for example, the content of past conversations and the results of those conversations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past communication history data into a generating AI, which can then analyze the data and suggest the optimal communication method.

[0117] A communication support system can analyze a user's current activity status and suggest the most appropriate communication method. For example, if the user is working, the system can suggest a communication method suitable for work. If the user is on a break, it can suggest a relaxed communication method. Furthermore, if the user is exercising, it can suggest a communication method suitable for exercise. This allows for more appropriate communication by analyzing the user's current activity status. Current activity status includes, for example, the current work content and environmental conditions. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's current activity status data into a generating AI, which can analyze the data and suggest the most appropriate communication method.

[0118] The communication support system can suggest the optimal communication method based on the user's geographical location information. For example, if the user is at home, the system can suggest a communication method suitable for home. It can also suggest a communication method suitable for the workplace if the user is at work. Furthermore, if the user is traveling, it can suggest a communication method suitable for their travel destination. This enables more appropriate communication by suggesting the optimal communication method based on the user's geographical location information. Geographical location information includes, for example, GPS data and location-based services. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's geographical location information data into a generating AI, which can then analyze the data and suggest the optimal communication method.

[0119] A communication support system can analyze a user's social media activity and suggest the most appropriate communication method. For example, if a user is experiencing stress on social media, the system can suggest communication methods to reduce that stress. It can also suggest communication methods to maintain relaxation if the user is feeling relaxed on social media. Furthermore, if the user is feeling excited on social media, the system can suggest communication methods to alleviate that excitement. This allows for more appropriate communication by analyzing social media activity. Social media activity includes, for example, analysis of post content and activity frequency. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input user social media activity data into a generating AI, which can then analyze the data and suggest the most appropriate communication method.

[0120] A communication support system can analyze a user's past emotional data and suggest the most suitable communication method. For example, the system can analyze communication methods used when the user was relaxed in the past and suggest them again in similar situations. It can also suggest avoiding communication methods used when the user was stressed in the past. Furthermore, it can suggest individually customized communication methods based on the user's past emotional data. This allows for more effective communication by analyzing past emotional data. Past emotional data includes, for example, past emotional states and changes in emotions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past emotional data into a generating AI, which can analyze the data and suggest the most suitable communication method.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The measurement unit measures brain waves. The measurement unit can measure brain waves using, for example, an earphone-type device. The earphone-type device measures brain waves when worn by the user. For example, the earphone-type device is equipped with a sensor that measures brain waves when worn in the ear. The earphone-type device is also equipped with a high-precision sensor for measuring brain waves. Furthermore, the earphone-type device is equipped with a noise-canceling function for measuring brain waves. Step 2: The conversion unit converts the brainwaves measured by the measurement unit into objective language. The conversion unit can convert brainwaves into objective language using, for example, a generating AI. The generating AI analyzes brainwaves and converts them into language that expresses the user's intentions and emotions. For example, the generating AI analyzes brainwave patterns and generates language that expresses the user's intentions and emotions. The generating AI can also analyze changes in brainwaves and generate language that expresses the user's intentions and emotions. Furthermore, the generating AI can analyze the characteristics of brainwaves and generate language that expresses the user's intentions and emotions. Step 3: The analysis unit analyzes the microexpressions of the face. The analysis unit can analyze the microexpressions of the face using, for example, AI. The AI ​​analyzes the microexpressions of the face and estimates the user's emotions. For example, the AI ​​analyzes changes in the microexpressions of the face and estimates the user's emotions. The AI ​​can also analyze the characteristics of the microexpressions of the face and estimate the user's emotions. Furthermore, the AI ​​can analyze patterns of the microexpressions of the face and estimate the user's emotions. Step 4: The support unit provides assistance and guidance based on the facial microexpressions analyzed by the analysis unit. The support unit can, for example, use AI to provide assistance and guidance. The AI ​​provides appropriate assistance and guidance to the user based on the analyzed facial microexpressions. For example, the AI ​​provides voice guidance to the user based on the analyzed facial microexpressions. The AI ​​can also provide visual instructions to the user based on the analyzed facial microexpressions. Furthermore, the AI ​​can provide text messages to the user based on the analyzed facial microexpressions. Step 5: The training unit performs facial training. The training unit can perform facial training using, for example, AI. The AI ​​analyzes the user's facial microexpressions and performs facial training. For example, the AI ​​analyzes changes in the user's facial microexpressions and performs facial training. The AI ​​can also analyze the characteristics of the user's facial microexpressions and perform facial training. Furthermore, the AI ​​can analyze patterns of the user's facial microexpressions and perform facial training.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the measurement unit, conversion unit, analysis unit, auxiliary unit, and training unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the measurement unit measures brain waves using the earphone-type device of the smart device 14. The conversion unit converts brain waves into objective language using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes facial microexpressions using the camera 42 of the smart device 14. The auxiliary unit provides assistance and guidance based on the facial microexpressions analyzed by the specific processing unit 290 of the data processing unit 12. The training unit can perform facial training using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the measurement unit, conversion unit, analysis unit, auxiliary unit, and training unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the measurement unit measures brain waves using the earphone-type device of the smart glasses 214. The conversion unit converts brain waves into objective language using, for example, the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes facial microexpressions using, for example, the camera 42 of the smart glasses 214. The auxiliary unit provides assistance and guidance based on the facial microexpressions analyzed by, for example, the specific processing unit 290 of the data processing unit 12. The training unit can perform facial training using, for example, the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the measurement unit, conversion unit, analysis unit, auxiliary unit, and training unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the measurement unit measures brain waves using the earphone-type device of the headset terminal 314. The conversion unit converts brain waves into objective language using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes facial microexpressions using the camera 42 of the headset terminal 314. The auxiliary unit provides assistance and guidance based on the facial microexpressions analyzed by the specific processing unit 290 of the data processing unit 12. The training unit can perform facial training using the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the measurement unit, conversion unit, analysis unit, auxiliary unit, and training unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the measurement unit measures brain waves using an earphone-type device on the robot 414. The conversion unit converts brain waves into objective language using, for example, the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes facial microexpressions using, for example, the camera 42 of the robot 414. The auxiliary unit provides assistance and guidance based on the facial microexpressions analyzed by, for example, the specific processing unit 290 of the data processing unit 12. The training unit can perform facial training using, for example, the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A measurement unit for measuring brain waves, A conversion unit that converts the brain waves measured by the measurement unit into objective words, An analysis unit that analyzes facial micro-expressions, An auxiliary unit that provides assistance and guidance based on the micro-expressions of the face analyzed by the aforementioned analysis unit, It includes a training section for facial training. A system characterized by the following features. (Note 2) The aforementioned measuring unit is Measure brain waves using an earphone-type device The system described in Appendix 1, characterized by the features described herein. (Note 3) The conversion unit is Converting measured brainwaves into objective language. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Analyzing facial microexpressions The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned auxiliary part is, Provide assistance and guidance based on analyzed facial microexpressions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned training department Perform facial exercises The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned measuring unit is The system estimates the user's emotions and adjusts the timing of brainwave measurements based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned measuring unit is The system analyzes the user's past electroencephalogram (EEG) data and selects the optimal measurement method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned measuring unit is During electroencephalogram (EEG) measurement, filtering is performed based on the user's current activity level and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned measuring unit is It estimates the user's emotions and determines the priority of brainwave measurements based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned measuring unit is During electroencephalogram (EEG) measurements, the system prioritizes measuring data that is highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned measuring unit is During electroencephalogram (EEG) measurements, the system analyzes the user's social media activity and measures relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The conversion unit is It estimates the user's emotions and adjusts the way words are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The conversion unit is During conversion, the level of detail in the conversion is adjusted based on the importance of the brainwaves. The system described in Appendix 1, characterized by the features described herein. (Note 15) The conversion unit is During conversion, different conversion algorithms are applied depending on the category of brainwaves. The system described in Appendix 1, characterized by the features described herein. (Note 16) The conversion unit is It estimates the user's emotions and adjusts the length of words based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The conversion unit is During conversion, the conversion priority is determined based on the timing of the electroencephalogram (EEG) measurements. The system described in Appendix 1, characterized by the features described herein. (Note 18) The conversion unit is During conversion, the order of words is adjusted based on the relevance of brainwaves. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the micro-expression analysis criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the interrelationships of facial microexpressions are taken into consideration to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, user attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During the analysis, the geographical distribution of faces will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis based on relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned auxiliary part is, It estimates the user's emotions and adjusts the methods of assistance and guidance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned auxiliary part is, When providing assistance or guidance, select the optimal method based on past data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned auxiliary part is, When providing assistance or guidance, customize the methods based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned auxiliary part is, It estimates the user's emotions and determines the priority of assistance and guidance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned auxiliary part is, When providing assistance or guidance, the optimal method is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned auxiliary part is, When providing assistance or guidance, we analyze the user's social media activity and suggest appropriate methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned training department It estimates the user's emotions and adjusts the training method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned training department During training, the system provides the optimal method based on the user's past training history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned training department During training, the training methods are customized based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned training department, It estimates the user's emotions and determines training priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned training department, During training, the system provides the optimal method based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned training department, During training, the system analyzes the user's social media activity and suggests training methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A measurement unit for measuring brain waves, A conversion unit that converts the brain waves measured by the measurement unit into objective words, An analysis unit that analyzes facial micro-expressions, An auxiliary unit that provides assistance and guidance based on the micro-expressions of the face analyzed by the aforementioned analysis unit, It includes a training section for facial training. A system characterized by the following features.

2. The aforementioned measuring unit is Measure brain waves using an earphone-type device The system according to feature 1.

3. The conversion unit is Converting measured brainwaves into objective language. The system according to feature 1.

4. The aforementioned analysis unit, Analyzing facial microexpressions The system according to feature 1.

5. The aforementioned auxiliary part is, Provide assistance and guidance based on analyzed facial microexpressions. The system according to feature 1.

6. The aforementioned training department Perform facial exercises The system according to feature 1.

7. The aforementioned measuring unit is The system estimates the user's emotions and adjusts the timing of brainwave measurements based on the estimated emotions. The system according to feature 1.

8. The aforementioned measuring unit is The system analyzes the user's past electroencephalogram (EEG) data and selects the optimal measurement method. The system according to feature 1.

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