System

Abstract

To provide a system capable of converting activity data of the brain into text or images / videos by analyzing in real-time.SOLUTION: A system in an embodiment includes a collection unit, an analysis unit, and a conversion unit. The collection unit collects activity data of the brain. The analysis unit analyzes the data collected by the collection unit. The conversion unit converts the data analyzed by the analysis unit into text or images / videos.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] Conventional technologies do not adequately analyze brain activity data in real time and convert it into text and images / video, leaving room for improvement.

[0005] The system according to the embodiment aims to analyze brain activity data in real time and convert it into text and images / videos. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a conversion unit. The collection unit collects brain activity data. The analysis unit analyzes the data collected by the collection unit. The conversion unit converts the data analyzed by the analysis unit into text or an image / video. Effect of the Invention

[0007] The system according to the embodiment can analyze brain activity data in real time and convert it into text and images / video. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The system according to the embodiment of the present invention is a system that learns brain activity in real time and converts language, thoughts, etc. into text or images / video. This system can be applied to conversation support for people with language disabilities, visualization of dreams, and crime solving by mind reading. For example, the system collects brain activity data using an EEG sensor. Next, AI analyzes the collected data and converts language and thoughts into text and images / video. This allows people with language disabilities to communicate their thoughts to others. In addition, the content of dreams can be visualized, and further, by reading the thoughts of suspects in criminal investigations, it can be useful in solving cases. For example, an EEG sensor detects activity of a specific part of the brain and collects the data. Next, AI analyzes the collected brain activity data. The AI ​​learns language and thought patterns based on the collected data. For example, it learns that a specific brain wave pattern corresponds to a specific word or image. This allows the AI ​​to analyze brain activity data and convert language and thoughts into text and images / video. The converted text and images / video are provided to the user. For example, a person with a language disability can use a device that displays text and images / videos to communicate his / her thoughts to others. Also, the content of a dream can be displayed as an image / video to visualize the content of the dream. Furthermore, in criminal investigations, reading the thoughts of a suspect can be useful in solving cases. This system allows a person with a language disability to communicate his / her thoughts to others and visualize the content of a dream, which is also useful in criminal investigations. As a result, the system can be used to support conversations for people with language disabilities, visualize dreams, and solve crimes by reading thoughts.

[0029] The system according to the embodiment includes a collection unit, an analysis unit, and a conversion unit. The collection unit collects brain activity data. The collection unit can collect brain activity data using, for example, an electroencephalogram sensor. The electroencephalogram sensor is attached to the head and monitors brain activity in real time. For example, the electroencephalogram sensor detects activity of a specific part of the brain and collects the data. The collected data is sent to the analysis unit. The analysis unit analyzes the collected data. The analysis unit can analyze the collected data using, for example, AI. The AI ​​learns language and thought patterns based on the collected data. For example, it learns that a specific brainwave pattern corresponds to a specific word or image. This allows the AI ​​to analyze the brain activity data and convert the language and thought into text or an image / video. The conversion unit converts the data analyzed by the analysis unit into text or an image / video. The conversion unit can convert the data analyzed using, for example, AI into text or an image / video. The converted text or image / video is provided to the user. For example, the conversion unit can include a device that displays the text or image / video. As a result, the system according to the embodiment can collect and analyze brain activity data and convert it into text and images / videos, which can be useful for assisting people with speech disorders in conversation, visualizing dreams, and criminal investigations.

[0030] The collecting unit collects brain activity data. The collecting unit can collect the brain activity data using, for example, an electroencephalogram (EEG) sensor. The EEG sensor is attached to the head and monitors brain activity in real time. Specifically, the EEG sensor detects brain waves through multiple electrodes placed on the scalp and converts the signals into digital data. This allows the activity of a specific part of the brain to be detected with high accuracy. For example, the EEG sensor detects brain waves of different frequency bands such as alpha waves, beta waves, and gamma waves, and records the activity levels of each in real time. The collected data is transmitted to the analyzing unit through wireless communication or wired connection. In addition, the collecting unit has a pre-processing function for noise removal and signal enhancement, which can improve the quality of the collected data. For example, the collecting unit filters out noise such as ambient electromagnetic interference and electromyogram (EMG) signals, and extracts pure EEG signals. This allows the collecting unit to collect brain activity data with high accuracy and quality and provide it to the analyzing unit.

[0031] The analysis unit analyzes the collected data. The analysis unit can analyze the collected data using, for example, AI. The AI ​​learns language and thought patterns based on the collected data. Specifically, the AI ​​uses deep learning technology to analyze the EEG data and learns that a specific EEG pattern corresponds to a specific word or image. For example, a recurrent neural network (RNN) or a convolutional neural network (CNN) is used to analyze temporal and spatial EEG patterns and extract features of language and thought. Furthermore, the AI ​​can combine supervised learning and unsupervised learning to efficiently learn diverse patterns of EEG data. For example, in supervised learning, a model is trained using EEG data corresponding to known words and images, and in unsupervised learning, unknown patterns are automatically clustered to discover new features. This allows the analysis unit to quickly and accurately analyze the collected EEG data and estimate the content of language and thought with high accuracy. Furthermore, the analysis unit can perform individualized analysis taking into account past data and the characteristics of individual users. For example, by learning the differences in each user's brainwave patterns and constructing an individual model, it is possible to provide more accurate analysis results. This allows the analysis unit to analyze the collected data from multiple angles and improve the performance of the entire system.

[0032] The conversion unit converts the data analyzed by the analysis unit into text or images / video. The conversion unit can convert the data analyzed by using, for example, AI into text or images / video. Specifically, the analyzed brainwave data is converted into text using natural language processing (NLP) technology. For example, a generation AI is used to generate the language and thought content estimated from the brainwave data as natural sentences. Furthermore, an image estimated from the brainwave data can be visually reproduced using image generation technology. For example, a generative adversarial network (GAN) is used to generate an image estimated from the brainwave data as a high-resolution image. This allows the conversion unit to provide the analyzed data to the user in various formats. Furthermore, the conversion unit is equipped with a device for displaying the generated text and images / video, and provides information so that the user can intuitively understand it. For example, the generated text and images / video are displayed in real time using a display or projector. In addition, the generated text can be output as voice using voice synthesis technology. This allows the conversion unit to provide information to the user in various formats and improve the convenience of the system. Furthermore, the conversion unit can collect user feedback and continuously improve the accuracy and quality of the generated results. For example, users can evaluate the generated text and images / videos, and the model can be retrained based on the evaluation results. This allows the conversion unit to always provide highly accurate conversion results based on the latest information and meet the needs of users.

[0033] The conversion unit includes a providing unit that displays the converted text or image / video. The providing unit displays the converted text or image / video. The providing unit can include, for example, a display, a projector, an audio output device, and the like. The display visually displays the converted text or image / video. The projector projects the converted text or image / video onto a large screen. The audio output device can output the converted text as audio. For example, the providing unit displays the converted text or image / video using a display. The providing unit can also project the converted text or image / video onto a large screen using a projector. Furthermore, the providing unit can also output the converted text as audio using an audio output device. Thereby, the providing unit displays the converted text or image / video, so that the user can confirm the result. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can optimize the display method using an AI model for displaying the converted text or image / video.

[0034] The collecting unit can collect brain activity data using an EEG sensor. The EEG sensor is a device for collecting brain activity data. The EEG sensor includes, for example, an EEG (Electroencephalography) sensor and an MEG (Magnetoencephalography) sensor. The EEG sensor places electrodes on the scalp and measures the electrical activity of the brain. The MEG sensor measures the magnetic activity of the brain. For example, the EEG sensor measures the electrical activity of the brain in real time through electrodes placed on the scalp. The MEG sensor can measure the magnetic activity of the brain with high accuracy. This allows the collecting unit to collect accurate brain activity data using the EEG sensor. A part or all of the above-mentioned processing in the collecting unit may be performed, for example, using AI, or may be performed without using AI. For example, the collecting unit can input data acquired from the EEG sensor to AI and cause the AI ​​to perform pre-processing and noise removal of the data.

[0035] The analysis unit can learn language or thought patterns based on the collected data. The analysis unit learns language and thought patterns based on the collected data. The analysis unit can analyze data using, for example, a machine learning algorithm. The machine learning algorithm has the ability to learn large amounts of data and identify specific patterns. For example, the analysis unit learns specific language patterns and thought patterns using collected brain wave data. The analysis unit can analyze data using, for example, a neural network and identify language and thought patterns. This allows the analysis unit to learn language and thought patterns based on the collected data, thereby enabling more accurate analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI and cause the AI ​​to learn language and thought patterns.

[0036] The providing unit may include a device for a person with a language disability to communicate his / her thoughts to others. The providing unit may include a device for a person with a language disability to communicate his / her thoughts to others. The providing unit may include, for example, a tablet, a smartphone, a dedicated terminal, or the like. The tablet is a device for displaying converted text and images / video. The smartphone is a portable device for displaying converted text and images / video. The dedicated terminal is a device specialized for a person with a language disability to communicate his / her thoughts to others. For example, the providing unit displays the converted text and images / video using a tablet. The providing unit may also display the converted text and images / video using a smartphone. Furthermore, the providing unit can use the dedicated terminal to allow a person with a language disability to communicate his / her thoughts to others. This allows the providing unit to allow a person with a language disability to communicate his / her thoughts to others. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can optimize the display method using an AI model for displaying the converted text and images / video.

[0037] The providing unit may include a device for visualizing the content of the dream. The providing unit may include a device for visualizing the content of the dream. The providing unit may include, for example, a VR (Virtual Reality) headset, a display, and the like. The VR headset is a device for a user to visually experience the content of the dream. The display is a device for visually displaying the content of the dream. For example, the providing unit visualizes the content of the dream using a VR headset. The providing unit may also visualize the content of the dream using a display. This allows the providing unit to visualize the content of the dream. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may optimize the visualization method using an AI model for visualizing the content of the dream.

[0038] The providing unit may include a device for reading the thoughts of a suspect in a criminal investigation. The providing unit may include a device for reading the thoughts of a suspect in a criminal investigation. The providing unit may include, for example, an electroencephalogram analysis device, a psychological analysis tool, and the like. The electroencephalogram analysis device is a device for analyzing the electroencephalogram data of a suspect and reading the thoughts. The psychological analysis tool is a device for analyzing the psychological state of a suspect and reading the thoughts. For example, the providing unit analyzes the electroencephalogram data of a suspect using an electroencephalogram analysis device and reads the thoughts. The providing unit may also analyze the psychological state of a suspect using a psychological analysis tool and read the thoughts. This allows the providing unit to read the thoughts of a suspect in a criminal investigation. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit may input data acquired from the electroencephalogram analysis device or the psychological analysis tool into an AI, and cause the AI ​​to read the thoughts.

[0039] The collecting unit can analyze the user's past electroencephalogram data and select the optimal collecting method. The collecting unit analyzes the user's past electroencephalogram data and selects the optimal collecting method. The collecting unit can, for example, identify the most stable collecting timing from the past electroencephalogram data and collect data at that timing. The collecting unit can also select a method of collecting during a specific activity based on the past electroencephalogram data. Furthermore, the collecting unit can analyze the past electroencephalogram data and select a collecting method under specific environmental conditions. For example, the collecting unit can identify the most stable collecting timing from the user's past electroencephalogram data and collect data at that timing. The collecting unit can also select a method of collecting during a specific activity based on the user's past electroencephalogram data. Furthermore, the collecting unit can analyze the user's past electroencephalogram data and select a collecting method under specific environmental conditions. As a result, the collecting unit can select the optimal collecting method by analyzing the past electroencephalogram data. A part or all of the above-mentioned processing in the collecting unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past EEG data into the generation AI and have the generation AI select the optimal collection method.

[0040] The collection unit can perform filtering based on the user's current activity status and environment when collecting the brainwave data. The collection unit performs filtering based on the user's current activity status and environment when collecting the brainwave data. For example, when the user is in a quiet environment, the collection unit can collect data with minimal noise. In addition, when the user is exercising, the collection unit can also collect data with noise due to exercise removed. Furthermore, when the user is performing multiple tasks, the collection unit can preferentially collect brainwave data related to a specific task. For example, when the user is in a quiet environment, the collection unit collects data with minimal noise. In addition, when the user is exercising, the collection unit can also collect data with noise due to exercise removed. Furthermore, when the user is performing multiple tasks, the collection unit can preferentially collect brainwave data related to a specific task. Thereby, the collection unit can collect data with less noise by filtering based on the current activity status and environment. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the current activity status and environmental data to the generation AI and cause the generation AI to perform filtering.

[0041] The collecting unit can preferentially collect highly relevant data based on the geographical location information of the user when collecting the electroencephalogram data. The collecting unit preferentially collects highly relevant data based on the geographical location information of the user when collecting the electroencephalogram data. For example, when the user is in a specific location, the collecting unit can preferentially collect electroencephalogram data related to the location. In addition, when the user is moving, the collecting unit can also preferentially collect electroencephalogram data related to the movement. Furthermore, when the user is under specific environmental conditions, the collecting unit can also preferentially collect electroencephalogram data related to the environment. For example, when the user is in a specific location, the collecting unit preferentially collects electroencephalogram data related to the location. In addition, when the user is moving, the collecting unit can also preferentially collect electroencephalogram data related to the movement. Furthermore, when the user is under specific environmental conditions, the collecting unit can also preferentially collect electroencephalogram data related to the environment. As a result, the collecting unit can obtain more useful data by preferentially collecting highly relevant data based on the geographical location information. A part or all of the above-mentioned processing in the collecting unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographic location information to the generation AI and have the generation AI select highly relevant data.

[0042] The collection unit can analyze the social media activity of the user when collecting the electroencephalogram data and collect related data. The collection unit analyzes the social media activity of the user when collecting the electroencephalogram data and collects related data. For example, when the user posts about a specific topic on social media, the collection unit can collect electroencephalogram data related to the topic. In addition, when the user expresses a specific emotion on social media, the collection unit can also collect electroencephalogram data related to the emotion. Furthermore, when the user participates in a specific event on social media, the collection unit can collect electroencephalogram data related to the event. For example, when the user posts about a specific topic on social media, the collection unit collects electroencephalogram data related to the topic. In addition, when the user expresses a specific emotion on social media, the collection unit can also collect electroencephalogram data related to the emotion. In addition, when the user participates in a specific event on social media, the collection unit can also collect electroencephalogram data related to the event. Thereby, the collection unit can collect related electroencephalogram data by analyzing the social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input social media activity data into the generation AI and cause the generation AI to collect related brainwave data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the electroencephalogram data during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the electroencephalogram data during the analysis. The analysis unit can perform a detailed analysis on electroencephalogram data with high importance, for example. The analysis unit can also perform a simple analysis on electroencephalogram data with low importance. Furthermore, the analysis unit can perform an analysis with a moderate level of detail on electroencephalogram data with medium importance. For example, the analysis unit performs a detailed analysis on electroencephalogram data with high importance. The analysis unit can also perform a simple analysis on electroencephalogram data with low importance. Furthermore, the analysis unit can perform an analysis with a moderate level of detail on electroencephalogram data with medium importance. Thereby, the analysis unit adjusts the level of detail of the analysis based on the importance of the electroencephalogram data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the electroencephalogram data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms according to the category of the electroencephalogram data during analysis. The analysis unit applies different analysis algorithms according to the category of the electroencephalogram data during analysis. The analysis unit can apply a language analysis algorithm to electroencephalogram data related to language, for example. The analysis unit can also apply an emotion analysis algorithm to electroencephalogram data related to emotion. Furthermore, the analysis unit can apply a thought analysis algorithm to electroencephalogram data related to thinking. For example, the analysis unit applies a language analysis algorithm to electroencephalogram data related to language. The analysis unit can also apply an emotion analysis algorithm to electroencephalogram data related to emotion. Furthermore, the analysis unit can apply a thought analysis algorithm to electroencephalogram data related to thinking. Thereby, the analysis unit can apply different analysis algorithms according to the category of the electroencephalogram data, thereby enabling more accurate analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the category of the electroencephalogram data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0045] The analysis unit can determine the priority of the analysis based on the collection time of the electroencephalogram data during the analysis. The analysis unit determines the priority of the analysis based on the collection time of the electroencephalogram data during the analysis. The analysis unit can, for example, preferentially analyze recently collected electroencephalogram data. The analysis unit can also preferentially analyze electroencephalogram data collected during a specific event. Furthermore, the analysis unit can also preferentially analyze electroencephalogram data collected while the user is performing a specific task. For example, the analysis unit preferentially analyzes recently collected electroencephalogram data. The analysis unit can also preferentially analyze electroencephalogram data collected during a specific event. Furthermore, the analysis unit can also preferentially analyze electroencephalogram data collected while the user is performing a specific task. Thereby, the analysis unit determines the priority of the analysis based on the collection time of the electroencephalogram data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the electroencephalogram data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the electroencephalogram data during analysis. The analysis unit adjusts the order of analysis based on the relevance of the electroencephalogram data during analysis. The analysis unit can, for example, preferentially analyze electroencephalogram data having high relevance. The analysis unit can also analyze electroencephalogram data having medium relevance next. Furthermore, the analysis unit can analyze electroencephalogram data having low relevance last. For example, the analysis unit preferentially analyzes electroencephalogram data having high relevance. The analysis unit can also analyze electroencephalogram data having medium relevance next. Furthermore, the analysis unit can analyze electroencephalogram data having low relevance last. Thereby, the analysis unit adjusts the order of analysis based on the relevance of the electroencephalogram data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the electroencephalogram data to the generation AI and cause the generation AI to adjust the order of analysis.

[0047] The conversion unit can adjust the level of detail of the conversion based on the importance of the electroencephalogram data at the time of conversion. The conversion unit adjusts the level of detail of the conversion based on the importance of the electroencephalogram data at the time of conversion. The conversion unit can perform detailed conversion on electroencephalogram data with high importance, for example. The conversion unit can also perform simple conversion on electroencephalogram data with low importance. Furthermore, the conversion unit can perform conversion with a moderate level of detail on electroencephalogram data with medium importance. For example, the conversion unit performs detailed conversion on electroencephalogram data with high importance. The conversion unit can also perform simple conversion on electroencephalogram data with low importance. Furthermore, the conversion unit can perform conversion with a moderate level of detail on electroencephalogram data with medium importance. Thereby, the conversion unit adjusts the level of detail of the conversion based on the importance of the electroencephalogram data, thereby enabling efficient conversion. Part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the importance of the electroencephalogram data to the generation AI and cause the generation AI to adjust the level of detail of the conversion.

[0048] The conversion unit can apply different conversion algorithms according to the category of the electroencephalogram data at the time of conversion. The conversion unit applies different conversion algorithms according to the category of the electroencephalogram data at the time of conversion. The conversion unit can apply a language conversion algorithm to electroencephalogram data related to language, for example. The conversion unit can also apply an emotion conversion algorithm to electroencephalogram data related to emotion. The conversion unit can also apply a thought conversion algorithm to electroencephalogram data related to thought. For example, the conversion unit applies a language conversion algorithm to electroencephalogram data related to language. The conversion unit can also apply an emotion conversion algorithm to electroencephalogram data related to emotion. The conversion unit can also apply a thought conversion algorithm to electroencephalogram data related to thought. Thereby, the conversion unit can apply different conversion algorithms according to the category of the electroencephalogram data, thereby enabling more accurate conversion. Part or all of the above-mentioned processing in the conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversion unit can input the category of the electroencephalogram data to the generation AI and cause the generation AI to apply the conversion algorithm.

[0049] The conversion unit can adjust the order of conversion based on the collection time of the electroencephalogram data at the time of conversion. The conversion unit adjusts the order of conversion based on the collection time of the electroencephalogram data at the time of conversion. The conversion unit can, for example, preferentially convert recently collected electroencephalogram data. The conversion unit can also preferentially convert electroencephalogram data collected during a specific event. Furthermore, the conversion unit can also preferentially convert electroencephalogram data collected while the user is performing a specific task. For example, the conversion unit preferentially converts recently collected electroencephalogram data. The conversion unit can also preferentially convert electroencephalogram data collected during a specific event. Furthermore, the conversion unit can also preferentially convert electroencephalogram data collected while the user is performing a specific task. Thereby, the conversion unit adjusts the order of conversion based on the collection time of the electroencephalogram data, thereby enabling efficient conversion. Part or all of the above-mentioned processing in the conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversion unit can input the collection time of the electroencephalogram data to the generation AI and cause the generation AI to adjust the order of conversion.

[0050] The conversion unit can adjust the order of conversion based on the relevance of the electroencephalogram data at the time of conversion. The conversion unit adjusts the order of conversion based on the relevance of the electroencephalogram data at the time of conversion. The conversion unit can, for example, preferentially convert electroencephalogram data having high relevance. The conversion unit can also convert electroencephalogram data having medium relevance next. Furthermore, the conversion unit can convert electroencephalogram data having low relevance last. For example, the conversion unit preferentially converts electroencephalogram data having high relevance. The conversion unit can also convert electroencephalogram data having medium relevance next. Furthermore, the conversion unit can convert electroencephalogram data having low relevance last. Thereby, the conversion unit adjusts the order of conversion based on the relevance of the electroencephalogram data, thereby enabling efficient conversion. Part or all of the above-mentioned processing in the conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversion unit can input the relevance of the electroencephalogram data to the generation AI and cause the generation AI to adjust the order of conversion.

[0051] The providing unit can select the optimal display method by referring to the user's past operation history at the time of providing. The providing unit selects the optimal display method by referring to the user's past operation history at the time of providing. The providing unit can, for example, preferentially provide a display method that the user has used in the past. The providing unit can also suggest a specific display method from the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and provide the most efficient display method. For example, the providing unit preferentially provides a display method that the user has used in the past. The providing unit can also suggest a specific display method from the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and provide the most efficient display method. Thereby, the providing unit can provide the optimal display method to the user by referring to the past operation history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data to the generating AI and cause the generating AI to select the optimal display method.

[0052] The providing unit can select the optimal display method based on the device information of the user at the time of providing. The providing unit selects the optimal display method based on the device information of the user at the time of providing. For example, when the user is using a smartphone, the providing unit can provide a display method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. As a result, the providing unit selects the optimal display method based on the device information, thereby enabling a display that is easy for the user to see. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information of the user to the generation AI and cause the generation AI to select the optimal display method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When analyzing the user's brainwave data, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral history. For example, the analysis unit learns what brainwave patterns the user showed in specific situations in the past, and analyzes the current brainwave data based on that information. In addition, by referring to the stress and relaxation states the user experienced in the past and comparing them with the current brainwave data, more accurate analysis results can be obtained. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to brainwave data related to specific tasks or activities the user performed in the past and comparing them with the current data. This allows the analysis unit to perform more accurate analysis by utilizing the user's past behavioral history.

[0055] When collecting the user's electroencephalogram data, the collection unit can simultaneously collect the user's physiological data (e.g., heart rate and body temperature) and combine and analyze these data. For example, if the user's heart rate is rising, it can be determined that the user is likely feeling stressed, and the frequency of collection of electroencephalogram data can be increased. In addition, if the user's body temperature is rising, the user may be exercising, so that electroencephalogram data related to exercise can be collected preferentially. Furthermore, by combining and analyzing the user's physiological data and electroencephalogram data, the user's condition can be grasped more accurately. This allows the collection unit to utilize the user's physiological data to improve the accuracy of collection and analysis of electroencephalogram data.

[0056] When analyzing the user's brainwave data, the analysis unit can perform the analysis while taking into consideration the user's current environmental information (e.g., the intensity of surrounding sounds and light). For example, when the user is in a quiet environment, it is highly likely that the user is relaxed, so the analysis of the brainwave pattern during relaxation is prioritized. Also, when the user is in a bright environment, it is highly likely that the user is concentrating, so it is also possible to analyze the brainwave pattern during concentration with priority. Furthermore, by adjusting the analysis result based on the user's environmental information, it is possible to obtain a more accurate analysis result. This allows the analysis unit to utilize the user's environmental information to improve the analysis accuracy of the brainwave data.

[0057] When collecting the user's electroencephalogram data, the collection unit can detect abnormalities by comparing the data with the user's past electroencephalogram data. For example, if the user's electroencephalogram data is significantly different from the past data, it is determined that an abnormality may have occurred, and an alert is issued. Also, if the user's electroencephalogram data matches the past data, it can be determined that the user is in a normal state. Furthermore, if an abnormality is detected, a detailed analysis can be performed to identify the cause of the abnormality. This allows the collection unit to utilize the user's past electroencephalogram data to detect abnormalities early and take measures against them.

[0058] When collecting the user's brainwave data, the collection unit can adjust the collection method in consideration of the user's current activity. For example, when the user is reading, brainwave data related to reading is preferentially collected. Also, when the user is exercising, brainwave data related to exercise can be preferentially collected. Furthermore, when the user is relaxing, brainwave data during relaxation can be preferentially collected. This allows the collection unit to collect more appropriate brainwave data in consideration of the user's current activity.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] Step 1: The collecting unit collects brain activity data. The collecting unit can collect brain activity data using, for example, an EEG sensor. The EEG sensor is attached to the head and monitors brain activity in real time. For example, the EEG sensor detects activity in a specific part of the brain and collects the data. Step 2: The analysis unit analyzes the collected data. The analysis unit can analyze the collected data using, for example, AI. The AI ​​learns language and thought patterns based on the collected data. For example, it learns that certain brainwave patterns correspond to certain words or images. Step 3: The conversion unit converts the data analyzed by the analysis unit into text or images / videos. The conversion unit can convert the analyzed data into text or images / videos using, for example, AI. The converted text or images / videos are provided to the user. For example, the conversion unit can include a device that displays the text or images / videos.

[0061] (Example 2) The system according to the embodiment of the present invention is a system that learns brain activity in real time and converts language, thoughts, etc. into text or images / video. This system can be applied to conversation support for people with language disabilities, visualization of dreams, and crime solving by mind reading. For example, the system collects brain activity data using an EEG sensor. Next, AI analyzes the collected data and converts language and thoughts into text and images / video. This allows people with language disabilities to communicate their thoughts to others. In addition, the content of dreams can be visualized, and further, by reading the thoughts of suspects in criminal investigations, it can be useful in solving cases. For example, an EEG sensor detects activity of a specific part of the brain and collects the data. Next, AI analyzes the collected brain activity data. The AI ​​learns language and thought patterns based on the collected data. For example, it learns that a specific brain wave pattern corresponds to a specific word or image. This allows the AI ​​to analyze brain activity data and convert language and thoughts into text and images / video. The converted text and images / video are provided to the user. For example, a person with a language disability can use a device that displays text and images / videos to communicate his / her thoughts to others. Also, the content of a dream can be displayed as an image / video to visualize the content of the dream. Furthermore, in criminal investigations, reading the thoughts of a suspect can be useful in solving cases. This system allows a person with a language disability to communicate his / her thoughts to others and visualize the content of a dream, which is also useful in criminal investigations. As a result, the system can be used to support conversations for people with language disabilities, visualize dreams, and solve crimes by reading thoughts.

[0062] The system according to the embodiment includes a collection unit, an analysis unit, and a conversion unit. The collection unit collects brain activity data. The collection unit can collect brain activity data using, for example, an electroencephalogram sensor. The electroencephalogram sensor is attached to the head and monitors brain activity in real time. For example, the electroencephalogram sensor detects activity of a specific part of the brain and collects the data. The collected data is sent to the analysis unit. The analysis unit analyzes the collected data. The analysis unit can analyze the collected data using, for example, AI. The AI ​​learns language and thought patterns based on the collected data. For example, it learns that a specific brainwave pattern corresponds to a specific word or image. This allows the AI ​​to analyze the brain activity data and convert the language and thought into text or an image / video. The conversion unit converts the data analyzed by the analysis unit into text or an image / video. The conversion unit can convert the data analyzed using, for example, AI into text or an image / video. The converted text or image / video is provided to the user. For example, the conversion unit can include a device that displays the text or image / video. As a result, the system according to the embodiment can collect and analyze brain activity data and convert it into text and images / videos, which can be useful for assisting people with speech disorders in conversation, visualizing dreams, and criminal investigations.

[0063] The collecting unit collects brain activity data. The collecting unit can collect the brain activity data using, for example, an electroencephalogram (EEG) sensor. The EEG sensor is attached to the head and monitors brain activity in real time. Specifically, the EEG sensor detects brain waves through multiple electrodes placed on the scalp and converts the signals into digital data. This allows the activity of a specific part of the brain to be detected with high accuracy. For example, the EEG sensor detects brain waves of different frequency bands such as alpha waves, beta waves, and gamma waves, and records the activity levels of each in real time. The collected data is transmitted to the analyzing unit through wireless communication or wired connection. In addition, the collecting unit has a pre-processing function for noise removal and signal enhancement, which can improve the quality of the collected data. For example, the collecting unit filters out noise such as ambient electromagnetic interference and electromyogram (EMG) signals, and extracts pure EEG signals. This allows the collecting unit to collect brain activity data with high accuracy and quality and provide it to the analyzing unit.

[0064] The analysis unit analyzes the collected data. The analysis unit can analyze the collected data using, for example, AI. The AI ​​learns language and thought patterns based on the collected data. Specifically, the AI ​​uses deep learning technology to analyze the EEG data and learns that a specific EEG pattern corresponds to a specific word or image. For example, a recurrent neural network (RNN) or a convolutional neural network (CNN) is used to analyze temporal and spatial EEG patterns and extract features of language and thought. Furthermore, the AI ​​can combine supervised learning and unsupervised learning to efficiently learn diverse patterns of EEG data. For example, in supervised learning, a model is trained using EEG data corresponding to known words and images, and in unsupervised learning, unknown patterns are automatically clustered to discover new features. This allows the analysis unit to quickly and accurately analyze the collected EEG data and estimate the content of language and thought with high accuracy. Furthermore, the analysis unit can perform individualized analysis taking into account past data and the characteristics of individual users. For example, by learning the differences in each user's brainwave patterns and constructing an individual model, it is possible to provide more accurate analysis results. This allows the analysis unit to analyze the collected data from multiple angles and improve the performance of the entire system.

[0065] The conversion unit converts the data analyzed by the analysis unit into text or images / video. The conversion unit can convert the data analyzed by using, for example, AI into text or images / video. Specifically, the analyzed brainwave data is converted into text using natural language processing (NLP) technology. For example, a generation AI is used to generate the language and thought content estimated from the brainwave data as natural sentences. Furthermore, an image estimated from the brainwave data can be visually reproduced using image generation technology. For example, a generative adversarial network (GAN) is used to generate an image estimated from the brainwave data as a high-resolution image. This allows the conversion unit to provide the analyzed data to the user in various formats. Furthermore, the conversion unit is equipped with a device for displaying the generated text and images / video, and provides information so that the user can intuitively understand it. For example, the generated text and images / video are displayed in real time using a display or projector. In addition, the generated text can be output as voice using voice synthesis technology. This allows the conversion unit to provide information to the user in various formats and improve the convenience of the system. Furthermore, the conversion unit can collect user feedback and continuously improve the accuracy and quality of the generated results. For example, users can evaluate the generated text and images / videos, and the model can be retrained based on the evaluation results. This allows the conversion unit to always provide highly accurate conversion results based on the latest information and meet the needs of users.

[0066] The conversion unit includes a providing unit that displays the converted text or image / video. The providing unit displays the converted text or image / video. The providing unit can include, for example, a display, a projector, an audio output device, and the like. The display visually displays the converted text or image / video. The projector projects the converted text or image / video onto a large screen. The audio output device can output the converted text as audio. For example, the providing unit displays the converted text or image / video using a display. The providing unit can also project the converted text or image / video onto a large screen using a projector. Furthermore, the providing unit can also output the converted text as audio using an audio output device. Thereby, the providing unit displays the converted text or image / video, so that the user can confirm the result. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can optimize the display method using an AI model for displaying the converted text or image / video.

[0067] The collecting unit can collect brain activity data using an EEG sensor. The EEG sensor is a device for collecting brain activity data. The EEG sensor includes, for example, an EEG (Electroencephalography) sensor and an MEG (Magnetoencephalography) sensor. The EEG sensor places electrodes on the scalp and measures the electrical activity of the brain. The MEG sensor measures the magnetic activity of the brain. For example, the EEG sensor measures the electrical activity of the brain in real time through electrodes placed on the scalp. The MEG sensor can measure the magnetic activity of the brain with high accuracy. This allows the collecting unit to collect accurate brain activity data using the EEG sensor. A part or all of the above-mentioned processing in the collecting unit may be performed, for example, using AI, or may be performed without using AI. For example, the collecting unit can input data acquired from the EEG sensor to AI and cause the AI ​​to perform pre-processing and noise removal of the data.

[0068] The analysis unit can learn language or thought patterns based on the collected data. The analysis unit learns language and thought patterns based on the collected data. The analysis unit can analyze data using, for example, a machine learning algorithm. The machine learning algorithm has the ability to learn large amounts of data and identify specific patterns. For example, the analysis unit learns specific language patterns and thought patterns using collected brain wave data. The analysis unit can analyze data using, for example, a neural network and identify language and thought patterns. This allows the analysis unit to learn language and thought patterns based on the collected data, thereby enabling more accurate analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI and cause the AI ​​to learn language and thought patterns.

[0069] The providing unit may include a device for a person with a language disability to communicate his / her thoughts to others. The providing unit may include a device for a person with a language disability to communicate his / her thoughts to others. The providing unit may include, for example, a tablet, a smartphone, a dedicated terminal, or the like. The tablet is a device for displaying converted text and images / video. The smartphone is a portable device for displaying converted text and images / video. The dedicated terminal is a device specialized for a person with a language disability to communicate his / her thoughts to others. For example, the providing unit displays the converted text and images / video using a tablet. The providing unit may also display the converted text and images / video using a smartphone. Furthermore, the providing unit can use the dedicated terminal to allow a person with a language disability to communicate his / her thoughts to others. This allows the providing unit to allow a person with a language disability to communicate his / her thoughts to others. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can optimize the display method using an AI model for displaying the converted text and images / video.

[0070] The providing unit may include a device for visualizing the content of the dream. The providing unit may include a device for visualizing the content of the dream. The providing unit may include, for example, a VR (Virtual Reality) headset, a display, and the like. The VR headset is a device for a user to visually experience the content of the dream. The display is a device for visually displaying the content of the dream. For example, the providing unit visualizes the content of the dream using a VR headset. The providing unit may also visualize the content of the dream using a display. This allows the providing unit to visualize the content of the dream. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may optimize the visualization method using an AI model for visualizing the content of the dream.

[0071] The providing unit may include a device for reading the thoughts of a suspect in a criminal investigation. The providing unit may include a device for reading the thoughts of a suspect in a criminal investigation. The providing unit may include, for example, an electroencephalogram analysis device, a psychological analysis tool, and the like. The electroencephalogram analysis device is a device for analyzing the electroencephalogram data of a suspect and reading the thoughts. The psychological analysis tool is a device for analyzing the psychological state of a suspect and reading the thoughts. For example, the providing unit analyzes the electroencephalogram data of a suspect using an electroencephalogram analysis device and reads the thoughts. The providing unit may also analyze the psychological state of a suspect using a psychological analysis tool and read the thoughts. This allows the providing unit to read the thoughts of a suspect in a criminal investigation. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit may input data acquired from the electroencephalogram analysis device or the psychological analysis tool into an AI, and cause the AI ​​to read the thoughts.

[0072] The collecting unit can estimate the user's emotion and adjust the timing of collecting the electroencephalogram data based on the estimated emotion of the user. The collecting unit can estimate the user's emotion and adjust the timing of collecting the electroencephalogram data based on the estimated emotion of the user. The collecting unit can estimate the user's emotion using techniques such as facial expression analysis, voice analysis, and electroencephalogram analysis. Facial expression analysis is a technique for capturing the user's facial expression with a camera and estimating the emotion. Voice analysis is a technique for analyzing the tone and speed of the user's voice and estimating the emotion. Electroencephalogram analysis is a technique for analyzing the user's electroencephalogram data and estimating the emotion. For example, when the user is relaxed, the collecting unit sets the frequency of collecting the electroencephalogram data low and collects data for a long period of time. In addition, when the user is feeling stressed, the collecting unit can also set the frequency of collecting the electroencephalogram data high and collect detailed data in a short period of time. Furthermore, when the user is concentrating, the collecting unit can also preferentially collect electroencephalogram data related to a specific task. As a result, the collecting unit can collect more appropriate data by adjusting the timing of collecting the electroencephalogram data according to the emotion of the user. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation 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-mentioned processing in the collection unit may be performed, for example, using an AI or may be performed without using an AI. For example, the collection unit may input the user's emotion data to the generation AI and cause the generation AI to perform emotion estimation.

[0073] The collecting unit can analyze the user's past electroencephalogram data and select the optimal collecting method. The collecting unit analyzes the user's past electroencephalogram data and selects the optimal collecting method. The collecting unit can, for example, identify the most stable collecting timing from the past electroencephalogram data and collect data at that timing. The collecting unit can also select a method of collecting during a specific activity based on the past electroencephalogram data. Furthermore, the collecting unit can analyze the past electroencephalogram data and select a collecting method under specific environmental conditions. For example, the collecting unit can identify the most stable collecting timing from the user's past electroencephalogram data and collect data at that timing. The collecting unit can also select a method of collecting during a specific activity based on the user's past electroencephalogram data. Furthermore, the collecting unit can analyze the user's past electroencephalogram data and select a collecting method under specific environmental conditions. As a result, the collecting unit can select the optimal collecting method by analyzing the past electroencephalogram data. A part or all of the above-mentioned processing in the collecting unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past EEG data into the generation AI and have the generation AI select the optimal collection method.

[0074] The collection unit can perform filtering based on the user's current activity status and environment when collecting the brainwave data. The collection unit performs filtering based on the user's current activity status and environment when collecting the brainwave data. For example, when the user is in a quiet environment, the collection unit can collect data with minimal noise. In addition, when the user is exercising, the collection unit can also collect data with noise due to exercise removed. Furthermore, when the user is performing multiple tasks, the collection unit can preferentially collect brainwave data related to a specific task. For example, when the user is in a quiet environment, the collection unit collects data with minimal noise. In addition, when the user is exercising, the collection unit can also collect data with noise due to exercise removed. Furthermore, when the user is performing multiple tasks, the collection unit can preferentially collect brainwave data related to a specific task. Thereby, the collection unit can collect data with less noise by filtering based on the current activity status and environment. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the current activity status and environmental data to the generation AI and cause the generation AI to perform filtering.

[0075] The collection unit can estimate the user's emotion and determine the priority of the brainwave data to be collected based on the estimated user's emotion. The collection unit estimates the user's emotion and determines the priority of the brainwave data to be collected based on the estimated user's emotion. For example, when the user is relaxed, the collection unit can preferentially collect brainwave data at the time of relaxation. Furthermore, when the user is stressed, the collection unit can also preferentially collect brainwave data at the time of stress. Furthermore, when the user is concentrating, the collection unit can also preferentially collect brainwave data at the time of concentration. For example, when the user is relaxed, the collection unit preferentially collects brainwave data at the time of relaxation. Furthermore, when the user is stressed, the collection unit can also preferentially collect brainwave data at the time of stress. Furthermore, when the user is concentrating, the collection unit can also preferentially collect brainwave data at the time of concentration. Thereby, the collection unit can preferentially collect important data by determining the priority of the brainwave data to be collected according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0076] The collecting unit can preferentially collect highly relevant data based on the geographical location information of the user when collecting the electroencephalogram data. The collecting unit preferentially collects highly relevant data based on the geographical location information of the user when collecting the electroencephalogram data. For example, when the user is in a specific location, the collecting unit can preferentially collect electroencephalogram data related to the location. In addition, when the user is moving, the collecting unit can also preferentially collect electroencephalogram data related to the movement. Furthermore, when the user is under specific environmental conditions, the collecting unit can also preferentially collect electroencephalogram data related to the environment. For example, when the user is in a specific location, the collecting unit preferentially collects electroencephalogram data related to the location. In addition, when the user is moving, the collecting unit can also preferentially collect electroencephalogram data related to the movement. Furthermore, when the user is under specific environmental conditions, the collecting unit can also preferentially collect electroencephalogram data related to the environment. As a result, the collecting unit can obtain more useful data by preferentially collecting highly relevant data based on the geographical location information. A part or all of the above-mentioned processing in the collecting unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographic location information to the generation AI and have the generation AI select highly relevant data.

[0077] The collection unit can analyze the social media activity of the user when collecting the electroencephalogram data and collect related data. The collection unit analyzes the social media activity of the user when collecting the electroencephalogram data and collects related data. For example, when the user posts about a specific topic on social media, the collection unit can collect electroencephalogram data related to the topic. In addition, when the user expresses a specific emotion on social media, the collection unit can also collect electroencephalogram data related to the emotion. Furthermore, when the user participates in a specific event on social media, the collection unit can collect electroencephalogram data related to the event. For example, when the user posts about a specific topic on social media, the collection unit collects electroencephalogram data related to the topic. In addition, when the user expresses a specific emotion on social media, the collection unit can also collect electroencephalogram data related to the emotion. In addition, when the user participates in a specific event on social media, the collection unit can also collect electroencephalogram data related to the event. Thereby, the collection unit can collect related electroencephalogram data by analyzing the social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input social media activity data into the generation AI and cause the generation AI to collect related brainwave data.

[0078] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the expression method of the analysis based on the estimated user's emotion. The analysis unit can display the analysis result in a visually easy-to-understand manner when the user is relaxed, for example. The analysis unit can also display the analysis result concisely when the user is stressed. Furthermore, the analysis unit can display detailed analysis results when the user is concentrating. For example, the analysis unit displays the analysis result in a visually easy-to-understand manner when the user is relaxed. The analysis unit can also display the analysis result concisely when the user is stressed. Furthermore, the analysis unit can display detailed analysis results when the user is concentrating. Thereby, the analysis unit can provide analysis results that are easier to understand by adjusting the expression method of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the electroencephalogram data during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the electroencephalogram data during the analysis. The analysis unit can perform a detailed analysis on electroencephalogram data with high importance, for example. The analysis unit can also perform a simple analysis on electroencephalogram data with low importance. Furthermore, the analysis unit can perform an analysis with a moderate level of detail on electroencephalogram data with medium importance. For example, the analysis unit performs a detailed analysis on electroencephalogram data with high importance. The analysis unit can also perform a simple analysis on electroencephalogram data with low importance. Furthermore, the analysis unit can perform an analysis with a moderate level of detail on electroencephalogram data with medium importance. Thereby, the analysis unit adjusts the level of detail of the analysis based on the importance of the electroencephalogram data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the electroencephalogram data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms according to the category of the electroencephalogram data during analysis. The analysis unit applies different analysis algorithms according to the category of the electroencephalogram data during analysis. The analysis unit can apply a language analysis algorithm to electroencephalogram data related to language, for example. The analysis unit can also apply an emotion analysis algorithm to electroencephalogram data related to emotion. Furthermore, the analysis unit can apply a thought analysis algorithm to electroencephalogram data related to thinking. For example, the analysis unit applies a language analysis algorithm to electroencephalogram data related to language. The analysis unit can also apply an emotion analysis algorithm to electroencephalogram data related to emotion. Furthermore, the analysis unit can apply a thought analysis algorithm to electroencephalogram data related to thinking. Thereby, the analysis unit can apply different analysis algorithms according to the category of the electroencephalogram data, thereby enabling more accurate analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the category of the electroencephalogram data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0081] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit can perform a detailed analysis and provide a long analysis result. Also, when the user is stressed, the analysis unit can perform a concise analysis and provide a short analysis result. Furthermore, when the user is concentrated, the analysis unit can provide an analysis result of a moderate length. For example, when the user is relaxed, the analysis unit can perform a detailed analysis and provide a long analysis result. Also, when the user is stressed, the analysis unit can perform a concise analysis and provide a short analysis result. Furthermore, when the user is concentrated, the analysis unit can provide an analysis result of a moderate length. Thereby, the analysis unit can provide an appropriate analysis result by adjusting the length of the analysis according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0082] The analysis unit can determine the priority of the analysis based on the collection time of the electroencephalogram data during the analysis. The analysis unit determines the priority of the analysis based on the collection time of the electroencephalogram data during the analysis. The analysis unit can, for example, preferentially analyze recently collected electroencephalogram data. The analysis unit can also preferentially analyze electroencephalogram data collected during a specific event. Furthermore, the analysis unit can also preferentially analyze electroencephalogram data collected while the user is performing a specific task. For example, the analysis unit preferentially analyzes recently collected electroencephalogram data. The analysis unit can also preferentially analyze electroencephalogram data collected during a specific event. Furthermore, the analysis unit can also preferentially analyze electroencephalogram data collected while the user is performing a specific task. Thereby, the analysis unit determines the priority of the analysis based on the collection time of the electroencephalogram data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the electroencephalogram data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the electroencephalogram data during analysis. The analysis unit adjusts the order of analysis based on the relevance of the electroencephalogram data during analysis. The analysis unit can, for example, preferentially analyze electroencephalogram data having high relevance. The analysis unit can also analyze electroencephalogram data having medium relevance next. Furthermore, the analysis unit can analyze electroencephalogram data having low relevance last. For example, the analysis unit preferentially analyzes electroencephalogram data having high relevance. The analysis unit can also analyze electroencephalogram data having medium relevance next. Furthermore, the analysis unit can analyze electroencephalogram data having low relevance last. Thereby, the analysis unit adjusts the order of analysis based on the relevance of the electroencephalogram data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the electroencephalogram data to the generation AI and cause the generation AI to adjust the order of analysis.

[0084] The conversion unit can estimate the user's emotion and adjust the conversion method based on the estimated user's emotion. The conversion unit can estimate the user's emotion and adjust the conversion method based on the estimated user's emotion. For example, when the user is relaxed, the conversion unit can apply a conversion method that proceeds at a leisurely pace. Also, when the user is stressed, the conversion unit can apply a method that performs conversion quickly. Furthermore, when the user is concentrated, the conversion unit can apply a detailed conversion method. For example, when the user is relaxed, the conversion unit applies a conversion method that proceeds at a leisurely pace. Also, when the user is stressed, the conversion unit can apply a method that performs conversion quickly. Furthermore, when the user is concentrated, the conversion unit can apply a detailed conversion method. Thereby, the conversion unit adjusts the conversion method according to the user's emotion, thereby enabling more appropriate conversion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0085] The conversion unit can adjust the level of detail of the conversion based on the importance of the electroencephalogram data at the time of conversion. The conversion unit adjusts the level of detail of the conversion based on the importance of the electroencephalogram data at the time of conversion. The conversion unit can perform detailed conversion on electroencephalogram data with high importance, for example. The conversion unit can also perform simple conversion on electroencephalogram data with low importance. Furthermore, the conversion unit can perform conversion with a moderate level of detail on electroencephalogram data with medium importance. For example, the conversion unit performs detailed conversion on electroencephalogram data with high importance. The conversion unit can also perform simple conversion on electroencephalogram data with low importance. Furthermore, the conversion unit can perform conversion with a moderate level of detail on electroencephalogram data with medium importance. Thereby, the conversion unit adjusts the level of detail of the conversion based on the importance of the electroencephalogram data, thereby enabling efficient conversion. Part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the importance of the electroencephalogram data to the generation AI and cause the generation AI to adjust the level of detail of the conversion.

[0086] The conversion unit can apply different conversion algorithms according to the category of the electroencephalogram data at the time of conversion. The conversion unit applies different conversion algorithms according to the category of the electroencephalogram data at the time of conversion. The conversion unit can apply a language conversion algorithm to electroencephalogram data related to language, for example. The conversion unit can also apply an emotion conversion algorithm to electroencephalogram data related to emotion. The conversion unit can also apply a thought conversion algorithm to electroencephalogram data related to thought. For example, the conversion unit applies a language conversion algorithm to electroencephalogram data related to language. The conversion unit can also apply an emotion conversion algorithm to electroencephalogram data related to emotion. The conversion unit can also apply a thought conversion algorithm to electroencephalogram data related to thought. Thereby, the conversion unit can apply different conversion algorithms according to the category of the electroencephalogram data, thereby enabling more accurate conversion. Part or all of the above-mentioned processing in the conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversion unit can input the category of the electroencephalogram data to the generation AI and cause the generation AI to apply the conversion algorithm.

[0087] The conversion unit can estimate the user's emotion and determine the priority of conversion based on the estimated user's emotion. The conversion unit estimates the user's emotion and determines the priority of conversion based on the estimated user's emotion. For example, when the user is relaxed, the conversion unit can preferentially convert the brainwave data at the time of relaxation. Also, when the user is stressed, the conversion unit can preferentially convert the brainwave data at the time of stress. Furthermore, when the user is concentrating, the conversion unit can preferentially convert the brainwave data at the time of concentration. For example, when the user is relaxed, the conversion unit preferentially converts the brainwave data at the time of relaxation. Also, when the user is stressed, the conversion unit can preferentially convert the brainwave data at the time of stress. Furthermore, when the user is concentrating, the conversion unit can preferentially convert the brainwave data at the time of concentration. Thereby, the conversion unit can preferentially convert important data by determining the priority of conversion according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0088] The conversion unit can adjust the order of conversion based on the collection time of the electroencephalogram data at the time of conversion. The conversion unit adjusts the order of conversion based on the collection time of the electroencephalogram data at the time of conversion. The conversion unit can, for example, preferentially convert recently collected electroencephalogram data. The conversion unit can also preferentially convert electroencephalogram data collected during a specific event. Furthermore, the conversion unit can also preferentially convert electroencephalogram data collected while the user is performing a specific task. For example, the conversion unit preferentially converts recently collected electroencephalogram data. The conversion unit can also preferentially convert electroencephalogram data collected during a specific event. Furthermore, the conversion unit can also preferentially convert electroencephalogram data collected while the user is performing a specific task. Thereby, the conversion unit adjusts the order of conversion based on the collection time of the electroencephalogram data, thereby enabling efficient conversion. Part or all of the above-mentioned processing in the conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversion unit can input the collection time of the electroencephalogram data to the generation AI and cause the generation AI to adjust the order of conversion.

[0089] The conversion unit can adjust the order of conversion based on the relevance of the electroencephalogram data at the time of conversion. The conversion unit adjusts the order of conversion based on the relevance of the electroencephalogram data at the time of conversion. The conversion unit can, for example, preferentially convert electroencephalogram data having high relevance. The conversion unit can also convert electroencephalogram data having medium relevance next. Furthermore, the conversion unit can convert electroencephalogram data having low relevance last. For example, the conversion unit preferentially converts electroencephalogram data having high relevance. The conversion unit can also convert electroencephalogram data having medium relevance next. Furthermore, the conversion unit can convert electroencephalogram data having low relevance last. Thereby, the conversion unit adjusts the order of conversion based on the relevance of the electroencephalogram data, thereby enabling efficient conversion. Part or all of the above-mentioned processing in the conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversion unit can input the relevance of the electroencephalogram data to the generation AI and cause the generation AI to adjust the order of conversion.

[0090] The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. For example, when the user is relaxed, the providing unit can provide a visually easy-to-understand display method. Furthermore, when the user is stressed, the providing unit can also provide a concise display method. Furthermore, when the user is concentrating, the providing unit can also provide a display method including detailed information. For example, when the user is relaxed, the providing unit can provide a visually easy-to-understand display method. Furthermore, when the user is stressed, the providing unit can also provide a concise display method. Furthermore, when the user is concentrating, the providing unit can also provide a display method including detailed information. Thereby, the providing unit can provide more appropriate information by adjusting the display method of the information according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's emotion data to the generating AI and cause the generating AI to estimate the emotion.

[0091] The providing unit can select the optimal display method by referring to the user's past operation history at the time of providing. The providing unit selects the optimal display method by referring to the user's past operation history at the time of providing. The providing unit can, for example, preferentially provide a display method that the user has used in the past. The providing unit can also suggest a specific display method from the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and provide the most efficient display method. For example, the providing unit preferentially provides a display method that the user has used in the past. The providing unit can also suggest a specific display method from the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and provide the most efficient display method. Thereby, the providing unit can provide the optimal display method to the user by referring to the past operation history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data to the generating AI and cause the generating AI to select the optimal display method.

[0092] The providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated user's emotion. The providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated user's emotion. For example, when the user is relaxed, the providing unit can provide information required at the time of relaxation with priority. Furthermore, when the user is stressed, the providing unit can also provide information required at the time of stress with priority. Furthermore, when the user is concentrating, the providing unit can also provide information required at the time of concentration with priority. For example, when the user is relaxed, the providing unit can provide information required at the time of relaxation with priority. Furthermore, when the user is stressed, the providing unit can provide information required at the time of stress with priority. Furthermore, when the user is concentrating, the providing unit can provide information required at the time of concentration with priority. Thereby, the providing unit can provide important information with priority by determining the priority of information according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's emotion data to the generating AI and cause the generating AI to estimate the emotion.

[0093] The providing unit can select the optimal display method based on the device information of the user at the time of providing. The providing unit selects the optimal display method based on the device information of the user at the time of providing. For example, when the user is using a smartphone, the providing unit can provide a display method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. As a result, the providing unit selects the optimal display method based on the device information, thereby enabling a display that is easy for the user to see. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information of the user to the generation AI and cause the generation AI to select the optimal display method.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] When analyzing the user's brainwave data, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral history. For example, the analysis unit learns what brainwave patterns the user showed in specific situations in the past, and analyzes the current brainwave data based on that information. In addition, by referring to the stress and relaxation states the user experienced in the past and comparing them with the current brainwave data, more accurate analysis results can be obtained. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to brainwave data related to specific tasks or activities the user performed in the past and comparing them with the current data. This allows the analysis unit to perform more accurate analysis by utilizing the user's past behavioral history.

[0096] The providing unit can provide information customized to the user's preferences based on the user's brainwave data. For example, when the user is relaxed, music and images suitable for relaxation can be provided. When the user is concentrating, information and tools for improving concentration can be provided. Furthermore, when the user is feeling stressed, information and activities useful for reducing stress can be suggested. In this way, the providing unit can utilize the user's brainwave data to provide optimal information according to the user's condition.

[0097] When collecting the user's electroencephalogram data, the collection unit can simultaneously collect the user's physiological data (e.g., heart rate and body temperature) and combine and analyze these data. For example, if the user's heart rate is rising, it can be determined that the user is likely feeling stressed, and the frequency of collection of electroencephalogram data can be increased. In addition, if the user's body temperature is rising, the user may be exercising, so that electroencephalogram data related to exercise can be collected preferentially. Furthermore, by combining and analyzing the user's physiological data and electroencephalogram data, the user's condition can be grasped more accurately. This allows the collection unit to utilize the user's physiological data to improve the accuracy of collection and analysis of electroencephalogram data.

[0098] When analyzing the user's brainwave data, the analysis unit can perform the analysis while taking into consideration the user's current environmental information (e.g., the intensity of surrounding sounds and light). For example, when the user is in a quiet environment, it is highly likely that the user is relaxed, so the analysis of the brainwave pattern during relaxation is prioritized. Also, when the user is in a bright environment, it is highly likely that the user is concentrating, so it is also possible to analyze the brainwave pattern during concentration with priority. Furthermore, by adjusting the analysis result based on the user's environmental information, it is possible to obtain a more accurate analysis result. This allows the analysis unit to utilize the user's environmental information to improve the analysis accuracy of the brainwave data.

[0099] The providing unit can provide feedback according to the user's emotional state based on the user's brainwave data. For example, when the user is relaxed, advice for maintaining relaxation and content suitable for relaxation are provided. In addition, when the user is feeling stressed, activities and relaxation techniques for reducing stress can be suggested. Furthermore, when the user is concentrating, tools and information for improving concentration can be provided. In this way, the providing unit can support the user's condition by providing appropriate feedback according to the user's emotional state.

[0100] When collecting the user's electroencephalogram data, the collection unit can detect abnormalities by comparing the data with the user's past electroencephalogram data. For example, if the user's electroencephalogram data is significantly different from the past data, it is determined that an abnormality may have occurred, and an alert is issued. Also, if the user's electroencephalogram data matches the past data, it can be determined that the user is in a normal state. Furthermore, if an abnormality is detected, a detailed analysis can be performed to identify the cause of the abnormality. This allows the collection unit to utilize the user's past electroencephalogram data to detect abnormalities early and take measures against them.

[0101] When analyzing the user's electroencephalogram data, the analysis unit can improve the accuracy of the analysis by taking into account the user's emotional state. For example, when the user is relaxed, the analysis is performed based on the electroencephalogram pattern when the user is relaxed. When the user is stressed, the analysis can be performed based on the electroencephalogram pattern when the user is stressed. Furthermore, when the user is concentrating, the analysis can be performed based on the electroencephalogram pattern when the user is concentrating. This allows the analysis unit to perform a more accurate analysis by taking into account the user's emotional state.

[0102] The providing unit can provide entertainment content according to the emotional state of the user based on the user's brainwave data. For example, when the user is relaxed, music and images suitable for relaxation can be provided. Also, when the user is feeling stressed, entertainment content useful for stress reduction can be provided. Furthermore, when the user is concentrating, games and activities for improving concentration can be provided. In this way, the providing unit can support the user's condition by providing entertainment content according to the user's emotional state.

[0103] When collecting the user's brainwave data, the collection unit can adjust the collection method in consideration of the user's current activity. For example, when the user is reading, brainwave data related to reading is preferentially collected. Also, when the user is exercising, brainwave data related to exercise can be preferentially collected. Furthermore, when the user is relaxing, brainwave data during relaxation can be preferentially collected. This allows the collection unit to collect more appropriate brainwave data in consideration of the user's current activity.

[0104] The providing unit can provide learning content according to the user's emotional state based on the user's brainwave data. For example, when the user is relaxed, content that allows the user to study while relaxing can be provided. When the user is feeling stressed, learning content that helps reduce stress can also be provided. Furthermore, when the user is concentrating, learning content to improve concentration can also be provided. In this way, the providing unit can improve the user's learning effect by providing learning content according to the user's emotional state.

[0105] The process flow of the second embodiment will be briefly described below.

[0106] Step 1: The collecting unit collects brain activity data. The collecting unit can collect brain activity data using, for example, an EEG sensor. The EEG sensor is attached to the head and monitors brain activity in real time. For example, the EEG sensor detects activity in a specific part of the brain and collects the data. Step 2: The analysis unit analyzes the collected data. The analysis unit can analyze the collected data using, for example, AI. The AI ​​learns language and thought patterns based on the collected data. For example, it learns that certain brainwave patterns correspond to certain words or images. Step 3: The conversion unit converts the data analyzed by the analysis unit into text or images / videos. The conversion unit can convert the analyzed data into text or images / videos using, for example, AI. The converted text or images / videos are provided to the user. For example, the conversion unit can include a device that displays the text or images / videos.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0109] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements including the above-mentioned collection unit, analysis unit, conversion unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects brain activity data using an electroencephalogram sensor of the smart device 14 and transmits it to the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the collected data is analyzed by AI. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts the analyzed data into text or images / video. The provision unit displays the converted text or images / video using, for example, the display or speaker of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0118] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements including the above-mentioned collection unit, analysis unit, conversion unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects brain activity data using an electroencephalogram sensor of the smart glasses 214 and transmits it to the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the collected data is analyzed by AI. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts the analyzed data into text or images / video. The provision unit displays the converted text or images / video using, for example, the display or speaker of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0130] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0134] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0137] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements including the above-mentioned collection unit, analysis unit, conversion unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects brain activity data using an electroencephalogram sensor of the headset type terminal 314 and transmits it to the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the collected data is analyzed by AI. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts the analyzed data into text or images / video. The provision unit displays the converted text or images / video using, for example, the display or speaker of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0151] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0154] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] Each of the multiple elements including the above-mentioned collection unit, analysis unit, conversion unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects brain activity data using an electroencephalogram sensor of the robot 414 and transmits it to the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the collected data is analyzed by AI. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts the analyzed data into text or images / video. The provision unit displays the converted text or images / video using, for example, a display or speaker of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0160] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0161] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0163] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0165] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0168] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0169] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0170] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0171] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0172] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0173] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0174] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0176] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0178] (Appendix 1) A collection unit that collects brain activity data; an analysis unit that analyzes the data collected by the collection unit; A conversion unit converts the data analyzed by the analysis unit into text or an image / video; Equipped A system characterized by: (Appendix 2) The conversion unit is Provides a presentation section for displaying the converted text or image / video 2. The system of claim 1, (Appendix 3) The collecting unit includes: Collecting brain activity data using an EEG sensor 2. The system of claim 1, (Appendix 4) The analysis unit is Learning language or thought patterns based on collected data 2. The system of claim 1, (Appendix 5) The providing unit is Equipping people with speech disabilities with devices to communicate their thoughts to others 3. The system of claim 2, (Appendix 6) The providing unit is Equipped with a device to visualize the contents of dreams 3. The system of claim 2, (Appendix 7) The providing unit is Equipping suspects with devices to read their thoughts in criminal investigations 3. The system of claim 2, (Appendix 8) The collecting unit includes: A user's emotion is estimated, and the timing of collecting electroencephalogram data is adjusted based on the estimated emotion of the user. 2. The system of claim 1, (Appendix 9) The collecting unit includes: Analyze the user's past brainwave data and select the optimal collection method 2. The system of claim 1, (Appendix 10) The collecting unit includes: Filtering EEG data as it is collected based on the user's current activity or environment 2. The system of claim 1, (Appendix 11) The collecting unit includes: A user's emotion is estimated, and a priority order of electroencephalogram data to be collected is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting EEG data, the system prioritizes collection of data that is more relevant to the user based on the user's geographic location. 2. The system of claim 1, (Appendix 13) The collecting unit includes: When collecting EEG data, the user's social media activity is analyzed to collect relevant data. 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the level of detail of the analysis based on the importance of the EEG data 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of EEG data. 2. The system of claim 1, (Appendix 17) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, prioritize analysis based on when the EEG data was collected. 2. The system of claim 1, (Appendix 19) The analysis unit is During analysis, adjust the order of analysis based on the relevance of EEG data 2. The system of claim 1, (Appendix 20) The conversion unit is Infer user emotions and adjust the conversion method based on the estimated user emotions 2. The system of claim 1, (Appendix 21) The conversion unit is During conversion, adjust the conversion detail level based on the importance of the EEG data 2. The system of claim 1, (Appendix 22) The conversion unit is During conversion, different conversion algorithms are applied depending on the category of EEG data. 2. The system of claim 1, (Appendix 23) The conversion unit is Infer user sentiment and prioritize conversions based on the estimated user sentiment. 2. The system of claim 1, (Appendix 24) The conversion unit is During conversion, adjust the order of conversion based on when the EEG data was collected 2. The system of claim 1, (Appendix 25) The conversion unit is During conversion, adjust the conversion order based on the relevance of the EEG data 2. The system of claim 1, (Appendix 26) The providing unit is Estimate the user's emotions and adjust the way information is displayed based on the estimated user's emotions 3. The system of claim 2, (Appendix 27) The providing unit is When providing the information, the optimal display method is selected by referring to the user's past operation history. 3. The system of claim 2, (Appendix 28) The providing unit is Estimate the user's emotions and prioritize the information to be provided based on the estimated user's emotions. 3. The system of claim 2, (Appendix 29) The providing unit is When providing the content, the optimal display method is selected based on the user's device information. 3. The system of claim 2, [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects brain activity data; an analysis unit that analyzes the data collected by the collection unit; A conversion unit converts the data analyzed by the analysis unit into text or an image / video; Equipped A system characterized by:

2. The conversion unit is Provides a presentation section for displaying the converted text or image / video 2. The system of claim 1 .

3. The collecting unit includes: Collecting brain activity data using an EEG sensor 2. The system of claim 1 .

4. The analysis unit is Learning language or thought patterns based on the data collected by the collection unit 2. The system of claim 1 .

5. The providing unit is Equipping people with speech disabilities with devices to communicate their thoughts to others 3. The system of claim 2.

6. The providing unit is Equipped with a device to visualize the contents of dreams 3. The system of claim 2.

7. The providing unit is Equipping suspects with devices to read their thoughts in criminal investigations 3. The system of claim 2.

8. The collecting unit includes: A user's emotion is estimated, and the timing of collecting electroencephalogram data is adjusted based on the estimated emotion of the user.

2. The system of claim 1 .

9. The collecting unit includes: Analyze the user's past brainwave data and select the optimal collection method 2. The system of claim 1 .

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