System

Abstract

An object of a system according to an embodiment is to accurately grasp thoughts and feelings of a person to be cared for and a person to be medically treated and to appropriately perform communication.SOLUTION: A system includes an acquisition unit, an analysis unit, a generation unit, and a display unit. The acquisition unit acquires brain waves of a person to be cared for or a person to be treated. The analysis unit analyzes the brainwave data acquired by the acquisition unit and analyzes an emotion. The generation unit generates a linguistic expression based on the emotion data analyzed by the analysis unit. The display unit displays the linguistic expression generated by the generation unit on a monitor.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to accurately grasp the thoughts and feelings of care recipients or medical patients and to communicate with them appropriately.

[0005] The system according to the embodiment aims to accurately grasp the thoughts and feelings of care recipients and medical recipients and to communicate with them appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a generation unit, and a display unit. The acquisition unit acquires electroencephalograms of a care recipient or medical recipient. The analysis unit analyzes the electroencephalogram data acquired by the acquisition unit to analyze emotions. The generation unit generates a linguistic expression based on the emotion data analyzed by the analysis unit. The display unit displays the linguistic expression generated by the generation unit on a monitor. [Effects of the Invention]

[0007] The system according to the embodiment can accurately grasp the thoughts and feelings of care recipients and medical recipients, and can communicate with them appropriately. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) A generative AI tool according to an embodiment of the present invention is a system that acquires the brain waves of a care recipient or medical care recipient, analyzes their emotions using deep learning, and displays the content verbalized by the generative AI on a monitor. The generative AI tool supports communication between caregivers and medical professionals by acquiring the brain waves of the care recipient or medical care recipient, analyzing their emotions using deep learning, and displaying the content verbalized by the generative AI on a monitor. For example, the generative AI tool acquires the brain waves of the care recipient or medical care recipient. For example, the generative AI tool can acquire brain waves in real time using a dedicated EEG sensor. The generative AI tool then analyzes the acquired brain wave data using deep learning. Deep learning uses a recurrent neural network (RNN) or long short-term memory (LSTM) to learn a model for analyzing emotions from brain wave data. For example, the generative AI tool learns brain wave patterns during relaxation and stress and analyzes emotions based on the learned brain wave patterns. Next, the generative AI tool generates linguistic expressions based on the analyzed emotional data. The generative AI uses a Transformer model to generate appropriate language expressions based on emotional data. For example, the generative AI tool generates language expressions such as "relaxed" or "stressed." The generative AI tool then displays the generated language expressions on a monitor. This allows caregivers and medical professionals to understand the emotions of the care recipient or medical care recipient in real time. For example, when the care recipient is relaxed, the generative AI tool displays "relaxed," and when the care recipient is stressed, it displays "stressed." This allows the generative AI tool to accurately understand the emotions of the care recipient or medical care recipient and provide appropriate responses. For example, when the care recipient is stressed, a relaxing environment can be provided. Furthermore, when the medical care recipient is relaxed, treatment can be carried out with peace of mind.In this way, by combining emotion analysis using brain waves with verbalization using generative AI, it is possible to resolve thoughts and communication that cannot be put into words, and support communication in nursing and medical settings.

[0029] The generation AI tool according to the embodiment includes an acquisition unit, an analysis unit, a generation unit, and a display unit. The acquisition unit acquires electroencephalography (EEG) data from a care recipient or a medical care recipient. The electroencephalography (EEG) data from a care recipient or a medical care recipient may be obtained by, but is not limited to, electroencephalography (EEG). The acquisition unit may acquire the electroencephalogram in real time using a dedicated EEG sensor. The acquisition unit may also consider a sensor placement method for obtaining electroencephalogram data with high accuracy and the accuracy of data acquisition. For example, the acquisition unit may place an EEG sensor on the head and acquire the electroencephalogram data in real time. The analysis unit may analyze the electroencephalogram data acquired by the acquisition unit using deep learning to analyze emotions. For example, algorithms such as a recurrent neural network (RNN) or a long short-term memory (LSTM) may be used to analyze emotions, but are not limited to, examples. For example, the analysis unit may analyze the electroencephalogram data using an RNN to analyze emotions with high accuracy. The analysis unit may also analyze the electroencephalogram data using an LSTM to analyze emotions. The analysis unit can also learn multiple brain wave patterns and identify different emotional states. For example, the analysis unit can learn brain wave patterns when relaxed and when stressed, and analyze emotions based on the learned patterns. The generation unit uses a generation AI to generate a linguistic expression based on the emotional data analyzed by the analysis unit. The generation AI can be, for example, a Transformer model, but is not limited to this example. For example, the generation unit can generate an appropriate linguistic expression based on the emotional data using a Transformer model. The generation unit can also evaluate the accuracy of the generated linguistic expression and improve the model. For example, the generation unit can evaluate the accuracy of the generated linguistic expression and improve the accuracy of the model. The display unit displays the linguistic expression generated by the generation unit on a monitor. For example, the display unit displays the generated linguistic expression on a monitor in real time. The display unit can also estimate the emotion of the care recipient or medical care recipient and adjust the display method based on the estimated emotion. For example, if the care recipient is relaxed, the display unit displays the image in a gentle color.As a result, the generation AI tool according to the embodiment can grasp the emotions of care recipients and medical recipients in real time and support communication between caregivers and medical professionals.

[0030] The acquisition unit can acquire brain waves in real time using an EEG sensor. EEG sensors include, but are not limited to, head-mounted and ear-worn types. The acquisition unit can acquire brain waves in real time using, for example, a head-mounted EEG sensor. The acquisition unit can also acquire brain waves in real time using an ear-worn EEG sensor. For example, the acquisition unit acquires brain waves with high accuracy using an ear-worn EEG sensor. The acquisition unit can also adjust the sensor placement method and data acquisition accuracy to improve the accuracy of brain wave data acquisition. For example, the acquisition unit places an EEG sensor at a specific position on the head and acquires brain wave data with high accuracy. By acquiring brain waves in real time, the condition of a care recipient or medical care recipient can be immediately grasped. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can input brain wave data acquired by the EEG sensor to a generation AI and cause the generation AI to perform real-time data analysis.

[0031] The analysis unit can analyze emotions by analyzing the EEG data using a recurrent neural network (RNN) or a long short-term memory (LSTM). Examples of recurrent neural networks (RNNs) include, but are not limited to, standard RNNs and gated RNNs. The analysis unit can analyze emotions by analyzing the EEG data using, for example, a standard RNN. The analysis unit can also analyze emotions by analyzing the EEG data using a gated RNN. For example, the analysis unit can analyze EEG data with high accuracy by using a gated RNN. Examples of long short-term memories (LSTMs) include, but are not limited to, standard LSTMs and bidirectional LSTMs. The analysis unit can analyze emotions by analyzing the EEG data using, for example, a standard LSTM. The analysis unit can also analyze emotions by analyzing the EEG data using a bidirectional LSTM. For example, the analysis unit can analyze EEG data with high accuracy by using a bidirectional LSTM. As a result, emotions can be analyzed from EEG data with high accuracy by using an RNN or LSTM. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input EEG data to the generation AI and have the generation AI analyze emotions.

[0032] The generation unit can generate a linguistic expression based on emotion data using a Transformer model. Transformer models include, but are not limited to, a standard Transformer and an improved Transformer. The generation unit can generate a linguistic expression based on emotion data using, for example, a standard Transformer. The generation unit can also generate a linguistic expression based on emotion data using an improved Transformer. For example, the generation unit converts emotion data into a linguistic expression with high accuracy using the improved Transformer. The generation unit can also evaluate the accuracy of the generated linguistic expression and improve the model. For example, the generation unit evaluates the accuracy of the generated linguistic expression and improves the accuracy of the model. In this way, appropriate linguistic expressions can be generated from emotion data by using a Transformer model. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input emotion data to a generation AI and cause the generation AI to generate a linguistic expression.

[0033] The display unit can display the generated linguistic expression on a monitor. For example, the display unit displays the generated linguistic expression on the monitor in real time. The display unit can also estimate the emotion of the care recipient or medical recipient and adjust the display method based on the estimated emotion. For example, if the care recipient is relaxed, the display unit displays the expression in a calm color. The display unit can also provide a simple, highly visible display method if the medical recipient is stressed. For example, the display unit provides a simple, highly visible display method for a stressed medical recipient. The display unit can also provide a visually emphasized display method for an excited care recipient. For example, the display unit provides a visually emphasized display method for an excited care recipient. By displaying the generated linguistic expression on the monitor, caregivers and medical professionals can visually grasp the emotion of the care recipient or medical recipient. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input the generated linguistic expression to a generation AI and have the generation AI adjust the display method.

[0034] The analysis unit can learn multiple brain wave patterns and identify different emotional states. For example, the analysis unit learns brain wave patterns when relaxed and brain wave patterns when stressed, and analyzes emotions based on the learned patterns. The analysis unit can also learn multiple brain wave patterns and build a model for identifying different emotional states. For example, the analysis unit learns brain wave patterns when relaxed and identifies a relaxed state based on the learned patterns. The analysis unit can also learn brain wave patterns when stressed and identifies a stressed state based on the learned patterns. The analysis unit can also combine multiple brain wave patterns to identify more detailed emotional states. For example, the analysis unit combines brain wave patterns when relaxed and when stressed to analyze emotional changes in detail. By learning multiple brain wave patterns, different emotional states can be identified with high accuracy. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input brain wave data to a generation AI and have the generation AI identify emotional states.

[0035] The generation unit can evaluate the accuracy of the generated linguistic expressions and improve the model. For example, the generation unit sets an evaluation index for evaluating the accuracy of the generated linguistic expressions. The generation unit can also evaluate the accuracy of the generated linguistic expressions based on the evaluation index and provide feedback for improving the model. For example, the generation unit evaluates the accuracy of the generated linguistic expressions and improves the accuracy of the model based on the evaluation results. The generation unit can also prepare a test dataset for evaluating the accuracy of the generated linguistic expressions and verify the accuracy of the model using the dataset. For example, the generation unit evaluates the accuracy of the generated linguistic expressions using the test dataset and improves the model based on the evaluation results. In this way, the accuracy of the model can be improved by evaluating the accuracy of the generated linguistic expressions. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the generated linguistic expressions to a generation AI and cause the generation AI to evaluate the accuracy.

[0036] The acquisition unit can analyze the past EEG data of the care recipient or medical care recipient and select the optimal acquisition method. The acquisition unit, for example, uses an algorithm for analyzing the past EEG data of the care recipient or medical care recipient. The acquisition unit can also set criteria for selecting the optimal acquisition method based on the past EEG data. For example, the acquisition unit can analyze the past EEG data of the care recipient and select an acquisition method for when the care recipient is relaxed. The acquisition unit can also analyze the past EEG data of the medical care recipient and select an acquisition method for when the care recipient is feeling stressed. Furthermore, the acquisition unit can analyze the past EEG data of the care recipient and select an acquisition method for when the care recipient is sleeping. In this way, the optimal acquisition method can be selected by analyzing the past EEG data. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI or without AI. For example, the acquisition unit can input the past EEG data into a generation AI and cause the generation AI to select the optimal acquisition method.

[0037] When acquiring EEG data, the acquisition unit can filter the data based on the current health condition and environment of the care recipient or medical care recipient. The acquisition unit, for example, uses an algorithm for evaluating the current health condition of the care recipient or medical care recipient. The acquisition unit can also set criteria for filtering EEG data based on the current environment. For example, the acquisition unit can acquire EEG data when the care recipient's health condition is good and filter data when the care recipient's health condition is poor. The acquisition unit can also acquire EEG data when the medical care recipient's treatment environment is quiet and filter data when there is noise. Furthermore, the acquisition unit can acquire EEG data when the care recipient's sleep environment is appropriate and filter data when the environment is inappropriate. By filtering based on the health condition and environment, more accurate EEG data can be acquired. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input health condition and environmental data into a generation AI and leave the filtering to the generation AI.

[0038] When acquiring EEG data, the acquisition unit can select the optimal acquisition means depending on the input method of the care recipient or medical care recipient. The acquisition unit, for example, uses an algorithm for evaluating the input method of the care recipient or medical care recipient. The acquisition unit can also set criteria for selecting the optimal acquisition means depending on the input method. For example, if the care recipient uses voice input, the acquisition unit can acquire EEG data based on the voice. If the medical care recipient uses text input, the acquisition unit can also acquire EEG data based on the text. Furthermore, if the care recipient uses image input, the acquisition unit can also acquire EEG data based on the image. This allows for more appropriate EEG data to be acquired by selecting the optimal acquisition means depending on the input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input input method data to a generation AI and cause the generation AI to select the optimal acquisition means.

[0039] When acquiring EEG data, the acquisition unit can prioritize acquisition of highly relevant data by taking into account the geographical location information of the care recipient or medical care recipient. The acquisition unit, for example, uses an algorithm for acquiring the geographical location information of the care recipient or medical care recipient. The acquisition unit can also set criteria for prioritized acquisition of highly relevant data based on the geographical location information. For example, the acquisition unit prioritizes acquisition of EEG data when the care recipient is at home. The acquisition unit can also prioritize acquisition of EEG data when the medical care recipient is in a hospital. Furthermore, the acquisition unit can also prioritize acquisition of EEG data when the care recipient is in a specific facility. This allows highly relevant data to be acquired preferentially by taking the geographical location information into account. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input geographical location information to a generation AI and cause the generation AI to acquire highly relevant data.

[0040] The acquisition unit can analyze the social media activity of the care recipient or medical care recipient when acquiring the brain waves and acquire related data. The acquisition unit, for example, uses an algorithm for analyzing the social media activity of the care recipient or medical care recipient. The acquisition unit can also set criteria for acquiring related data based on the social media activity. For example, the acquisition unit can analyze posts made by the care recipient on social media when they are relaxing and acquire brain wave data. The acquisition unit can also analyze posts made by the medical care recipient on social media when they are feeling stressed and acquire brain wave data. Furthermore, the acquisition unit can analyze posts made by the care recipient on social media when they are sleeping and acquire brain wave data. In this way, related brain wave data can be acquired by analyzing social media activity. Some or all of the above-mentioned processing in the acquisition unit can be performed, for example, using AI or without AI. For example, the acquisition unit can input social media activity data to a generation AI and cause the generation AI to acquire related brain wave data.

[0041] The acquisition unit can customize the acquisition method by reflecting past feedback from the care recipient or medical care recipient when acquiring EEG data. The acquisition unit, for example, uses an algorithm for analyzing past feedback from the care recipient or medical care recipient. The acquisition unit can also set criteria for customizing the acquisition method based on past feedback. For example, the acquisition unit customizes the acquisition method for when the care recipient is relaxed based on past feedback from the care recipient. The acquisition unit can also customize the acquisition method for when the care recipient is stressed based on past feedback from the medical care recipient. The acquisition unit can also customize the acquisition method for when the care recipient is sleeping based on past feedback from the care recipient. This allows the acquisition method to be customized by reflecting past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input past feedback data into a generation AI and cause the generation AI to customize the acquisition method.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the EEG data. The analysis unit, for example, uses an algorithm for evaluating the importance of the EEG data. The analysis unit can also set criteria for adjusting the level of detail of the analysis based on the importance. For example, the analysis unit can analyze EEG data with high importance in detail and analyze data with low importance in a simplified manner. The analysis unit can also prioritize analysis of EEG data with high importance and postpone analysis of data with low importance. Furthermore, the analysis unit can analyze EEG data with high importance in detail and filter data with low importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the EEG data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the EEG data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of EEG data. The analysis unit, for example, uses an algorithm for classifying the category of EEG data. The analysis unit can also set criteria for applying different analysis algorithms depending on the category. For example, the analysis unit can apply a specific algorithm to EEG data when the subject is relaxed. The analysis unit can also apply a different algorithm to EEG data when the subject is stressed. Furthermore, the analysis unit can apply a dedicated algorithm to EEG data when the subject is sleeping. This allows for more accurate analysis by applying different analysis algorithms depending on the category of EEG data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the category of EEG data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of the care recipient or medical care recipient. The analysis unit, for example, uses an algorithm for referencing past analysis results of the care recipient or medical care recipient. The analysis unit can also set standards for improving the accuracy of the analysis based on past analysis results. For example, the analysis unit can improve the analysis accuracy when the care recipient is relaxed based on past analysis results of the care recipient. The analysis unit can also improve the analysis accuracy when the medical care recipient is feeling stressed based on past analysis results of the care recipient. Furthermore, the analysis unit can improve the analysis accuracy when the care recipient is sleeping based on past analysis results of the care recipient. In this way, the accuracy of the analysis can be improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time when the EEG data was acquired. The analysis unit, for example, uses an algorithm for evaluating the time when the EEG data was acquired. The analysis unit can also set criteria for determining the priority of analysis based on the time when the data was acquired. For example, the analysis unit can prioritize analyzing the most recent EEG data and postpone analyzing older data. The analysis unit can also prioritize analyzing EEG data from important events. Furthermore, the analysis unit can prioritize analyzing EEG data from specific time periods. This enables efficient analysis by determining the priority of analysis based on the time when the EEG data was acquired. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the EEG data was acquired into the generation AI and have the generation AI determine the priority of analysis.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the EEG data. The analysis unit, for example, uses an algorithm for evaluating the relevance of the EEG data. The analysis unit can also set criteria for adjusting the order of analysis based on the relevance. For example, the analysis unit prioritizes analysis of highly relevant EEG data. The analysis unit can also postpone analysis of less relevant EEG data. Furthermore, the analysis unit can analyze highly relevant EEG data in detail and analyze less relevant data simply. This enables efficient analysis by adjusting the order of analysis based on the relevance of the EEG data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the EEG data to the generation AI and cause the generation AI to adjust the order of analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the expertise level of the care recipient or medical recipient. The analysis unit, for example, uses an algorithm for evaluating the expertise level of the care recipient or medical recipient. The analysis unit can also set criteria for adjusting the use of technical terms in the analysis according to the expertise level. For example, the analysis unit uses a lot of technical terms if the care recipient has specialized knowledge. The analysis unit can also avoid technical terms if the medical recipient does not have specialized knowledge. Furthermore, the analysis unit can adjust the use of technical terms according to the expertise level of the care recipient. By adjusting the use of technical terms according to the expertise level, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input expertise level data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0048] The generation unit can adjust the level of detail of the generated linguistic expression based on the importance of the emotional data when generating the linguistic expression. The generation unit, for example, uses an algorithm for evaluating the importance of the emotional data. The generation unit can also set criteria for adjusting the level of detail of the generated linguistic expression based on the importance. For example, the generation unit can generate a detailed linguistic expression based on emotional data with high importance. The generation unit can also generate a simplified linguistic expression based on emotional data with low importance. Furthermore, the generation unit can prioritize verbalizing emotional data with high importance and postpone verbalizing data with low importance. This enables efficient generation of linguistic expressions by adjusting the level of detail of the generated linguistic expression based on the importance of the emotional data. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the importance of the emotional data to the generation AI and cause the generation AI to adjust the level of detail of the generated linguistic expression.

[0049] When generating a linguistic expression, the generation unit can apply different generation algorithms depending on the category of emotional data. The generation unit, for example, uses an algorithm for classifying the category of emotional data. The generation unit can also set criteria for applying different generation algorithms depending on the category. For example, the generation unit can apply a specific generation algorithm to emotional data representing a relaxed state. The generation unit can also apply a different generation algorithm to emotional data representing a stressed state. Furthermore, the generation unit can also apply a dedicated generation algorithm to emotional data representing an excited state. In this way, by applying different generation algorithms depending on the category of emotional data, more appropriate linguistic expressions can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the category of emotional data into the generation AI and cause the generation AI to apply different generation algorithms.

[0050] When generating a linguistic expression, the generation unit can improve the accuracy of the generation by referring to past generation results of the care recipient or medical care recipient. The generation unit, for example, uses an algorithm for referencing past generation results of the care recipient or medical care recipient. The generation unit can also set a standard for improving the accuracy of the generation based on the past generation results. For example, the generation unit can improve the accuracy of the linguistic expression when the care recipient is relaxed based on the past generation results of the care recipient. The generation unit can also improve the accuracy of the linguistic expression when the care recipient is feeling stressed based on the past generation results of the medical care recipient. Furthermore, the generation unit can improve the accuracy of the linguistic expression when the care recipient is excited based on the past generation results of the care recipient. In this way, the accuracy of the generation can be improved by referring to the past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past generation results into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0051] When generating linguistic expressions, the generation unit can determine the generation priority based on the acquisition time of emotion data. The generation unit, for example, uses an algorithm for evaluating the acquisition time of emotion data. The generation unit can also set criteria for determining the generation priority based on the acquisition time. For example, the generation unit prioritizes generating linguistic expressions based on the latest emotion data. The generation unit can also generate linguistic expressions based on emotion data from an important event. Furthermore, the generation unit can generate linguistic expressions based on emotion data from a specific time period. This enables efficient generation of linguistic expressions by determining the generation priority based on the acquisition time of emotion data. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the acquisition time of emotion data to the generation AI and have the generation AI determine the generation priority.

[0052] The generation unit can adjust the order of generation based on the relevance of emotional data when generating linguistic expressions. The generation unit, for example, uses an algorithm for evaluating the relevance of emotional data. The generation unit can also set criteria for adjusting the order of generation based on the relevance. For example, the generation unit prioritizes generating linguistic expressions based on highly relevant emotional data. The generation unit can also postpone generating less relevant emotional data. Furthermore, the generation unit can generate detailed linguistic expressions based on highly relevant emotional data and generate less relevant data simply. This enables efficient generation of linguistic expressions by adjusting the order of generation based on the relevance of emotional data. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the relevance of emotional data to the generation AI and cause the generation AI to adjust the order of generation.

[0053] When generating a linguistic expression, the generation unit can adjust the use of technical terminology in the generated expression according to the expertise level of the care recipient or medical recipient. The generation unit, for example, uses an algorithm for evaluating the expertise level of the care recipient or medical recipient. The generation unit can also set criteria for adjusting the use of technical terminology in the generated expression according to the expertise level. For example, the generation unit uses a lot of technical terminology if the care recipient has specialized knowledge. The generation unit can also avoid technical terminology if the medical recipient does not have specialized knowledge. The generation unit can also adjust the use of technical terminology according to the expertise level of the care recipient. This allows for the generation of a linguistic expression that is easier to understand by adjusting the use of technical terminology according to the expertise level. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input expertise level data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0054] The display unit can select the optimal display method by referring to the past display history of the care recipient or medical recipient when displaying the image. The display unit, for example, uses an algorithm for referencing the past display history of the care recipient or medical recipient. The display unit can also set criteria for selecting the optimal display method based on the past display history. For example, the display unit selects a display method for when the care recipient is relaxed based on the past display history of the care recipient. The display unit can also select a display method for when the care recipient is feeling stressed based on the past display history of the medical recipient. Furthermore, the display unit can select a display method for when the care recipient is excited based on the past display history of the care recipient. In this way, the optimal display method can be selected by referring to the past display history. Some or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input past display history data to a generation AI and have the generation AI select the optimal display method.

[0055] The display unit can customize the display content according to the current task of the care recipient or medical care recipient when displaying the display. The display unit, for example, uses an algorithm for evaluating the current task of the care recipient or medical care recipient. The display unit can also set criteria for customizing the display content according to the current task. For example, the display unit customizes the display content according to the task when the care recipient is relaxed. The display unit can also customize the display content according to the task when the medical care recipient is feeling stressed. Furthermore, the display unit can also customize the display content according to the task when the care recipient is excited. This enables more appropriate display by customizing the display content according to the current task. Some or all of the above-mentioned processing in the display unit may be performed, for example, using AI or without AI. For example, the display unit can input current task data into a generation AI and have the generation AI customize the display content.

[0056] The display unit can select the optimal display method by taking into account the device information of the care recipient or medical recipient. The display unit, for example, uses an algorithm to acquire the device information of the care recipient or medical recipient. The display unit can also set criteria for selecting the optimal display method based on the device information. For example, if the care recipient is using a smartphone, the display unit can provide a display method tailored to the screen size. If the medical recipient is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, if the care recipient is using a smartwatch, the display unit can provide a simple, highly visible display method. This allows the optimal display method to be selected by taking into account the device information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input device information to a generation AI and cause the generation AI to select the optimal display method.

[0057] The display unit can make the display content multilingual according to the language setting of the care recipient or medical care recipient. The display unit, for example, uses an algorithm to acquire the language setting of the care recipient or medical care recipient. The display unit can also set criteria for making the display content multilingual according to the language setting. For example, the display unit automatically sets the display content based on the language setting of the care recipient's device. The display unit can also provide a language switching function when the medical care recipient uses multiple languages. Furthermore, if the care recipient selects a specific language, the display unit can provide the display content in that language. This allows the display content to be multilingual according to the language setting, thereby accommodating a wider range of users. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input language setting data to a generation AI and cause the generation AI to execute multilingual display content.

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

[0059] The acquisition unit can filter environmental sounds and background noise when acquiring EEG data from a care recipient or medical care recipient. For example, the acquisition unit can use noise canceling technology to remove environmental sounds from the EEG data. The acquisition unit can also filter noise in a specific frequency band. Furthermore, the acquisition unit can acquire more accurate EEG data by detecting and filtering noise in real time. This can minimize the influence of environmental sounds and background noise and improve the accuracy of the EEG data.

[0060] When analyzing the EEG data of a care recipient or medical care recipient, the analysis unit can refer to past health data and medical history. For example, the analysis unit can refer to the results of a care recipient's past health checkups and reflect them in the analysis of the EEG data. The analysis unit can also refer to the medical care recipient's past treatment history and use it to analyze the EEG data. Furthermore, the analysis unit can refer to the lifestyle data of the care recipient or medical care recipient and use it in the analysis of the EEG data. This makes it possible to provide more accurate analysis results by taking into account past health data and medical history.

[0061] When displaying the generated linguistic expression, the display unit can adjust the font size and color according to the visual needs of the care recipient or medical care recipient. For example, the display unit can display the expression in a larger font size for a care recipient with poor eyesight. The display unit can also adjust the color contrast for a medical care recipient with color vision deficiency. Furthermore, the display unit can provide a night mode to ensure visibility even in dark environments. This allows the display unit to accommodate a wider range of users by providing a display method according to visual needs.

[0062] When acquiring EEG data from a care recipient or medical care recipient, the acquisition unit can detect abnormalities in the data in real time and issue an alert. For example, if the acquisition unit detects an abnormal EEG pattern, it notifies the caregiver or medical professional. The acquisition unit can also issue an alert if a specific threshold is exceeded. Furthermore, the acquisition unit can adjust the frequency of abnormality detection and change the sensitivity of the alert as needed. This allows for real-time detection of abnormalities and enables rapid response.

[0063] When analyzing the EEG data of care recipients or medical care recipients, the analysis unit can apply an anomaly detection algorithm to detect abnormal patterns. For example, if the analysis unit detects an abnormal EEG pattern, it can issue an alert. The analysis unit can also refer to past data to improve the accuracy of anomaly detection. Furthermore, the analysis unit can display the results of anomaly detection in real time to support rapid response. This allows for early detection of abnormal patterns and appropriate response.

[0064] When displaying the generated linguistic expression, the display unit can accept voice feedback from the care recipient or medical recipient and update the display content in real time. For example, when the care recipient provides voice feedback, the display unit updates the display based on the content of that feedback. Also, when the medical recipient issues a voice instruction, the display unit can change the display content in accordance with that instruction. Furthermore, the display unit can analyze the content of the voice feedback and provide an appropriate display method. This makes it possible to utilize voice feedback to enable a more interactive display.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The acquisition unit acquires the brain waves of the care recipient or medical care recipient. For example, a dedicated EEG sensor is used to acquire brain waves in real time, and the sensor placement method and data acquisition accuracy can be taken into consideration in order to acquire brain wave data with high accuracy. Step 2: The analysis unit uses deep learning to analyze the EEG data acquired by the acquisition unit and analyze emotions. For example, it uses algorithms such as recurrent neural networks (RNN) and long short-term memory (LSTM) to learn multiple EEG patterns and identify different emotional states. Step 3: The generation unit uses a generative AI to generate language expressions based on the emotion data analyzed by the analysis unit. For example, it uses a Transformer model to generate appropriate language expressions based on the emotion data, evaluates the accuracy of the generated language expressions, and improves the model. Step 4: The display unit displays the linguistic expression generated by the generation unit on a monitor. For example, the display unit displays the generated linguistic expression on the monitor in real time, estimates the emotion of the care recipient or medical care recipient, and adjusts the display method based on the estimated emotion.

[0067] (Example 2) A generative AI tool according to an embodiment of the present invention is a system that acquires the brain waves of a care recipient or medical care recipient, analyzes their emotions using deep learning, and displays the content verbalized by the generative AI on a monitor. The generative AI tool supports communication between caregivers and medical professionals by acquiring the brain waves of the care recipient or medical care recipient, analyzing their emotions using deep learning, and displaying the content verbalized by the generative AI on a monitor. For example, the generative AI tool acquires the brain waves of the care recipient or medical care recipient. For example, the generative AI tool can acquire brain waves in real time using a dedicated EEG sensor. The generative AI tool then analyzes the acquired brain wave data using deep learning. Deep learning uses a recurrent neural network (RNN) or long short-term memory (LSTM) to learn a model for analyzing emotions from brain wave data. For example, the generative AI tool learns brain wave patterns during relaxation and stress and analyzes emotions based on the learned brain wave patterns. Next, the generative AI tool generates linguistic expressions based on the analyzed emotional data. The generative AI uses a Transformer model to generate appropriate language expressions based on emotional data. For example, the generative AI tool generates language expressions such as "relaxed" or "stressed." The generative AI tool then displays the generated language expressions on a monitor. This allows caregivers and medical professionals to understand the emotions of the care recipient or medical care recipient in real time. For example, when the care recipient is relaxed, the generative AI tool displays "relaxed," and when the care recipient is stressed, it displays "stressed." This allows the generative AI tool to accurately understand the emotions of the care recipient or medical care recipient and provide appropriate responses. For example, when the care recipient is stressed, a relaxing environment can be provided. Furthermore, when the medical care recipient is relaxed, treatment can be carried out with peace of mind.In this way, by combining emotion analysis using brain waves with verbalization using generative AI, it is possible to resolve thoughts and communication that cannot be put into words, and support communication in nursing and medical settings.

[0068] The generation AI tool according to the embodiment includes an acquisition unit, an analysis unit, a generation unit, and a display unit. The acquisition unit acquires electroencephalography (EEG) data from a care recipient or a medical care recipient. The electroencephalography (EEG) data from a care recipient or a medical care recipient may be obtained by, but is not limited to, electroencephalography (EEG). The acquisition unit may acquire the electroencephalogram in real time using a dedicated EEG sensor. The acquisition unit may also consider a sensor placement method for obtaining electroencephalogram data with high accuracy and the accuracy of data acquisition. For example, the acquisition unit may place an EEG sensor on the head and acquire the electroencephalogram data in real time. The analysis unit may analyze the electroencephalogram data acquired by the acquisition unit using deep learning to analyze emotions. For example, algorithms such as a recurrent neural network (RNN) or a long short-term memory (LSTM) may be used to analyze emotions, but are not limited to, examples. For example, the analysis unit may analyze the electroencephalogram data using an RNN to analyze emotions with high accuracy. The analysis unit may also analyze the electroencephalogram data using an LSTM to analyze emotions. The analysis unit can also learn multiple brain wave patterns and identify different emotional states. For example, the analysis unit can learn brain wave patterns when relaxed and when stressed, and analyze emotions based on the learned patterns. The generation unit uses a generation AI to generate a linguistic expression based on the emotional data analyzed by the analysis unit. The generation AI can be, for example, a Transformer model, but is not limited to this example. For example, the generation unit can generate an appropriate linguistic expression based on the emotional data using a Transformer model. The generation unit can also evaluate the accuracy of the generated linguistic expression and improve the model. For example, the generation unit can evaluate the accuracy of the generated linguistic expression and improve the accuracy of the model. The display unit displays the linguistic expression generated by the generation unit on a monitor. For example, the display unit displays the generated linguistic expression on a monitor in real time. The display unit can also estimate the emotion of the care recipient or medical care recipient and adjust the display method based on the estimated emotion. For example, if the care recipient is relaxed, the display unit displays the image in a gentle color.As a result, the generation AI tool according to the embodiment can grasp the emotions of care recipients and medical recipients in real time and support communication between caregivers and medical professionals.

[0069] The acquisition unit can acquire brain waves in real time using an EEG sensor. EEG sensors include, but are not limited to, head-mounted and ear-worn types. The acquisition unit can acquire brain waves in real time using, for example, a head-mounted EEG sensor. The acquisition unit can also acquire brain waves in real time using an ear-worn EEG sensor. For example, the acquisition unit acquires brain waves with high accuracy using an ear-worn EEG sensor. The acquisition unit can also adjust the sensor placement method and data acquisition accuracy to improve the accuracy of brain wave data acquisition. For example, the acquisition unit places an EEG sensor at a specific position on the head and acquires brain wave data with high accuracy. By acquiring brain waves in real time, the condition of a care recipient or medical care recipient can be immediately grasped. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can input brain wave data acquired by the EEG sensor to a generation AI and cause the generation AI to perform real-time data analysis.

[0070] The analysis unit can analyze emotions by analyzing the EEG data using a recurrent neural network (RNN) or a long short-term memory (LSTM). Examples of recurrent neural networks (RNNs) include, but are not limited to, standard RNNs and gated RNNs. The analysis unit can analyze emotions by analyzing the EEG data using, for example, a standard RNN. The analysis unit can also analyze emotions by analyzing the EEG data using a gated RNN. For example, the analysis unit can analyze EEG data with high accuracy by using a gated RNN. Examples of long short-term memories (LSTMs) include, but are not limited to, standard LSTMs and bidirectional LSTMs. The analysis unit can analyze emotions by analyzing the EEG data using, for example, a standard LSTM. The analysis unit can also analyze emotions by analyzing the EEG data using a bidirectional LSTM. For example, the analysis unit can analyze EEG data with high accuracy by using a bidirectional LSTM. As a result, emotions can be analyzed from EEG data with high accuracy by using an RNN or LSTM. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input EEG data to the generation AI and have the generation AI analyze emotions.

[0071] The generation unit can generate a linguistic expression based on emotion data using a Transformer model. Transformer models include, but are not limited to, a standard Transformer and an improved Transformer. The generation unit can generate a linguistic expression based on emotion data using, for example, a standard Transformer. The generation unit can also generate a linguistic expression based on emotion data using an improved Transformer. For example, the generation unit converts emotion data into a linguistic expression with high accuracy using the improved Transformer. The generation unit can also evaluate the accuracy of the generated linguistic expression and improve the model. For example, the generation unit evaluates the accuracy of the generated linguistic expression and improves the accuracy of the model. In this way, appropriate linguistic expressions can be generated from emotion data by using a Transformer model. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input emotion data to a generation AI and cause the generation AI to generate a linguistic expression.

[0072] The display unit can display the generated linguistic expression on a monitor. For example, the display unit displays the generated linguistic expression on the monitor in real time. The display unit can also estimate the emotion of the care recipient or medical recipient and adjust the display method based on the estimated emotion. For example, if the care recipient is relaxed, the display unit displays the expression in a calm color. The display unit can also provide a simple, highly visible display method if the medical recipient is stressed. For example, the display unit provides a simple, highly visible display method for a stressed medical recipient. The display unit can also provide a visually emphasized display method for an excited care recipient. For example, the display unit provides a visually emphasized display method for an excited care recipient. By displaying the generated linguistic expression on the monitor, caregivers and medical professionals can visually grasp the emotion of the care recipient or medical recipient. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input the generated linguistic expression to a generation AI and have the generation AI adjust the display method.

[0073] The analysis unit can learn multiple brain wave patterns and identify different emotional states. For example, the analysis unit learns brain wave patterns when relaxed and brain wave patterns when stressed, and analyzes emotions based on the learned patterns. The analysis unit can also learn multiple brain wave patterns and build a model for identifying different emotional states. For example, the analysis unit learns brain wave patterns when relaxed and identifies a relaxed state based on the learned patterns. The analysis unit can also learn brain wave patterns when stressed and identifies a stressed state based on the learned patterns. The analysis unit can also combine multiple brain wave patterns to identify more detailed emotional states. For example, the analysis unit combines brain wave patterns when relaxed and when stressed to analyze emotional changes in detail. By learning multiple brain wave patterns, different emotional states can be identified with high accuracy. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input brain wave data to a generation AI and have the generation AI identify emotional states.

[0074] The generation unit can evaluate the accuracy of the generated linguistic expressions and improve the model. For example, the generation unit sets an evaluation index for evaluating the accuracy of the generated linguistic expressions. The generation unit can also evaluate the accuracy of the generated linguistic expressions based on the evaluation index and provide feedback for improving the model. For example, the generation unit evaluates the accuracy of the generated linguistic expressions and improves the accuracy of the model based on the evaluation results. The generation unit can also prepare a test dataset for evaluating the accuracy of the generated linguistic expressions and verify the accuracy of the model using the dataset. For example, the generation unit evaluates the accuracy of the generated linguistic expressions using the test dataset and improves the model based on the evaluation results. In this way, the accuracy of the model can be improved by evaluating the accuracy of the generated linguistic expressions. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the generated linguistic expressions to a generation AI and cause the generation AI to evaluate the accuracy.

[0075] The acquisition unit can estimate the emotion of the care recipient or medical care recipient and adjust the timing of brain wave acquisition based on the estimated emotion. The acquisition unit, for example, uses an algorithm for estimating the emotion of the care recipient or medical care recipient. The acquisition unit can also set a standard for adjusting the timing of brain wave acquisition based on the estimated emotion. For example, the acquisition unit acquires brain waves when the care recipient is relaxed and refrains from acquiring them when the care recipient is stressed. The acquisition unit can also acquire brain waves when the medical care recipient is relaxed during treatment and refrain from acquiring them when the care recipient is tense. Furthermore, the acquisition unit can acquire brain waves when the care recipient is relaxed during sleep and refrain from acquiring them when the care recipient is awake. In this way, by adjusting the timing of brain wave acquisition based on emotion, brain waves can be acquired at more appropriate timing. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI or without AI. For example, the acquisition unit can cause a generation AI to execute an emotion estimation algorithm and adjust the timing of brain wave acquisition based on the estimated emotion.

[0076] The acquisition unit can analyze the past EEG data of the care recipient or medical care recipient and select the optimal acquisition method. The acquisition unit, for example, uses an algorithm for analyzing the past EEG data of the care recipient or medical care recipient. The acquisition unit can also set criteria for selecting the optimal acquisition method based on the past EEG data. For example, the acquisition unit can analyze the past EEG data of the care recipient and select an acquisition method for when the care recipient is relaxed. The acquisition unit can also analyze the past EEG data of the medical care recipient and select an acquisition method for when the care recipient is feeling stressed. Furthermore, the acquisition unit can analyze the past EEG data of the care recipient and select an acquisition method for when the care recipient is sleeping. In this way, the optimal acquisition method can be selected by analyzing the past EEG data. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI or without AI. For example, the acquisition unit can input the past EEG data into a generation AI and cause the generation AI to select the optimal acquisition method.

[0077] When acquiring EEG data, the acquisition unit can filter the data based on the current health condition and environment of the care recipient or medical care recipient. The acquisition unit, for example, uses an algorithm for evaluating the current health condition of the care recipient or medical care recipient. The acquisition unit can also set criteria for filtering EEG data based on the current environment. For example, the acquisition unit can acquire EEG data when the care recipient's health condition is good and filter data when the care recipient's health condition is poor. The acquisition unit can also acquire EEG data when the medical care recipient's treatment environment is quiet and filter data when there is noise. Furthermore, the acquisition unit can acquire EEG data when the care recipient's sleep environment is appropriate and filter data when the environment is inappropriate. By filtering based on the health condition and environment, more accurate EEG data can be acquired. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input health condition and environmental data into a generation AI and leave the filtering to the generation AI.

[0078] When acquiring EEG data, the acquisition unit can select the optimal acquisition means depending on the input method of the care recipient or medical care recipient. The acquisition unit, for example, uses an algorithm for evaluating the input method of the care recipient or medical care recipient. The acquisition unit can also set criteria for selecting the optimal acquisition means depending on the input method. For example, if the care recipient uses voice input, the acquisition unit can acquire EEG data based on the voice. If the medical care recipient uses text input, the acquisition unit can also acquire EEG data based on the text. Furthermore, if the care recipient uses image input, the acquisition unit can also acquire EEG data based on the image. This allows for more appropriate EEG data to be acquired by selecting the optimal acquisition means depending on the input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input input method data to a generation AI and cause the generation AI to select the optimal acquisition means.

[0079] The acquisition unit can estimate the emotion of the care recipient or medical recipient and prioritize the brain wave data to be acquired based on the estimated emotion. The acquisition unit, for example, uses an algorithm for estimating the emotion of the care recipient or medical recipient. The acquisition unit can also set criteria for prioritizing the brain wave data based on the estimated emotion. For example, the acquisition unit prioritizes acquiring brain wave data when the care recipient is relaxed. The acquisition unit can also prioritize acquiring brain wave data when the medical recipient is feeling stressed. Furthermore, the acquisition unit can prioritize acquiring brain wave data when the care recipient is sleeping. In this way, by prioritizing the brain wave data based on the emotion, important data can be acquired preferentially. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can cause a generation AI to execute an emotion estimation algorithm and prioritize the brain wave data based on the estimated emotion.

[0080] When acquiring EEG data, the acquisition unit can prioritize acquisition of highly relevant data by taking into account the geographical location information of the care recipient or medical care recipient. The acquisition unit, for example, uses an algorithm for acquiring the geographical location information of the care recipient or medical care recipient. The acquisition unit can also set criteria for prioritized acquisition of highly relevant data based on the geographical location information. For example, the acquisition unit prioritizes acquisition of EEG data when the care recipient is at home. The acquisition unit can also prioritize acquisition of EEG data when the medical care recipient is in a hospital. Furthermore, the acquisition unit can also prioritize acquisition of EEG data when the care recipient is in a specific facility. This allows highly relevant data to be acquired preferentially by taking the geographical location information into account. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input geographical location information to a generation AI and cause the generation AI to acquire highly relevant data.

[0081] The acquisition unit can analyze the social media activity of the care recipient or medical care recipient when acquiring the brain waves and acquire related data. The acquisition unit, for example, uses an algorithm for analyzing the social media activity of the care recipient or medical care recipient. The acquisition unit can also set criteria for acquiring related data based on the social media activity. For example, the acquisition unit can analyze posts made by the care recipient on social media when they are relaxing and acquire brain wave data. The acquisition unit can also analyze posts made by the medical care recipient on social media when they are feeling stressed and acquire brain wave data. Furthermore, the acquisition unit can analyze posts made by the care recipient on social media when they are sleeping and acquire brain wave data. In this way, related brain wave data can be acquired by analyzing social media activity. Some or all of the above-mentioned processing in the acquisition unit can be performed, for example, using AI or without AI. For example, the acquisition unit can input social media activity data to a generation AI and cause the generation AI to acquire related brain wave data.

[0082] The acquisition unit can customize the acquisition method by reflecting past feedback from the care recipient or medical care recipient when acquiring EEG data. The acquisition unit, for example, uses an algorithm for analyzing past feedback from the care recipient or medical care recipient. The acquisition unit can also set criteria for customizing the acquisition method based on past feedback. For example, the acquisition unit customizes the acquisition method for when the care recipient is relaxed based on past feedback from the care recipient. The acquisition unit can also customize the acquisition method for when the care recipient is stressed based on past feedback from the medical care recipient. The acquisition unit can also customize the acquisition method for when the care recipient is sleeping based on past feedback from the care recipient. This allows the acquisition method to be customized by reflecting past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input past feedback data into a generation AI and cause the generation AI to customize the acquisition method.

[0083] The analysis unit can estimate the emotions of the care recipient or medical care recipient and adjust the presentation method of the analysis based on the estimated emotions. The analysis unit, for example, uses an algorithm for estimating the emotions of the care recipient or medical care recipient. The analysis unit can also set criteria for adjusting the presentation method of the analysis based on the estimated emotions. For example, if the care recipient is relaxed, the analysis unit can display the analysis results in a calm manner. If the medical care recipient is feeling stressed, the analysis unit can also display the analysis results in a concise manner. Furthermore, if the care recipient is excited, the analysis unit can visually emphasize the analysis results and display them. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis based on emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input emotion data into a generation AI and have the generation AI adjust the presentation method of the analysis.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the EEG data. The analysis unit, for example, uses an algorithm for evaluating the importance of the EEG data. The analysis unit can also set criteria for adjusting the level of detail of the analysis based on the importance. For example, the analysis unit can analyze EEG data with high importance in detail and analyze data with low importance in a simplified manner. The analysis unit can also prioritize analysis of EEG data with high importance and postpone analysis of data with low importance. Furthermore, the analysis unit can analyze EEG data with high importance in detail and filter data with low importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the EEG data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the EEG data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the category of EEG data. The analysis unit, for example, uses an algorithm for classifying the category of EEG data. The analysis unit can also set criteria for applying different analysis algorithms depending on the category. For example, the analysis unit can apply a specific algorithm to EEG data when the subject is relaxed. The analysis unit can also apply a different algorithm to EEG data when the subject is stressed. Furthermore, the analysis unit can apply a dedicated algorithm to EEG data when the subject is sleeping. This allows for more accurate analysis by applying different analysis algorithms depending on the category of EEG data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the category of EEG data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of the care recipient or medical care recipient. The analysis unit, for example, uses an algorithm for referencing past analysis results of the care recipient or medical care recipient. The analysis unit can also set standards for improving the accuracy of the analysis based on past analysis results. For example, the analysis unit can improve the analysis accuracy when the care recipient is relaxed based on past analysis results of the care recipient. The analysis unit can also improve the analysis accuracy when the medical care recipient is feeling stressed based on past analysis results of the care recipient. Furthermore, the analysis unit can improve the analysis accuracy when the care recipient is sleeping based on past analysis results of the care recipient. In this way, the accuracy of the analysis can be improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0087] The analysis unit can estimate the emotions of the care recipient or medical recipient and adjust the length of the analysis based on the estimated emotions. The analysis unit, for example, uses an algorithm for estimating the emotions of the care recipient or medical recipient. The analysis unit can also set criteria for adjusting the length of the analysis based on the estimated emotions. For example, the analysis unit can shorten the length of the analysis if the care recipient is relaxed. The analysis unit can also shorten the length of the analysis if the medical recipient is stressed. Furthermore, the analysis unit can also lengthen the length of the analysis if the care recipient is excited. In this way, by adjusting the length of the analysis based on emotions, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input emotion data to a generation AI and have the generation AI adjust the length of the analysis.

[0088] During analysis, the analysis unit can determine the priority of analysis based on the time when the EEG data was acquired. The analysis unit, for example, uses an algorithm for evaluating the time when the EEG data was acquired. The analysis unit can also set criteria for determining the priority of analysis based on the time when the data was acquired. For example, the analysis unit can prioritize analyzing the most recent EEG data and postpone analyzing older data. The analysis unit can also prioritize analyzing EEG data from important events. Furthermore, the analysis unit can prioritize analyzing EEG data from specific time periods. This enables efficient analysis by determining the priority of analysis based on the time when the EEG data was acquired. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the EEG data was acquired into the generation AI and have the generation AI determine the priority of analysis.

[0089] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the EEG data. The analysis unit, for example, uses an algorithm for evaluating the relevance of the EEG data. The analysis unit can also set criteria for adjusting the order of analysis based on the relevance. For example, the analysis unit prioritizes analysis of highly relevant EEG data. The analysis unit can also postpone analysis of less relevant EEG data. Furthermore, the analysis unit can analyze highly relevant EEG data in detail and analyze less relevant data simply. This enables efficient analysis by adjusting the order of analysis based on the relevance of the EEG data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the EEG data to the generation AI and cause the generation AI to adjust the order of analysis.

[0090] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the expertise level of the care recipient or medical recipient. The analysis unit, for example, uses an algorithm for evaluating the expertise level of the care recipient or medical recipient. The analysis unit can also set criteria for adjusting the use of technical terms in the analysis according to the expertise level. For example, the analysis unit uses a lot of technical terms if the care recipient has specialized knowledge. The analysis unit can also avoid technical terms if the medical recipient does not have specialized knowledge. Furthermore, the analysis unit can adjust the use of technical terms according to the expertise level of the care recipient. By adjusting the use of technical terms according to the expertise level, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input expertise level data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0091] The generation unit can estimate the emotion of the care recipient or medical care recipient and adjust the generated linguistic expression method based on the estimated emotion. The generation unit, for example, uses an algorithm for estimating the emotion of the care recipient or medical care recipient. The generation unit can also set a criterion for adjusting the linguistic expression method based on the estimated emotion. For example, the generation unit can generate a calm linguistic expression when the care recipient is relaxed. The generation unit can also generate a concise linguistic expression when the medical care recipient is stressed. Furthermore, the generation unit can generate a visually emphasized linguistic expression when the care recipient is excited. In this way, by adjusting the linguistic expression method based on the emotion, more appropriate linguistic expression can be generated. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input emotion data into the generation AI and cause the generation AI to adjust the linguistic expression method.

[0092] The generation unit can adjust the level of detail of the generated linguistic expression based on the importance of the emotional data when generating the linguistic expression. The generation unit, for example, uses an algorithm for evaluating the importance of the emotional data. The generation unit can also set criteria for adjusting the level of detail of the generated linguistic expression based on the importance. For example, the generation unit can generate a detailed linguistic expression based on emotional data with high importance. The generation unit can also generate a simplified linguistic expression based on emotional data with low importance. Furthermore, the generation unit can prioritize verbalizing emotional data with high importance and postpone verbalizing data with low importance. This enables efficient generation of linguistic expressions by adjusting the level of detail of the generated linguistic expression based on the importance of the emotional data. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the importance of the emotional data to the generation AI and cause the generation AI to adjust the level of detail of the generated linguistic expression.

[0093] When generating a linguistic expression, the generation unit can apply different generation algorithms depending on the category of emotional data. The generation unit, for example, uses an algorithm for classifying the category of emotional data. The generation unit can also set criteria for applying different generation algorithms depending on the category. For example, the generation unit can apply a specific generation algorithm to emotional data representing a relaxed state. The generation unit can also apply a different generation algorithm to emotional data representing a stressed state. Furthermore, the generation unit can also apply a dedicated generation algorithm to emotional data representing an excited state. In this way, by applying different generation algorithms depending on the category of emotional data, more appropriate linguistic expressions can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the category of emotional data into the generation AI and cause the generation AI to apply different generation algorithms.

[0094] When generating a linguistic expression, the generation unit can improve the accuracy of the generation by referring to past generation results of the care recipient or medical care recipient. The generation unit, for example, uses an algorithm for referencing past generation results of the care recipient or medical care recipient. The generation unit can also set a standard for improving the accuracy of the generation based on the past generation results. For example, the generation unit can improve the accuracy of the linguistic expression when the care recipient is relaxed based on the past generation results of the care recipient. The generation unit can also improve the accuracy of the linguistic expression when the care recipient is feeling stressed based on the past generation results of the medical care recipient. Furthermore, the generation unit can improve the accuracy of the linguistic expression when the care recipient is excited based on the past generation results of the care recipient. In this way, the accuracy of the generation can be improved by referring to the past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past generation results into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0095] The generation unit can estimate the emotion of the care recipient or medical care recipient and adjust the length of the generated linguistic expression based on the estimated emotion. The generation unit, for example, uses an algorithm for estimating the emotion of the care recipient or medical care recipient. The generation unit can also set a criterion for adjusting the length of the linguistic expression based on the estimated emotion. For example, the generation unit can generate a short linguistic expression when the care recipient is relaxed. The generation unit can also generate a short linguistic expression when the medical care recipient is stressed. Furthermore, the generation unit can generate a long linguistic expression when the care recipient is excited. In this way, by adjusting the length of the linguistic expression based on the emotion, more appropriate linguistic expressions can be generated. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI or without using a generation AI. For example, the generation unit can input emotion data to the generation AI and cause the generation AI to adjust the length of the linguistic expression.

[0096] When generating linguistic expressions, the generation unit can determine the generation priority based on the acquisition time of emotion data. The generation unit, for example, uses an algorithm for evaluating the acquisition time of emotion data. The generation unit can also set criteria for determining the generation priority based on the acquisition time. For example, the generation unit prioritizes generating linguistic expressions based on the latest emotion data. The generation unit can also generate linguistic expressions based on emotion data from an important event. Furthermore, the generation unit can generate linguistic expressions based on emotion data from a specific time period. This enables efficient generation of linguistic expressions by determining the generation priority based on the acquisition time of emotion data. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the acquisition time of emotion data to the generation AI and have the generation AI determine the generation priority.

[0097] The generation unit can adjust the order of generation based on the relevance of emotional data when generating linguistic expressions. The generation unit, for example, uses an algorithm for evaluating the relevance of emotional data. The generation unit can also set criteria for adjusting the order of generation based on the relevance. For example, the generation unit prioritizes generating linguistic expressions based on highly relevant emotional data. The generation unit can also postpone generating less relevant emotional data. Furthermore, the generation unit can generate detailed linguistic expressions based on highly relevant emotional data and generate less relevant data simply. This enables efficient generation of linguistic expressions by adjusting the order of generation based on the relevance of emotional data. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the relevance of emotional data to the generation AI and cause the generation AI to adjust the order of generation.

[0098] When generating a linguistic expression, the generation unit can adjust the use of technical terminology in the generated expression according to the expertise level of the care recipient or medical recipient. The generation unit, for example, uses an algorithm for evaluating the expertise level of the care recipient or medical recipient. The generation unit can also set criteria for adjusting the use of technical terminology in the generated expression according to the expertise level. For example, the generation unit uses a lot of technical terminology if the care recipient has specialized knowledge. The generation unit can also avoid technical terminology if the medical recipient does not have specialized knowledge. The generation unit can also adjust the use of technical terminology according to the expertise level of the care recipient. This allows for the generation of a linguistic expression that is easier to understand by adjusting the use of technical terminology according to the expertise level. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input expertise level data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0099] The display unit can estimate the emotion of the care recipient or medical care recipient and adjust the display method based on the estimated emotion. The display unit, for example, uses an algorithm for estimating the emotion of the care recipient or medical care recipient. The display unit can also set criteria for adjusting the display method based on the estimated emotion. For example, if the care recipient is relaxed, the display unit displays the image in a calm color. If the medical care recipient is feeling stressed, the display unit can provide a simple, highly visible display method. Furthermore, if the care recipient is excited, the display unit can provide a visually emphasized display method. This allows for more appropriate display by adjusting the display method based on the emotion. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input emotion data to a generation AI and have the generation AI adjust the display method.

[0100] The display unit can select the optimal display method by referring to the past display history of the care recipient or medical recipient when displaying the image. The display unit, for example, uses an algorithm for referencing the past display history of the care recipient or medical recipient. The display unit can also set criteria for selecting the optimal display method based on the past display history. For example, the display unit selects a display method for when the care recipient is relaxed based on the past display history of the care recipient. The display unit can also select a display method for when the care recipient is feeling stressed based on the past display history of the medical recipient. Furthermore, the display unit can select a display method for when the care recipient is excited based on the past display history of the care recipient. In this way, the optimal display method can be selected by referring to the past display history. Some or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input past display history data to a generation AI and have the generation AI select the optimal display method.

[0101] The display unit can customize the display content according to the current task of the care recipient or medical care recipient when displaying the display. The display unit, for example, uses an algorithm for evaluating the current task of the care recipient or medical care recipient. The display unit can also set criteria for customizing the display content according to the current task. For example, the display unit customizes the display content according to the task when the care recipient is relaxed. The display unit can also customize the display content according to the task when the medical care recipient is feeling stressed. Furthermore, the display unit can also customize the display content according to the task when the care recipient is excited. This enables more appropriate display by customizing the display content according to the current task. Some or all of the above-mentioned processing in the display unit may be performed, for example, using AI or without AI. For example, the display unit can input current task data into a generation AI and have the generation AI customize the display content.

[0102] The display unit can estimate the emotions of the care recipient or medical recipient and adjust the displayed operation procedures based on the estimated emotions. The display unit, for example, uses an algorithm for estimating the emotions of the care recipient or medical recipient. The display unit can also set criteria for adjusting the displayed operation procedures based on the estimated emotions. For example, the display unit can simplify the operation procedures when the care recipient is relaxed. The display unit can also simplify the operation procedures when the medical recipient is stressed. Furthermore, the display unit can make the operation procedures more detailed when the care recipient is excited. This allows for more appropriate operation by adjusting the displayed operation procedures based on the emotions. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input emotion data to a generation AI and have the generation AI adjust the operation procedures.

[0103] The display unit can select the optimal display method by taking into account the device information of the care recipient or medical recipient. The display unit, for example, uses an algorithm to acquire the device information of the care recipient or medical recipient. The display unit can also set criteria for selecting the optimal display method based on the device information. For example, if the care recipient is using a smartphone, the display unit can provide a display method tailored to the screen size. If the medical recipient is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, if the care recipient is using a smartwatch, the display unit can provide a simple, highly visible display method. This allows the optimal display method to be selected by taking into account the device information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input device information to a generation AI and cause the generation AI to select the optimal display method.

[0104] The display unit can make the display content multilingual according to the language setting of the care recipient or medical care recipient. The display unit, for example, uses an algorithm to acquire the language setting of the care recipient or medical care recipient. The display unit can also set criteria for making the display content multilingual according to the language setting. For example, the display unit automatically sets the display content based on the language setting of the care recipient's device. The display unit can also provide a language switching function when the medical care recipient uses multiple languages. Furthermore, if the care recipient selects a specific language, the display unit can provide the display content in that language. This allows the display content to be multilingual according to the language setting, thereby accommodating a wider range of users. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input language setting data to a generation AI and cause the generation AI to execute multilingual display content. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, analysis unit, generation unit, and display unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit can acquire the brain waves of a care recipient or medical recipient in real time using an EEG sensor of the smart device 14. For example, the analysis unit analyzes the brain wave data using deep learning by the specific processing unit 290 of the data processing device 12 to analyze emotions. For example, the generation unit generates an appropriate linguistic expression based on the emotion data using a Transformer model by the specific processing unit 290 of the data processing device 12. For example, the display unit displays the generated linguistic expression on a monitor by the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described acquisition unit, analysis unit, generation unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit can acquire the brain waves of the care recipient or medical recipient in real time using an EEG sensor of the smart glasses 214. For example, the analysis unit analyzes the brain wave data using deep learning by the specific processing unit 290 of the data processing device 12 to analyze emotions. For example, the generation unit generates an appropriate linguistic expression based on the emotion data using a Transformer model by the specific processing unit 290 of the data processing device 12. For example, the display unit displays the generated linguistic expression on a monitor by the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-described acquisition unit, analysis unit, generation unit, and display unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit can acquire the brain waves of a care recipient or medical recipient in real time using an EEG sensor of the headset-type terminal 314. For example, the analysis unit analyzes the brain wave data using deep learning by the specific processing unit 290 of the data processing device 12 to analyze emotions. For example, the generation unit generates an appropriate linguistic expression based on the emotion data using a Transformer model by the specific processing unit 290 of the data processing device 12. For example, the display unit displays the generated linguistic expression on a monitor by the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, analysis unit, generation unit, and display unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit can acquire the brain waves of the care recipient or medical care recipient in real time using an EEG sensor of the robot 414. For example, the analysis unit analyzes the brain wave data using deep learning by the specific processing unit 290 of the data processing device 12 to analyze emotions. For example, the generation unit generates an appropriate linguistic expression based on the emotion data using a Transformer model by the specific processing unit 290 of the data processing device 12. For example, the display unit displays the linguistic expression generated by the display of the robot 414 on a monitor.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The acquisition unit can filter environmental sounds and background noise when acquiring EEG data from a care recipient or medical care recipient. For example, the acquisition unit can use noise canceling technology to remove environmental sounds from the EEG data. The acquisition unit can also filter noise in a specific frequency band. Furthermore, the acquisition unit can acquire more accurate EEG data by detecting and filtering noise in real time. This can minimize the influence of environmental sounds and background noise and improve the accuracy of the EEG data.

[0107] When analyzing the EEG data of a care recipient or medical care recipient, the analysis unit can refer to past health data and medical history. For example, the analysis unit can refer to the results of a care recipient's past health checkups and reflect them in the analysis of the EEG data. The analysis unit can also refer to the medical care recipient's past treatment history and use it to analyze the EEG data. Furthermore, the analysis unit can refer to the lifestyle data of the care recipient or medical care recipient and use it in the analysis of the EEG data. This makes it possible to provide more accurate analysis results by taking into account past health data and medical history.

[0108] The generation unit can estimate the emotion of the care recipient or medical recipient and adjust the tone of the generated linguistic expression based on the estimated emotion. For example, if the care recipient is relaxed, the generation unit can generate a linguistic expression in a calm tone. If the medical recipient is stressed, the generation unit can also generate a linguistic expression in an encouraging tone. Furthermore, if the care recipient is excited, the generation unit can also generate a linguistic expression in a calm tone. In this way, by adjusting the tone of the linguistic expression based on the emotion, more appropriate communication can be supported.

[0109] When displaying the generated linguistic expression, the display unit can adjust the font size and color according to the visual needs of the care recipient or medical care recipient. For example, the display unit can display the expression in a larger font size for a care recipient with poor eyesight. The display unit can also adjust the color contrast for a medical care recipient with color vision deficiency. Furthermore, the display unit can provide a night mode to ensure visibility even in dark environments. This allows the display unit to accommodate a wider range of users by providing a display method according to visual needs.

[0110] When acquiring EEG data from a care recipient or medical care recipient, the acquisition unit can detect abnormalities in the data in real time and issue an alert. For example, if the acquisition unit detects an abnormal EEG pattern, it notifies the caregiver or medical professional. The acquisition unit can also issue an alert if a specific threshold is exceeded. Furthermore, the acquisition unit can adjust the frequency of abnormality detection and change the sensitivity of the alert as needed. This allows for real-time detection of abnormalities and enables rapid response.

[0111] The analysis unit can estimate the emotions of the care recipient or medical recipient and customize the display method of the analysis results based on the estimated emotions. For example, if the care recipient is relaxed, the analysis unit can display the analysis results in calm colors. If the medical recipient is feeling stressed, the analysis unit can also display the analysis results in a simple, highly visible format. Furthermore, if the care recipient is excited, the analysis unit can also display the analysis results in a visually emphasized manner. This makes it possible to provide more appropriate information by customizing the display method of the analysis results based on emotions.

[0112] The generation unit can estimate the emotion of the care recipient or medical recipient and adjust the format of the generated linguistic expression based on the estimated emotion. For example, if the care recipient is relaxed, the generation unit can generate a linguistic expression in a casual format. If the medical recipient is stressed, the generation unit can also generate a linguistic expression in a formal format. Furthermore, if the care recipient is excited, the generation unit can also generate a linguistic expression in a concise format. In this way, by adjusting the format of the linguistic expression based on the emotion, more appropriate communication can be supported.

[0113] The acquisition unit can dynamically adjust the data acquisition frequency when acquiring brain wave data of a care recipient or medical recipient. For example, the acquisition unit can set the data acquisition frequency low when the care recipient is relaxed. The acquisition unit can also set the data acquisition frequency high when the medical recipient is feeling stressed. Furthermore, the acquisition unit can also appropriately adjust the data acquisition frequency when the care recipient is sleeping. This allows for more efficient data collection by dynamically adjusting the data acquisition frequency according to the situation.

[0114] When analyzing the EEG data of care recipients or medical care recipients, the analysis unit can apply an anomaly detection algorithm to detect abnormal patterns. For example, if the analysis unit detects an abnormal EEG pattern, it can issue an alert. The analysis unit can also refer to past data to improve the accuracy of anomaly detection. Furthermore, the analysis unit can display the results of anomaly detection in real time to support rapid response. This allows for early detection of abnormal patterns and appropriate response.

[0115] When displaying the generated linguistic expression, the display unit can accept voice feedback from the care recipient or medical recipient and update the display content in real time. For example, when the care recipient provides voice feedback, the display unit updates the display based on the content of that feedback. Also, when the medical recipient issues a voice instruction, the display unit can change the display content in accordance with that instruction. Furthermore, the display unit can analyze the content of the voice feedback and provide an appropriate display method. This makes it possible to utilize voice feedback to enable a more interactive display.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The acquisition unit acquires the brain waves of the care recipient or medical care recipient. For example, a dedicated EEG sensor is used to acquire brain waves in real time, and the sensor placement method and data acquisition accuracy can be taken into consideration in order to acquire brain wave data with high accuracy. Step 2: The analysis unit uses deep learning to analyze the EEG data acquired by the acquisition unit and analyze emotions. For example, it uses algorithms such as recurrent neural networks (RNN) and long short-term memory (LSTM) to learn multiple EEG patterns and identify different emotional states. Step 3: The generation unit uses a generative AI to generate language expressions based on the emotion data analyzed by the analysis unit. For example, it uses a Transformer model to generate appropriate language expressions based on the emotion data, evaluates the accuracy of the generated language expressions, and improves the model. Step 4: The display unit displays the linguistic expression generated by the generation unit on a monitor. For example, the display unit displays the generated linguistic expression on the monitor in real time, estimates the emotion of the care recipient or medical care recipient, and adjusts the display method based on the estimated emotion.

[0118] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] 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.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit for acquiring the brain waves of a care recipient or a medical recipient; an analysis unit that analyzes the electroencephalogram data acquired by the acquisition unit and analyzes emotions; a generation unit that generates a linguistic expression based on the emotion data analyzed by the analysis unit; a display unit that displays the linguistic expression generated by the generation unit on a monitor. A system characterized by:

2. The acquisition unit EEG sensors are used to capture brain waves in real time.

2. The system of claim 1.

3. The analysis unit Analyzing emotions by analyzing EEG data using recurrent neural networks (RNN) and long short-term memory (LSTM) 2. The system of claim 1.

4. The generation unit Generate language expressions from emotion data using a transformer model 2. The system of claim 1.

5. The display unit Display the generated linguistic expression on the monitor 2. The system of claim 1.

6. The analysis unit Learn multiple brainwave patterns to identify different emotional states 2. The system of claim 1.

7. The generation unit Evaluate the accuracy of the generated language expressions and improve the model 2. The system of claim 1.

8. The acquisition unit Estimate the emotions of care recipients and medical care recipients, and adjust the timing of brain wave acquisition based on the estimated emotions.

2. The system of claim 1.

Images