system

Abstract

The system according to this embodiment aims to understand the user's thoughts and feelings in real time and provide appropriate feedback. [Solution] The system according to the embodiment comprises a reading unit, an analysis unit, an understanding unit, and a feedback unit. The reading unit reads brain waves or nerve signals. The analysis unit analyzes the signals read by the reading unit. The understanding unit understands the user's thoughts or emotions based on the results analyzed by the analysis unit. The feedback unit provides feedback of the information understood by the understanding unit.

Description

Technical Field

[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 conventional technology, there is a problem that it is difficult to accurately understand a user's thoughts and feelings in real time and provide appropriate feedback.

[0005] The system according to the embodiment aims to understand a user's thoughts and feelings in real time and provide appropriate feedback.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reading unit, an analysis unit, an understanding unit, and a feedback unit. The reading unit reads brain waves or nerve signals. The analysis unit analyzes the signals read by the reading unit. The understanding unit understands the user's thoughts or emotions based on the results analyzed by the analysis unit. The feedback unit provides feedback of the information understood by the understanding unit. [Effects of the Invention]

[0007] The system according to this embodiment can understand the user's thoughts and feelings in real time and provide appropriate feedback. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied 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 NeuroAICompanion system, according to an embodiment of the present invention, is a personal assistant system that integrates a brain implant with generative AI. The NeuroAICompanion system reads the user's brainwaves and neural signals, which are then analyzed and understood by the generative AI. The generative AI instantly grasps the user's thoughts and emotions and directly feeds optimal information and advice back into the brain. This allows the user to access information and perform tasks simply by "thinking." For example, the NeuroAICompanion system is designed to target professionals performing highly intellectual work, business people who require efficient information processing and decision-making, and people with physical limitations. The challenges faced by these targets include delayed decision-making due to information overload, difficulty in solving complex problems, and communication difficulties due to physical limitations. To address these challenges, the NeuroAICompanion system provides instant information access and decision-making support, enhanced advanced problem-solving abilities, and thought-directed communication. The generative AI learns brainwave patterns and understands the user's thoughts and emotions with high accuracy. It also considers the user's situation and past experiences to generate optimal information and solutions. Furthermore, it complements and expands the user's fragmented thoughts, expressing them in a more refined form. It learns the user's thought patterns and preferences, supporting more effective information presentation and problem-solving. Thoughts are output not only in language, but also in diverse formats such as images, audio, and haptic feedback. This enables the NeuroAICompanion system to provide information based on the user's thoughts.

[0029] The NeuroAICompanion system according to this embodiment comprises a reading unit, an analysis unit, an understanding unit, and a feedback unit. The reading unit reads brain waves or nerve signals. The reading unit can read, for example, EEG signals, MEG signals, or signals of specific neurotransmitters. The reading unit uses advanced sensor technology to read brain waves and nerve signals with high accuracy. For example, the reading unit monitors brain waves in real time and accurately captures signal fluctuations. The reading unit also includes filtering technology to reduce noise. The analysis unit analyzes the signals read by the reading unit. For example, the analysis unit analyzes brain waves and nerve signals using signal processing algorithms. The analysis unit preprocesses the data, removes noise, and extracts signal features. For example, the analysis unit removes noise using filtering technology and analyzes the frequency components of the signal. The analysis unit also identifies signal patterns using machine learning models. The understanding unit understands the user's thoughts and emotions based on the results analyzed by the analysis unit. The understanding unit understands the user's thoughts and feelings, for example, using machine learning models or psychological evaluation criteria. The understanding unit estimates the user's thoughts and feelings based on the analyzed signal patterns. For example, the understanding unit uses machine learning models to analyze signal patterns and estimate the user's thoughts and feelings. The understanding unit also evaluates the user's feelings using psychological evaluation criteria. The feedback unit provides feedback based on the information understood by the understanding unit. The feedback unit provides feedback in various ways, such as visual feedback, audio feedback, and haptic feedback. The feedback unit provides the user with optimal information and advice. For example, the feedback unit displays information on a display as visual feedback. The feedback unit can also provide information through a speaker as audio feedback. Furthermore, the feedback unit can convey information using vibrations or pressure as haptic feedback. As a result, the NeuroAICompanion system according to this embodiment can provide information based on the user's thoughts.

[0030] The reading unit reads brain waves or nerve signals. For example, it can read EEG signals, MEG signals, and signals of specific neurotransmitters. The reading unit employs advanced sensor technology to read brain waves and nerve signals with high accuracy. Specifically, EEG sensors are attached to the scalp to capture the brain's electrical activity in real time. MEG sensors detect magnetic field fluctuations in the brain to measure nerve activity with high accuracy. Furthermore, chemical sensors may be used to read signals of specific neurotransmitters. These sensors detect fluctuations in the concentration of chemicals in the brain, providing detailed information about nerve activity. The reading unit collects data from these sensors in real time and applies advanced filtering techniques to reduce noise. For example, digital filtering techniques are used to remove environmental and physiological noise and extract pure brain wave signals. In addition, the reading unit employs a high sampling rate to accurately capture signal fluctuations. This allows it to capture subtle fluctuations in brain waves and nerve signals, tracking changes in the user's thoughts and emotions in real time. The reading unit transmits this data to a central database, making it accessible to the analysis unit. This allows the reading unit to read the user's brainwaves and neural signals with high accuracy, improving the overall system performance.

[0031] The analysis unit analyzes the signals read by the reading unit. For example, the analysis unit uses signal processing algorithms to analyze brain waves and neural signals. Specifically, it preprocesses the data, removes noise, and extracts signal features. For example, it uses filtering techniques to remove noise and analyze the frequency components of the signal. Bandpass filters and notch filters are used for this purpose. Furthermore, the analysis unit uses machine learning models to identify signal patterns. For example, it uses deep learning techniques to automatically extract features of brain wave signals and identify patterns corresponding to specific thoughts and emotions. Based on these patterns, the analysis unit can track changes in the user's thoughts and emotions in real time. Additionally, the analysis unit can utilize historical data and statistical information to analyze long-term trends and patterns. For example, it can predict fluctuations in thoughts and emotions in specific situations and environments based on the user's past brain wave data. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal signals, issuing early warnings. This allows the analysis unit to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The understanding unit understands the user's thoughts and emotions based on the results analyzed by the analysis unit. The understanding unit understands the user's thoughts and emotions using, for example, machine learning models and psychological evaluation criteria. Specifically, it estimates the user's thoughts and emotions based on the analyzed signal patterns. For example, it uses a deep learning model to analyze the characteristics of brainwave signals and estimate what the user is currently thinking and feeling. It also evaluates the user's emotions using psychological evaluation criteria. This utilizes research results showing that specific brainwave patterns correspond to specific emotional states. The understanding unit integrates this information to grasp the overall picture of the user's thoughts and emotions. Furthermore, the understanding unit can make more accurate estimations by considering the user's past data and behavioral history. For example, it can predict the user's thoughts and emotions in the current situation based on what thoughts and emotions the user showed in specific situations in the past. In addition, the understanding unit can collect user feedback and continuously improve the accuracy of the model. As a result, the understanding unit can understand the user's thoughts and emotions with high accuracy and improve the overall system performance.

[0033] The feedback unit provides feedback based on information understood by the understanding unit. The feedback unit provides information through methods such as visual feedback, audio feedback, and haptic feedback. Specifically, as visual feedback, information is displayed on the screen. For example, it provides advice and information based on the user's thoughts and feelings through a graphical interface. It can also provide information through the speaker as audio feedback. For example, it can deliver advice and reminders in voice that are tailored to the user's thoughts and feelings. Furthermore, it can convey information using vibration and pressure as haptic feedback. For example, if the user is in a specific thought or emotional state, the device vibrates to draw their attention. The feedback unit can combine these feedback methods to provide the user with the most optimal information and advice. In addition, the feedback unit can collect user feedback and continuously improve the accuracy and effectiveness of the feedback. For example, it can record how the user reacted to the feedback provided and adjust the feedback method based on that information. The feedback unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only display notifications but also voice calls, SMS, and email. This allows the feedback unit to provide information to the user quickly and reliably, improving the overall performance of the system.

[0034] The learning unit can learn the user's thinking patterns or preferences. For example, the learning unit learns the user's thinking patterns and preferences based on past behavioral data or survey results. The learning unit uses machine learning algorithms to analyze the user's behavioral data and identify thinking patterns and preferences. For example, the learning unit collects the user's past behavioral data and analyzes it using machine learning algorithms. The learning unit can also learn the user's preferences based on survey results. For example, the learning unit analyzes the survey results answered by the user and identifies their preferences. By learning the user's thinking patterns and preferences, it becomes possible to provide more personalized information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavioral data into a generating AI and have the generating AI perform the identification of thinking patterns and preferences.

[0035] The output unit can output thoughts in multiple formats. For example, it can output thoughts in text, graphical, or audio formats. The output unit uses a generative AI to express the user's thoughts in diverse formats. For example, the output unit can use a text generation AI to output the user's thoughts in text format. The output unit can also use a graphical generation AI to output the user's thoughts in graphical format. Furthermore, the output unit can use an audio generation AI to output the user's thoughts in audio format. This allows for the provision of information tailored to the user's needs by outputting thoughts in diverse formats. Some or all of the above-described processes in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's thought data into a generative AI and have the generative AI produce output in formats such as text, graphical, or audio.

[0036] The reading unit can analyze the user's past EEG data and select the optimal reading method. For example, the reading unit can select the most stable reading method from the user's past EEG data. The reading unit can also suggest the optimal reading method for a specific situation based on the user's past EEG data. The reading unit can also analyze the user's past EEG data and provide an individually customized reading method. This allows for the provision of individually customized reading methods by analyzing past EEG data. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the user's past EEG data into a generating AI and have the generating AI select the optimal reading method.

[0037] The reading unit can filter the data based on the user's current activity level during reading. For example, if the user is exercising, the reading unit can filter out movement-related noise before reading the brainwaves. If the user is resting, the reading unit can also prioritize reading brainwaves in a relaxed state. If the user is working, the reading unit can also filter out brainwaves in a focused state before reading them. This allows for the acquisition of accurate brainwave data with reduced noise by filtering based on the user's activity level. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input user activity data into a generating AI and have the generating AI perform the filtering.

[0038] The reading unit can prioritize reading signals that are highly relevant based on the user's geographical location information during reading. For example, if the user is at home, the reading unit can prioritize reading brainwaves indicating a relaxed state. If the user is at work, the reading unit can also prioritize reading brainwaves indicating a focused state. If the user is out, the reading unit can also prioritize reading brainwaves appropriate to the environment. This allows for the acquisition of appropriate data tailored to the environment by prioritizing signal reading based on geographical location information. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the user's geographical location information into a generating AI and cause the generating AI to prioritize reading signals that are highly relevant.

[0039] The reading unit can analyze the user's social media activity and read relevant signals during the reading process. For example, if the user is relaxed on social media, the reading unit will prioritize reading brainwaves indicating a relaxed state. If the user is stressed on social media, the reading unit can also prioritize reading brainwaves indicating a stressed state. If the user is focused on social media, the reading unit can also prioritize reading brainwaves indicating a focused state. This allows for the acquisition of data tailored to the user's activity by reading signals based on social media activity. Some or all of the processing described above in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the user's social media activity data into a generating AI and have the generating AI perform the reading of relevant signals.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the signals during the analysis. For example, the analysis unit can perform a detailed analysis for signals of high importance. The analysis unit can also perform a simplified analysis for signals of low importance. The analysis unit can also perform an analysis with an appropriate level of detail for signals of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the signals. 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 signal importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the signal category during analysis. For example, for signals in a relaxed state, the analysis unit can apply an analysis algorithm specialized for a relaxed state. For signals in a stressed state, the analysis unit can also apply an analysis algorithm specialized for a stressed state. For signals in a focused state, the analysis unit can also apply an analysis algorithm specialized for a focused state. By applying an analysis algorithm according to the signal category, more accurate analysis becomes possible. 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 signal category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0042] The analysis unit can determine the priority of analysis based on the timing of signal acquisition during the analysis process. For example, the analysis unit may prioritize the analysis of recently acquired signals. The analysis unit may also postpone the analysis of signals acquired in the past. The analysis unit may also prioritize the analysis of signals acquired during a specific time period. This allows for the prioritization of the latest data by determining the priority of analysis based on the timing of signal acquisition. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input signal acquisition timing data into a generating AI and have the generating AI determine the priority of analysis.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the signals during analysis. For example, the analysis unit may prioritize the analysis of highly relevant signals. The analysis unit may also postpone the analysis of less relevant signals. The analysis unit may also prioritize the analysis of signals related to a specific category. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the signals. 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 signal relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0044] The understanding unit can adjust the level of detail of its understanding based on the importance of the analysis results. For example, the understanding unit will perform a detailed understanding of analysis results with high importance. The understanding unit can also perform a simplified understanding of analysis results with low importance. The understanding unit can also perform an understanding with an appropriate level of detail of analysis results with moderate importance. This allows for efficient understanding by adjusting the level of detail of the understanding based on the importance of the analysis results. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input importance data of the analysis results into a generating AI and have the generating AI perform the adjustment of the level of detail of the understanding.

[0045] The understanding unit can apply different understanding algorithms depending on the category of the analysis result during the understanding process. For example, the understanding unit can apply an understanding algorithm specialized for relaxation to analysis results of a relaxed state. The understanding unit can also apply an understanding algorithm specialized for stress to analysis results of a stressed state. The understanding unit can also apply an understanding algorithm specialized for concentration to analysis results of a concentrated state. By applying an understanding algorithm according to the category of the analysis result, a more accurate understanding becomes possible. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input the category data of the analysis result into a generating AI and cause the generating AI to execute the application of different understanding algorithms.

[0046] The understanding unit can determine the priority of understanding based on when the analysis results were acquired. For example, the understanding unit may prioritize understanding recently acquired analysis results. The understanding unit may also postpone understanding analysis results acquired in the past. The understanding unit may also prioritize understanding analysis results acquired during a specific time period. This allows the latest analysis results to be understood preferentially by determining the priority of understanding based on when the analysis results were acquired. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input data on when the analysis results were acquired into a generating AI and have the generating AI perform the determination of the priority of understanding.

[0047] The understanding unit can adjust the order of understanding based on the relevance of the analysis results during the understanding process. For example, the understanding unit may prioritize understanding highly relevant analysis results. The understanding unit may also postpone understanding less relevant analysis results. The understanding unit may also prioritize understanding analysis results related to a specific category. This allows for efficient understanding by adjusting the order of understanding based on the relevance of the analysis results. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input relevance data of the analysis results into a generating AI and have the generating AI perform the adjustment of the order of understanding.

[0048] The feedback unit can adjust the level of detail of the feedback based on the importance of the understanding result. For example, the feedback unit can provide detailed feedback for understanding results of high importance. The feedback unit can also provide simplified feedback for understanding results of low importance. The feedback unit can also provide feedback with an appropriate level of detail for understanding results of medium importance. This allows for efficient feedback by adjusting the level of detail of the feedback based on the importance of the understanding result. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the importance data of the understanding result into a generating AI and have the generating AI perform the adjustment of the level of detail of the feedback.

[0049] The feedback unit can apply different feedback methods depending on the category of the understanding result during the feedback process. For example, the feedback unit can apply a feedback method specifically tailored to relaxation for understanding results of a relaxed state. The feedback unit can also apply a feedback method specifically tailored to stress for understanding results of a stressed state. The feedback unit can also apply a feedback method specifically tailored to concentration for understanding results of a focused state. This allows for more appropriate feedback by applying feedback methods according to the category of the understanding result. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the category data of the understanding result into a generating AI and have the generating AI execute the application of different feedback methods.

[0050] The feedback unit can determine the priority of feedback based on when the understanding results were obtained. For example, the feedback unit can prioritize feedback on recently obtained understanding results. The feedback unit can also postpone feedback on previously obtained understanding results. The feedback unit can also prioritize feedback on understanding results obtained during a specific time period. This allows the latest understanding results to be prioritized by determining the priority of feedback based on when the understanding results were obtained. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input data on when the understanding results were obtained into a generating AI and have the generating AI perform the task of determining the priority of feedback.

[0051] The feedback unit can adjust the order of feedback based on the relevance of the understanding results. For example, the feedback unit can prioritize feedback on highly relevant understanding results. The feedback unit can also postpone feedback on less relevant understanding results. The feedback unit can also prioritize feedback on understanding results related to a specific category. This allows for efficient feedback by adjusting the order of feedback based on the relevance of the understanding results. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input relevance data of the understanding results into a generating AI and have the generating AI perform the adjustment of the feedback order.

[0052] The learning unit can adjust the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust the learning algorithm. The learning unit can also optimize the parameters of the learning algorithm by referring to past learning data. This enables efficient learning by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the adjustment of the learning algorithm.

[0053] The learning unit can weight the training data based on the timing of signal acquisition during training. For example, the learning unit can assign high weights to recently acquired signal data during training. The learning unit can also assign low weights to signal data acquired in the past during training. The learning unit can also assign appropriate weights to signal data acquired during a specific time period during training. This enables efficient training by weighting the training data based on the timing of signal acquisition. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input signal acquisition timing data into a generating AI and have the generating AI perform the weighting of the training data.

[0054] The output unit can select an appropriate output method by referring to the user's past output history when outputting data. For example, the output unit can select the optimal output method based on the user's past output history. The output unit can also analyze the user's past output history and adjust the output method. The output unit can also optimize the parameters of the output method by referring to the user's past output history. This enables efficient information provision by selecting the optimal output method by referring to past output history. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's past output history data into a generating AI and have the generating AI select the optimal output method.

[0055] The output unit can select an appropriate output method based on the user's device information at the time of output. For example, if the user is using a smartphone, the output unit provides an output method that matches the screen size. If the user is using a tablet, the output unit can also provide an output method optimized for a larger screen. If the user is using a smartwatch, the output unit can also provide a concise and highly visible output method. This makes it possible to provide appropriate information according to the user's device by selecting the optimal output method based on device information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's device information into a generating AI and have the generating AI select the optimal output method.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The NeuroAICompanion system can also be equipped with a health management unit that monitors the user's health status. This unit acquires biometric data such as heart rate, blood pressure, and body temperature, in addition to the user's brain waves and nerve signals. For example, if the health management unit detects an abnormally high heart rate, it can determine that the user may be experiencing stress and provide advice for relaxation. It can also detect signs of fever if the user's body temperature is elevated and encourage rest. Furthermore, if the user's blood pressure is high, the health management unit can warn of health risks and recommend seeking medical attention. This allows the NeuroAICompanion system to comprehensively manage the user's health status and provide appropriate advice.

[0058] The NeuroAICompanion system can also be equipped with a sleep management unit that monitors the user's sleep patterns. This unit analyzes the user's brainwaves and neural signals to evaluate sleep quality and patterns. For example, it can detect whether the user is in deep sleep and, if deep sleep is insufficient, suggest improvements to the sleep environment. Furthermore, the sleep management unit can analyze the user's brainwave patterns during sleep and provide advice to improve sleep quality. It can also learn the user's sleep patterns and suggest an optimal sleep schedule. This enables the NeuroAICompanion system to improve the user's sleep quality and support a healthy lifestyle.

[0059] The NeuroAICompanion system can also be equipped with a learning support unit to further enhance the user's learning effectiveness. This unit analyzes the user's brainwaves and neural signals to evaluate learning progress and comprehension. For example, it can detect whether the user is effectively learning new information and suggest improvements to the learning method. Furthermore, it can adjust the learning content according to the user's level of comprehension. In addition, it can learn the user's learning patterns and suggest an optimal learning schedule. This enables the NeuroAICompanion system to enhance the user's learning effectiveness and support efficient learning.

[0060] The NeuroAICompanion system can also be equipped with an exercise management unit that monitors the user's movement patterns. This unit acquires movement data using accelerometers and gyroscopes, in addition to the user's brainwaves and neural signals. For example, if the user's exercise level is insufficient, the exercise management unit can provide advice to encourage more exercise. It can also analyze the user's movement patterns and propose an optimal exercise plan. Furthermore, it can analyze the user's brainwave patterns during exercise and provide advice to maximize the effectiveness of the exercise. This enables the NeuroAICompanion system to improve the user's exercise habits and support a healthy lifestyle.

[0061] The NeuroAICompanion system can also be equipped with a meal management unit that monitors the user's eating patterns. This unit acquires meal record data in addition to the user's brainwaves and neural signals. For example, if the user's diet is unbalanced, the meal management unit can suggest a balanced diet. It can also analyze the user's eating patterns and propose a healthy meal plan. Furthermore, the meal management unit can analyze the user's brainwave patterns during meals and evaluate their satisfaction with the food. This allows the NeuroAICompanion system to improve the user's eating habits and support a healthy lifestyle.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reading unit reads brain waves or nerve signals. For example, it can read EEG signals, MEG signals, or signals of specific neurotransmitters. The reading unit uses advanced sensor technology to read brain waves and nerve signals with high accuracy. For example, it monitors brain waves in real time and accurately captures signal fluctuations. It also features filtering technology to reduce noise. Step 2: The analysis unit analyzes the signals read by the reading unit. For example, it analyzes electroencephalograms and neural signals using signal processing algorithms. It preprocesses the data to remove noise and extract signal features. It removes noise using filtering techniques and analyzes the frequency components of the signal. It also uses machine learning models to identify signal patterns. Step 3: The understanding unit understands the user's thoughts and feelings based on the results analyzed by the analysis unit. For example, it uses machine learning models and psychological evaluation criteria to understand the user's thoughts and feelings. It estimates the user's thoughts and feelings based on the analyzed signal patterns. It uses machine learning models to analyze signal patterns and estimate the user's thoughts and feelings. It also evaluates the user's emotions using psychological evaluation criteria. Step 4: The feedback unit provides feedback based on the information understood by the understanding unit. For example, it provides information through methods such as visual feedback, audio feedback, and haptic feedback. It provides the user with the most appropriate information and advice. Visual feedback involves displaying information on a screen. Audio feedback can also be provided through a speaker. Furthermore, haptic feedback can convey information using vibrations or pressure.

[0064] (Example of form 2) The NeuroAICompanion system, according to an embodiment of the present invention, is a personal assistant system that integrates a brain implant with generative AI. The NeuroAICompanion system reads the user's brainwaves and neural signals, which are then analyzed and understood by the generative AI. The generative AI instantly grasps the user's thoughts and emotions and directly feeds optimal information and advice back into the brain. This allows the user to access information and perform tasks simply by "thinking." For example, the NeuroAICompanion system is designed to target professionals performing highly intellectual work, business people who require efficient information processing and decision-making, and people with physical limitations. The challenges faced by these targets include delayed decision-making due to information overload, difficulty in solving complex problems, and communication difficulties due to physical limitations. To address these challenges, the NeuroAICompanion system provides instant information access and decision-making support, enhanced advanced problem-solving abilities, and thought-directed communication. The generative AI learns brainwave patterns and understands the user's thoughts and emotions with high accuracy. It also considers the user's situation and past experiences to generate optimal information and solutions. Furthermore, it complements and expands the user's fragmented thoughts, expressing them in a more refined form. It learns the user's thought patterns and preferences, supporting more effective information presentation and problem-solving. Thoughts are output not only in language, but also in diverse formats such as images, audio, and haptic feedback. This enables the NeuroAICompanion system to provide information based on the user's thoughts.

[0065] The NeuroAICompanion system according to this embodiment comprises a reading unit, an analysis unit, an understanding unit, and a feedback unit. The reading unit reads brain waves or nerve signals. The reading unit can read, for example, EEG signals, MEG signals, or signals of specific neurotransmitters. The reading unit uses advanced sensor technology to read brain waves and nerve signals with high accuracy. For example, the reading unit monitors brain waves in real time and accurately captures signal fluctuations. The reading unit also includes filtering technology to reduce noise. The analysis unit analyzes the signals read by the reading unit. For example, the analysis unit analyzes brain waves and nerve signals using signal processing algorithms. The analysis unit preprocesses the data, removes noise, and extracts signal features. For example, the analysis unit removes noise using filtering technology and analyzes the frequency components of the signal. The analysis unit also identifies signal patterns using machine learning models. The understanding unit understands the user's thoughts and emotions based on the results analyzed by the analysis unit. The understanding unit understands the user's thoughts and feelings, for example, using machine learning models or psychological evaluation criteria. The understanding unit estimates the user's thoughts and feelings based on the analyzed signal patterns. For example, the understanding unit uses machine learning models to analyze signal patterns and estimate the user's thoughts and feelings. The understanding unit also evaluates the user's feelings using psychological evaluation criteria. The feedback unit provides feedback based on the information understood by the understanding unit. The feedback unit provides feedback in various ways, such as visual feedback, audio feedback, and haptic feedback. The feedback unit provides the user with optimal information and advice. For example, the feedback unit displays information on a display as visual feedback. The feedback unit can also provide information through a speaker as audio feedback. Furthermore, the feedback unit can convey information using vibrations or pressure as haptic feedback. As a result, the NeuroAICompanion system according to this embodiment can provide information based on the user's thoughts.

[0066] The reading unit reads brain waves or nerve signals. For example, it can read EEG signals, MEG signals, and signals of specific neurotransmitters. The reading unit employs advanced sensor technology to read brain waves and nerve signals with high accuracy. Specifically, EEG sensors are attached to the scalp to capture the brain's electrical activity in real time. MEG sensors detect magnetic field fluctuations in the brain to measure nerve activity with high accuracy. Furthermore, chemical sensors may be used to read signals of specific neurotransmitters. These sensors detect fluctuations in the concentration of chemicals in the brain, providing detailed information about nerve activity. The reading unit collects data from these sensors in real time and applies advanced filtering techniques to reduce noise. For example, digital filtering techniques are used to remove environmental and physiological noise and extract pure brain wave signals. In addition, the reading unit employs a high sampling rate to accurately capture signal fluctuations. This allows it to capture subtle fluctuations in brain waves and nerve signals, tracking changes in the user's thoughts and emotions in real time. The reading unit transmits this data to a central database, making it accessible to the analysis unit. This allows the reading unit to read the user's brainwaves and neural signals with high accuracy, improving the overall system performance.

[0067] The analysis unit analyzes the signals read by the reading unit. For example, the analysis unit uses signal processing algorithms to analyze brain waves and neural signals. Specifically, it preprocesses the data, removes noise, and extracts signal features. For example, it uses filtering techniques to remove noise and analyze the frequency components of the signal. Bandpass filters and notch filters are used for this purpose. Furthermore, the analysis unit uses machine learning models to identify signal patterns. For example, it uses deep learning techniques to automatically extract features of brain wave signals and identify patterns corresponding to specific thoughts and emotions. Based on these patterns, the analysis unit can track changes in the user's thoughts and emotions in real time. Additionally, the analysis unit can utilize historical data and statistical information to analyze long-term trends and patterns. For example, it can predict fluctuations in thoughts and emotions in specific situations and environments based on the user's past brain wave data. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal signals, issuing early warnings. This allows the analysis unit to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0068] The understanding unit understands the user's thoughts and emotions based on the results analyzed by the analysis unit. The understanding unit understands the user's thoughts and emotions using, for example, machine learning models and psychological evaluation criteria. Specifically, it estimates the user's thoughts and emotions based on the analyzed signal patterns. For example, it uses a deep learning model to analyze the characteristics of brainwave signals and estimate what the user is currently thinking and feeling. It also evaluates the user's emotions using psychological evaluation criteria. This utilizes research results showing that specific brainwave patterns correspond to specific emotional states. The understanding unit integrates this information to grasp the overall picture of the user's thoughts and emotions. Furthermore, the understanding unit can make more accurate estimations by considering the user's past data and behavioral history. For example, it can predict the user's thoughts and emotions in the current situation based on what thoughts and emotions the user showed in specific situations in the past. In addition, the understanding unit can collect user feedback and continuously improve the accuracy of the model. As a result, the understanding unit can understand the user's thoughts and emotions with high accuracy and improve the overall system performance.

[0069] The feedback unit provides feedback based on information understood by the understanding unit. The feedback unit provides information through methods such as visual feedback, audio feedback, and haptic feedback. Specifically, as visual feedback, information is displayed on the screen. For example, it provides advice and information based on the user's thoughts and feelings through a graphical interface. It can also provide information through the speaker as audio feedback. For example, it can deliver advice and reminders in voice that are tailored to the user's thoughts and feelings. Furthermore, it can convey information using vibration and pressure as haptic feedback. For example, if the user is in a specific thought or emotional state, the device vibrates to draw their attention. The feedback unit can combine these feedback methods to provide the user with the most optimal information and advice. In addition, the feedback unit can collect user feedback and continuously improve the accuracy and effectiveness of the feedback. For example, it can record how the user reacted to the feedback provided and adjust the feedback method based on that information. The feedback unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only display notifications but also voice calls, SMS, and email. This allows the feedback unit to provide information to the user quickly and reliably, improving the overall performance of the system.

[0070] The learning unit can learn the user's thinking patterns or preferences. For example, the learning unit learns the user's thinking patterns and preferences based on past behavioral data or survey results. The learning unit uses machine learning algorithms to analyze the user's behavioral data and identify thinking patterns and preferences. For example, the learning unit collects the user's past behavioral data and analyzes it using machine learning algorithms. The learning unit can also learn the user's preferences based on survey results. For example, the learning unit analyzes the survey results answered by the user and identifies their preferences. By learning the user's thinking patterns and preferences, it becomes possible to provide more personalized information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavioral data into a generating AI and have the generating AI perform the identification of thinking patterns and preferences.

[0071] The output unit can output thoughts in multiple formats. For example, it can output thoughts in text, graphical, or audio formats. The output unit uses a generative AI to express the user's thoughts in diverse formats. For example, the output unit can use a text generation AI to output the user's thoughts in text format. The output unit can also use a graphical generation AI to output the user's thoughts in graphical format. Furthermore, the output unit can use an audio generation AI to output the user's thoughts in audio format. This allows for the provision of information tailored to the user's needs by outputting thoughts in diverse formats. Some or all of the above-described processes in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's thought data into a generative AI and have the generative AI produce output in formats such as text, graphical, or audio.

[0072] The reading unit can estimate the user's emotions and adjust the accuracy of reading brainwaves and neural signals based on the estimated emotions. For example, if the user is relaxed, the reading unit can increase the reading accuracy to obtain more detailed brainwave data. If the user is stressed, the reading unit can also decrease the reading accuracy to reduce noise. If the user is focused, the reading unit can also focus on reading specific brainwave patterns. This allows for the acquisition of more accurate brainwave data by adjusting the reading accuracy 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 reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The reading unit can analyze the user's past EEG data and select the optimal reading method. For example, the reading unit can select the most stable reading method from the user's past EEG data. The reading unit can also suggest the optimal reading method for a specific situation based on the user's past EEG data. The reading unit can also analyze the user's past EEG data and provide an individually customized reading method. This allows for the provision of individually customized reading methods by analyzing past EEG data. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the user's past EEG data into a generating AI and have the generating AI select the optimal reading method.

[0074] The reading unit can filter the data based on the user's current activity level during reading. For example, if the user is exercising, the reading unit can filter out movement-related noise before reading the brainwaves. If the user is resting, the reading unit can also prioritize reading brainwaves in a relaxed state. If the user is working, the reading unit can also filter out brainwaves in a focused state before reading them. This allows for the acquisition of accurate brainwave data with reduced noise by filtering based on the user's activity level. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input user activity data into a generating AI and have the generating AI perform the filtering.

[0075] The reading unit can estimate the user's emotions and determine the priority of signals to read based on the estimated user emotions. For example, if the user is relaxed, the reading unit will prioritize reading brainwaves indicating a relaxed state. If the user is stressed, the reading unit can also prioritize reading brainwaves indicating a stressed state. If the user is focused, the reading unit can also prioritize reading brainwaves indicating a focused state. This allows for the priority of reading important signals by determining the priority of signals based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 reading unit may be performed using AI, or not using AI. For example, the reading unit can input user emotion data into a generative AI and have the generative AI determine the priority of signals.

[0076] The reading unit can prioritize reading signals that are highly relevant based on the user's geographical location information during reading. For example, if the user is at home, the reading unit can prioritize reading brainwaves indicating a relaxed state. If the user is at work, the reading unit can also prioritize reading brainwaves indicating a focused state. If the user is out, the reading unit can also prioritize reading brainwaves appropriate to the environment. This allows for the acquisition of appropriate data tailored to the environment by prioritizing signal reading based on geographical location information. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the user's geographical location information into a generating AI and cause the generating AI to prioritize reading signals that are highly relevant.

[0077] The reading unit can analyze the user's social media activity and read relevant signals during the reading process. For example, if the user is relaxed on social media, the reading unit will prioritize reading brainwaves indicating a relaxed state. If the user is stressed on social media, the reading unit can also prioritize reading brainwaves indicating a stressed state. If the user is focused on social media, the reading unit can also prioritize reading brainwaves indicating a focused state. This allows for the acquisition of data tailored to the user's activity by reading signals based on social media activity. Some or all of the processing described above in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the user's social media activity data into a generating AI and have the generating AI perform the reading of relevant signals.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can apply a detailed analysis algorithm. If the user is stressed, the analysis unit can also apply a simplified analysis algorithm. If the user is focused, the analysis unit can also apply an analysis algorithm that focuses on specific brainwave patterns. This allows for more appropriate analysis by adjusting the analysis algorithm 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 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, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the signals during the analysis. For example, the analysis unit can perform a detailed analysis for signals of high importance. The analysis unit can also perform a simplified analysis for signals of low importance. The analysis unit can also perform an analysis with an appropriate level of detail for signals of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the signals. 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 signal importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the signal category during analysis. For example, for signals in a relaxed state, the analysis unit can apply an analysis algorithm specialized for a relaxed state. For signals in a stressed state, the analysis unit can also apply an analysis algorithm specialized for a stressed state. For signals in a focused state, the analysis unit can also apply an analysis algorithm specialized for a focused state. By applying an analysis algorithm according to the signal category, more accurate analysis becomes possible. 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 signal category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0081] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit may prioritize analyzing signals indicating a relaxed state. If the user is stressed, the analysis unit may also prioritize analyzing signals indicating a stressed state. If the user is focused, the analysis unit may also prioritize analyzing signals indicating a focused state. In this way, by determining the priority of analysis based on the user's emotions, important signals can be prioritized for analysis. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0082] The analysis unit can determine the priority of analysis based on the timing of signal acquisition during the analysis process. For example, the analysis unit may prioritize the analysis of recently acquired signals. The analysis unit may also postpone the analysis of signals acquired in the past. The analysis unit may also prioritize the analysis of signals acquired during a specific time period. This allows for the prioritization of the latest data by determining the priority of analysis based on the timing of signal acquisition. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input signal acquisition timing data into a generating AI and have the generating AI determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the signals during analysis. For example, the analysis unit may prioritize the analysis of highly relevant signals. The analysis unit may also postpone the analysis of less relevant signals. The analysis unit may also prioritize the analysis of signals related to a specific category. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the signals. 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 signal relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0084] The understanding unit can estimate the user's emotions and adjust the understanding algorithm based on the estimated emotions. For example, if the user is relaxed, the understanding unit can apply a detailed understanding algorithm. If the user is stressed, the understanding unit can also apply a simplified understanding algorithm. If the user is focused, the understanding unit can also apply an understanding algorithm that focuses on specific brainwave patterns. This allows for a more appropriate understanding by adjusting the understanding algorithm 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 understanding unit may be performed using AI, for example, or not using AI. For example, the understanding unit can input user emotion data into a generative AI and have the generative AI adjust the understanding algorithm.

[0085] The understanding unit can adjust the level of detail of its understanding based on the importance of the analysis results. For example, the understanding unit will perform a detailed understanding of analysis results with high importance. The understanding unit can also perform a simplified understanding of analysis results with low importance. The understanding unit can also perform an understanding with an appropriate level of detail of analysis results with moderate importance. This allows for efficient understanding by adjusting the level of detail of the understanding based on the importance of the analysis results. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input importance data of the analysis results into a generating AI and have the generating AI perform the adjustment of the level of detail of the understanding.

[0086] The understanding unit can apply different understanding algorithms depending on the category of the analysis result during the understanding process. For example, the understanding unit can apply an understanding algorithm specialized for relaxation to analysis results of a relaxed state. The understanding unit can also apply an understanding algorithm specialized for stress to analysis results of a stressed state. The understanding unit can also apply an understanding algorithm specialized for concentration to analysis results of a concentrated state. By applying an understanding algorithm according to the category of the analysis result, a more accurate understanding becomes possible. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input the category data of the analysis result into a generating AI and cause the generating AI to execute the application of different understanding algorithms.

[0087] The understanding unit can estimate the user's emotions and determine the priority of understanding based on the estimated user emotions. For example, if the user is relaxed, the understanding unit will prioritize understanding the analysis results for a relaxed state. If the user is stressed, the understanding unit can also prioritize understanding the analysis results for a stressed state. If the user is focused, the understanding unit can also prioritize understanding the analysis results for a focused state. In this way, by determining the priority of understanding based on the user's emotions, important analysis results can be understood preferentially. 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 understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input user emotion data into a generative AI and have the generative AI perform the determination of the priority of understanding.

[0088] The understanding unit can determine the priority of understanding based on when the analysis results were acquired. For example, the understanding unit may prioritize understanding recently acquired analysis results. The understanding unit may also postpone understanding analysis results acquired in the past. The understanding unit may also prioritize understanding analysis results acquired during a specific time period. This allows the latest analysis results to be understood preferentially by determining the priority of understanding based on when the analysis results were acquired. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input data on when the analysis results were acquired into a generating AI and have the generating AI perform the determination of the priority of understanding.

[0089] The understanding unit can adjust the order of understanding based on the relevance of the analysis results during the understanding process. For example, the understanding unit may prioritize understanding highly relevant analysis results. The understanding unit may also postpone understanding less relevant analysis results. The understanding unit may also prioritize understanding analysis results related to a specific category. This allows for efficient understanding by adjusting the order of understanding based on the relevance of the analysis results. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input relevance data of the analysis results into a generating AI and have the generating AI perform the adjustment of the order of understanding.

[0090] The feedback unit can estimate the user's emotions and adjust the way it expresses the feedback based on the estimated emotions. For example, if the user is relaxed, the feedback unit will provide feedback in a gentle manner. If the user is stressed, the feedback unit can also provide feedback in a concise and clear manner. If the user is focused, the feedback unit can also provide feedback in a manner that includes detailed information. This allows for more appropriate feedback by adjusting the way it expresses the feedback 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 feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the way it expresses the feedback.

[0091] The feedback unit can adjust the level of detail of the feedback based on the importance of the understanding result. For example, the feedback unit can provide detailed feedback for understanding results of high importance. The feedback unit can also provide simplified feedback for understanding results of low importance. The feedback unit can also provide feedback with an appropriate level of detail for understanding results of medium importance. This allows for efficient feedback by adjusting the level of detail of the feedback based on the importance of the understanding result. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the importance data of the understanding result into a generating AI and have the generating AI perform the adjustment of the level of detail of the feedback.

[0092] The feedback unit can apply different feedback methods depending on the category of the understanding result during the feedback process. For example, the feedback unit can apply a feedback method specifically tailored to relaxation for understanding results of a relaxed state. The feedback unit can also apply a feedback method specifically tailored to stress for understanding results of a stressed state. The feedback unit can also apply a feedback method specifically tailored to concentration for understanding results of a focused state. This allows for more appropriate feedback by applying feedback methods according to the category of the understanding result. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the category data of the understanding result into a generating AI and have the generating AI execute the application of different feedback methods.

[0093] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is relaxed, the feedback unit can prioritize feedback based on its understanding of the relaxed state. If the user is stressed, the feedback unit can also prioritize feedback based on its understanding of the stressed state. If the user is focused, the feedback unit can also prioritize feedback based on its understanding of the focused state. In this way, by determining the priority of feedback based on the user's emotions, important understandings can be prioritized. 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 feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI determine the priority of feedback.

[0094] The feedback unit can determine the priority of feedback based on when the understanding results were obtained. For example, the feedback unit can prioritize feedback on recently obtained understanding results. The feedback unit can also postpone feedback on previously obtained understanding results. The feedback unit can also prioritize feedback on understanding results obtained during a specific time period. This allows the latest understanding results to be prioritized by determining the priority of feedback based on when the understanding results were obtained. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input data on when the understanding results were obtained into a generating AI and have the generating AI perform the task of determining the priority of feedback.

[0095] The feedback unit can adjust the order of feedback based on the relevance of the understanding results. For example, the feedback unit can prioritize feedback on highly relevant understanding results. The feedback unit can also postpone feedback on less relevant understanding results. The feedback unit can also prioritize feedback on understanding results related to a specific category. This allows for efficient feedback by adjusting the order of feedback based on the relevance of the understanding results. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input relevance data of the understanding results into a generating AI and have the generating AI perform the adjustment of the feedback order.

[0096] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit will prioritize learning data representing a relaxed state. If the user is stressed, the learning unit can also prioritize learning data representing a stressed state. If the user is focused, the learning unit can also prioritize learning data representing a focused state. This allows for more effective learning by selecting training data 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 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 learning unit may be performed using AI, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform the selection of training data.

[0097] The learning unit can adjust the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust the learning algorithm. The learning unit can also optimize the parameters of the learning algorithm by referring to past learning data. This enables efficient learning by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the adjustment of the learning algorithm.

[0098] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can increase the learning frequency when the user is relaxed. It can also decrease the learning frequency when the user is stressed. It can also appropriately adjust the learning frequency when the user is focused. This allows for more effective learning by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into the generative AI and have the generative AI adjust the learning frequency.

[0099] The learning unit can weight the training data based on the timing of signal acquisition during training. For example, the learning unit can assign high weights to recently acquired signal data during training. The learning unit can also assign low weights to signal data acquired in the past during training. The learning unit can also assign appropriate weights to signal data acquired during a specific time period during training. This enables efficient training by weighting the training data based on the timing of signal acquisition. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input signal acquisition timing data into a generating AI and have the generating AI perform the weighting of the training data.

[0100] The output unit can estimate the user's emotions and adjust the output format based on the estimated emotions. For example, if the user is relaxed, the output unit will output in a calm format. If the user is stressed, the output unit can also output in a concise and clear format. If the user is focused, the output unit can also output in a format that includes detailed information. This allows for more appropriate information to be provided by adjusting the output format 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 output unit may be performed using AI, for example, or not using AI. For example, the output unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the output format.

[0101] The output unit can select an appropriate output method by referring to the user's past output history when outputting data. For example, the output unit can select the optimal output method based on the user's past output history. The output unit can also analyze the user's past output history and adjust the output method. The output unit can also optimize the parameters of the output method by referring to the user's past output history. This enables efficient information provision by selecting the optimal output method by referring to past output history. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's past output history data into a generating AI and have the generating AI select the optimal output method.

[0102] The output unit can estimate the user's emotions and determine the priority of output based on the estimated emotions. For example, if the user is relaxed, the output unit will prioritize outputting information about the relaxed state. If the user is stressed, the output unit can also prioritize outputting information about the stressed state. If the user is focused, the output unit can also prioritize outputting information about the focused state. In this way, by determining the priority of output based on the user's emotions, important information can be provided preferentially. 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 output unit may be performed using AI, for example, or not using AI. For example, the output unit can input user emotion data into a generative AI and have the generative AI determine the priority of output.

[0103] The output unit can select an appropriate output method based on the user's device information at the time of output. For example, if the user is using a smartphone, the output unit provides an output method that matches the screen size. If the user is using a tablet, the output unit can also provide an output method optimized for a larger screen. If the user is using a smartwatch, the output unit can also provide a concise and highly visible output method. This makes it possible to provide appropriate information according to the user's device by selecting the optimal output method based on device information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's device information into a generating AI and have the generating AI select the optimal output method.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The NeuroAICompanion system can also be equipped with a health management unit that monitors the user's health status. This unit acquires biometric data such as heart rate, blood pressure, and body temperature, in addition to the user's brain waves and nerve signals. For example, if the health management unit detects an abnormally high heart rate, it can determine that the user may be experiencing stress and provide advice for relaxation. It can also detect signs of fever if the user's body temperature is elevated and encourage rest. Furthermore, if the user's blood pressure is high, the health management unit can warn of health risks and recommend seeking medical attention. This allows the NeuroAICompanion system to comprehensively manage the user's health status and provide appropriate advice.

[0106] The NeuroAICompanion system can also include a music recommendation unit that estimates the user's emotions and selects music based on those emotions. This unit analyzes the user's brainwaves and neural signals, recommending calming music when the user is relaxed, relaxing music when stressed, and music that enhances concentration when the user is concentrating. For example, if the unit estimates the user is relaxed, it might recommend classical music or nature sounds. If it estimates the user is stressed, it could recommend relaxing ambient music. Furthermore, if it estimates the user is concentrating, it could recommend instrumental music to enhance concentration. This allows the NeuroAICompanion system to provide music tailored to the user's emotions, creating a more comfortable environment.

[0107] The NeuroAICompanion system can also be equipped with a sleep management unit that monitors the user's sleep patterns. This unit analyzes the user's brainwaves and neural signals to evaluate sleep quality and patterns. For example, it can detect whether the user is in deep sleep and, if deep sleep is insufficient, suggest improvements to the sleep environment. Furthermore, the sleep management unit can analyze the user's brainwave patterns during sleep and provide advice to improve sleep quality. It can also learn the user's sleep patterns and suggest an optimal sleep schedule. This enables the NeuroAICompanion system to improve the user's sleep quality and support a healthy lifestyle.

[0108] The NeuroAICompanion system can further estimate the user's emotions and adjust the timing of feedback based on those emotions. For example, if the user is relaxed, the feedback timing can be delayed to allow the user to maintain their relaxed state. If the user is stressed, the feedback timing can be advanced to provide advice quickly. If the user is concentrating, the feedback timing can be adjusted so as not to interrupt their concentration. This allows the NeuroAICompanion system to provide feedback at the appropriate time according to the user's emotions, enabling more effective support.

[0109] The NeuroAICompanion system can also be equipped with a learning support unit to further enhance the user's learning effectiveness. This unit analyzes the user's brainwaves and neural signals to evaluate learning progress and comprehension. For example, it can detect whether the user is effectively learning new information and suggest improvements to the learning method. Furthermore, it can adjust the learning content according to the user's level of comprehension. In addition, it can learn the user's learning patterns and suggest an optimal learning schedule. This enables the NeuroAICompanion system to enhance the user's learning effectiveness and support efficient learning.

[0110] The NeuroAICompanion system can further estimate the user's emotions and adjust its communication style based on those estimates. For example, if the user is relaxed, it can communicate in a calm tone. If the user is stressed, it can communicate in a concise and clear tone. If the user is focused, it can communicate in a tone that includes detailed information. This allows the NeuroAICompanion system to provide an appropriate communication style according to the user's emotions, enabling more effective dialogue.

[0111] The NeuroAICompanion system can also be equipped with an exercise management unit that monitors the user's movement patterns. This unit acquires movement data using accelerometers and gyroscopes, in addition to the user's brainwaves and neural signals. For example, if the user's exercise level is insufficient, the exercise management unit can provide advice to encourage more exercise. It can also analyze the user's movement patterns and propose an optimal exercise plan. Furthermore, it can analyze the user's brainwave patterns during exercise and provide advice to maximize the effectiveness of the exercise. This enables the NeuroAICompanion system to improve the user's exercise habits and support a healthy lifestyle.

[0112] The NeuroAICompanion system can further estimate the user's emotions and adjust the content of reminders based on those emotions. For example, if the user is relaxed, it can provide reminders in a calm tone. If the user is stressed, it can provide reminders in a concise and clear tone. If the user is focused, it can provide reminders with detailed information. This allows the NeuroAICompanion system to provide appropriate reminders tailored to the user's emotions, enabling more effective task management.

[0113] The NeuroAICompanion system can also be equipped with a meal management unit that monitors the user's eating patterns. This unit acquires meal record data in addition to the user's brainwaves and neural signals. For example, if the user's diet is unbalanced, the meal management unit can suggest a balanced diet. It can also analyze the user's eating patterns and propose a healthy meal plan. Furthermore, the meal management unit can analyze the user's brainwave patterns during meals and evaluate their satisfaction with the food. This allows the NeuroAICompanion system to improve the user's eating habits and support a healthy lifestyle.

[0114] The NeuroAICompanion system can further estimate the user's emotions and prioritize notifications based on those emotions. For example, if the user is relaxed, it can postpone less important notifications. If the user is stressed, it can prioritize more important notifications. If the user is concentrating, it can adjust the timing of notifications to avoid interrupting their concentration. This allows the NeuroAICompanion system to manage notifications appropriately according to the user's emotions, resulting in more effective information delivery.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reading unit reads brain waves or nerve signals. For example, it can read EEG signals, MEG signals, or signals of specific neurotransmitters. The reading unit uses advanced sensor technology to read brain waves and nerve signals with high accuracy. For example, it monitors brain waves in real time and accurately captures signal fluctuations. It also features filtering technology to reduce noise. Step 2: The analysis unit analyzes the signals read by the reading unit. For example, it analyzes electroencephalograms and neural signals using signal processing algorithms. It preprocesses the data to remove noise and extract signal features. It removes noise using filtering techniques and analyzes the frequency components of the signal. It also uses machine learning models to identify signal patterns. Step 3: The understanding unit understands the user's thoughts and feelings based on the results analyzed by the analysis unit. For example, it uses machine learning models and psychological evaluation criteria to understand the user's thoughts and feelings. It estimates the user's thoughts and feelings based on the analyzed signal patterns. It uses machine learning models to analyze signal patterns and estimate the user's thoughts and feelings. It also evaluates the user's emotions using psychological evaluation criteria. Step 4: The feedback unit provides feedback based on the information understood by the understanding unit. For example, it provides information through methods such as visual feedback, audio feedback, and haptic feedback. It provides the user with the most appropriate information and advice. Visual feedback involves displaying information on a screen. Audio feedback can also be provided through a speaker. Furthermore, haptic feedback can convey information using vibrations or pressure.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the reading unit, analysis unit, understanding unit, feedback unit, learning unit, and output unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reading unit can read brain waves and neural signals using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the read signals. The understanding unit is implemented by the specific processing unit 290 of the data processing unit 12 and understands the user's thoughts and emotions based on the analysis results. The feedback unit is implemented by the control unit 46A of the smart device 14 and provides visual, auditory, and haptic feedback. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's thought patterns and preferences. The output unit is implemented by the control unit 46A of the smart device 14 and outputs thoughts in the form of text, graphical, or audio. 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.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the reading unit, analysis unit, understanding unit, feedback unit, learning unit, and output unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reading unit can read brain waves and nerve signals using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the read signals. The understanding unit is implemented by the specific processing unit 290 of the data processing unit 12 and understands the user's thoughts and emotions based on the analysis results. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and provides visual, auditory, and haptic feedback. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's thought patterns and preferences. The output unit is implemented by the control unit 46A of the smart glasses 214 and outputs thoughts in the form of text, graphical, or audio. 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.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the reading unit, analysis unit, understanding unit, feedback unit, learning unit, and output unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reading unit can read brain waves and neural signals using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the read signals. The understanding unit is implemented by the specific processing unit 290 of the data processing unit 12 and understands the user's thoughts and emotions based on the analysis results. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and provides visual, auditory, and haptic feedback. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's thought patterns and preferences. The output unit is implemented by the control unit 46A of the headset terminal 314 and outputs thoughts in the form of text, graphical, or audio. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the reading unit, analysis unit, understanding unit, feedback unit, learning unit, and output unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reading unit can read brain waves and neural signals using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the read signals. The understanding unit is implemented by the specific processing unit 290 of the data processing unit 12 and understands the user's thoughts and emotions based on the analysis results. The feedback unit is implemented by the control unit 46A of the robot 414 and provides visual, auditory, and tactile feedback. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's thought patterns and preferences. The output unit is implemented by the control unit 46A of the robot 414 and outputs thoughts in the form of text, graphical, or audio. 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) A system characterized by comprising: a reading unit that reads brain waves or nerve signals; an analysis unit that analyzes the signals read by the reading unit; an understanding unit that understands the user's thoughts or emotions based on the results of the analysis by the analysis unit; and a feedback unit that provides feedback of the information understood by the understanding unit. (Note 2) The system according to Appendix 1, characterized by having a learning unit that learns the user's thinking patterns or preferences. (Note 3) The system described in Appendix 1, characterized by having an output unit that outputs thoughts in multiple formats. (Note 4) The system according to Appendix 1, characterized in that the reading unit estimates the user's emotions and adjusts the accuracy of reading brain waves or nerve signals based on the estimated user's emotions. (Note 5) The system described in Appendix 1 is characterized in that the reading unit analyzes the user's past brainwave data and selects an appropriate reading method. (Note 6) The reading unit is When reading, filtering is performed based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 7) The reading unit is It estimates the user's emotions and determines the priority of signals to read based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The system according to Appendix 1, characterized in that the reading unit, when reading, prioritizes reading signals that are highly relevant based on the user's geographical location information. (Note 9) The reading unit is During reading, the system analyzes the user's social media activity and reads relevant signals. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the signals. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the signal category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the signals were acquired. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationships between the signals. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned understanding unit is, It estimates the user's emotions and adjusts the understanding algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned understanding unit is, When understanding the data, adjust the level of detail based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned understanding unit is, During the understanding process, different understanding algorithms are applied depending on the category of the analysis result. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned understanding unit is, It estimates the user's emotions and determines the priority of understanding based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned understanding unit is, When understanding the concepts, prioritize the understanding based on when the analysis results were obtained. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned understanding unit is, During the understanding process, the order of understanding is adjusted based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is When providing feedback, adjust the level of detail based on the importance of the understanding gained. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is When providing feedback, different feedback methods will be applied depending on the category of the understanding result. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When providing feedback, prioritize the feedback based on when the understanding was obtained. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is When providing feedback, adjust the order of feedback based on the relevance of the understanding results. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The system described in Appendix 2, characterized in that the learning unit adjusts the learning algorithm by referring to past learning data during learning. (Note 30) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned learning unit, During training, the training data is weighted based on when the signals were acquired. The system described in Appendix 2, characterized by the features described herein. (Note 32) The output unit is, It estimates the user's emotions and adjusts the output format based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The system described in Appendix 3, characterized in that the output unit selects an appropriate output method by referring to the user's past output history when outputting. (Note 34) The output unit is, It estimates the user's emotions and determines the priority of the output based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The system according to Appendix 3, characterized in that the output unit selects an appropriate output method based on the user's device information when outputting. [Explanation of Symbols]

[0189] 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 system characterized by comprising: a reading unit that reads brain waves or nerve signals; an analysis unit that analyzes the signals read by the reading unit; an understanding unit that understands the user's thoughts or emotions based on the results of the analysis by the analysis unit; and a feedback unit that provides feedback of the information understood by the understanding unit.

2. The system according to claim 1, characterized by comprising a learning unit that learns the user's thinking patterns or preferences.

3. The system according to claim 1, characterized in that it includes an output unit that outputs thoughts in multiple formats.

4. The system according to claim 1, characterized in that the reading unit estimates the user's emotions and adjusts the accuracy of reading brain waves or nerve signals based on the estimated user's emotions.

5. The system according to claim 1, characterized in that the reading unit analyzes the user's past brainwave data and selects an appropriate reading method.

6. The reading unit is When reading, filtering is performed based on the user's current activity status. The system according to feature 1.

7. The reading unit is It estimates the user's emotions and determines the priority of signals to read based on the estimated user emotions. The system according to feature 1.

8. The system according to claim 1, characterized in that the reading unit, when reading, prioritizes reading signals that are highly relevant based on the user's geographical location information.

9. The reading unit is During reading, the system analyzes the user's social media activity and reads relevant signals. The system according to feature 1.

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