system

Abstract

The system according to this embodiment aims to understand the user's mental state and emotions in real time and provide personalized support. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects the user's brainwave data and biometric information. The analysis unit analyzes the data collected by the collection unit and estimates the user's mental state and emotions. The provision unit provides personalized support based on the analysis results obtained by the analysis unit.

Description

Technical Field

[0005]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

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 grasp the mental state and emotions of a user in real time and provide corresponding support.

[0005] The system according to the embodiment aims to grasp the mental state and emotions of a user in real time and provide personalized support.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects the user's electroencephalogram (EEG) data and biometric information. The analysis unit analyzes the data collected by the data collection unit and estimates the user's mental state and emotions. The data provision unit provides personalized support based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can grasp the user's mental state and emotions in real time and provide personalized support. [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 ۱۲ 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 MindWaveCompanion system, according to an embodiment of the present invention, is an innovative application that combines electroencephalogram (EEG) sensing technology with a wearable device. The MindWaveCompanion system analyzes the user's EEG data in real time, utilizes generative AI to understand the individual's mental state and emotions, and provides personalized support accordingly. For example, the MindWaveCompanion system aims to improve the user's overall mental well-being, such as stress management, improved concentration, and improved sleep quality. Specifically, the user wears a wearable device such as a headband or smartwatch with an EEG sensor. These devices then collect the user's EEG data and biometric information in real time. The collected data is analyzed by generative AI to estimate the user's mental state and emotions with high accuracy. Based on this, personalized support is provided. For example, it can detect stress in real time and suggest coping strategies; recommend micro-breaks to improve concentration; suggest an optimal sleep cycle and adjust the sleep environment; recommend music and meditation content based on emotion recognition; and analyze long-term mental state trends to provide a personalized improvement plan. The MindWaveCompanion system aims to improve the user's overall mental well-being. The generative AI continuously learns and improves the accuracy of its suggestions and analyses, supporting the long-term improvement of the user's mental well-being. This allows the MindWaveCompanion system to enhance the user's mental well-being.

[0029] The MindWaveCompanion system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects the user's brainwave data and biometric information. The data collection unit can collect the user's brainwave data and biometric information using, for example, a wearable device such as a headband with an EEG sensor or a smartwatch. For example, the data collection unit collects the user's brainwave data in real time using a headband with an EEG sensor. The data collection unit can also collect biometric information such as the user's heart rate and skin electrical activity using a smartwatch. Furthermore, the data collection unit can collect more detailed data by combining multiple wearable devices. The analysis unit analyzes the data collected by the data collection unit and estimates the user's mental state and emotions. The analysis unit can analyze the collected brainwave data and biometric information using, for example, a generative AI. The generative AI can estimate the user's mental state and emotions with high accuracy using, for example, a text generation AI (e.g., LLM). The analysis unit can also comprehensively analyze the brainwave data and biometric information using a multimodal generative AI. Furthermore, the analysis unit can use generative AI to monitor the user's mental state and emotional changes in real time. The service unit provides personalized support based on the analysis results obtained by the analysis unit. For example, the service unit can detect stress in real time and suggest coping strategies. It can also recommend micro-breaks to improve concentration. Furthermore, the service unit can suggest an optimal sleep cycle and adjust the sleep environment. For example, the service unit can suggest an optimal sleep cycle based on the user's brainwave data and biometric information. It can also recommend music and meditation content based on emotion recognition. Furthermore, the service unit can analyze long-term trends in mental state and provide a personalized improvement plan. As a result, the MindWaveCompanion system according to this embodiment can improve the user's mental well-being. Some or all of the above-described processes in the service unit may be performed using AI, for example, or without AI.For example, the support unit can provide support using an AI model that takes the analysis results obtained by the analysis unit as input and outputs personalized support.

[0030] The data collection unit collects the user's brainwave data and biometric information. For example, the unit can collect this data using wearable devices such as a headband with an EEG sensor or a smartwatch. Specifically, a headband with an EEG sensor is worn on the user's head and detects subtle fluctuations in brainwaves with high precision. This allows for real-time monitoring of the user's concentration level, relaxation level, and stress level. A smartwatch, worn on the user's wrist, continuously monitors biometric information such as heart rate, skin electrical activity, and body temperature. This data is transmitted to the data collection unit via Bluetooth® or Wi-Fi and stored in a central database. Furthermore, the data collection unit can combine multiple wearable devices to collect more detailed data. For example, using a headband with an EEG sensor and a smartwatch simultaneously allows for a comprehensive understanding of the user's mental and physiological state. This enables the data collection unit to evaluate the user's condition from multiple perspectives and provide more accurate data. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and provisioning departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit to estimate the user's mental state and emotions. The analysis unit can, for example, use generative AI to analyze collected brainwave data and biometric information. The generative AI, for example, uses text generation AI (e.g., LLM) to estimate the user's mental state and emotions with high accuracy. Specifically, the generative AI analyzes patterns in brainwave data to determine whether the user is relaxed, focused, or stressed. By combining this with biometric information such as heart rate and skin electrical activity, changes in the user's emotions can be estimated more accurately. Furthermore, the analysis unit can also use multimodal generative AI to comprehensively analyze brainwave data and biometric information. This allows for a comprehensive evaluation of different types of data, providing a more detailed understanding of the user's mental state. The analysis unit can also use generative AI to monitor changes in the user's mental state and emotions in real time. For example, if a user begins to feel stressed, the analysis unit can immediately detect the change and take appropriate measures. Furthermore, the analysis unit can utilize past data and statistical information to analyze long-term trends in mental state. This allows for the prediction of fluctuations in the user's mental state and the development of future countermeasures. The analysis unit uses anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The service provider provides personalized support based on the analysis results obtained by the analysis unit. For example, the service provider can detect stress in real time and suggest coping strategies. Specifically, if the analysis unit determines that the user is experiencing stress, the service provider will suggest deep breathing exercises or short meditation sessions to help the user relax. The service provider can also recommend micro-breaks to improve concentration. For example, it can provide alerts to help users maintain focus by suggesting short breaks during long work sessions. Furthermore, the service provider can suggest an optimal sleep cycle and adjust the sleep environment. For example, based on the user's brainwave data and biometric information, it can suggest optimal bedtimes and wake-up times to support comfortable sleep. The service provider can also recommend music and meditation content based on emotion recognition. For example, if the user feels the need to relax, the service provider will suggest relaxing music and meditation guides. In addition, the service provider can analyze long-term mental state trends and provide personalized improvement plans. For example, based on the user's past data, it can suggest specific action plans for stress management and concentration improvement. This allows the service provider to improve the user's mental well-being. Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit can provide support using an AI model that takes the analysis results obtained by the analysis unit as input and outputs personalized support. This allows the service provision unit to quickly and effectively provide optimal support tailored to the user's needs.

[0033] The data collection unit can collect the user's brainwave data and biometric information using wearable devices such as a headband with an EEG sensor or a smartwatch. For example, the data collection unit can collect the user's brainwave data in real time using a headband with an EEG sensor. The data collection unit can also collect biometric information such as the user's heart rate and skin electrical activity using a smartwatch. Furthermore, the data collection unit can combine multiple wearable devices to collect more detailed data. This allows for the efficient collection of the user's brainwave data and biometric information using wearable devices. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from a headband with an EEG sensor or a smartwatch into a generating AI and have the generating AI perform data analysis.

[0034] The analysis unit can analyze the collected data and estimate the user's mental state and emotions with high accuracy. The analysis unit can analyze collected brainwave data and biometric information using, for example, a generative AI. The generative AI can estimate the user's mental state and emotions with high accuracy using, for example, a text generation AI (e.g., LLM). The analysis unit can also comprehensively analyze brainwave data and biometric information using a multimodal generative AI. Furthermore, the analysis unit can monitor changes in the user's mental state and emotions in real time using the generative AI. This allows for accurate understanding of the user's mental state and emotions through high-precision analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input collected data into the generative AI and have the generative AI perform the estimation of the user's mental state and emotions.

[0035] The service provider can detect stress in real time based on the analysis results and propose coping strategies. For example, the service provider can monitor the user's stress level in real time based on the analysis results and propose coping strategies when stress levels rise. The service provider can also identify the cause of stress and propose appropriate coping strategies. Furthermore, the service provider can propose relaxation techniques and counseling to reduce the user's stress level. In this way, by detecting stress in real time and proposing appropriate coping strategies, it supports the user's stress management. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide support using an AI model that takes the analysis results as input and performs stress detection and proposes coping strategies.

[0036] The service provider can recommend microbreaks to improve concentration. For example, the service provider might recommend a short break if the user's concentration decreases. The service provider can also suggest exercises and relaxation techniques to maintain concentration. Furthermore, the service provider can provide advice to optimize the user's work environment. By recommending microbreaks to improve concentration, the service provider helps maintain and improve the user's concentration. Some or all of the above processes performed by the service provider may be carried out using AI, for example, or not. For example, the service provider can provide support using an AI model that takes the user's concentration data as input and recommends microbreaks.

[0037] The service provider can propose an optimal sleep cycle and adjust the sleep environment. For example, the service provider can propose an optimal sleep cycle based on the user's brainwave data and biometric information. The service provider can also provide advice to optimize the user's sleep environment. Furthermore, the service provider can propose relaxation techniques and counseling to improve the user's sleep quality. In this way, the service provider improves the user's sleep quality by proposing an optimal sleep cycle and adjusting the sleep environment. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide support using an AI model that takes the user's sleep data as input and proposes an optimal sleep cycle and adjusts the sleep environment.

[0038] The service provider can recommend music and meditation content based on emotion recognition. For example, the service provider can analyze the user's emotional state and recommend relaxation music or guided meditation based on that analysis. The service provider can also suggest different types of music and meditation content depending on the user's emotional state. Furthermore, the service provider can recommend music and meditation content that includes counseling or relaxation techniques to improve the user's emotional state, based on emotion recognition. In this way, recommending music and meditation content based on emotion recognition improves the user's mental well-being. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can provide support using an AI model that takes the user's emotional data as input and recommends music and meditation content.

[0039] The service provider can analyze long-term trends in mental state and provide personalized improvement plans. For example, the service provider can collect data on the user's mental state over a long period and analyze its trends. Based on the analysis results, the service provider can also propose improvement plans to enhance the user's mental well-being. Furthermore, the service provider can monitor changes in the user's mental state and adjust the improvement plan as needed. This allows for the continuous improvement of the user's mental well-being by analyzing long-term trends in mental state and providing personalized improvement plans. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can provide support using an AI model that takes the user's long-term mental state data as input and proposes improvement plans.

[0040] The data collection unit can analyze the user's past EEG data and select the optimal collection method. For example, the data collection unit can identify the most effective collection timing based on the user's past EEG data. The data collection unit can also analyze the user's past EEG data and optimize the collection method under specific circumstances. Furthermore, the data collection unit can adjust the collection frequency based on the user's past EEG data to improve data accuracy. In this way, by analyzing past data, the optimal collection method is selected and data accuracy is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past EEG data into a generating AI and have the generating AI select the optimal collection method.

[0041] The data collection unit can filter EEG data based on the user's current activity level and environment. For example, if the user is exercising, the unit can filter out exercise-related noise before collecting EEG data. Furthermore, if the user is in a quiet environment, the unit can minimize environmental noise while collecting EEG data. Additionally, if the user is working, the unit can filter the EEG data considering work-related stress. This allows for the collection of less noisy data by filtering based on activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input user activity and environmental data into a generating AI and have the generating AI perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting electroencephalogram (EEG) data. For example, if the user is at home, the data collection unit can prioritize the collection of EEG data indicating a relaxed state. Similarly, if the user is at work, the data collection unit can prioritize the collection of EEG data related to work-related stress. Furthermore, if the user is outside, the data collection unit can prioritize the collection of EEG data corresponding to changes in the environment. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI determine the priority of highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data when collecting electroencephalogram (EEG) data. For example, if the user is experiencing stress on social media, the data collection unit can collect stress-related EEG data. It can also collect relaxed EEG data if the user is relaxed on social media. Furthermore, if the user is focused on social media, the data collection unit can collect EEG data related to concentration. This allows for the collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the electroencephalogram (EEG) data during the analysis. For example, if important EEG data is collected, the analysis unit will perform a detailed analysis. If less important EEG data is collected, the analysis unit can also perform a simplified analysis. Furthermore, if highly important EEG data is collected, the analysis unit can generate a detailed report. This allows for efficient analysis by adjusting the level of detail based on importance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the EEG data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the electroencephalogram (EEG) data during analysis. For example, the analysis unit can apply a stress analysis algorithm to stress-related EEG data. It can also apply a relaxation analysis algorithm to relaxation-related EEG data. Furthermore, it can apply a concentration analysis algorithm to concentration-related EEG data. By applying an analysis algorithm according to the 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 the category of the EEG data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the timing of EEG data collection during analysis. For example, the analysis unit may prioritize the analysis of recently collected EEG data. The analysis unit can also analyze current data while referring to past EEG data. Furthermore, the analysis unit can prioritize the analysis of EEG data related to specific events or situations. This allows for the prioritization of the latest data by determining the analysis priority based on the collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of EEG data collection into a generating AI and have the generating AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the electroencephalogram (EEG) data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant EEG data. It can also postpone the analysis of less relevant EEG data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the EEG data. This allows for efficient analysis by adjusting the order of analysis based on relevance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the EEG data into a generating AI and have the generating AI adjust the order of analysis.

[0048] The service provider can analyze the user's past mental state at the time of service delivery to select the most suitable support method. For example, the service provider can suggest the most suitable relaxation method based on the user's past mental state. The service provider can also analyze the user's past mental state and suggest methods for stress management. Furthermore, the service provider can suggest methods for improving concentration, taking into account the user's past mental state. In this way, by analyzing the user's past mental state, the service provider can select the most suitable support method and provide effective support. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past mental state data into a generating AI and have the generating AI select the most suitable support method.

[0049] The service provider can customize the means of support based on the user's current living situation at the time of delivery. For example, if the user is at work, the service provider can suggest relaxation methods that can be performed in a short amount of time. If the user is at home, the service provider can also provide advice on creating a relaxing environment. Furthermore, if the user is out, the service provider can suggest relaxation methods that can be performed while out. In this way, by customizing the means of support based on the user's living situation, the service provider can provide the most suitable support for the user. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's living situation data into a generating AI and have the generating AI perform the customization of the means of support.

[0050] The service provider can select the optimal support method by considering the user's geographical location information at the time of delivery. For example, if the user is at home, the service provider can suggest relaxation methods that can be performed at home. If the user is at work, the service provider can also suggest stress management methods that can be performed at work. Furthermore, if the user is out, the service provider can also suggest methods to improve concentration that can be performed while out. In this way, by considering geographical location information, the service provider can select the optimal support method and provide effective support. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal support method.

[0051] The service provider can analyze the user's social media activity and suggest support measures at the time of delivery. For example, if the user is experiencing stress on social media, the service provider can suggest methods for stress management. Furthermore, if the user is feeling relaxed on social media, the service provider can suggest ways to maintain that relaxed state. Additionally, if the user is concentrating on social media, the service provider can suggest ways to improve concentration. In this way, by analyzing social media activity, relevant support measures can be suggested. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI generate support measure suggestions.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The data collection unit can collect ambient sound data in addition to the user's brainwave data. For example, the unit can monitor the sound environment around the user in real time and analyze the combined brainwave and ambient sound data. This allows for the identification of external factors that influence the user's mental state and emotions, enabling more accurate analysis. Furthermore, the unit can analyze the impact of specific sound environments on the user's mental state and suggest the optimal sound environment. For example, if a quiet environment enhances concentration, it can provide advice on maintaining that environment.

[0054] The analysis unit can estimate the user's cognitive load based on the user's electroencephalogram (EEG) data. For example, the analysis unit can monitor the user's cognitive load level in real time from the EEG data and recommend a break if the cognitive load increases. The analysis unit can also identify the cause of the cognitive load and suggest appropriate countermeasures. Furthermore, the analysis unit can suggest exercises and relaxation techniques to reduce the user's cognitive load. This allows for the management of the user's cognitive load and supports efficient work.

[0055] The data collection unit can collect not only the user's brainwave data but also their eye-tracking data. For example, the unit can monitor the user's eye movements in real time and analyze the combined brainwave and eye-tracking data. This allows for a more accurate understanding of the user's attentional direction and level of concentration. Furthermore, based on the eye-tracking data, the unit can evaluate the user's visual load and suggest appropriate break times. This enables the management of the user's concentration and attention using eye-tracking data.

[0056] The data collection unit can collect not only the user's electroencephalogram (EEG) data but also their exercise data. For example, the unit can monitor the user's exercise volume and patterns in real time and analyze the combined EEG and exercise data. This allows for an evaluation of the impact of exercise on the user's mental state and the suggestion of an appropriate exercise plan. Furthermore, the unit can provide advice to improve the user's exercise habits based on the exercise data. In this way, exercise data can be used to improve the user's mental well-being.

[0057] The data collection unit can collect not only the user's brainwave data but also their dietary data. For example, the unit can monitor the user's meal content and timing in real time and analyze the combined brainwave and dietary data. This allows for an evaluation of the impact of diet on the user's mental state and the suggestion of an appropriate meal plan. Furthermore, the unit can provide advice to improve the user's eating habits based on the dietary data. In this way, dietary data can be used to improve the user's mental well-being.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data collection unit collects the user's brainwave data and biometric information. The data collection unit can collect the user's brainwave data and biometric information using wearable devices such as a headband with an EEG sensor or a smartwatch. For example, the data collection unit can collect the user's brainwave data in real time using a headband with an EEG sensor. The data collection unit can also collect biometric information such as the user's heart rate and skin electrical activity using a smartwatch. Furthermore, the data collection unit can combine multiple wearable devices to collect more detailed data. Step 2: The analysis unit analyzes the data collected by the collection unit to estimate the user's mental state and emotions. The analysis unit can analyze the collected brainwave data and biometric information using, for example, a generative AI. The generative AI can estimate the user's mental state and emotions with high accuracy using, for example, a text generation AI (e.g., LLM). The analysis unit can also comprehensively analyze brainwave data and biometric information using a multimodal generative AI. Furthermore, the analysis unit can monitor changes in the user's mental state and emotions in real time using the generative AI. Step 3: The service provider provides personalized support based on the analysis results obtained by the analysis unit. For example, the service provider can detect stress in real time and suggest coping strategies. It can also recommend micro-breaks to improve concentration. Furthermore, the service provider can suggest an optimal sleep cycle and adjust the sleep environment. For example, the service provider can suggest an optimal sleep cycle based on the user's brainwave data and biometric information. It can also recommend music and meditation content based on emotion recognition. Furthermore, the service provider can analyze long-term trends in mental state and provide a personalized improvement plan. In this way, the MindWaveCompanion system according to the embodiment can improve the user's mental well-being. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide support using an AI model that takes the analysis results obtained by the analysis unit as input and outputs personalized support.

[0060] (Example of form 2) The MindWaveCompanion system, according to an embodiment of the present invention, is an innovative application that combines electroencephalogram (EEG) sensing technology with a wearable device. The MindWaveCompanion system analyzes the user's EEG data in real time, utilizes generative AI to understand the individual's mental state and emotions, and provides personalized support accordingly. For example, the MindWaveCompanion system aims to improve the user's overall mental well-being, such as stress management, improved concentration, and improved sleep quality. Specifically, the user wears a wearable device such as a headband or smartwatch with an EEG sensor. These devices then collect the user's EEG data and biometric information in real time. The collected data is analyzed by generative AI to estimate the user's mental state and emotions with high accuracy. Based on this, personalized support is provided. For example, it can detect stress in real time and suggest coping strategies; recommend micro-breaks to improve concentration; suggest an optimal sleep cycle and adjust the sleep environment; recommend music and meditation content based on emotion recognition; and analyze long-term mental state trends to provide a personalized improvement plan. The MindWaveCompanion system aims to improve the user's overall mental well-being. The generative AI continuously learns and improves the accuracy of its suggestions and analyses, supporting the long-term improvement of the user's mental well-being. This allows the MindWaveCompanion system to enhance the user's mental well-being.

[0061] The MindWaveCompanion system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects the user's brainwave data and biometric information. The data collection unit can collect the user's brainwave data and biometric information using, for example, a wearable device such as a headband with an EEG sensor or a smartwatch. For example, the data collection unit collects the user's brainwave data in real time using a headband with an EEG sensor. The data collection unit can also collect biometric information such as the user's heart rate and skin electrical activity using a smartwatch. Furthermore, the data collection unit can collect more detailed data by combining multiple wearable devices. The analysis unit analyzes the data collected by the data collection unit and estimates the user's mental state and emotions. The analysis unit can analyze the collected brainwave data and biometric information using, for example, a generative AI. The generative AI can estimate the user's mental state and emotions with high accuracy using, for example, a text generation AI (e.g., LLM). The analysis unit can also comprehensively analyze the brainwave data and biometric information using a multimodal generative AI. Furthermore, the analysis unit can use generative AI to monitor the user's mental state and emotional changes in real time. The service unit provides personalized support based on the analysis results obtained by the analysis unit. For example, the service unit can detect stress in real time and suggest coping strategies. It can also recommend micro-breaks to improve concentration. Furthermore, the service unit can suggest an optimal sleep cycle and adjust the sleep environment. For example, the service unit can suggest an optimal sleep cycle based on the user's brainwave data and biometric information. It can also recommend music and meditation content based on emotion recognition. Furthermore, the service unit can analyze long-term trends in mental state and provide a personalized improvement plan. As a result, the MindWaveCompanion system according to this embodiment can improve the user's mental well-being. Some or all of the above-described processes in the service unit may be performed using AI, for example, or without AI.For example, the support unit can provide support using an AI model that takes the analysis results obtained by the analysis unit as input and outputs personalized support.

[0062] The data collection unit collects the user's brainwave data and biometric information. For example, the unit can collect this data using wearable devices such as a headband with an EEG sensor or a smartwatch. Specifically, a headband with an EEG sensor is worn on the user's head and detects subtle fluctuations in brainwaves with high precision. This allows for real-time monitoring of the user's concentration level, relaxation level, and stress level. A smartwatch, worn on the user's wrist, continuously monitors biometric information such as heart rate, skin electrical activity, and body temperature. This data is transmitted to the data collection unit via Bluetooth or Wi-Fi and stored in a central database. Furthermore, the data collection unit can combine multiple wearable devices to collect more detailed data. For example, using a headband with an EEG sensor and a smartwatch simultaneously allows for a comprehensive understanding of the user's mental and physiological state. This enables the data collection unit to evaluate the user's condition from multiple perspectives and provide more accurate data. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and provisioning departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0063] The analysis unit analyzes the data collected by the collection unit to estimate the user's mental state and emotions. The analysis unit can, for example, use generative AI to analyze collected brainwave data and biometric information. The generative AI, for example, uses text generation AI (e.g., LLM) to estimate the user's mental state and emotions with high accuracy. Specifically, the generative AI analyzes patterns in brainwave data to determine whether the user is relaxed, focused, or stressed. By combining this with biometric information such as heart rate and skin electrical activity, changes in the user's emotions can be estimated more accurately. Furthermore, the analysis unit can also use multimodal generative AI to comprehensively analyze brainwave data and biometric information. This allows for a comprehensive evaluation of different types of data, providing a more detailed understanding of the user's mental state. The analysis unit can also use generative AI to monitor changes in the user's mental state and emotions in real time. For example, if a user begins to feel stressed, the analysis unit can immediately detect the change and take appropriate measures. Furthermore, the analysis unit can utilize past data and statistical information to analyze long-term trends in mental state. This allows for the prediction of fluctuations in the user's mental state and the development of future countermeasures. The analysis unit uses anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0064] The service provider provides personalized support based on the analysis results obtained by the analysis unit. For example, the service provider can detect stress in real time and suggest coping strategies. Specifically, if the analysis unit determines that the user is experiencing stress, the service provider will suggest deep breathing exercises or short meditation sessions to help the user relax. The service provider can also recommend micro-breaks to improve concentration. For example, it can provide alerts to help users maintain focus by suggesting short breaks during long work sessions. Furthermore, the service provider can suggest an optimal sleep cycle and adjust the sleep environment. For example, based on the user's brainwave data and biometric information, it can suggest optimal bedtimes and wake-up times to support comfortable sleep. The service provider can also recommend music and meditation content based on emotion recognition. For example, if the user feels the need to relax, the service provider will suggest relaxing music and meditation guides. In addition, the service provider can analyze long-term mental state trends and provide personalized improvement plans. For example, based on the user's past data, it can suggest specific action plans for stress management and concentration improvement. This allows the service provider to improve the user's mental well-being. Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit can provide support using an AI model that takes the analysis results obtained by the analysis unit as input and outputs personalized support. This allows the service provision unit to quickly and effectively provide optimal support tailored to the user's needs.

[0065] The data collection unit can collect the user's brainwave data and biometric information using wearable devices such as a headband with an EEG sensor or a smartwatch. For example, the data collection unit can collect the user's brainwave data in real time using a headband with an EEG sensor. The data collection unit can also collect biometric information such as the user's heart rate and skin electrical activity using a smartwatch. Furthermore, the data collection unit can combine multiple wearable devices to collect more detailed data. This allows for the efficient collection of the user's brainwave data and biometric information using wearable devices. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from a headband with an EEG sensor or a smartwatch into a generating AI and have the generating AI perform data analysis.

[0066] The analysis unit can analyze the collected data and estimate the user's mental state and emotions with high accuracy. The analysis unit can analyze collected brainwave data and biometric information using, for example, a generative AI. The generative AI can estimate the user's mental state and emotions with high accuracy using, for example, a text generation AI (e.g., LLM). The analysis unit can also comprehensively analyze brainwave data and biometric information using a multimodal generative AI. Furthermore, the analysis unit can monitor changes in the user's mental state and emotions in real time using the generative AI. This allows for accurate understanding of the user's mental state and emotions through high-precision analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input collected data into the generative AI and have the generative AI perform the estimation of the user's mental state and emotions.

[0067] The service provider can detect stress in real time based on the analysis results and propose coping strategies. For example, the service provider can monitor the user's stress level in real time based on the analysis results and propose coping strategies when stress levels rise. The service provider can also identify the cause of stress and propose appropriate coping strategies. Furthermore, the service provider can propose relaxation techniques and counseling to reduce the user's stress level. In this way, by detecting stress in real time and proposing appropriate coping strategies, it supports the user's stress management. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide support using an AI model that takes the analysis results as input and performs stress detection and proposes coping strategies.

[0068] The service provider can recommend microbreaks to improve concentration. For example, the service provider might recommend a short break if the user's concentration decreases. The service provider can also suggest exercises and relaxation techniques to maintain concentration. Furthermore, the service provider can provide advice to optimize the user's work environment. By recommending microbreaks to improve concentration, the service provider helps maintain and improve the user's concentration. Some or all of the above processes performed by the service provider may be carried out using AI, for example, or not. For example, the service provider can provide support using an AI model that takes the user's concentration data as input and recommends microbreaks.

[0069] The service provider can propose an optimal sleep cycle and adjust the sleep environment. For example, the service provider can propose an optimal sleep cycle based on the user's brainwave data and biometric information. The service provider can also provide advice to optimize the user's sleep environment. Furthermore, the service provider can propose relaxation techniques and counseling to improve the user's sleep quality. In this way, the service provider improves the user's sleep quality by proposing an optimal sleep cycle and adjusting the sleep environment. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide support using an AI model that takes the user's sleep data as input and proposes an optimal sleep cycle and adjusts the sleep environment.

[0070] The service provider can recommend music and meditation content based on emotion recognition. For example, the service provider can analyze the user's emotional state and recommend relaxation music or guided meditation based on that analysis. The service provider can also suggest different types of music and meditation content depending on the user's emotional state. Furthermore, the service provider can recommend music and meditation content that includes counseling or relaxation techniques to improve the user's emotional state, based on emotion recognition. In this way, recommending music and meditation content based on emotion recognition improves the user's mental well-being. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can provide support using an AI model that takes the user's emotional data as input and recommends music and meditation content.

[0071] The service provider can analyze long-term trends in mental state and provide personalized improvement plans. For example, the service provider can collect data on the user's mental state over a long period and analyze its trends. Based on the analysis results, the service provider can also propose improvement plans to enhance the user's mental well-being. Furthermore, the service provider can monitor changes in the user's mental state and adjust the improvement plan as needed. This allows for the continuous improvement of the user's mental well-being by analyzing long-term trends in mental state and providing personalized improvement plans. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can provide support using an AI model that takes the user's long-term mental state data as input and proposes improvement plans.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of EEG data collection based on the estimated emotions. For example, the data collection unit can monitor the user's emotional state in real time and adjust the timing of EEG data collection according to changes in emotion. The data collection unit can also start collecting EEG data when the user's emotional state exceeds a certain threshold. Furthermore, the data collection unit can adjust the frequency and duration of EEG data collection based on the user's emotional state. This allows for the collection of more appropriate data by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of collection timing.

[0073] The data collection unit can analyze the user's past EEG data and select the optimal collection method. For example, the data collection unit can identify the most effective collection timing based on the user's past EEG data. The data collection unit can also analyze the user's past EEG data and optimize the collection method under specific circumstances. Furthermore, the data collection unit can adjust the collection frequency based on the user's past EEG data to improve data accuracy. In this way, by analyzing past data, the optimal collection method is selected and data accuracy is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past EEG data into a generating AI and have the generating AI select the optimal collection method.

[0074] The data collection unit can filter EEG data based on the user's current activity level and environment. For example, if the user is exercising, the unit can filter out exercise-related noise before collecting EEG data. Furthermore, if the user is in a quiet environment, the unit can minimize environmental noise while collecting EEG data. Additionally, if the user is working, the unit can filter the EEG data considering work-related stress. This allows for the collection of less noisy data by filtering based on activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input user activity and environmental data into a generating AI and have the generating AI perform the filtering.

[0075] The data collection unit can estimate the user's emotions and determine the priority of brainwave data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related brainwave data. It can also prioritize collecting relaxed brainwave data if the user is relaxed. Furthermore, if the user is focused, the data collection unit can prioritize collecting brainwave data related to concentration. This allows for the priority collection of important data by prioritizing data based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and data prioritization.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting electroencephalogram (EEG) data. For example, if the user is at home, the data collection unit can prioritize the collection of EEG data indicating a relaxed state. Similarly, if the user is at work, the data collection unit can prioritize the collection of EEG data related to work-related stress. Furthermore, if the user is outside, the data collection unit can prioritize the collection of EEG data corresponding to changes in the environment. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI determine the priority of highly relevant data.

[0077] The data collection unit can analyze the user's social media activity and collect relevant data when collecting electroencephalogram (EEG) data. For example, if the user is experiencing stress on social media, the data collection unit can collect stress-related EEG data. It can also collect relaxed EEG data if the user is relaxed on social media. Furthermore, if the user is focused on social media, the data collection unit can collect EEG data related to concentration. This allows for the collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can identify the cause of the stress and suggest coping strategies. If the user is relaxed, the analysis unit can also provide advice on maintaining that relaxed state. Furthermore, if the user is focused, the analysis unit can suggest ways to improve their concentration. By adjusting the presentation of the analysis based on emotions, the system can provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as 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, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the electroencephalogram (EEG) data during the analysis. For example, if important EEG data is collected, the analysis unit will perform a detailed analysis. If less important EEG data is collected, the analysis unit can also perform a simplified analysis. Furthermore, if highly important EEG data is collected, the analysis unit can generate a detailed report. This allows for efficient analysis by adjusting the level of detail based on importance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the EEG data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of the electroencephalogram (EEG) data during analysis. For example, the analysis unit can apply a stress analysis algorithm to stress-related EEG data. It can also apply a relaxation analysis algorithm to relaxation-related EEG data. Furthermore, it can apply a concentration analysis algorithm to concentration-related EEG data. By applying an analysis algorithm according to the 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 the category of the EEG data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can quickly identify the cause of the stress. If the user is relaxed, the analysis unit can perform a detailed analysis and provide advice on how to maintain that relaxed state. Furthermore, if the user is focused, the analysis unit can suggest ways to improve their concentration. By adjusting the length of the analysis based on emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation and adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the timing of EEG data collection during analysis. For example, the analysis unit may prioritize the analysis of recently collected EEG data. The analysis unit can also analyze current data while referring to past EEG data. Furthermore, the analysis unit can prioritize the analysis of EEG data related to specific events or situations. This allows for the prioritization of the latest data by determining the analysis priority based on the collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of EEG data collection into a generating AI and have the generating AI determine the analysis priority.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the electroencephalogram (EEG) data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant EEG data. It can also postpone the analysis of less relevant EEG data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the EEG data. This allows for efficient analysis by adjusting the order of analysis based on relevance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the EEG data into a generating AI and have the generating AI adjust the order of analysis.

[0084] The service provider can estimate the user's emotions and adjust the support provided based on those emotions. For example, if the user is feeling stressed, the service provider can suggest ways to relax. If the user is relaxed, the service provider can also provide advice on how to maintain that relaxed state. Furthermore, if the user is focused, the service provider can suggest ways to improve their concentration. By adjusting the support based on emotions, the service provider can provide the optimal support for the user. Emotion estimation is achieved using an emotion estimation function, for example, 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjust the support method.

[0085] The service provider can analyze the user's past mental state at the time of service delivery to select the most suitable support method. For example, the service provider can suggest the most suitable relaxation method based on the user's past mental state. The service provider can also analyze the user's past mental state and suggest methods for stress management. Furthermore, the service provider can suggest methods for improving concentration, taking into account the user's past mental state. In this way, by analyzing the user's past mental state, the service provider can select the most suitable support method and provide effective support. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past mental state data into a generating AI and have the generating AI select the most suitable support method.

[0086] The service provider can customize the means of support based on the user's current living situation at the time of delivery. For example, if the user is at work, the service provider can suggest relaxation methods that can be performed in a short amount of time. If the user is at home, the service provider can also provide advice on creating a relaxing environment. Furthermore, if the user is out, the service provider can suggest relaxation methods that can be performed while out. In this way, by customizing the means of support based on the user's living situation, the service provider can provide the most suitable support for the user. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's living situation data into a generating AI and have the generating AI perform the customization of the means of support.

[0087] The service provider can estimate the user's emotions and determine the priority of support to provide based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize suggesting stress management methods. If the user is relaxed, the service provider can also prioritize suggesting methods to maintain that relaxed state. Furthermore, if the user is focused, the service provider can prioritize suggesting methods to improve concentration. This allows for the priority provision of important support by prioritizing it based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation and support priority determination.

[0088] The service provider can select the optimal support method by considering the user's geographical location information at the time of delivery. For example, if the user is at home, the service provider can suggest relaxation methods that can be performed at home. If the user is at work, the service provider can also suggest stress management methods that can be performed at work. Furthermore, if the user is out, the service provider can also suggest methods to improve concentration that can be performed while out. In this way, by considering geographical location information, the service provider can select the optimal support method and provide effective support. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal support method.

[0089] The service provider can analyze the user's social media activity and suggest support measures at the time of delivery. For example, if the user is experiencing stress on social media, the service provider can suggest methods for stress management. Furthermore, if the user is feeling relaxed on social media, the service provider can suggest ways to maintain that relaxed state. Additionally, if the user is concentrating on social media, the service provider can suggest ways to improve concentration. In this way, by analyzing social media activity, relevant support measures can be suggested. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI generate support measure suggestions.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The data collection unit can collect ambient sound data in addition to the user's brainwave data. For example, the unit can monitor the sound environment around the user in real time and analyze the combined brainwave and ambient sound data. This allows for the identification of external factors that influence the user's mental state and emotions, enabling more accurate analysis. Furthermore, the unit can analyze the impact of specific sound environments on the user's mental state and suggest the optimal sound environment. For example, if a quiet environment enhances concentration, it can provide advice on maintaining that environment.

[0092] The analysis unit can estimate the user's cognitive load based on the user's electroencephalogram (EEG) data. For example, the analysis unit can monitor the user's cognitive load level in real time from the EEG data and recommend a break if the cognitive load increases. The analysis unit can also identify the cause of the cognitive load and suggest appropriate countermeasures. Furthermore, the analysis unit can suggest exercises and relaxation techniques to reduce the user's cognitive load. This allows for the management of the user's cognitive load and supports efficient work.

[0093] The service provider can estimate the user's emotions and provide appropriate feedback based on those estimates. For example, if the user is feeling stressed, it can suggest ways to relax. If the user is relaxed, it can also provide advice on how to maintain that relaxed state. Furthermore, if the user is focused, it can suggest ways to improve their concentration. In this way, by providing feedback based on emotions, the service can offer optimal support to the user.

[0094] The data collection unit can collect not only the user's brainwave data but also their eye-tracking data. For example, the unit can monitor the user's eye movements in real time and analyze the combined brainwave and eye-tracking data. This allows for a more accurate understanding of the user's attentional direction and level of concentration. Furthermore, based on the eye-tracking data, the unit can evaluate the user's visual load and suggest appropriate break times. This enables the management of the user's concentration and attention using eye-tracking data.

[0095] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on those emotions. For example, if the user is stressed, the analysis results can be displayed concisely, clearly indicating the cause of the stress and how to deal with it. If the user is relaxed, detailed analysis results can be provided, along with advice on maintaining that relaxed state. Furthermore, if the user is focused, methods for improving concentration can be suggested. In this way, by adjusting how the analysis results are displayed based on emotions, information that is easy for the user to understand can be provided.

[0096] The service provider can estimate the user's emotions and adjust the type of content provided based on those estimates. For example, if the user is stressed, it can provide relaxation music or meditation content. If the user is relaxed, it can provide music or content to maintain that relaxed state. Furthermore, if the user is concentrating, it can provide music or content to enhance their concentration. By adjusting the type of content provided based on emotions, the service provider can offer optimal support to the user.

[0097] The data collection unit can collect not only the user's electroencephalogram (EEG) data but also their exercise data. For example, the unit can monitor the user's exercise volume and patterns in real time and analyze the combined EEG and exercise data. This allows for an evaluation of the impact of exercise on the user's mental state and the suggestion of an appropriate exercise plan. Furthermore, the unit can provide advice to improve the user's exercise habits based on the exercise data. In this way, exercise data can be used to improve the user's mental well-being.

[0098] The analysis unit can estimate the user's emotions and adjust the frequency of analysis based on those estimates. For example, if the user is stressed, the analysis unit will analyze the data more frequently to identify the cause of the stress. If the user is relaxed, the analysis unit can reduce the frequency of analysis and provide advice to help maintain that relaxed state. Furthermore, if the user is focused, the unit can suggest ways to improve their concentration. By adjusting the frequency of analysis based on emotions, the system can provide the user with the most optimal analysis results.

[0099] The support unit can estimate the user's emotions and adjust the timing of support based on those estimates. For example, if the user is feeling stressed, it can immediately suggest ways to relax. If the user is relaxed, it can also provide advice at the appropriate time to help them maintain that relaxed state. Furthermore, if the user is concentrating, it can suggest ways to improve their concentration at the appropriate time. By adjusting the timing of support based on emotions, the system can provide the optimal support for the user.

[0100] The data collection unit can collect not only the user's brainwave data but also their dietary data. For example, the unit can monitor the user's meal content and timing in real time and analyze the combined brainwave and dietary data. This allows for an evaluation of the impact of diet on the user's mental state and the suggestion of an appropriate meal plan. Furthermore, the unit can provide advice to improve the user's eating habits based on the dietary data. In this way, dietary data can be used to improve the user's mental well-being.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The data collection unit collects the user's brainwave data and biometric information. The data collection unit can collect the user's brainwave data and biometric information using wearable devices such as a headband with an EEG sensor or a smartwatch. For example, the data collection unit can collect the user's brainwave data in real time using a headband with an EEG sensor. The data collection unit can also collect biometric information such as the user's heart rate and skin electrical activity using a smartwatch. Furthermore, the data collection unit can combine multiple wearable devices to collect more detailed data. Step 2: The analysis unit analyzes the data collected by the collection unit to estimate the user's mental state and emotions. The analysis unit can analyze the collected brainwave data and biometric information using, for example, a generative AI. The generative AI can estimate the user's mental state and emotions with high accuracy using, for example, a text generation AI (e.g., LLM). The analysis unit can also comprehensively analyze brainwave data and biometric information using a multimodal generative AI. Furthermore, the analysis unit can monitor changes in the user's mental state and emotions in real time using the generative AI. Step 3: The service provider provides personalized support based on the analysis results obtained by the analysis unit. For example, the service provider can detect stress in real time and suggest coping strategies. It can also recommend micro-breaks to improve concentration. Furthermore, the service provider can suggest an optimal sleep cycle and adjust the sleep environment. For example, the service provider can suggest an optimal sleep cycle based on the user's brainwave data and biometric information. It can also recommend music and meditation content based on emotion recognition. Furthermore, the service provider can analyze long-term trends in mental state and provide a personalized improvement plan. In this way, the MindWaveCompanion system according to the embodiment can improve the user's mental well-being. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide support using an AI model that takes the analysis results obtained by the analysis unit as input and outputs personalized support.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's brainwave data and biometric information using a headband or smartwatch with an electroencephalogram sensor on the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to estimate the user's mental state and emotions. The provision unit provides personalized support based on the analysis results using the specific processing unit 290 of the data processing unit 12. 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.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's brainwave data and biometric information using the headband with an electroencephalogram sensor of the smart glasses 214 or a smartwatch. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and estimates the user's mental state and emotions. The provision unit provides personalized support based on the analysis results using the identification processing unit 290 of the data processing unit 12. 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.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's brainwave data and biometric information using the headband with an electroencephalogram sensor or a smartwatch of the headset terminal 314. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and estimates the user's mental state and emotions. The provision unit provides personalized support based on the analysis results using the identification processing unit 290 of the data processing unit 12. 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.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's brainwave data and biometric information using a headband or smartwatch with an electroencephalogram sensor on the robot 414. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 to estimate the user's mental state and emotions. The provision unit provides personalized support based on the analysis results using the identification processing unit 290 of the data processing unit 12. 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) A collection unit that collects the user's brainwave data and biometric information, An analysis unit analyzes the data collected by the aforementioned collection unit to estimate the user's mental state and emotions, The system includes a provisioning unit that provides personalized support based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is This involves collecting user brainwave data and biometric information using wearable devices such as headbands with EEG sensors and smartwatches. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to estimate the user's mental state and emotions with high accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Based on the analysis results, stress is detected in real time, and coping strategies are suggested. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, I recommend taking microbreaks to improve concentration. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We propose an optimal sleep cycle and adjust your sleep environment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Recommend music and meditation content based on emotional recognition. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, We analyze long-term trends in mental state and provide personalized improvement plans. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of EEG data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past brainwave data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting electroencephalogram (EEG) data, filtering is performed based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and determines the priority of EEG data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting electroencephalogram (EEG) data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting electroencephalogram (EEG) data, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on the timing of EEG data collection. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, We estimate the user's emotions and adjust the support we provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, At the time of provision, the system analyzes the user's past mental state to select the most suitable support method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, the support methods will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of support to provide based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, the optimal support method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and suggest ways to support them. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 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 collection unit that collects the user's brainwave data and biometric information, An analysis unit analyzes the data collected by the aforementioned collection unit to estimate the user's mental state and emotions, The system includes a provisioning unit that provides personalized support based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is This involves collecting user brainwave data and biometric information using wearable devices such as headbands with EEG sensors and smartwatches. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to estimate the user's mental state and emotions with high accuracy. The system according to feature 1.

4. The aforementioned supply unit is, Based on the analysis results, stress is detected in real time, and coping strategies are suggested. The system according to feature 1.

5. The aforementioned supply unit is, I recommend taking microbreaks to improve concentration. The system according to feature 1.

6. The aforementioned supply unit is, We propose an optimal sleep cycle and adjust your sleep environment. The system according to feature 1.

7. The aforementioned supply unit is, Recommend music and meditation content based on emotional recognition. The system according to feature 1.

8. The aforementioned supply unit is, We analyze long-term trends in mental state and provide personalized improvement plans. The system according to feature 1.

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