system

Abstract

The system according to this embodiment aims to accurately understand the thoughts and feelings of those receiving care or medical treatment, and to communicate with them appropriately. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a generation unit, a display unit, and a judgment unit. The acquisition unit acquires the brainwaves of the person receiving care or medical treatment. The analysis unit analyzes the brainwave data acquired by the acquisition unit. The generation unit uses a generating AI to verbalize or visualize the data based on the data analyzed by the analysis unit. The display unit displays the content generated by the generation unit on a monitor. The judgment unit determines which output is more appropriate.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to accurately grasp the thoughts and feelings of care recipients and medical patients and to communicate appropriately. [[ID=A]]

[0005] The system according to the embodiment aims to accurately grasp the thoughts and feelings of care recipients and medical patients and to communicate appropriately.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a generation unit, a display unit, and a decision unit. The acquisition unit acquires the brainwaves of the person receiving care or medical treatment. The analysis unit analyzes the brainwave data acquired by the acquisition unit. The generation unit uses a generating AI to verbalize or visualize the data based on the data analyzed by the analysis unit. The display unit displays the content generated by the generation unit on a monitor. The decision unit determines which output is more appropriate. [Effects of the Invention]

[0007] The system according to this embodiment can accurately grasp the thoughts and feelings of those receiving care or medical treatment, and communicate with them appropriately. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The communication support system according to an embodiment of the present invention is a system for resolving thoughts and communication that cannot be expressed in words using electroencephalography (EEG) technology. This communication support system acquires the brainwaves of a person receiving care or medical treatment and analyzes the brainwaves and emotions using deep learning. Next, it uses a generative AI to verbalize or visualize the analysis results and displays the content on a monitor. Since verbal and visual thinking differ depending on the timing, the agent determines and generates which output is optimal. This system is used as a communication support tool for caregivers and medical professionals. For example, it acquires the brainwaves of a person receiving care or medical treatment. In this case, it utilizes an EEG, a highly real-time and versatile electroencephalogram (EEG) measurement device. EEG has high temporal resolution and can measure brainwaves in real time. For example, when a person receiving care is excited, beta waves are measured, and when they are calm, alpha waves are measured. This makes it possible to understand the emotional state of the person receiving care. Next, the acquired brainwave data is analyzed using deep learning. Deep learning is used to measure concentration and emotions by performing frequency analysis from the brainwave data and calculating a spectrum. For example, when a person receiving care is concentrating, brainwaves in a specific frequency band appear strongly. Based on this information, the emotional state of the person receiving care is analyzed. The analysis results are then verbalized or visualized using a generative AI. For example, text can be generated from brainwave data through an LLM (Large-Scale Language Model). This allows the system to output the text the person receiving care is thinking. It is also possible to generate images from brain signals. For example, if the person receiving care is imagining a cat, the generative AI will draw an image of a cat based on that image. Since linguistic and visual thinking differ depending on the timing, the agent determines which output is most appropriate and generates accordingly. Based on the person receiving care's brainwave data and analysis results, the agent generates text when verbalization is appropriate and an image when visualization is appropriate. In this way, the thoughts and emotions of the person receiving care can be expressed in the most optimal form. This system is used as a communication support tool for caregivers and medical professionals.For example, in the care of individuals with aphasia, verbalizing or visualizing the thoughts and feelings of those being cared for makes it easier for caregivers to understand their needs. It is also helpful in understanding the needs of individuals with dementia. Furthermore, it allows for understanding what patients with limb or hearing impairments require. In this way, communication in care and medical settings can be facilitated, improving the quality of life for those receiving care and medical treatment. Thus, communication support systems can express the thoughts and feelings of those receiving care and medical treatment in the most optimal way, supporting communication between caregivers and medical professionals.

[0029] The communication support system according to the embodiment comprises an acquisition unit, an analysis unit, a generation unit, a display unit, and a judgment unit. The acquisition unit acquires the brainwaves of the person receiving care or medical treatment. The acquisition unit acquires brainwaves using, for example, a highly real-time EEG. EEG has high temporal resolution and can measure brainwaves in real time. For example, beta waves are measured when the person receiving care is excited, and alpha waves are measured when they are calm. This makes it possible to understand the emotional state of the person receiving care. The analysis unit analyzes the brainwave data acquired by the acquisition unit. For example, the analysis unit performs frequency analysis from the brainwave data and calculates a spectrum. Deep learning is used to measure concentration and emotion by performing frequency analysis from brainwave data and calculating a spectrum. For example, when the person receiving care is concentrating, brainwaves in a specific frequency band appear strongly. Based on this information, the emotional state of the person receiving care is analyzed. The generation unit uses a generating AI to verbalize or visualize the data analyzed by the analysis unit. The generation unit can, for example, generate text using an LLM (Large-Scale Language Model) based on brainwaves. This allows the system to output text that the person receiving care is thinking. The generation unit can also generate images based on brain signals. For example, if the person receiving care is imagining a cat, the generation AI will draw an image of a cat based on that image. The display unit displays the content generated by the generation unit on a monitor. The display unit visualizes the thoughts and feelings of the person receiving care by displaying the generated text and images on the monitor. The judgment unit determines which output is more appropriate. For example, based on the person receiving care's brainwave data and analysis results, the judgment unit generates text if verbalization is appropriate, and generates an image if visualization is appropriate. In this way, the communication support system can express the thoughts and feelings of the person receiving care or medical treatment in the most optimal way, supporting communication between caregivers and medical professionals.

[0030] The acquisition unit acquires the brainwaves (EEG) of the person being cared for or receiving medical treatment. The acquisition unit acquires the EEG using, for example, a highly real-time EEG. EEG has high temporal resolution and can measure brainwaves in real time. Specifically, the EEG sensor is attached to the scalp and detects weak electrical signals. This allows for a detailed understanding of the brain activity state of the person being cared for. The EEG sensor uses multiple electrodes to acquire signals from different parts of the brain and analyzes the activity of each part. For example, activity in the frontal lobe is related to attention, planning, and emotional control, while activity in the occipital lobe is related to the processing of visual information. This allows for a detailed understanding of which parts of the care recipient's brain are active and how. When the care recipient is excited, beta waves are measured, and when they are calm, alpha waves are measured. Beta waves are in the frequency band of 14Hz to 30Hz and indicate mental activity and tension. On the other hand, alpha waves are in the frequency band of 8Hz to 13Hz and appear when relaxed or at rest. This allows for an understanding of the emotional state of the person being cared for. Furthermore, the EEG sensor incorporates filtering technology to reduce noise, minimizing the influence of the external environment. This allows the acquisition unit to collect accurate and reliable electroencephalogram data.

[0031] The analysis unit analyzes the electroencephalogram (EEG) data acquired by the acquisition unit. For example, the analysis unit performs frequency analysis on the EEG data and calculates a spectrum. A spectrum is a graph that contains both time and frequency information, and can visually represent fluctuations in brain waves. Deep learning is used to measure concentration and emotion by performing frequency analysis on EEG data and calculating a spectrum. Deep learning models have the ability to learn from large amounts of EEG data and identify specific patterns. For example, when a person receiving care is concentrating, brain waves in a specific frequency band appear strongly. Based on this information, the emotional state of the person receiving care is analyzed. The analysis unit extracts features from the EEG data and uses this to evaluate the emotions and concentration state of the person receiving care. For example, it analyzes the intensity and fluctuation patterns of a specific frequency band to determine whether the person receiving care is relaxed or tense. In addition, the analysis unit can track changes in the person receiving care by comparing them with past data. This allows the analysis unit to grasp the emotional state of the person receiving care in real time and provide information for appropriate responses. Furthermore, the analysis unit can detect unusual brainwave patterns using an anomaly detection algorithm, enabling early detection of abnormalities. This allows the analysis unit to continuously monitor the health status of the person receiving care and support prompt responses.

[0032] The generation unit uses a generating AI to verbalize or visualize data analyzed by the analysis unit. For example, the generation unit can generate text through an LLM (Large-Scale Language Model) based on brainwaves. The LLM has learned from a vast amount of text data and has the ability to generate natural language based on the input data. This allows it to output text that the person being cared for is thinking. For example, if the person being cared for is thinking "I want a drink of water," the LLM analyzes the brainwave pattern and generates the text "I want a drink of water." The generation unit can also generate images based on brain signals. For example, if the person being cared for is imagining a cat, the generating AI will draw an image of a cat based on that image. The generating AI can use an image generation model to generate high-quality images based on the input brainwave data. This allows it to visually represent the thoughts and feelings of the person being cared for. Furthermore, the generation unit has a format conversion function to output the generated text and images in an appropriate format, providing information in a way that is easily understood by the user. In this way, the generation unit can accurately and effectively express the thoughts and feelings of the person being cared for and support communication.

[0033] The display unit displays the content generated by the generation unit on a monitor. For example, the display unit visualizes the thoughts and feelings of the person receiving care by displaying generated text and images on the monitor. The monitor is equipped with a high-resolution display, allowing for clear display of the generated content. This enables caregivers and medical professionals to intuitively understand the care recipient's condition. The display unit also allows manipulation of the generated content through a user interface, enabling users to enlarge, reduce, or scroll the displayed content as needed. Furthermore, the display unit has an audio output function, allowing it to read the generated text aloud. This allows care recipients with visual impairments or those who have difficulty understanding visual information to confirm the generated content aloud. The display unit has multiple display modes, allowing users to select the optimal display method according to their needs. For example, there are text mode, image mode, and audio mode, allowing users to select the appropriate mode depending on the situation. This enables the display unit to visualize the thoughts and feelings of the person receiving care in various ways, deepening the understanding of caregivers and medical professionals.

[0034] The decision unit determines which output is more appropriate. For example, based on the care recipient's electroencephalogram (EEG) data and analysis results, the decision unit generates text if verbalization is appropriate, and generates an image if image generation is appropriate. The decision unit uses AI to analyze the characteristics of the EEG data and select the optimal output format. For example, if the care recipient is thinking of specific instructions or requests, it determines that text output is appropriate, and if the care recipient is trying to express images or emotions, it determines that image output is appropriate. The decision unit can continuously improve the accuracy of output format selection based on past data and user feedback. This allows the decision unit to express the care recipient's thoughts and emotions in the most optimal way, supporting communication between caregivers and medical professionals. Furthermore, the decision unit can combine multiple output formats and generate text and images simultaneously as needed. This allows for a richer expression of the care recipient's thoughts and emotions, deepening the understanding of caregivers and medical professionals. The decision unit can select the output format in real time and respond quickly and appropriately. This allows the decision unit to effectively support the care recipient's communication and improve the quality of care and medical treatment.

[0035] The acquisition unit can acquire brain waves using a real-time EEG. The acquisition unit acquires brain waves using a real-time EEG, for example. EEGs have high temporal resolution and can measure brain waves in real time. For example, beta waves are measured when the person being cared for is agitated, and alpha waves are measured when they are calm. This allows for immediate understanding of the emotional state of the person being cared for by acquiring brain waves in real time. Real-time EEGs include, for example, those with low data acquisition latency and the EEG device used. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input a real-time EEG device into a generating AI and have the generating AI perform the acquisition of brain wave data.

[0036] The analysis unit can perform frequency analysis from electroencephalogram (EEG) data and calculate a spectrum. The analysis unit can, for example, perform frequency analysis from EEG data and calculate a spectrum. Frequency analysis is performed using methods such as FFT (Fast Fourier Transform) or wavelet transform. The spectrum is generated considering, for example, temporal resolution and frequency resolution. This allows for a detailed analysis of the emotional state of the person receiving care by performing frequency analysis on EEG data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input EEG data into a generating AI and have the generating AI perform frequency analysis and spectrum calculation.

[0037] The generation unit can generate text through an LLM (Large-Scale Language Model). For example, the generation unit generates text through an LLM based on electroencephalogram (EEG). This allows the thoughts of the person being cared for to be output as text using an LLM. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input EEG data into a generation AI and have the generation AI perform text generation.

[0038] The generation unit can generate images based on brain signals. For example, the generation unit generates images based on brain signals. Brain signals include, for example, EEG signals and MEG signals. By generating images based on brain signals, the thoughts of the person being cared for can be visually represented. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input brain signal data into a generation AI and have the generation AI perform image generation.

[0039] The decision unit can generate text when verbalization is appropriate and images when image representation is appropriate. For example, based on the care recipient's electroencephalogram (EEG) data and analysis results, the decision unit generates text when verbalization is appropriate and images when image representation is appropriate. This allows for the optimal representation of the care recipient's thoughts and emotions. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input EEG data and analysis results into a generation AI and have the generation AI determine the optimal output format.

[0040] The data acquisition unit can analyze the care recipient's past brainwave data and select the optimal acquisition method. For example, the acquisition unit can select the most stable acquisition method based on the care recipient's past brainwave data. The acquisition unit can also analyze the care recipient's past brainwave data and select an acquisition method for a specific time period. Furthermore, the acquisition unit can select an acquisition method for specific environmental conditions based on the care recipient's past brainwave data. This allows for the acquisition of stable data by selecting the optimal acquisition method based on past brainwave data. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the care recipient's past brainwave data into a generating AI and have the generating AI select the optimal acquisition method.

[0041] The acquisition unit can filter brainwave data based on the care recipient's current health status and activity level when acquiring brainwave data. For example, if the care recipient is exercising, the acquisition unit can filter out noise caused by exercise. Furthermore, if the care recipient is ill, the acquisition unit can filter the brainwave data considering the effects of the illness. Additionally, if the care recipient is taking medication, the acquisition unit can filter the brainwave data considering the effects of the medication. This allows for the acquisition of less noisy data by filtering based on health status and activity level. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the care recipient's health status and activity level data into a generating AI and have the generating AI perform the filtering.

[0042] The data acquisition unit can prioritize the acquisition of highly relevant data by considering the geographical location information of the person receiving care when acquiring electroencephalogram (EEG) data. For example, if the person receiving care is at home, the data acquisition unit will prioritize acquiring EEG data from home. It can also prioritize acquiring EEG data from a hospital if the person receiving care is in a hospital. Furthermore, if the person receiving care is out, the data acquisition unit can prioritize acquiring EEG data from their location. This allows for the priority acquisition of highly relevant data by considering geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the geographical location information of the person receiving care into a generating AI and have the generating AI prioritize the acquisition of highly relevant data.

[0043] The acquisition unit can analyze the care recipient's social media activity and acquire relevant data when acquiring brainwave data. For example, if the care recipient is experiencing stress on social media, the acquisition unit can acquire brainwave data related to stress. It can also acquire brainwave data related to relaxation if the care recipient is relaxing on social media. Furthermore, if the care recipient is concentrating on social media, the acquisition unit can acquire brainwave data related to concentration. Thus, relevant brainwave data can be acquired by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the care recipient's social media activity data into a generating AI and have the generating AI acquire the relevant brainwave data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the electroencephalogram (EEG) data during the analysis. For example, the analysis unit can perform a detailed analysis on EEG data with high importance. It can also perform a simplified analysis on EEG data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on EEG data of moderate importance. By adjusting the level of detail of the analysis based on importance, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the EEG data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the electroencephalogram (EEG) data during analysis. For example, the analysis unit can apply a stress analysis algorithm to EEG data related to stress. It can also apply a relaxation analysis algorithm to EEG data related to relaxation. Furthermore, it can apply a concentration analysis algorithm to EEG data related to concentration. By applying an analysis algorithm according to the category, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the EEG data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the acquisition timing of electroencephalogram (EEG) data during analysis. For example, the analysis unit may prioritize the analysis of the most recent EEG data. The analysis unit can also perform analysis while referring to past EEG data. Furthermore, the analysis unit can prioritize the analysis of EEG data from a specific time period. This enables efficient analysis by determining the priority of analysis based on the acquisition timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the acquisition timing of the EEG data into a generating AI and have the generating AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the electroencephalogram (EEG) data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant EEG data. It can also postpone the analysis of less relevant EEG data. Furthermore, the analysis unit can appropriately analyze EEG data with a moderate degree of relevance. By adjusting the order of analysis based on relevance, efficient analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the EEG data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The generation unit can adjust the level of detail of the generated text based on the importance of the electroencephalogram (EEG) data during generation. For example, the generation unit can generate detailed text for EEG data of high importance. It can also generate simplified text for EEG data of low importance. Furthermore, it can generate text with an appropriate level of detail for EEG data of moderate importance. This allows for efficient generation by adjusting the level of detail based on importance. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance of the EEG data into the generation AI and have the generation AI adjust the level of detail of the generated text.

[0049] The generation unit can apply different generation algorithms depending on the category of the electroencephalogram (EEG) data during generation. For example, the generation unit can apply a stress generation algorithm to EEG data related to stress. It can also apply a relaxation generation algorithm to EEG data related to relaxation. Furthermore, it can apply a concentration generation algorithm to EEG data related to concentration. By applying a generation algorithm according to the category, highly accurate generation becomes possible. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the category of the EEG data into the generation AI and have the generation AI execute the application of the generation algorithm.

[0050] The generation unit can determine the generation priority based on the acquisition timing of electroencephalogram (EEG) data during the generation process. For example, the generation unit can generate text based on the latest EEG data. It can also generate text while referring to past EEG data. Furthermore, the generation unit can generate text based on EEG data from a specific time period. This enables efficient generation by determining the generation priority based on the acquisition timing. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the acquisition timing of the EEG data to the generation AI and have the generation AI determine the generation priority.

[0051] The generation unit can adjust the generation order based on the relevance of the electroencephalogram (EEG) data during generation. For example, the generation unit can generate text based on highly relevant EEG data. It can also generate text later, prioritizing less relevant EEG data. Furthermore, it can generate text in an appropriate order based on moderately relevant EEG data. This allows for efficient generation by adjusting the generation order based on relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the EEG data into the generation AI and have the generation AI adjust the generation order.

[0052] The display unit can select a suitable display method by referring to the care recipient's past display history when displaying information. For example, the display unit can select the optimal display method based on the display method the care recipient has preferred in the past. The display unit can also select display methods to be avoided based on the display methods the care recipient has avoided in the past. Furthermore, the display unit can analyze the care recipient's past display history and select the most effective display method. This makes it possible to display information in a way that is appropriate for the care recipient by selecting the optimal display method based on past display history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the care recipient's past display history into a generating AI and have the generating AI select the optimal display method.

[0053] The display unit can select the optimal display method when displaying information, taking into account the care recipient's device information. For example, if the care recipient is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the care recipient is using a tablet, the display unit can provide a display method optimized for a larger screen. In addition, if the care recipient is using a smartwatch, the display unit can provide a concise and highly visible display method. This allows the display unit to provide the optimal display method by considering device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the care recipient's device information into a generating AI and have the generating AI select the optimal display method.

[0054] The decision-making unit can select the optimal decision-making method by referring to the care recipient's past decision-making history when making a decision. For example, the decision-making unit can select the optimal decision-making method based on the decision-making method the care recipient has preferred in the past. The decision-making unit can also select decision-making methods to avoid based on the decision-making method the care recipient has avoided in the past. Furthermore, the decision-making unit can analyze the care recipient's past decision-making history and select the most effective decision-making method. This makes it possible to make decisions that are appropriate for the care recipient by selecting the optimal decision-making method based on past decision-making history. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without using AI. For example, the decision-making unit can input the care recipient's past decision-making history into a generating AI and have the generating AI perform the selection of the optimal decision-making method.

[0055] The decision unit can apply different decision algorithms depending on the category of the electroencephalogram (EEG) data during the decision-making process. For example, the decision unit can apply a stress decision algorithm to EEG data related to stress. It can also apply a relaxation decision algorithm to EEG data related to relaxation. Furthermore, it can apply a concentration decision algorithm to EEG data related to concentration. By applying a decision algorithm according to the category, highly accurate decisions can be made. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input the category of the EEG data into a generating AI and have the generating AI execute the application of the decision algorithm.

[0056] The decision-making unit can determine the priority of decisions based on the timing of EEG data acquisition. For example, the decision-making unit can make decisions based on the most recent EEG data. It can also make decisions by referring to past EEG data. Furthermore, the decision-making unit can make decisions based on EEG data from a specific time period. This enables efficient decision-making by determining the priority of decisions based on the acquisition timing. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the timing of EEG data acquisition into a generating AI and have the generating AI determine the priority of decisions.

[0057] The decision-making unit can adjust the order of decisions based on the relevance of the electroencephalogram (EEG) data during the decision-making process. For example, the decision-making unit can make decisions based on highly relevant EEG data. It can also postpone decisions based on less relevant EEG data. Furthermore, it can make decisions in an appropriate order based on moderately relevant EEG data. By adjusting the order of decisions based on relevance, efficient decision-making becomes possible. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the relevance of the EEG data into a generating AI and have the generating AI adjust the order of decisions.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The acquisition unit can analyze the care recipient's past behavioral patterns to determine the optimal timing for acquiring brainwave data. For example, if the care recipient is relaxed at a specific time each day, acquiring brainwave data during that time can yield more accurate results. Furthermore, acquiring brainwave data while the care recipient is performing a specific activity can collect activity-related brainwave data. It is also possible to adjust the frequency of brainwave acquisition based on the care recipient's past behavioral patterns. This allows for the collection of more accurate data by acquiring brainwave data based on the care recipient's behavioral patterns.

[0060] The analysis unit can adjust the analysis algorithm when analyzing electroencephalogram (EEG) data, taking into account the care recipient's past health condition and lifestyle. For example, if the care recipient has experienced stressful situations in the past, applying an analysis algorithm tailored to those situations can yield more accurate results. It is also possible to adjust parameters to improve the accuracy of the analysis based on the care recipient's lifestyle. Furthermore, the analysis algorithm can be dynamically changed in response to changes in the care recipient's health condition. This allows for the provision of more reliable analysis results by performing analysis based on the care recipient's health condition and lifestyle.

[0061] The generation unit can customize the content generated based on the care recipient's brainwave data according to their preferences. For example, if the care recipient prefers text generation in a specific language or style, the system can generate text according to those preferences. Furthermore, if the care recipient is interested in a particular theme or topic, the system can generate content related to that theme. It can also analyze the care recipient's past responses and select the most effective method of expression. This allows for the generation of customized content tailored to the care recipient's preferences, resulting in a more satisfying outcome.

[0062] The display unit can display content generated based on the care recipient's brainwave data, according to the care recipient's visual preferences. For example, if the care recipient prefers a particular color or font, the display method can be tailored to that preference. Furthermore, if the care recipient prefers a specific layout or design, the content can be displayed based on that layout. In addition, the system can analyze the care recipient's visual responses and select the most effective display method. This allows for a more comfortable user experience by providing a display method that aligns with the care recipient's visual preferences.

[0063] The decision-making unit can predict the care recipient's behavior based on their electroencephalogram (EEG) data and make optimal decisions. For example, if a care recipient tends to exhibit a particular behavior, the unit can predict that behavior and take appropriate action. Furthermore, by analyzing the care recipient's past behavioral data and understanding their behavioral patterns, the accuracy of predictions can be improved. In addition, behavioral predictions can be dynamically adjusted according to the care recipient's current situation. This allows for more effective support by making optimal decisions based on the care recipient's behavioral predictions.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The acquisition unit acquires the brainwaves of the person receiving care or medical treatment. The acquisition unit acquires the brainwaves using, for example, a highly real-time EEG. EEG has high temporal resolution and can measure brainwaves in real time. For example, beta waves are measured when the person receiving care is agitated, and alpha waves are measured when they are calm. This makes it possible to understand the emotional state of the person receiving care. Step 2: The analysis unit analyzes the electroencephalogram (EEG) data acquired by the acquisition unit. For example, the analysis unit performs frequency analysis on the EEG data and calculates a spectrum. Deep learning is used to measure concentration and emotions by performing frequency analysis on EEG data and calculating a spectrum. For example, when a person receiving care is concentrating, brainwaves in a specific frequency band appear strongly. Based on this information, the emotional state of the person receiving care is analyzed. Step 3: The generation unit uses the generation AI to verbalize or visualize the data analyzed by the analysis unit. For example, the generation unit can generate text through an LLM (Large-Scale Language Model) based on brainwaves. This allows it to output the text that the person receiving care is thinking. The generation unit can also generate images based on brain signals. For example, if the person receiving care is imagining a cat, the generation AI will draw an image of a cat based on that image. Step 4: The display unit displays the content generated by the generation unit on the monitor. The display unit visualizes the thoughts and feelings of the person receiving care by displaying, for example, the generated text or images on the monitor. Step 5: The decision unit determines which output is appropriate. For example, based on the care recipient's electroencephalogram data and analysis results, the decision unit generates text if verbalization is appropriate, and generates an image if visualization is appropriate. In this way, the communication support system can express the thoughts and feelings of the care recipient or medical patient in the most optimal way, and support communication between caregivers and medical professionals.

[0066] (Example of form 2) The communication support system according to an embodiment of the present invention is a system for resolving thoughts and communication that cannot be expressed in words using electroencephalography (EEG) technology. This communication support system acquires the brainwaves of a person receiving care or medical treatment and analyzes the brainwaves and emotions using deep learning. Next, it uses a generative AI to verbalize or visualize the analysis results and displays the content on a monitor. Since verbal and visual thinking differ depending on the timing, the agent determines and generates which output is optimal. This system is used as a communication support tool for caregivers and medical professionals. For example, it acquires the brainwaves of a person receiving care or medical treatment. In this case, it utilizes an EEG, a highly real-time and versatile electroencephalogram (EEG) measurement device. EEG has high temporal resolution and can measure brainwaves in real time. For example, when a person receiving care is excited, beta waves are measured, and when they are calm, alpha waves are measured. This makes it possible to understand the emotional state of the person receiving care. Next, the acquired brainwave data is analyzed using deep learning. Deep learning is used to measure concentration and emotions by performing frequency analysis from the brainwave data and calculating a spectrum. For example, when a person receiving care is concentrating, brainwaves in a specific frequency band appear strongly. Based on this information, the emotional state of the person receiving care is analyzed. The analysis results are then verbalized or visualized using a generative AI. For example, text can be generated from brainwave data through an LLM (Large-Scale Language Model). This allows the system to output the text the person receiving care is thinking. It is also possible to generate images from brain signals. For example, if the person receiving care is imagining a cat, the generative AI will draw an image of a cat based on that image. Since linguistic and visual thinking differ depending on the timing, the agent determines which output is most appropriate and generates accordingly. Based on the person receiving care's brainwave data and analysis results, the agent generates text when verbalization is appropriate and an image when visualization is appropriate. In this way, the thoughts and emotions of the person receiving care can be expressed in the most optimal form. This system is used as a communication support tool for caregivers and medical professionals.For example, in the care of individuals with aphasia, verbalizing or visualizing the thoughts and feelings of those being cared for makes it easier for caregivers to understand their needs. It is also helpful in understanding the needs of individuals with dementia. Furthermore, it allows for understanding what patients with limb or hearing impairments require. In this way, communication in care and medical settings can be facilitated, improving the quality of life for those receiving care and medical treatment. Thus, communication support systems can express the thoughts and feelings of those receiving care and medical treatment in the most optimal way, supporting communication between caregivers and medical professionals.

[0067] The communication support system according to the embodiment comprises an acquisition unit, an analysis unit, a generation unit, a display unit, and a judgment unit. The acquisition unit acquires the brainwaves of the person receiving care or medical treatment. The acquisition unit acquires brainwaves using, for example, a highly real-time EEG. EEG has high temporal resolution and can measure brainwaves in real time. For example, beta waves are measured when the person receiving care is excited, and alpha waves are measured when they are calm. This makes it possible to understand the emotional state of the person receiving care. The analysis unit analyzes the brainwave data acquired by the acquisition unit. For example, the analysis unit performs frequency analysis from the brainwave data and calculates a spectrum. Deep learning is used to measure concentration and emotion by performing frequency analysis from brainwave data and calculating a spectrum. For example, when the person receiving care is concentrating, brainwaves in a specific frequency band appear strongly. Based on this information, the emotional state of the person receiving care is analyzed. The generation unit uses a generating AI to verbalize or visualize the data analyzed by the analysis unit. The generation unit can, for example, generate text using an LLM (Large-Scale Language Model) based on brainwaves. This allows the system to output text that the person receiving care is thinking. The generation unit can also generate images based on brain signals. For example, if the person receiving care is imagining a cat, the generation AI will draw an image of a cat based on that image. The display unit displays the content generated by the generation unit on a monitor. The display unit visualizes the thoughts and feelings of the person receiving care by displaying the generated text and images on the monitor. The judgment unit determines which output is more appropriate. For example, based on the person receiving care's brainwave data and analysis results, the judgment unit generates text if verbalization is appropriate, and generates an image if visualization is appropriate. In this way, the communication support system can express the thoughts and feelings of the person receiving care or medical treatment in the most optimal way, supporting communication between caregivers and medical professionals.

[0068] The acquisition unit acquires the brainwaves (EEG) of the person being cared for or receiving medical treatment. The acquisition unit acquires the EEG using, for example, a highly real-time EEG. EEG has high temporal resolution and can measure brainwaves in real time. Specifically, the EEG sensor is attached to the scalp and detects weak electrical signals. This allows for a detailed understanding of the brain activity state of the person being cared for. The EEG sensor uses multiple electrodes to acquire signals from different parts of the brain and analyzes the activity of each part. For example, activity in the frontal lobe is related to attention, planning, and emotional control, while activity in the occipital lobe is related to the processing of visual information. This allows for a detailed understanding of which parts of the care recipient's brain are active and how. When the care recipient is excited, beta waves are measured, and when they are calm, alpha waves are measured. Beta waves are in the frequency band of 14Hz to 30Hz and indicate mental activity and tension. On the other hand, alpha waves are in the frequency band of 8Hz to 13Hz and appear when relaxed or at rest. This allows for an understanding of the emotional state of the person being cared for. Furthermore, the EEG sensor incorporates filtering technology to reduce noise, minimizing the influence of the external environment. This allows the acquisition unit to collect accurate and reliable electroencephalogram data.

[0069] The analysis unit analyzes the electroencephalogram (EEG) data acquired by the acquisition unit. For example, the analysis unit performs frequency analysis on the EEG data and calculates a spectrum. A spectrum is a graph that contains both time and frequency information, and can visually represent fluctuations in brain waves. Deep learning is used to measure concentration and emotion by performing frequency analysis on EEG data and calculating a spectrum. Deep learning models have the ability to learn from large amounts of EEG data and identify specific patterns. For example, when a person receiving care is concentrating, brain waves in a specific frequency band appear strongly. Based on this information, the emotional state of the person receiving care is analyzed. The analysis unit extracts features from the EEG data and uses this to evaluate the emotions and concentration state of the person receiving care. For example, it analyzes the intensity and fluctuation patterns of a specific frequency band to determine whether the person receiving care is relaxed or tense. In addition, the analysis unit can track changes in the person receiving care by comparing them with past data. This allows the analysis unit to grasp the emotional state of the person receiving care in real time and provide information for appropriate responses. Furthermore, the analysis unit can detect unusual brainwave patterns using an anomaly detection algorithm, enabling early detection of abnormalities. This allows the analysis unit to continuously monitor the health status of the person receiving care and support prompt responses.

[0070] The generation unit uses a generating AI to verbalize or visualize data analyzed by the analysis unit. For example, the generation unit can generate text through an LLM (Large-Scale Language Model) based on brainwaves. The LLM has learned from a vast amount of text data and has the ability to generate natural language based on the input data. This allows it to output text that the person being cared for is thinking. For example, if the person being cared for is thinking "I want a drink of water," the LLM analyzes the brainwave pattern and generates the text "I want a drink of water." The generation unit can also generate images based on brain signals. For example, if the person being cared for is imagining a cat, the generating AI will draw an image of a cat based on that image. The generating AI can use an image generation model to generate high-quality images based on the input brainwave data. This allows it to visually represent the thoughts and feelings of the person being cared for. Furthermore, the generation unit has a format conversion function to output the generated text and images in an appropriate format, providing information in a way that is easily understood by the user. In this way, the generation unit can accurately and effectively express the thoughts and feelings of the person being cared for and support communication.

[0071] The display unit displays the content generated by the generation unit on a monitor. For example, the display unit visualizes the thoughts and feelings of the person receiving care by displaying generated text and images on the monitor. The monitor is equipped with a high-resolution display, allowing for clear display of the generated content. This enables caregivers and medical professionals to intuitively understand the care recipient's condition. The display unit also allows manipulation of the generated content through a user interface, enabling users to enlarge, reduce, or scroll the displayed content as needed. Furthermore, the display unit has an audio output function, allowing it to read the generated text aloud. This allows care recipients with visual impairments or those who have difficulty understanding visual information to confirm the generated content aloud. The display unit has multiple display modes, allowing users to select the optimal display method according to their needs. For example, there are text mode, image mode, and audio mode, allowing users to select the appropriate mode depending on the situation. This enables the display unit to visualize the thoughts and feelings of the person receiving care in various ways, deepening the understanding of caregivers and medical professionals.

[0072] The decision unit determines which output is more appropriate. For example, based on the care recipient's electroencephalogram (EEG) data and analysis results, the decision unit generates text if verbalization is appropriate, and generates an image if image generation is appropriate. The decision unit uses AI to analyze the characteristics of the EEG data and select the optimal output format. For example, if the care recipient is thinking of specific instructions or requests, it determines that text output is appropriate, and if the care recipient is trying to express images or emotions, it determines that image output is appropriate. The decision unit can continuously improve the accuracy of output format selection based on past data and user feedback. This allows the decision unit to express the care recipient's thoughts and emotions in the most optimal way, supporting communication between caregivers and medical professionals. Furthermore, the decision unit can combine multiple output formats and generate text and images simultaneously as needed. This allows for a richer expression of the care recipient's thoughts and emotions, deepening the understanding of caregivers and medical professionals. The decision unit can select the output format in real time and respond quickly and appropriately. This allows the decision unit to effectively support the care recipient's communication and improve the quality of care and medical treatment.

[0073] The acquisition unit can acquire brain waves using a real-time EEG. The acquisition unit acquires brain waves using a real-time EEG, for example. EEGs have high temporal resolution and can measure brain waves in real time. For example, beta waves are measured when the person being cared for is agitated, and alpha waves are measured when they are calm. This allows for immediate understanding of the emotional state of the person being cared for by acquiring brain waves in real time. Real-time EEGs include, for example, those with low data acquisition latency and the EEG device used. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input a real-time EEG device into a generating AI and have the generating AI perform the acquisition of brain wave data.

[0074] The analysis unit can perform frequency analysis from electroencephalogram (EEG) data and calculate a spectrum. The analysis unit can, for example, perform frequency analysis from EEG data and calculate a spectrum. Frequency analysis is performed using methods such as FFT (Fast Fourier Transform) or wavelet transform. The spectrum is generated considering, for example, temporal resolution and frequency resolution. This allows for a detailed analysis of the emotional state of the person receiving care by performing frequency analysis on EEG data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input EEG data into a generating AI and have the generating AI perform frequency analysis and spectrum calculation.

[0075] The generation unit can generate text through an LLM (Large-Scale Language Model). For example, the generation unit generates text through an LLM based on electroencephalogram (EEG). This allows the thoughts of the person being cared for to be output as text using an LLM. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input EEG data into a generation AI and have the generation AI perform text generation.

[0076] The generation unit can generate images based on brain signals. For example, the generation unit generates images based on brain signals. Brain signals include, for example, EEG signals and MEG signals. By generating images based on brain signals, the thoughts of the person being cared for can be visually represented. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input brain signal data into a generation AI and have the generation AI perform image generation.

[0077] The decision unit can generate text when verbalization is appropriate and images when image representation is appropriate. For example, based on the care recipient's electroencephalogram (EEG) data and analysis results, the decision unit generates text when verbalization is appropriate and images when image representation is appropriate. This allows for the optimal representation of the care recipient's thoughts and emotions. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input EEG data and analysis results into a generation AI and have the generation AI determine the optimal output format.

[0078] The data acquisition unit can estimate the emotions of the person being cared for and adjust the timing of brainwave acquisition based on the estimated emotions. For example, the data acquisition unit can obtain low-noise data by acquiring brainwaves when the person being cared for is relaxed. The data acquisition unit can also acquire brainwaves when the person being cared for is excited and track changes in emotions in real time. Furthermore, the data acquisition unit can acquire brainwaves when the person being cared for is asleep and analyze brainwave patterns during sleep. This allows for the acquisition of more accurate data by adjusting the timing of brainwave acquisition according to the emotions of the person being cared for. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the emotional data of the person being cared for into the generative AI and have the generative AI adjust the timing of brainwave acquisition.

[0079] The data acquisition unit can analyze the care recipient's past brainwave data and select the optimal acquisition method. For example, the acquisition unit can select the most stable acquisition method based on the care recipient's past brainwave data. The acquisition unit can also analyze the care recipient's past brainwave data and select an acquisition method for a specific time period. Furthermore, the acquisition unit can select an acquisition method for specific environmental conditions based on the care recipient's past brainwave data. This allows for the acquisition of stable data by selecting the optimal acquisition method based on past brainwave data. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the care recipient's past brainwave data into a generating AI and have the generating AI select the optimal acquisition method.

[0080] The acquisition unit can filter brainwave data based on the care recipient's current health status and activity level when acquiring brainwave data. For example, if the care recipient is exercising, the acquisition unit can filter out noise caused by exercise. Furthermore, if the care recipient is ill, the acquisition unit can filter the brainwave data considering the effects of the illness. Additionally, if the care recipient is taking medication, the acquisition unit can filter the brainwave data considering the effects of the medication. This allows for the acquisition of less noisy data by filtering based on health status and activity level. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the care recipient's health status and activity level data into a generating AI and have the generating AI perform the filtering.

[0081] The data acquisition unit can estimate the emotions of the person being cared for and determine the priority of brainwave data to acquire based on the estimated emotions. For example, if the person being cared for is stressed, the data acquisition unit will prioritize acquiring brainwave data related to stress. It can also prioritize acquiring brainwave data related to relaxation if the person is relaxed. Furthermore, if the person being cared for is concentrating, the data acquisition unit can prioritize acquiring brainwave data related to concentration. This allows for the priority acquisition of important data by prioritizing brainwave data based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input the person's emotional data into a generative AI and have the generative AI determine the priority of the brainwave data.

[0082] The data acquisition unit can prioritize the acquisition of highly relevant data by considering the geographical location information of the person receiving care when acquiring electroencephalogram (EEG) data. For example, if the person receiving care is at home, the data acquisition unit will prioritize acquiring EEG data from home. It can also prioritize acquiring EEG data from a hospital if the person receiving care is in a hospital. Furthermore, if the person receiving care is out, the data acquisition unit can prioritize acquiring EEG data from their location. This allows for the priority acquisition of highly relevant data by considering geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the geographical location information of the person receiving care into a generating AI and have the generating AI prioritize the acquisition of highly relevant data.

[0083] The acquisition unit can analyze the care recipient's social media activity and acquire relevant data when acquiring brainwave data. For example, if the care recipient is experiencing stress on social media, the acquisition unit can acquire brainwave data related to stress. It can also acquire brainwave data related to relaxation if the care recipient is relaxing on social media. Furthermore, if the care recipient is concentrating on social media, the acquisition unit can acquire brainwave data related to concentration. Thus, relevant brainwave data can be acquired by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the care recipient's social media activity data into a generating AI and have the generating AI acquire the relevant brainwave data.

[0084] The analysis unit can estimate the emotions of the person being cared for and adjust the presentation of the analysis based on the estimated emotions. For example, if the person being cared for is relaxed, the analysis unit can display the analysis results in a calm expression. If the person being cared for is agitated, the analysis unit can also display the analysis results in an emphasized expression. Furthermore, if the person being cared for is stressed, the analysis unit can display the analysis results in a simple expression. In this way, by adjusting the presentation of the analysis based on emotions, it is possible to provide analysis results that are appropriate for the person being cared for. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the person being cared for's emotional data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the electroencephalogram (EEG) data during the analysis. For example, the analysis unit can perform a detailed analysis on EEG data with high importance. It can also perform a simplified analysis on EEG data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on EEG data of moderate importance. By adjusting the level of detail of the analysis based on importance, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the EEG data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the category of the electroencephalogram (EEG) data during analysis. For example, the analysis unit can apply a stress analysis algorithm to EEG data related to stress. It can also apply a relaxation analysis algorithm to EEG data related to relaxation. Furthermore, it can apply a concentration analysis algorithm to EEG data related to concentration. By applying an analysis algorithm according to the category, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the EEG data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0087] The analysis unit can estimate the emotions of the person being cared for and adjust the length of the analysis based on the estimated emotions. For example, if the person being cared for is relaxed, the analysis unit can perform a longer analysis. It can also perform a shorter analysis if the person being cared for is in a hurry. Furthermore, if the person being cared for is agitated, the analysis unit can perform an analysis of an appropriate length. This allows for an analysis tailored to the person being cared for by adjusting the length of the analysis based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the person's emotional data into the generative AI and have the generative AI adjust the length of the analysis.

[0088] The analysis unit can determine the priority of analysis based on the acquisition timing of electroencephalogram (EEG) data during analysis. For example, the analysis unit may prioritize the analysis of the most recent EEG data. The analysis unit can also perform analysis while referring to past EEG data. Furthermore, the analysis unit can prioritize the analysis of EEG data from a specific time period. This enables efficient analysis by determining the priority of analysis based on the acquisition timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the acquisition timing of the EEG data into a generating AI and have the generating AI determine the priority of analysis.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the electroencephalogram (EEG) data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant EEG data. It can also postpone the analysis of less relevant EEG data. Furthermore, the analysis unit can appropriately analyze EEG data with a moderate degree of relevance. By adjusting the order of analysis based on relevance, efficient analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the EEG data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0090] The generation unit can estimate the emotions of the person being cared for and adjust the expression of the generated content based on the estimated emotions. For example, if the person being cared for is relaxed, the generation unit can generate text using calm language. If the person being cared for is excited, the generation unit can also generate text using emphatic language. Furthermore, if the person being cared for is stressed, the generation unit can generate text using simple language. This allows for expression appropriate to the person being cared for by adjusting the expression of the generated content based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the person being cared for's emotional data into a generation AI and have the generation AI adjust the expression of the generated content.

[0091] The generation unit can adjust the level of detail of the generated text based on the importance of the electroencephalogram (EEG) data during generation. For example, the generation unit can generate detailed text for EEG data of high importance. It can also generate simplified text for EEG data of low importance. Furthermore, it can generate text with an appropriate level of detail for EEG data of moderate importance. This allows for efficient generation by adjusting the level of detail based on importance. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance of the EEG data into the generation AI and have the generation AI adjust the level of detail of the generated text.

[0092] The generation unit can apply different generation algorithms depending on the category of the electroencephalogram (EEG) data during generation. For example, the generation unit can apply a stress generation algorithm to EEG data related to stress. It can also apply a relaxation generation algorithm to EEG data related to relaxation. Furthermore, it can apply a concentration generation algorithm to EEG data related to concentration. By applying a generation algorithm according to the category, highly accurate generation becomes possible. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the category of the EEG data into the generation AI and have the generation AI execute the application of the generation algorithm.

[0093] The generation unit can estimate the emotions of the person being cared for and adjust the length of the generated content based on the estimated emotions. For example, if the person being cared for is relaxed, the generation unit will generate longer text. It can also generate shorter text if the person is in a hurry. Furthermore, if the person is agitated, the generation unit can generate text of an appropriate length. This allows for generation tailored to the person being cared for by adjusting the length of the generated content based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the person's emotional data into a generation AI and have the generation AI adjust the length of the generated content.

[0094] The generation unit can determine the generation priority based on the acquisition timing of electroencephalogram (EEG) data during the generation process. For example, the generation unit can generate text based on the latest EEG data. It can also generate text while referring to past EEG data. Furthermore, the generation unit can generate text based on EEG data from a specific time period. This enables efficient generation by determining the generation priority based on the acquisition timing. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the acquisition timing of the EEG data to the generation AI and have the generation AI determine the generation priority.

[0095] The generation unit can adjust the generation order based on the relevance of the electroencephalogram (EEG) data during generation. For example, the generation unit can generate text based on highly relevant EEG data. It can also generate text later, prioritizing less relevant EEG data. Furthermore, it can generate text in an appropriate order based on moderately relevant EEG data. This allows for efficient generation by adjusting the generation order based on relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the EEG data into the generation AI and have the generation AI adjust the generation order.

[0096] The display unit can estimate the emotions of the person being cared for and adjust the display method based on the estimated emotions. For example, if the person being cared for is relaxed, the display unit will display in calm colors. If the person being cared for is excited, the display unit can also display in emphasized colors. Furthermore, if the person being cared for is stressed, the display unit can also display in simple colors. In this way, by adjusting the display method based on emotions, it is possible to provide a display that is appropriate for the person being cared for. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the person being cared for's emotion data into the generative AI and have the generative AI adjust the display method.

[0097] The display unit can select a suitable display method by referring to the care recipient's past display history when displaying information. For example, the display unit can select the optimal display method based on the display method the care recipient has preferred in the past. The display unit can also select display methods to be avoided based on the display methods the care recipient has avoided in the past. Furthermore, the display unit can analyze the care recipient's past display history and select the most effective display method. This makes it possible to display information in a way that is appropriate for the care recipient by selecting the optimal display method based on past display history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the care recipient's past display history into a generating AI and have the generating AI select the optimal display method.

[0098] The display unit can estimate the emotions of the person being cared for and determine the display priority based on the estimated emotions. For example, if the person being cared for is feeling stressed, the display unit will prioritize displaying information related to stress. It can also prioritize displaying information related to relaxation if the person being cared for is relaxed. Furthermore, if the person being cared for is concentrating, the display unit can prioritize displaying information related to concentration. This allows important information to be displayed preferentially by determining the display priority based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, or not. For example, the display unit can input the person's emotional data into a generative AI and have the generative AI determine the display priority.

[0099] The display unit can select the optimal display method when displaying information, taking into account the care recipient's device information. For example, if the care recipient is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the care recipient is using a tablet, the display unit can provide a display method optimized for a larger screen. In addition, if the care recipient is using a smartwatch, the display unit can provide a concise and highly visible display method. This allows the display unit to provide the optimal display method by considering device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the care recipient's device information into a generating AI and have the generating AI select the optimal display method.

[0100] The decision-making unit can estimate the emotions of the person being cared for and adjust its decision-making method based on the estimated emotions. For example, if the person being cared for is relaxed, the decision-making unit can provide a calm decision-making method. It can also provide a rapid decision-making method if the person being cared for is agitated. Furthermore, it can provide a simple decision-making method if the person being cared for is stressed. This allows for decisions to be made that are appropriate for the person being cared for by adjusting the decision-making method based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or not using AI. For example, the decision-making unit can input the person being cared for's emotional data into the generative AI and have the generative AI adjust the decision-making method.

[0101] The decision-making unit can select the optimal decision-making method by referring to the care recipient's past decision-making history when making a decision. For example, the decision-making unit can select the optimal decision-making method based on the decision-making method the care recipient has preferred in the past. The decision-making unit can also select decision-making methods to avoid based on the decision-making method the care recipient has avoided in the past. Furthermore, the decision-making unit can analyze the care recipient's past decision-making history and select the most effective decision-making method. This makes it possible to make decisions that are appropriate for the care recipient by selecting the optimal decision-making method based on past decision-making history. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without using AI. For example, the decision-making unit can input the care recipient's past decision-making history into a generating AI and have the generating AI perform the selection of the optimal decision-making method.

[0102] The decision unit can apply different decision algorithms depending on the category of the electroencephalogram (EEG) data during the decision-making process. For example, the decision unit can apply a stress decision algorithm to EEG data related to stress. It can also apply a relaxation decision algorithm to EEG data related to relaxation. Furthermore, it can apply a concentration decision algorithm to EEG data related to concentration. By applying a decision algorithm according to the category, highly accurate decisions can be made. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input the category of the EEG data into a generating AI and have the generating AI execute the application of the decision algorithm.

[0103] The decision-making unit can estimate the emotions of the person being cared for and determine the priority of decisions based on the estimated emotions. For example, if the person being cared for is stressed, the decision-making unit will prioritize decisions related to stress. It can also prioritize decisions related to relaxation if the person being cared for is relaxed. Furthermore, if the person being cared for is focused, the decision-making unit will prioritize decisions related to concentration. This allows important decisions to be prioritized by determining the priority of decisions based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision-making unit may be performed using AI, or not. For example, the decision-making unit can input the person being cared for's emotional data into a generative AI and have the generative AI determine the priority of decisions.

[0104] The decision-making unit can determine the priority of decisions based on the timing of EEG data acquisition. For example, the decision-making unit can make decisions based on the most recent EEG data. It can also make decisions by referring to past EEG data. Furthermore, the decision-making unit can make decisions based on EEG data from a specific time period. This enables efficient decision-making by determining the priority of decisions based on the acquisition timing. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the timing of EEG data acquisition into a generating AI and have the generating AI determine the priority of decisions.

[0105] The decision-making unit can adjust the order of decisions based on the relevance of the electroencephalogram (EEG) data during the decision-making process. For example, the decision-making unit can make decisions based on highly relevant EEG data. It can also postpone decisions based on less relevant EEG data. Furthermore, it can make decisions in an appropriate order based on moderately relevant EEG data. By adjusting the order of decisions based on relevance, efficient decision-making becomes possible. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the relevance of the EEG data into a generating AI and have the generating AI adjust the order of decisions.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The acquisition unit can analyze the care recipient's past behavioral patterns to determine the optimal timing for acquiring brainwave data. For example, if the care recipient is relaxed at a specific time each day, acquiring brainwave data during that time can yield more accurate results. Furthermore, acquiring brainwave data while the care recipient is performing a specific activity can collect activity-related brainwave data. It is also possible to adjust the frequency of brainwave acquisition based on the care recipient's past behavioral patterns. This allows for the collection of more accurate data by acquiring brainwave data based on the care recipient's behavioral patterns.

[0108] The analysis unit can adjust the analysis algorithm when analyzing electroencephalogram (EEG) data, taking into account the care recipient's past health condition and lifestyle. For example, if the care recipient has experienced stressful situations in the past, applying an analysis algorithm tailored to those situations can yield more accurate results. It is also possible to adjust parameters to improve the accuracy of the analysis based on the care recipient's lifestyle. Furthermore, the analysis algorithm can be dynamically changed in response to changes in the care recipient's health condition. This allows for the provision of more reliable analysis results by performing analysis based on the care recipient's health condition and lifestyle.

[0109] The generation unit can customize the content generated based on the care recipient's brainwave data according to their preferences. For example, if the care recipient prefers text generation in a specific language or style, the system can generate text according to those preferences. Furthermore, if the care recipient is interested in a particular theme or topic, the system can generate content related to that theme. It can also analyze the care recipient's past responses and select the most effective method of expression. This allows for the generation of customized content tailored to the care recipient's preferences, resulting in a more satisfying outcome.

[0110] The display unit can display content generated based on the care recipient's brainwave data, according to the care recipient's visual preferences. For example, if the care recipient prefers a particular color or font, the display method can be tailored to that preference. Furthermore, if the care recipient prefers a specific layout or design, the content can be displayed based on that layout. In addition, the system can analyze the care recipient's visual responses and select the most effective display method. This allows for a more comfortable user experience by providing a display method that aligns with the care recipient's visual preferences.

[0111] The decision-making unit can predict the care recipient's behavior based on their electroencephalogram (EEG) data and make optimal decisions. For example, if a care recipient tends to exhibit a particular behavior, the unit can predict that behavior and take appropriate action. Furthermore, by analyzing the care recipient's past behavioral data and understanding their behavioral patterns, the accuracy of predictions can be improved. In addition, behavioral predictions can be dynamically adjusted according to the care recipient's current situation. This allows for more effective support by making optimal decisions based on the care recipient's behavioral predictions.

[0112] The acquisition unit can estimate the emotions of the person being cared for and adjust the timing of brainwave acquisition based on the estimated emotions. For example, by acquiring brainwaves when the person being cared for is relaxed, less noisy data can be obtained. It is also possible to acquire brainwaves when the person being cared for is excited and track changes in emotions in real time. Furthermore, it is possible to acquire brainwaves when the person being cared for is asleep and analyze brainwave patterns during sleep. By adjusting the timing of brainwave acquisition according to the emotions of the person being cared for, more accurate data can be obtained.

[0113] The analysis unit can estimate the emotions of the person being cared for and adjust the way the analysis is presented based on those estimated emotions. For example, if the person being cared for is relaxed, the analysis results will be displayed in a calm manner. If the person being cared for is agitated, the analysis results can be displayed in an emphasized manner. Furthermore, if the person being cared for is stressed, the analysis results can be displayed in a simple manner. In this way, by adjusting the way the analysis is presented based on emotions, it is possible to provide analysis results that are appropriate for the person being cared for.

[0114] The generation unit can estimate the emotions of the person being cared for and adjust the expression of the generated content based on those estimated emotions. For example, if the person being cared for is relaxed, the text will be generated using calm language. If the person being cared for is excited, the text can be generated using emphatic language. Furthermore, if the person being cared for is stressed, the text can be generated using simple language. In this way, by adjusting the expression of the generated content based on emotions, it becomes possible to create language that is appropriate for the person being cared for.

[0115] The display unit can estimate the emotions of the person being cared for and adjust the display method based on the estimated emotions. For example, if the person being cared for is relaxed, it will display in calm colors. If the person being cared for is excited, it can display in more emphasized colors. Furthermore, if the person being cared for is stressed, it can display in simple colors. In this way, by adjusting the display method based on emotions, it is possible to provide a display that is appropriate for the person being cared for.

[0116] The decision-making unit can estimate the emotions of the person receiving care and adjust its decision-making method based on those emotions. For example, if the person receiving care is relaxed, it can provide a calm decision-making method. If the person receiving care is agitated, it can provide a rapid decision-making method. Furthermore, if the person receiving care is stressed, it can provide a simple decision-making method. By adjusting the decision-making method based on emotions, it becomes possible to make decisions that are appropriate for the person receiving care.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The acquisition unit acquires the brainwaves of the person receiving care or medical treatment. The acquisition unit acquires the brainwaves using, for example, a highly real-time EEG. EEG has high temporal resolution and can measure brainwaves in real time. For example, beta waves are measured when the person receiving care is agitated, and alpha waves are measured when they are calm. This makes it possible to understand the emotional state of the person receiving care. Step 2: The analysis unit analyzes the electroencephalogram (EEG) data acquired by the acquisition unit. For example, the analysis unit performs frequency analysis on the EEG data and calculates a spectrum. Deep learning is used to measure concentration and emotions by performing frequency analysis on EEG data and calculating a spectrum. For example, when a person receiving care is concentrating, brainwaves in a specific frequency band appear strongly. Based on this information, the emotional state of the person receiving care is analyzed. Step 3: The generation unit uses the generation AI to verbalize or visualize the data analyzed by the analysis unit. For example, the generation unit can generate text through an LLM (Large-Scale Language Model) based on brainwaves. This allows it to output the text that the person receiving care is thinking. The generation unit can also generate images based on brain signals. For example, if the person receiving care is imagining a cat, the generation AI will draw an image of a cat based on that image. Step 4: The display unit displays the content generated by the generation unit on the monitor. The display unit visualizes the thoughts and feelings of the person receiving care by displaying, for example, the generated text or images on the monitor. Step 5: The decision unit determines which output is appropriate. For example, based on the care recipient's electroencephalogram data and analysis results, the decision unit generates text if verbalization is appropriate, and generates an image if visualization is appropriate. In this way, the communication support system can express the thoughts and feelings of the care recipient or medical patient in the most optimal way, and support communication between caregivers and medical professionals.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0121] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, display unit, and decision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires brain waves using the EEG sensor of the smart device 14 and processes them with the processor 46. The analysis unit analyzes the brain wave data using the specific processing unit 290 of the data processing unit 12 and calculates a spectrum. The generation unit generates text or images based on the analysis results using the specific processing unit 290 of the data processing unit 12. The display unit displays the generated content using the display 40A of the smart device 14. The decision unit determines the optimal output format using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, display unit, and decision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires brain waves using the EEG sensor of the smart glasses 214 and processes them with the processor 46. The analysis unit analyzes the brain wave data with the specific processing unit 290 of the data processing unit 12 and calculates a spectrum. The generation unit generates text or images based on the analysis results with the specific processing unit 290 of the data processing unit 12. The display unit displays the generated content with the display of the smart glasses 214. The decision unit determines the optimal output format with the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, display unit, and decision unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires brain waves using the EEG sensor of the headset terminal 314 and processes them with the processor 46. The analysis unit analyzes the brain wave data using, for example, the specific processing unit 290 of the data processing unit 12 and calculates a spectrum. The generation unit generates text or images based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The display unit displays the generated content using, for example, the display 343 of the headset terminal 314. The decision unit determines the optimal output format using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0163] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, display unit, and decision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires brain waves using the EEG sensor of the robot 414 and processes them with the processor 46. The analysis unit analyzes the brain wave data using, for example, the specific processing unit 290 of the data processing unit 12 and calculates a spectrum. The generation unit generates text or images based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The display unit displays the generated content using, for example, the display of the robot 414. The decision unit determines the optimal output format using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0172] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0180] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0181] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0182] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0183] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0184] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0185] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] (Note 1) An acquisition unit that acquires the electroencephalogram (EEG) of the person receiving care or medical treatment, An analysis unit analyzes the electroencephalogram data acquired by the acquisition unit, Based on the data analyzed by the aforementioned analysis unit, the generation unit generates a language or visualization using a generation AI, A display unit that displays the content generated by the generation unit on a monitor, It includes a determination unit that determines which output is more suitable. A system characterized by the following features. (Note 2) The acquisition unit is, We acquire brainwaves using real-time EEG. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Frequency analysis is performed on electroencephalogram (EEG) data to calculate a spectrum. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate LLMA text The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Images are generated based on brain signals. The system described in Appendix 1, characterized by the features described herein. (Note 6) The unit that makes the determination said, If verbal representation is appropriate, generate text; if image representation is appropriate, generate an image. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the emotions of the person receiving care and adjusts the timing of brainwave acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Analyze the care recipient's past electroencephalogram (EEG) data and select the most appropriate acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring electroencephalograms (EEGs), filtering is performed based on the care recipient's current health status and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, The system estimates the emotions of the person receiving care and prioritizes the acquisition of electroencephalogram (EEG) data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring electroencephalogram (EEG) data, the system prioritizes the acquisition of highly relevant data, taking into account the geographical location of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, During the acquisition of electroencephalogram (EEG) data, the social media activity of the care recipient is analyzed, and relevant data is obtained. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the emotions of the person receiving care and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the emotions of the person receiving care and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when the electroencephalogram (EEG) data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system estimates the emotions of the person receiving care and adjusts the way content is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the level of detail is adjusted based on the importance of the EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different generation algorithms are applied depending on the category of EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the emotions of the person receiving care and adjusts the length of the content generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the generation priority is determined based on the timing of EEG data acquisition. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the generation order is adjusted based on the relevance of the EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is The system estimates the emotions of the person receiving care and adjusts the display method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is When displaying information, the system selects the most appropriate display method by referring to the care recipient's past display history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is The system estimates the emotions of the person receiving care and determines the priority of the displayed information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is When displaying information, the optimal display method is selected considering the care recipient's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The unit that makes the determination said, The system estimates the emotions of the person receiving care and adjusts the decision-making process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The unit that makes the determination said, When making a decision, the most appropriate decision-making method is selected by referring to the care recipient's past decision-making history. The system described in Appendix 1, characterized by the features described herein. (Note 31) The unit that makes the determination said, When making a decision, different decision algorithms are applied depending on the category of the EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The unit that makes the determination said, The system estimates the emotions of the person receiving care and determines the priority of decisions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The unit that makes the determination said, When making a decision, the priority of the decision is determined based on when the electroencephalogram (EEG) data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 34) The unit that makes the determination said, When making a decision, the order of decisions is adjusted based on the correlation of the electroencephalogram (EEG) data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires the electroencephalogram (EEG) of the person receiving care or medical treatment, An analysis unit analyzes the electroencephalogram data acquired by the acquisition unit, Based on the data analyzed by the aforementioned analysis unit, the generation unit generates the data using AI to verbalize or visualize it. A display unit that displays the content generated by the generation unit on a monitor, It includes a determination unit that determines which output is more suitable. A system characterized by the following features.

2. The acquisition unit is, We acquire brainwaves using real-time EEG. The system according to feature 1.

3. The aforementioned analysis unit, Frequency analysis is performed on electroencephalogram (EEG) data to calculate a spectrum. The system according to feature 1.

4. The generating unit is Generate LLMA text The system according to feature 1.

5. The generating unit is Images are generated based on brain signals. The system according to feature 1.

6. The unit that makes the determination said, If verbal representation is appropriate, generate text; if image representation is appropriate, generate an image. The system according to feature 1.

7. The acquisition unit is, The system estimates the emotions of the person receiving care and adjusts the timing of brainwave acquisition based on the estimated emotions. The system according to feature 1.

8. The acquisition unit is, Analyze the care recipient's past electroencephalogram (EEG) data and select the most appropriate acquisition method. The system according to feature 1.

Images