system

Abstract

The system according to the embodiment aims to realize natural communication with and monitoring of dementia patients, thereby reducing the burden on caregivers. [Solution] The system according to the embodiment includes a communication unit, a monitoring unit, an acquisition unit, and an analysis unit. The communication unit uses a generative AI to have natural conversations with dementia patients. The monitoring unit monitors the patient's condition using a camera or microphone. The acquisition unit acquires the patient's physical data. The analysis unit analyzes the collected data.

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 does not provide sufficient communication with or supervision over dementia patients, placing a heavy burden on caregivers.

[0005] The system according to the embodiment aims to realize natural communication with and monitoring of dementia patients, thereby reducing the burden on caregivers. [Means for solving the problem]

[0006] The system according to the embodiment includes a communication unit, a monitoring unit, an acquisition unit, and an analysis unit. The communication unit uses a generation AI to have natural conversations with dementia patients. The monitoring unit monitors the patient's condition using a camera or microphone. The acquisition unit acquires the patient's physical data. The analysis unit analyzes the collected data. [Effects of the Invention]

[0007] The system according to the embodiment enables natural communication with and monitoring of dementia patients, thereby reducing the burden on caregivers. [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 system according to an embodiment of the present invention uses generative AI to engage in natural conversations with dementia patients, monitors the patient's condition using cameras and microphones, acquires the patient's physical data, and analyzes the collected data. This system reduces the burden on caregivers by naturally communicating with and monitoring the dementia patient, and by acquiring and analyzing the physical data. This system can reduce the burden on caregivers by naturally communicating with and monitoring the dementia patient, and by acquiring and analyzing the physical data. For example, the system uses generative AI to engage in natural conversations with the dementia patient. For example, when the patient asks, "What day is it today?", the generative AI provides an appropriate answer. Next, the system monitors the patient's condition using cameras and microphones. For example, the system can monitor the patient's movements around the room and what they are talking about in real time. Furthermore, the system acquires the patient's physical data. For example, the system periodically measures vital signs such as heart rate and body temperature and collects the data. The collected data is analyzed using ICT technology and provided to caregivers. For example, the system can analyze changes in the patient's behavioral patterns and health condition and provide appropriate advice to the caregiver. This allows the system to reduce the burden on caregivers and improve patient safety.

[0029] A care support system according to an embodiment includes a communication unit, a monitoring unit, an acquisition unit, and an analysis unit. The communication unit uses a generation AI to have natural conversations with a dementia patient. For example, when the patient asks, "What day is it today?", the generation AI provides an appropriate answer. The generation AI can also estimate the patient's emotions and select conversation topics based on the estimated emotions. The monitoring unit monitors the patient's condition using a camera and a microphone. For example, the monitoring unit monitors the patient's movements around the room and what they are talking about in real time. The monitoring unit can also estimate the patient's emotions and adjust the frequency of monitoring based on the estimated emotions. The acquisition unit periodically measures vital signs such as heart rate and body temperature and collects data. For example, the acquisition unit measures heart rate and body temperature to constantly monitor the patient's health condition. The acquisition unit can also estimate the patient's emotions and adjust the frequency of vital sign measurements based on the estimated emotions. The analysis unit analyzes the collected data using ICT technology. For example, the analysis unit can analyze changes in the patient's behavioral patterns and health status and provide appropriate advice to caregivers. The analysis unit can also estimate the patient's emotions and determine the priority of analysis based on the estimated emotions. As a result, the care support system according to the embodiment can reduce the burden on caregivers by naturally communicating with and monitoring dementia patients and acquiring and analyzing their physical data.

[0030] The care support system includes a detection unit that detects abnormalities. The detection unit detects abnormalities. For example, the detection unit can detect abnormalities in heart rate. The detection unit can also detect abnormalities in behavior. Furthermore, the detection unit can estimate the patient's emotions and adjust the criteria for abnormality detection based on the estimated emotions. For example, if the patient is feeling anxious, the detection unit can set stricter criteria for abnormality detection. Also, if the patient is relaxed, the detection unit can set lenient criteria for abnormality detection. This allows for rapid response by detecting abnormalities.

[0031] The care support system includes a notification unit that notifies a caregiver. When an abnormality is detected, the notification unit notifies the caregiver. For example, the notification unit can notify the caregiver by email or an alert. The notification unit can also estimate the patient's emotions and adjust the notification method based on the estimated emotions. For example, if the patient is feeling anxious, the notification unit can notify in a gentle tone. Also, if the patient is relaxed, the notification unit can notify in a normal tone. This allows the caregiver to be quickly notified when an abnormality is detected.

[0032] The care support system includes a providing unit that provides advice based on collected data. The providing unit provides advice based on the collected data. For example, the providing unit can provide advice regarding health management for the patient. The providing unit can also make suggestions for improving the patient's lifestyle habits. Furthermore, the providing unit can estimate the patient's emotions and adjust the content of the advice based on the estimated emotions. For example, if the patient is feeling anxious, the providing unit can provide advice that gives a sense of security. Also, if the patient is relaxed, the providing unit can provide normal advice. In this way, appropriate advice can be provided based on the collected data.

[0033] The communication department can use generative AI to have natural conversations. Generative AI conducts natural conversations. For example, when a patient asks, "What day is it today?", the generative AI provides an appropriate answer. Generative AI can also estimate the patient's emotions and select a conversation topic based on the estimated emotions. For example, if the patient is feeling anxious, the generative AI can select a relaxing topic and engage in a conversation that gives the patient a sense of security. Also, if the patient is excited, the generative AI can select a calming topic to calm the patient down and engage in a conversation that helps them regain their composure. This makes natural conversation possible using generative AI.

[0034] The monitoring unit can monitor the patient's condition in real time using a camera or a microphone. The monitoring unit can monitor the patient's condition in real time using a camera or a microphone. For example, the monitoring unit can monitor the patient's movements around the room and what they are talking about in real time. The monitoring unit can also estimate the patient's emotions and adjust the frequency of monitoring based on the estimated emotions. For example, if the patient is feeling anxious, the monitoring frequency can be increased to provide a sense of security. Also, if the patient is relaxed, the monitoring frequency can be reduced to respect their privacy. This makes it possible to monitor the patient's condition in real time.

[0035] The acquisition unit can periodically measure vital signs such as heart rate or body temperature. The acquisition unit periodically measures vital signs such as heart rate or body temperature. For example, the acquisition unit can measure heart rate or body temperature to constantly monitor the patient's health condition. The acquisition unit can also estimate the patient's emotions and adjust the frequency of vital sign measurements based on the estimated emotions. For example, if the patient is feeling anxious, the frequency of vital sign measurements can be increased to monitor the patient's health condition. Also, if the patient is relaxed, the frequency of vital sign measurements can be reduced to respect the patient's privacy. This allows the patient's vital signs to be periodically measured.

[0036] The analysis unit can analyze the collected data using ICT technology. The analysis unit analyzes the collected data using ICT technology. For example, the analysis unit can analyze changes in a patient's behavioral patterns and health condition and provide appropriate advice to caregivers. The analysis unit can also estimate the patient's emotions and determine the priority of analysis based on the estimated emotions. For example, if the patient is feeling anxious, priority can be given to analyzing emotional data. Also, if the patient is relaxed, priority can be given to analyzing health data. In this way, the analysis of collected data is possible using ICT technology.

[0037] The communication unit can analyze the patient's past conversation history and provide optimal conversation content. The communication unit uses generation AI to analyze the patient's past conversation history and provide optimal conversation content. For example, based on what the patient has said in the past, the generation AI can provide related topics and continue the conversation. The generation AI can also revisit topics that the patient has shown interest in in the past and engage in more in-depth conversation. Furthermore, the generation AI can avoid topics that the patient has avoided in the past and provide new topics. This makes it possible to provide optimal conversation content based on past conversation history.

[0038] The communication unit can adjust the depth of conversation based on the patient's interests and concerns during a conversation. The communication unit uses the generation AI to adjust the depth of conversation based on the patient's interests and concerns during a conversation. For example, if the patient shows strong interest in a particular topic, the generation AI can provide detailed information and engage in an in-depth conversation. Also, if the patient shows interest in a general topic, the generation AI can provide concise information and engage in light conversation. Furthermore, if the patient shows interest in a new topic, the generation AI can provide basic information and engage in introductory conversation. This makes it possible to adjust the depth of conversation based on the patient's interests and concerns.

[0039] The communication unit can adjust the speed and expression of conversation according to the patient's cognitive state during conversation. The communication unit uses the generation AI to adjust the speed and expression of conversation according to the patient's cognitive state during conversation. For example, if the patient's cognitive state is declining, the generation AI can converse at a slower speed using simple expressions. On the other hand, if the patient's cognitive state is good, the generation AI can converse at a normal speed using detailed expressions. Furthermore, if the patient's cognitive state fluctuates, the generation AI can also adjust the speed and expression in real time while conversing. This makes it possible to adjust the speed and expression of conversation according to the patient's cognitive state.

[0040] The communication unit can provide highly relevant topics based on the patient's geographic location information during conversation. The communication unit uses the generation AI to provide highly relevant topics taking the patient's geographic location information into consideration during conversation. For example, if the patient is at home, the generation AI can provide topics related to the patient's home and engage in friendly conversation. If the patient is in a hospital, the generation AI can provide topics related to the hospital and engage in conversation that gives the patient a sense of security. Furthermore, if the patient is out and about, the generation AI can provide topics related to the patient's outing and engage in interesting conversation. This makes it possible to provide highly relevant topics based on the patient's geographic location information.

[0041] The communication unit can analyze the patient's social media activity and provide relevant topics during conversations. The communication unit uses a generative AI to analyze the patient's social media activity and provide relevant topics during conversations. For example, based on the content the patient has shared on social media, the generative AI can provide relevant topics and engage in empathetic conversations. The generative AI can also provide in-depth topics based on the content the patient has shown interest in on social media. Furthermore, based on the activity of the patient's friends on social media, the generative AI can provide related topics and engage in conversations on common topics. This makes it possible to provide relevant topics based on the patient's social media activity.

[0042] The communication unit can customize the content of conversations by reflecting the patient's past feedback. The communication unit uses the generation AI to customize the content of conversations by reflecting the patient's past feedback. For example, based on topics that the patient liked in the past, the generation AI can offer those topics again, leading to an enjoyable conversation. Also, based on topics that the patient avoided in the past, the generation AI can offer new topics and lead to an interesting conversation. Furthermore, based on the patient's past feedback, the generation AI can adjust the content of the conversation and hold an optimal conversation. This makes it possible to customize the content of conversations based on the patient's past feedback.

[0043] The monitoring unit analyzes the patient's behavior patterns during monitoring and can detect abnormal behavior early. The monitoring unit uses a camera and a microphone to analyze the patient's behavior patterns during monitoring and can detect abnormal behavior early. For example, if the patient behaves differently than usual, the camera and microphone can detect the abnormal behavior. Also, if the patient wakes up frequently at night, the behavior pattern can be analyzed to detect abnormal behavior early. Furthermore, if the patient behaves abnormally during a specific time period, the pattern can be analyzed to detect the abnormal behavior early. In this way, abnormal behavior can be detected early by analyzing the patient's behavior pattern.

[0044] The monitoring unit can adjust the timing of monitoring according to the patient's lifestyle rhythm when monitoring. The monitoring unit adjusts the timing of monitoring according to the patient's lifestyle rhythm when monitoring using a camera or microphone. For example, if the patient is active during the day, the monitoring frequency during the day can be increased. Also, if the patient rests at night, the monitoring frequency at night can be reduced. Furthermore, if the patient's lifestyle rhythm fluctuates, the monitoring timing can also be adjusted according to that rhythm. In this way, the monitoring timing can be adjusted according to the patient's lifestyle rhythm.

[0045] The monitoring unit can improve the accuracy of monitoring by referring to the patient's past history of abnormal behavior during monitoring. The monitoring unit can improve the accuracy of monitoring by referring to the patient's past history of abnormal behavior during monitoring using a camera or microphone. For example, the accuracy of monitoring using a camera or microphone can be improved based on the patient's past history of abnormal behavior. The accuracy of monitoring can also be improved by referring to patterns of abnormal behavior that the patient has exhibited in the past. Furthermore, the accuracy of monitoring can also be improved by analyzing the patient's history of abnormal behavior. In this way, the accuracy of monitoring can be improved by referring to the patient's past history of abnormal behavior.

[0046] The monitoring unit can adjust the monitoring range based on the geographical location information of the patient when monitoring. When monitoring using a camera or microphone, the monitoring unit adjusts the monitoring range taking into account the geographical location information of the patient. For example, if the patient is at home, the monitoring range is limited to the home. Also, if the patient is out, the monitoring range can be expanded to include the patient's location. Furthermore, if the patient is in a specific location, the monitoring range can also be adjusted according to that location. This makes it possible to adjust the monitoring range based on the geographical location information of the patient.

[0047] The monitoring unit can improve the accuracy of monitoring by referring to literature related to the patient during monitoring. The monitoring unit can improve the accuracy of monitoring by referring to literature related to the patient during monitoring using a camera or microphone. For example, the accuracy of monitoring can be improved by referring to literature related to the patient's medical history. The accuracy of monitoring can also be improved by referring to literature related to the patient's behavioral patterns. Furthermore, the accuracy of monitoring can also be improved by referring to literature related to the patient's health condition. In this way, the accuracy of monitoring can be improved by referring to literature related to the patient.

[0048] The monitoring unit can adjust the monitoring method based on the market value of the patient when monitoring. When monitoring using a camera or microphone, the monitoring unit adjusts the monitoring method taking into account the market value of the patient. For example, if the market value of the patient is high, the monitoring method can be strengthened to ensure safety. Also, if the market value of the patient is low, the monitoring method can be simplified to reduce costs. Furthermore, the monitoring method can also be appropriately adjusted according to the market value of the patient. This makes it possible to adjust the monitoring method based on the market value of the patient.

[0049] The acquisition unit can improve the accuracy of measurement by referring to the patient's past health data when measuring vital signs. The acquisition unit periodically measures vital signs such as heart rate and body temperature, and improves the accuracy of measurement by referring to the patient's past health data when measuring vital signs. For example, the acquisition unit can improve the accuracy of current heart rate measurement by referring to the patient's past heart rate data. The acquisition unit can also improve the accuracy of current body temperature measurement by referring to the patient's past body temperature data. Furthermore, the acquisition unit can improve the accuracy of current measurement by referring to the patient's past vital sign data. In this way, the accuracy of measurement can be improved by referring to the patient's past health data.

[0050] The acquisition unit can adjust the timing of measurement according to the patient's lifestyle rhythm when measuring vital signs. The acquisition unit periodically measures vital signs such as heart rate and body temperature, and adjusts the timing of measurement according to the patient's lifestyle rhythm when measuring vital signs. For example, if the patient is active in the morning, the frequency of measurement can be increased in the morning. Also, if the patient rests at night, the frequency of measurement can be reduced in the evening. Furthermore, if the patient's lifestyle rhythm fluctuates, the timing of measurement can also be adjusted according to that rhythm. This makes it possible to adjust the timing of measurement according to the patient's lifestyle rhythm.

[0051] The acquisition unit can improve the measurement method by reflecting patient feedback when measuring vital signs. The acquisition unit periodically measures vital signs such as heart rate and body temperature, and improves the measurement method by reflecting patient feedback when measuring vital signs. For example, if the patient is dissatisfied with the measurement method, the measurement method can be improved based on the feedback. Also, if the patient is satisfied with the measurement method, that method can be continued to be used. Furthermore, a new measurement method can be introduced and improved based on patient feedback. This makes it possible to improve the measurement method based on patient feedback.

[0052] The acquisition unit can adjust the measurement range based on the patient's geographical location information when measuring vital signs. The acquisition unit periodically measures vital signs such as heart rate and body temperature, and adjusts the measurement range taking the patient's geographical location information into consideration when measuring vital signs. For example, if the patient is at home, the measurement range can be set within the home. Also, if the patient is out, the measurement range can be set when the patient is away from home. Furthermore, if the patient is in a specific location, the measurement range can also be adjusted according to that location. This makes it possible to adjust the measurement range based on the patient's geographical location information.

[0053] The acquisition unit can analyze the patient's social media activity when measuring vital signs to improve the accuracy of the measurement. The acquisition unit periodically measures vital signs such as heart rate and body temperature, and analyzes the patient's social media activity when measuring vital signs to improve the accuracy of the measurement. For example, the accuracy of the measurement can be improved based on health information shared by the patient on social media. The patient's social media activity can also be analyzed to improve the accuracy of the measurement. Furthermore, the accuracy of the measurement can be improved by referring to the health information of the patient's friends on social media. In this way, the accuracy of the measurement can be improved based on the patient's social media activity.

[0054] The acquisition unit can customize the measurement method by reflecting the patient's past feedback when measuring vital signs. The acquisition unit periodically measures vital signs such as heart rate and body temperature, and customizes the measurement method by reflecting the patient's past feedback when measuring vital signs. For example, a customized measurement method can be provided based on a measurement method that the patient has preferred in the past. Also, a new measurement method can be provided based on a measurement method that the patient has avoided in the past. Furthermore, the optimal measurement method can be customized based on the patient's past feedback. This makes it possible to customize the measurement method based on the patient's past feedback.

[0055] The analysis unit can optimize the analysis algorithm by referring to past analysis data during analysis. The analysis unit uses ICT technology to optimize the analysis algorithm by referring to past analysis data during analysis. For example, the current analysis algorithm is optimized based on past analysis data. The accuracy of the algorithm can also be improved by referring to past analysis results. Furthermore, past data can be analyzed and the optimal analysis algorithm can be introduced. This makes it possible to optimize the analysis algorithm by referring to past analysis data.

[0056] The analysis unit analyzes the patient's behavioral patterns during analysis, enabling early detection of abnormal behavior. The analysis unit uses ICT technology to analyze the patient's behavioral patterns during analysis, enabling early detection of abnormal behavior. For example, the analysis unit analyzes the patient's behavioral patterns and detects abnormal behavior early. It can also identify abnormal behavior patterns based on the patient's behavioral data. It can also refer to the patient's behavioral history to detect abnormal behavior early. This allows early detection of abnormal behavior by analyzing the patient's behavioral patterns.

[0057] The analysis unit can adjust the timing of analysis during analysis according to the patient's lifestyle rhythm. The analysis unit uses ICT technology to adjust the timing of analysis during analysis according to the patient's lifestyle rhythm. For example, if the patient is active during the day, the frequency of analysis during the day can be increased. Also, if the patient rests at night, the frequency of analysis at night can be reduced. Furthermore, if the patient's lifestyle rhythm fluctuates, the timing of analysis can also be adjusted according to that rhythm. This makes it possible to adjust the timing of analysis according to the patient's lifestyle rhythm.

[0058] The analysis unit can adjust the range of analysis based on the patient's geographic location information during analysis. The analysis unit uses ICT technology to adjust the range of analysis taking into account the patient's geographic location information during analysis. For example, if the patient is at home, the analysis range is set to within the home. Also, if the patient is out, the analysis range can be set to when the patient is away from home. Furthermore, if the patient is in a specific location, the analysis range can also be adjusted according to that location. This makes it possible to adjust the range of analysis based on the patient's geographic location information.

[0059] The analysis unit can improve the accuracy of the analysis by referring to literature related to the patient during analysis. The analysis unit uses ICT technology to improve the accuracy of the analysis by referring to literature related to the patient during analysis. For example, the analysis accuracy can be improved by referring to literature related to the patient's medical history. The analysis accuracy can also be improved by referring to literature related to the patient's behavioral patterns. Furthermore, the analysis accuracy can be improved by referring to literature related to the patient's health condition. In this way, the analysis accuracy can be improved by referring to literature related to the patient.

[0060] The analysis unit can adjust the analysis method based on the patient's market value during analysis. The analysis unit uses ICT technology to adjust the analysis method taking into account the patient's market value during analysis. For example, if the patient's market value is high, the analysis method can be strengthened to improve accuracy. Also, if the patient's market value is low, the analysis method can be simplified to reduce costs. Furthermore, the analysis method can also be appropriately adjusted according to the patient's market value. This makes it possible to adjust the analysis method based on the patient's market value.

[0061] When detecting an abnormality, the detection unit can improve the accuracy of detection by referring to the patient's past abnormal behavior history. When detecting an abnormality, the detection unit improves the accuracy of detection by referring to the patient's past abnormal behavior history. For example, the accuracy of current abnormality detection can be improved based on the patient's past abnormal behavior history. In addition, the detection accuracy can be improved by referring to the patient's past abnormal behavior pattern. Furthermore, the patient's abnormal behavior history can be analyzed to improve the accuracy of detection. In this way, by referring to the patient's past abnormal behavior history, the detection accuracy can be improved.

[0062] The detection unit analyzes the patient's behavioral patterns when an abnormality is detected, and can detect abnormal behavior early. The detection unit analyzes the patient's behavioral patterns when an abnormality is detected, and can detect abnormal behavior early. For example, the detection unit analyzes the patient's behavioral patterns and can detect abnormal behavior early. Furthermore, the detection unit can identify abnormal behavior patterns based on the patient's behavioral data. Furthermore, the detection unit can also refer to the patient's behavioral history to detect abnormal behavior early. As a result, abnormal behavior can be detected early by analyzing the patient's behavioral patterns.

[0063] When detecting an abnormality, the detection unit can adjust the detection range based on the geographical location information of the patient. When detecting an abnormality, the detection unit adjusts the detection range taking into account the geographical location information of the patient. For example, if the patient is at home, the abnormality detection range can be set within the home. Also, if the patient is out, the abnormality detection range can be set when the patient is away from home. Furthermore, if the patient is in a specific location, the abnormality detection range can also be adjusted according to that location. In this way, the detection range can be adjusted based on the geographical location information of the patient.

[0064] The detection unit can improve the accuracy of detection by referring to literature related to the patient when detecting an abnormality. The detection unit can improve the accuracy of detection by referring to literature related to the patient when detecting an abnormality. For example, the detection unit can improve the accuracy of abnormality detection by referring to literature related to the patient's medical history. The detection unit can also improve the accuracy of abnormality detection by referring to literature related to the patient's behavioral patterns. The detection unit can also improve the accuracy of abnormality detection by referring to literature related to the patient's health condition. In this way, the detection accuracy can be improved by referring to literature related to the patient.

[0065] The notification unit can improve the accuracy of the notification by referring to the patient's past abnormal behavior history when making a notification. The notification unit can improve the accuracy of the notification by referring to the patient's past abnormal behavior history when making a notification. For example, the accuracy of the current notification can be improved based on the patient's past abnormal behavior history. The notification accuracy can also be improved by referring to the patient's past abnormal behavior patterns. Furthermore, the patient's abnormal behavior history can be analyzed to improve the accuracy of the notification. In this way, the accuracy of the notification can be improved by referring to the patient's past abnormal behavior history.

[0066] The notification unit can analyze the patient's behavioral patterns at the time of notification and provide early notification of abnormal behavior. The notification unit can analyze the patient's behavioral patterns at the time of notification and provide early notification of abnormal behavior. For example, the notification unit analyzes the patient's behavioral patterns and provides early notification of abnormal behavior. Furthermore, it can identify and provide notification of abnormal behavior patterns based on the patient's behavioral data. Furthermore, it can also refer to the patient's behavioral history and provide early notification of abnormal behavior. In this way, it is possible to analyze the patient's behavioral patterns and provide early notification of abnormal behavior.

[0067] The notification unit can adjust the range of notification based on the geographical location information of the patient at the time of notification. The notification unit adjusts the range of notification taking into account the geographical location information of the patient at the time of notification. For example, if the patient is at home, the notification range within the home can be set. Also, if the patient is out, the notification range can be set when the patient is away from home. Furthermore, if the patient is in a specific location, the notification range can also be adjusted according to that location. In this way, the range of notification can be adjusted based on the geographical location information of the patient.

[0068] The notification unit can improve the accuracy of the notification by referring to literature related to the patient when making a notification. The notification unit can improve the accuracy of the notification by referring to literature related to the patient when making a notification. For example, the notification unit can improve the accuracy of the notification by referring to literature related to the patient's medical history. The notification unit can also improve the accuracy of the notification by referring to literature related to the patient's behavioral patterns. The notification unit can also improve the accuracy of the notification by referring to literature related to the patient's health condition. In this way, the notification accuracy can be improved by referring to literature related to the patient.

[0069] When providing advice, the providing unit can provide optimal advice by referring to the patient's past behavioral history. When providing advice, the providing unit can provide optimal advice by referring to the patient's past behavioral history. For example, optimal advice is provided based on the patient's past behavioral history. Also, optimal advice can be provided by referring to the patient's past behavioral patterns. Furthermore, the patient's behavioral history can be analyzed and optimal advice can be provided. In this way, optimal advice can be provided by referring to the patient's past behavioral history.

[0070] The providing unit can adjust the timing of advice according to the patient's lifestyle rhythm when providing advice. The providing unit can adjust the timing of advice according to the patient's lifestyle rhythm when providing advice. For example, if the patient is active in the morning, the frequency of providing advice in the morning can be increased. Also, if the patient rests at night, the frequency of providing advice in the evening can be reduced. Furthermore, if the patient's lifestyle rhythm fluctuates, the timing of advice can also be adjusted according to that rhythm. In this way, the timing of advice can be adjusted according to the patient's lifestyle rhythm.

[0071] When providing advice, the providing unit can provide optimal advice based on the geographical location information of the patient. When providing advice, the providing unit provides optimal advice taking into account the geographical location information of the patient. For example, if the patient is at home, optimal advice for the home can be provided. Also, if the patient is out, optimal advice for when the patient is out can be provided. Furthermore, if the patient is in a specific location, optimal advice can be provided according to that location. This makes it possible to provide optimal advice based on the geographical location information of the patient.

[0072] The providing unit, when providing advice, can analyze the patient's social media activity and provide relevant advice. The providing unit, when providing advice, can analyze the patient's social media activity and provide relevant advice. For example, relevant advice can be provided based on content shared by the patient on social media. The providing unit can also analyze the patient's social media activity and provide relevant advice. Furthermore, relevant advice can be provided by referring to the activities of the patient's friends on social media. In this way, relevant advice can be provided based on the patient's social media activity.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The communication department uses generative AI to have natural conversations with dementia patients. For example, when a patient asks, "What day is it today?", the generative AI provides an appropriate answer. The generative AI can also estimate the patient's emotions and select conversation topics based on the estimated emotions. Step 2: The monitoring unit monitors the patient's condition using a camera and microphone. For example, the monitoring unit monitors in real time how the patient walks around the room and what they are talking about. The monitoring unit can also estimate the patient's emotions and adjust the frequency of monitoring based on the estimated emotions. Step 3: The acquisition unit periodically measures vital signs, such as heart rate and body temperature, and collects data. For example, the acquisition unit measures heart rate and body temperature to constantly monitor the patient's health condition. The acquisition unit can also estimate the patient's emotions and adjust the frequency of vital sign measurements based on the estimated emotions. Step 4: The analysis unit uses ICT technology to analyze the collected data. For example, the analysis unit analyzes changes in the patient's behavioral patterns and health status and provides appropriate advice to caregivers. The analysis unit can also estimate the patient's emotions and determine the priority of analysis based on the estimated emotions.

[0076] (Example 2) A system according to an embodiment of the present invention uses generative AI to engage in natural conversations with dementia patients, monitors the patient's condition using cameras and microphones, acquires the patient's physical data, and analyzes the collected data. This system reduces the burden on caregivers by naturally communicating with and monitoring the dementia patient, and by acquiring and analyzing the physical data. This system can reduce the burden on caregivers by naturally communicating with and monitoring the dementia patient, and by acquiring and analyzing the physical data. For example, the system uses generative AI to engage in natural conversations with the dementia patient. For example, when the patient asks, "What day is it today?", the generative AI provides an appropriate answer. Next, the system monitors the patient's condition using cameras and microphones. For example, the system can monitor the patient's movements around the room and what they are talking about in real time. Furthermore, the system acquires the patient's physical data. For example, the system periodically measures vital signs such as heart rate and body temperature and collects the data. The collected data is analyzed using ICT technology and provided to caregivers. For example, the system can analyze changes in the patient's behavioral patterns and health condition and provide appropriate advice to the caregiver. This allows the system to reduce the burden on caregivers and improve patient safety.

[0077] A care support system according to an embodiment includes a communication unit, a monitoring unit, an acquisition unit, and an analysis unit. The communication unit uses a generation AI to have natural conversations with a dementia patient. For example, when the patient asks, "What day is it today?", the generation AI provides an appropriate answer. The generation AI can also estimate the patient's emotions and select conversation topics based on the estimated emotions. The monitoring unit monitors the patient's condition using a camera and a microphone. For example, the monitoring unit monitors the patient's movements around the room and what they are talking about in real time. The monitoring unit can also estimate the patient's emotions and adjust the frequency of monitoring based on the estimated emotions. The acquisition unit periodically measures vital signs such as heart rate and body temperature and collects data. For example, the acquisition unit measures heart rate and body temperature to constantly monitor the patient's health condition. The acquisition unit can also estimate the patient's emotions and adjust the frequency of vital sign measurements based on the estimated emotions. The analysis unit analyzes the collected data using ICT technology. For example, the analysis unit can analyze changes in the patient's behavioral patterns and health status and provide appropriate advice to caregivers. The analysis unit can also estimate the patient's emotions and determine the priority of analysis based on the estimated emotions. As a result, the care support system according to the embodiment can reduce the burden on caregivers by naturally communicating with and monitoring dementia patients and acquiring and analyzing their physical data.

[0078] The care support system includes a detection unit that detects abnormalities. The detection unit detects abnormalities. For example, the detection unit can detect abnormalities in heart rate. The detection unit can also detect abnormalities in behavior. Furthermore, the detection unit can estimate the patient's emotions and adjust the criteria for abnormality detection based on the estimated emotions. For example, if the patient is feeling anxious, the detection unit can set stricter criteria for abnormality detection. Also, if the patient is relaxed, the detection unit can set lenient criteria for abnormality detection. This allows for rapid response by detecting abnormalities.

[0079] The care support system includes a notification unit that notifies a caregiver. When an abnormality is detected, the notification unit notifies the caregiver. For example, the notification unit can notify the caregiver by email or an alert. The notification unit can also estimate the patient's emotions and adjust the notification method based on the estimated emotions. For example, if the patient is feeling anxious, the notification unit can notify in a gentle tone. Also, if the patient is relaxed, the notification unit can notify in a normal tone. This allows the caregiver to be quickly notified when an abnormality is detected.

[0080] The care support system includes a providing unit that provides advice based on collected data. The providing unit provides advice based on the collected data. For example, the providing unit can provide advice regarding health management for the patient. The providing unit can also make suggestions for improving the patient's lifestyle habits. Furthermore, the providing unit can estimate the patient's emotions and adjust the content of the advice based on the estimated emotions. For example, if the patient is feeling anxious, the providing unit can provide advice that gives a sense of security. Also, if the patient is relaxed, the providing unit can provide normal advice. In this way, appropriate advice can be provided based on the collected data.

[0081] The communication department can use generative AI to have natural conversations. Generative AI conducts natural conversations. For example, when a patient asks, "What day is it today?", the generative AI provides an appropriate answer. Generative AI can also estimate the patient's emotions and select a conversation topic based on the estimated emotions. For example, if the patient is feeling anxious, the generative AI can select a relaxing topic and engage in a conversation that gives the patient a sense of security. Also, if the patient is excited, the generative AI can select a calming topic to calm the patient down and engage in a conversation that helps them regain their composure. This makes natural conversation possible using generative AI.

[0082] The monitoring unit can monitor the patient's condition in real time using a camera or a microphone. The monitoring unit can monitor the patient's condition in real time using a camera or a microphone. For example, the monitoring unit can monitor the patient's movements around the room and what they are talking about in real time. The monitoring unit can also estimate the patient's emotions and adjust the frequency of monitoring based on the estimated emotions. For example, if the patient is feeling anxious, the monitoring frequency can be increased to provide a sense of security. Also, if the patient is relaxed, the monitoring frequency can be reduced to respect their privacy. This makes it possible to monitor the patient's condition in real time.

[0083] The acquisition unit can periodically measure vital signs such as heart rate or body temperature. The acquisition unit periodically measures vital signs such as heart rate or body temperature. For example, the acquisition unit can measure heart rate or body temperature to constantly monitor the patient's health condition. The acquisition unit can also estimate the patient's emotions and adjust the frequency of vital sign measurements based on the estimated emotions. For example, if the patient is feeling anxious, the frequency of vital sign measurements can be increased to monitor the patient's health condition. Also, if the patient is relaxed, the frequency of vital sign measurements can be reduced to respect the patient's privacy. This allows the patient's vital signs to be periodically measured.

[0084] The analysis unit can analyze the collected data using ICT technology. The analysis unit analyzes the collected data using ICT technology. For example, the analysis unit can analyze changes in a patient's behavioral patterns and health condition and provide appropriate advice to caregivers. The analysis unit can also estimate the patient's emotions and determine the priority of analysis based on the estimated emotions. For example, if the patient is feeling anxious, priority can be given to analyzing emotional data. Also, if the patient is relaxed, priority can be given to analyzing health data. In this way, the analysis of collected data is possible using ICT technology.

[0085] The communication unit can estimate the patient's emotions and select a conversation topic based on the estimated patient emotions. The communication unit uses the generation AI to estimate the patient's emotions and select a conversation topic based on the estimated emotions. For example, if the patient is feeling anxious, the generation AI can select a relaxing topic and engage in a conversation that provides reassurance. Also, if the patient is agitated, the generation AI can select a calming topic to calm the patient and engage in a conversation that restores composure. Furthermore, if the patient is feeling lonely, the generation AI can select a topic that shows empathy and engage in a conversation that is considerate of the patient's feelings. This makes it possible to select an appropriate conversation topic based on the patient's emotions.

[0086] The communication unit can analyze the patient's past conversation history and provide optimal conversation content. The communication unit uses generation AI to analyze the patient's past conversation history and provide optimal conversation content. For example, based on what the patient has said in the past, the generation AI can provide related topics and continue the conversation. The generation AI can also revisit topics that the patient has shown interest in in the past and engage in more in-depth conversation. Furthermore, the generation AI can avoid topics that the patient has avoided in the past and provide new topics. This makes it possible to provide optimal conversation content based on past conversation history.

[0087] The communication unit can adjust the depth of conversation based on the patient's interests and concerns during a conversation. The communication unit uses the generation AI to adjust the depth of conversation based on the patient's interests and concerns during a conversation. For example, if the patient shows strong interest in a particular topic, the generation AI can provide detailed information and engage in an in-depth conversation. Also, if the patient shows interest in a general topic, the generation AI can provide concise information and engage in light conversation. Furthermore, if the patient shows interest in a new topic, the generation AI can provide basic information and engage in introductory conversation. This makes it possible to adjust the depth of conversation based on the patient's interests and concerns.

[0088] The communication unit can adjust the speed and expression of conversation according to the patient's cognitive state during conversation. The communication unit uses the generation AI to adjust the speed and expression of conversation according to the patient's cognitive state during conversation. For example, if the patient's cognitive state is declining, the generation AI can converse at a slower speed using simple expressions. On the other hand, if the patient's cognitive state is good, the generation AI can converse at a normal speed using detailed expressions. Furthermore, if the patient's cognitive state fluctuates, the generation AI can also adjust the speed and expression in real time while conversing. This makes it possible to adjust the speed and expression of conversation according to the patient's cognitive state.

[0089] The communication unit can estimate the patient's emotions and adjust the tone of the conversation based on the estimated patient's emotions. The communication unit uses the generation AI to estimate the patient's emotions and adjust the tone of the conversation based on the estimated emotions. For example, if the patient is sad, the generation AI can speak to them in a gentle tone and engage in comforting conversation. If the patient is happy, the generation AI can speak to them in a bright tone and engage in empathetic conversation. Furthermore, if the patient is angry, the generation AI can speak to them in a calm tone and engage in calming conversation. This makes it possible to adjust the tone of the conversation based on the patient's emotions.

[0090] The communication unit can provide highly relevant topics based on the patient's geographic location information during conversation. The communication unit uses the generation AI to provide highly relevant topics taking the patient's geographic location information into consideration during conversation. For example, if the patient is at home, the generation AI can provide topics related to the patient's home and engage in friendly conversation. If the patient is in a hospital, the generation AI can provide topics related to the hospital and engage in conversation that gives the patient a sense of security. Furthermore, if the patient is out and about, the generation AI can provide topics related to the patient's outing and engage in interesting conversation. This makes it possible to provide highly relevant topics based on the patient's geographic location information.

[0091] The communication unit can analyze the patient's social media activity and provide relevant topics during conversations. The communication unit uses a generative AI to analyze the patient's social media activity and provide relevant topics during conversations. For example, based on the content the patient has shared on social media, the generative AI can provide relevant topics and engage in empathetic conversations. The generative AI can also provide in-depth topics based on the content the patient has shown interest in on social media. Furthermore, based on the activity of the patient's friends on social media, the generative AI can provide related topics and engage in conversations on common topics. This makes it possible to provide relevant topics based on the patient's social media activity.

[0092] The communication unit can customize the content of conversations by reflecting the patient's past feedback. The communication unit uses the generation AI to customize the content of conversations by reflecting the patient's past feedback. For example, based on topics that the patient liked in the past, the generation AI can offer those topics again, leading to an enjoyable conversation. Also, based on topics that the patient avoided in the past, the generation AI can offer new topics and lead to an interesting conversation. Furthermore, based on the patient's past feedback, the generation AI can adjust the content of the conversation and hold an optimal conversation. This makes it possible to customize the content of conversations based on the patient's past feedback.

[0093] The monitoring unit can estimate the patient's emotions and adjust the frequency of monitoring based on the estimated patient's emotions. The monitoring unit estimates the patient's emotions using a camera or microphone and adjusts the frequency of monitoring based on the estimated emotions. For example, if the patient is feeling anxious, the monitoring frequency can be increased to provide a sense of security. Also, if the patient is relaxed, the monitoring frequency can be reduced to respect privacy. Furthermore, if the patient is excited, the monitoring frequency can be adjusted appropriately to ensure safety. In this way, the monitoring frequency can be adjusted based on the patient's emotions.

[0094] The monitoring unit analyzes the patient's behavior patterns during monitoring and can detect abnormal behavior early. The monitoring unit uses a camera and a microphone to analyze the patient's behavior patterns during monitoring and can detect abnormal behavior early. For example, if the patient behaves differently than usual, the camera and microphone can detect the abnormal behavior. Also, if the patient wakes up frequently at night, the behavior pattern can be analyzed to detect abnormal behavior early. Furthermore, if the patient behaves abnormally during a specific time period, the pattern can be analyzed to detect the abnormal behavior early. In this way, abnormal behavior can be detected early by analyzing the patient's behavior pattern.

[0095] The monitoring unit can adjust the timing of monitoring according to the patient's lifestyle rhythm when monitoring. The monitoring unit adjusts the timing of monitoring according to the patient's lifestyle rhythm when monitoring using a camera or microphone. For example, if the patient is active during the day, the monitoring frequency during the day can be increased. Also, if the patient rests at night, the monitoring frequency at night can be reduced. Furthermore, if the patient's lifestyle rhythm fluctuates, the monitoring timing can also be adjusted according to that rhythm. In this way, the monitoring timing can be adjusted according to the patient's lifestyle rhythm.

[0096] The monitoring unit can improve the accuracy of monitoring by referring to the patient's past history of abnormal behavior during monitoring. The monitoring unit can improve the accuracy of monitoring by referring to the patient's past history of abnormal behavior during monitoring using a camera or microphone. For example, the accuracy of monitoring using a camera or microphone can be improved based on the patient's past history of abnormal behavior. The accuracy of monitoring can also be improved by referring to patterns of abnormal behavior that the patient has exhibited in the past. Furthermore, the accuracy of monitoring can also be improved by analyzing the patient's history of abnormal behavior. In this way, the accuracy of monitoring can be improved by referring to the patient's past history of abnormal behavior.

[0097] The monitoring unit can estimate the patient's emotions and determine the monitoring priority based on the estimated patient's emotions. The monitoring unit estimates the patient's emotions using a camera or microphone and determines the monitoring priority based on the estimated emotions. For example, if the patient is feeling anxious, the monitoring priority can be increased to provide a sense of security. Also, if the patient is relaxed, the monitoring priority can be lowered to respect the patient's privacy. Furthermore, if the patient is excited, the monitoring priority can be adjusted appropriately to ensure safety. In this way, the monitoring priority can be determined based on the patient's emotions.

[0098] The monitoring unit can adjust the monitoring range based on the geographical location information of the patient when monitoring. When monitoring using a camera or microphone, the monitoring unit adjusts the monitoring range taking into account the geographical location information of the patient. For example, if the patient is at home, the monitoring range is limited to the home. Also, if the patient is out, the monitoring range can be expanded to include the patient's location. Furthermore, if the patient is in a specific location, the monitoring range can also be adjusted according to that location. This makes it possible to adjust the monitoring range based on the geographical location information of the patient.

[0099] The monitoring unit can improve the accuracy of monitoring by referring to literature related to the patient during monitoring. The monitoring unit can improve the accuracy of monitoring by referring to literature related to the patient during monitoring using a camera or microphone. For example, the accuracy of monitoring can be improved by referring to literature related to the patient's medical history. The accuracy of monitoring can also be improved by referring to literature related to the patient's behavioral patterns. Furthermore, the accuracy of monitoring can also be improved by referring to literature related to the patient's health condition. In this way, the accuracy of monitoring can be improved by referring to literature related to the patient.

[0100] The monitoring unit can adjust the monitoring method based on the market value of the patient when monitoring. When monitoring using a camera or microphone, the monitoring unit adjusts the monitoring method taking into account the market value of the patient. For example, if the market value of the patient is high, the monitoring method can be strengthened to ensure safety. Also, if the market value of the patient is low, the monitoring method can be simplified to reduce costs. Furthermore, the monitoring method can also be appropriately adjusted according to the market value of the patient. This makes it possible to adjust the monitoring method based on the market value of the patient.

[0101] The acquisition unit can estimate the patient's emotions and adjust the frequency of vital sign measurements based on the estimated patient emotions. The acquisition unit periodically measures vital signs such as heart rate and body temperature, estimates the patient's emotions, and adjusts the frequency of vital sign measurements based on the estimated emotions. For example, if the patient feels anxious, the frequency of vital sign measurements can be increased to monitor the patient's health condition. Also, if the patient feels relaxed, the frequency of vital sign measurements can be reduced to respect the patient's privacy. Furthermore, if the patient feels excited, the frequency of vital sign measurements can be appropriately adjusted to monitor the patient's health condition. In this way, the frequency of vital sign measurements can be adjusted based on the patient's emotions.

[0102] The acquisition unit can improve the accuracy of measurement by referring to the patient's past health data when measuring vital signs. The acquisition unit periodically measures vital signs such as heart rate and body temperature, and improves the accuracy of measurement by referring to the patient's past health data when measuring vital signs. For example, the acquisition unit can improve the accuracy of current heart rate measurement by referring to the patient's past heart rate data. The acquisition unit can also improve the accuracy of current body temperature measurement by referring to the patient's past body temperature data. Furthermore, the acquisition unit can improve the accuracy of current measurement by referring to the patient's past vital sign data. In this way, the accuracy of measurement can be improved by referring to the patient's past health data.

[0103] The acquisition unit can adjust the timing of measurement according to the patient's lifestyle rhythm when measuring vital signs. The acquisition unit periodically measures vital signs such as heart rate and body temperature, and adjusts the timing of measurement according to the patient's lifestyle rhythm when measuring vital signs. For example, if the patient is active in the morning, the frequency of measurement can be increased in the morning. Also, if the patient rests at night, the frequency of measurement can be reduced in the evening. Furthermore, if the patient's lifestyle rhythm fluctuates, the timing of measurement can also be adjusted according to that rhythm. This makes it possible to adjust the timing of measurement according to the patient's lifestyle rhythm.

[0104] The acquisition unit can improve the measurement method by reflecting patient feedback when measuring vital signs. The acquisition unit periodically measures vital signs such as heart rate and body temperature, and improves the measurement method by reflecting patient feedback when measuring vital signs. For example, if the patient is dissatisfied with the measurement method, the measurement method can be improved based on the feedback. Also, if the patient is satisfied with the measurement method, that method can be continued to be used. Furthermore, a new measurement method can be introduced and improved based on patient feedback. This makes it possible to improve the measurement method based on patient feedback.

[0105] The acquisition unit can estimate the patient's emotions and adjust the vital sign measurement method based on the estimated patient's emotions. The acquisition unit periodically measures vital signs such as heart rate and body temperature, estimates the patient's emotions, and adjusts the vital sign measurement method based on the estimated emotions. For example, if the patient is feeling anxious, a simple and quick measurement method can be used. If the patient is relaxed, a detailed measurement method can be used. Furthermore, if the patient is excited, an appropriately adjusted measurement method can be used. In this way, the vital sign measurement method can be adjusted based on the patient's emotions.

[0106] The acquisition unit can adjust the measurement range based on the patient's geographical location information when measuring vital signs. The acquisition unit periodically measures vital signs such as heart rate and body temperature, and adjusts the measurement range taking the patient's geographical location information into consideration when measuring vital signs. For example, if the patient is at home, the measurement range can be set within the home. Also, if the patient is out, the measurement range can be set when the patient is away from home. Furthermore, if the patient is in a specific location, the measurement range can also be adjusted according to that location. This makes it possible to adjust the measurement range based on the patient's geographical location information.

[0107] The acquisition unit can analyze the patient's social media activity when measuring vital signs to improve the accuracy of the measurement. The acquisition unit periodically measures vital signs such as heart rate and body temperature, and analyzes the patient's social media activity when measuring vital signs to improve the accuracy of the measurement. For example, the accuracy of the measurement can be improved based on health information shared by the patient on social media. The patient's social media activity can also be analyzed to improve the accuracy of the measurement. Furthermore, the accuracy of the measurement can be improved by referring to the health information of the patient's friends on social media. In this way, the accuracy of the measurement can be improved based on the patient's social media activity.

[0108] The acquisition unit can customize the measurement method by reflecting the patient's past feedback when measuring vital signs. The acquisition unit periodically measures vital signs such as heart rate and body temperature, and customizes the measurement method by reflecting the patient's past feedback when measuring vital signs. For example, a customized measurement method can be provided based on a measurement method that the patient has preferred in the past. Also, a new measurement method can be provided based on a measurement method that the patient has avoided in the past. Furthermore, the optimal measurement method can be customized based on the patient's past feedback. This makes it possible to customize the measurement method based on the patient's past feedback.

[0109] The analysis unit can estimate the patient's emotions and determine the priority of analysis based on the estimated patient's emotions. The analysis unit estimates the patient's emotions using ICT technology and determines the priority of analysis based on the estimated emotions. For example, if the patient is feeling anxious, analysis of emotional data can be prioritized. Also, if the patient is relaxed, analysis of health data can be prioritized. Furthermore, if the patient is excited, analysis of behavioral data can be prioritized. In this way, the priority of analysis can be determined based on the patient's emotions.

[0110] The analysis unit can optimize the analysis algorithm by referring to past analysis data during analysis. The analysis unit uses ICT technology to optimize the analysis algorithm by referring to past analysis data during analysis. For example, the current analysis algorithm is optimized based on past analysis data. The accuracy of the algorithm can also be improved by referring to past analysis results. Furthermore, past data can be analyzed and the optimal analysis algorithm can be introduced. This makes it possible to optimize the analysis algorithm by referring to past analysis data.

[0111] The analysis unit analyzes the patient's behavioral patterns during analysis, enabling early detection of abnormal behavior. The analysis unit uses ICT technology to analyze the patient's behavioral patterns during analysis, enabling early detection of abnormal behavior. For example, the analysis unit analyzes the patient's behavioral patterns and detects abnormal behavior early. It can also identify abnormal behavior patterns based on the patient's behavioral data. It can also refer to the patient's behavioral history to detect abnormal behavior early. This allows early detection of abnormal behavior by analyzing the patient's behavioral patterns.

[0112] The analysis unit can adjust the timing of analysis during analysis according to the patient's lifestyle rhythm. The analysis unit uses ICT technology to adjust the timing of analysis during analysis according to the patient's lifestyle rhythm. For example, if the patient is active during the day, the frequency of analysis during the day can be increased. Also, if the patient rests at night, the frequency of analysis at night can be reduced. Furthermore, if the patient's lifestyle rhythm fluctuates, the timing of analysis can also be adjusted according to that rhythm. This makes it possible to adjust the timing of analysis according to the patient's lifestyle rhythm.

[0113] The analysis unit can estimate the patient's emotions and adjust the analysis method based on the estimated patient's emotions. The analysis unit estimates the patient's emotions using ICT technology and adjusts the analysis method based on the estimated emotions. For example, if the patient is feeling anxious, a simple and quick analysis method can be used. If the patient is relaxed, a detailed analysis method can be used. Furthermore, if the patient is excited, a moderately adjusted analysis method can be used. This makes it possible to adjust the analysis method based on the patient's emotions.

[0114] The analysis unit can adjust the range of analysis based on the patient's geographic location information during analysis. The analysis unit uses ICT technology to adjust the range of analysis taking into account the patient's geographic location information during analysis. For example, if the patient is at home, the analysis range is set to within the home. Also, if the patient is out, the analysis range can be set to when the patient is away from home. Furthermore, if the patient is in a specific location, the analysis range can also be adjusted according to that location. This makes it possible to adjust the range of analysis based on the patient's geographic location information.

[0115] The analysis unit can improve the accuracy of the analysis by referring to literature related to the patient during analysis. The analysis unit uses ICT technology to improve the accuracy of the analysis by referring to literature related to the patient during analysis. For example, the analysis accuracy can be improved by referring to literature related to the patient's medical history. The analysis accuracy can also be improved by referring to literature related to the patient's behavioral patterns. Furthermore, the analysis accuracy can be improved by referring to literature related to the patient's health condition. In this way, the analysis accuracy can be improved by referring to literature related to the patient.

[0116] The analysis unit can adjust the analysis method based on the patient's market value during analysis. The analysis unit uses ICT technology to adjust the analysis method taking into account the patient's market value during analysis. For example, if the patient's market value is high, the analysis method can be strengthened to improve accuracy. Also, if the patient's market value is low, the analysis method can be simplified to reduce costs. Furthermore, the analysis method can also be appropriately adjusted according to the patient's market value. This makes it possible to adjust the analysis method based on the patient's market value.

[0117] The detection unit can estimate the patient's emotions and adjust the abnormality detection criteria based on the estimated patient's emotions. The detection unit can estimate the patient's emotions and adjust the abnormality detection criteria based on the estimated emotions. For example, if the patient is feeling anxious, the detection unit can set the abnormality detection criteria to be strict. Also, if the patient is relaxed, the detection unit can set the abnormality detection criteria to be lenient. Furthermore, if the patient is excited, the detection unit can also adjust the abnormality detection criteria to be appropriate. In this way, the abnormality detection criteria can be adjusted based on the patient's emotions.

[0118] When detecting an abnormality, the detection unit can improve the accuracy of detection by referring to the patient's past abnormal behavior history. When detecting an abnormality, the detection unit improves the accuracy of detection by referring to the patient's past abnormal behavior history. For example, the accuracy of current abnormality detection can be improved based on the patient's past abnormal behavior history. In addition, the detection accuracy can be improved by referring to the patient's past abnormal behavior pattern. Furthermore, the patient's abnormal behavior history can be analyzed to improve the accuracy of detection. In this way, by referring to the patient's past abnormal behavior history, the detection accuracy can be improved.

[0119] The detection unit analyzes the patient's behavioral patterns when an abnormality is detected, and can detect abnormal behavior early. The detection unit analyzes the patient's behavioral patterns when an abnormality is detected, and can detect abnormal behavior early. For example, the detection unit analyzes the patient's behavioral patterns and can detect abnormal behavior early. Furthermore, the detection unit can identify abnormal behavior patterns based on the patient's behavioral data. Furthermore, the detection unit can also refer to the patient's behavioral history to detect abnormal behavior early. As a result, abnormal behavior can be detected early by analyzing the patient's behavioral patterns.

[0120] The detection unit can estimate the patient's emotion and determine the priority of abnormality detection based on the estimated emotion of the patient. The detection unit can estimate the patient's emotion and determine the priority of abnormality detection based on the estimated emotion. For example, if the patient is feeling anxious, the priority of abnormality detection can be increased. Also, if the patient is relaxed, the priority of abnormality detection can be decreased. Furthermore, if the patient is excited, the priority of abnormality detection can be adjusted appropriately. In this way, the priority of abnormality detection can be determined based on the patient's emotion.

[0121] When detecting an abnormality, the detection unit can adjust the detection range based on the geographical location information of the patient. When detecting an abnormality, the detection unit adjusts the detection range taking into account the geographical location information of the patient. For example, if the patient is at home, the abnormality detection range can be set within the home. Also, if the patient is out, the abnormality detection range can be set when the patient is away from home. Furthermore, if the patient is in a specific location, the abnormality detection range can also be adjusted according to that location. In this way, the detection range can be adjusted based on the geographical location information of the patient.

[0122] The detection unit can improve the accuracy of detection by referring to literature related to the patient when detecting an abnormality. The detection unit can improve the accuracy of detection by referring to literature related to the patient when detecting an abnormality. For example, the detection unit can improve the accuracy of abnormality detection by referring to literature related to the patient's medical history. The detection unit can also improve the accuracy of abnormality detection by referring to literature related to the patient's behavioral patterns. The detection unit can also improve the accuracy of abnormality detection by referring to literature related to the patient's health condition. In this way, the detection accuracy can be improved by referring to literature related to the patient.

[0123] The notification unit can estimate the patient's emotion and adjust the notification method based on the estimated patient's emotion. The notification unit can estimate the patient's emotion and adjust the notification method based on the estimated emotion. For example, if the patient is feeling anxious, the notification unit can provide notification in a gentle tone. Also, if the patient is relaxed, the notification unit can provide notification in a normal tone. Furthermore, if the patient is excited, the notification unit can provide notification in a calm tone. In this way, the notification method can be adjusted based on the patient's emotion.

[0124] The notification unit can improve the accuracy of the notification by referring to the patient's past abnormal behavior history when making a notification. The notification unit can improve the accuracy of the notification by referring to the patient's past abnormal behavior history when making a notification. For example, the accuracy of the current notification can be improved based on the patient's past abnormal behavior history. The notification accuracy can also be improved by referring to the patient's past abnormal behavior patterns. Furthermore, the patient's abnormal behavior history can be analyzed to improve the accuracy of the notification. In this way, the accuracy of the notification can be improved by referring to the patient's past abnormal behavior history.

[0125] The notification unit can analyze the patient's behavioral patterns at the time of notification and provide early notification of abnormal behavior. The notification unit can analyze the patient's behavioral patterns at the time of notification and provide early notification of abnormal behavior. For example, the notification unit analyzes the patient's behavioral patterns and provides early notification of abnormal behavior. Furthermore, it can identify and provide notification of abnormal behavior patterns based on the patient's behavioral data. Furthermore, it can also refer to the patient's behavioral history and provide early notification of abnormal behavior. In this way, it is possible to analyze the patient's behavioral patterns and provide early notification of abnormal behavior.

[0126] The notification unit can estimate the patient's emotion and determine the priority of notifications based on the estimated emotion of the patient. The notification unit can estimate the patient's emotion and determine the priority of notifications based on the estimated emotion. For example, if the patient is feeling anxious, the priority of notifications can be increased. Also, if the patient is relaxed, the priority of notifications can be decreased. Furthermore, if the patient is excited, the priority of notifications can be adjusted appropriately. In this way, the priority of notifications can be determined based on the patient's emotion.

[0127] The notification unit can adjust the range of notification based on the geographical location information of the patient at the time of notification. The notification unit adjusts the range of notification taking into account the geographical location information of the patient at the time of notification. For example, if the patient is at home, the notification range within the home can be set. Also, if the patient is out, the notification range can be set when the patient is away from home. Furthermore, if the patient is in a specific location, the notification range can also be adjusted according to that location. In this way, the range of notification can be adjusted based on the geographical location information of the patient.

[0128] The notification unit can improve the accuracy of the notification by referring to literature related to the patient when making a notification. The notification unit can improve the accuracy of the notification by referring to literature related to the patient when making a notification. For example, the notification unit can improve the accuracy of the notification by referring to literature related to the patient's medical history. The notification unit can also improve the accuracy of the notification by referring to literature related to the patient's behavioral patterns. The notification unit can also improve the accuracy of the notification by referring to literature related to the patient's health condition. In this way, the notification accuracy can be improved by referring to literature related to the patient.

[0129] The providing unit can estimate the patient's emotions and adjust the content of the advice based on the estimated patient's emotions. The providing unit can estimate the patient's emotions and adjust the content of the advice based on the estimated emotions. For example, if the patient is feeling anxious, advice that gives a sense of security can be provided. Also, if the patient is relaxed, normal advice can be provided. Furthermore, if the patient is excited, advice to help the patient regain their composure can be provided. In this way, the content of the advice can be adjusted based on the patient's emotions.

[0130] When providing advice, the providing unit can provide optimal advice by referring to the patient's past behavioral history. When providing advice, the providing unit can provide optimal advice by referring to the patient's past behavioral history. For example, optimal advice is provided based on the patient's past behavioral history. Also, optimal advice can be provided by referring to the patient's past behavioral patterns. Furthermore, the patient's behavioral history can be analyzed and optimal advice can be provided. In this way, optimal advice can be provided by referring to the patient's past behavioral history.

[0131] The providing unit can adjust the timing of advice according to the patient's lifestyle rhythm when providing advice. The providing unit can adjust the timing of advice according to the patient's lifestyle rhythm when providing advice. For example, if the patient is active in the morning, the frequency of providing advice in the morning can be increased. Also, if the patient rests at night, the frequency of providing advice in the evening can be reduced. Furthermore, if the patient's lifestyle rhythm fluctuates, the timing of advice can also be adjusted according to that rhythm. In this way, the timing of advice can be adjusted according to the patient's lifestyle rhythm.

[0132] The providing unit can estimate the patient's emotions and determine the priority of advice based on the estimated patient's emotions. The providing unit can estimate the patient's emotions and determine the priority of advice based on the estimated emotions. For example, if the patient is feeling anxious, the priority of advice can be increased. Also, if the patient is relaxed, the priority of advice can be decreased. Furthermore, if the patient is excited, the priority of advice can be adjusted appropriately. In this way, the priority of advice can be determined based on the patient's emotions.

[0133] When providing advice, the providing unit can provide optimal advice based on the geographical location information of the patient. When providing advice, the providing unit provides optimal advice taking into account the geographical location information of the patient. For example, if the patient is at home, optimal advice for the home can be provided. Also, if the patient is out, optimal advice for when the patient is out can be provided. Furthermore, if the patient is in a specific location, optimal advice can be provided according to that location. This makes it possible to provide optimal advice based on the geographical location information of the patient.

[0134] The providing unit, when providing advice, can analyze the patient's social media activity and provide relevant advice. The providing unit, when providing advice, can analyze the patient's social media activity and provide relevant advice. For example, relevant advice can be provided based on content shared by the patient on social media. The providing unit can also analyze the patient's social media activity and provide relevant advice. Furthermore, relevant advice can be provided by referring to the activities of the patient's friends on social media. In this way, relevant advice can be provided based on the patient's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the communication unit, monitoring unit, acquisition unit, analysis unit, detection unit, notification unit, and provision 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 communication unit is realized by the control unit 46A of the smart device 14 and uses a generative AI to have a natural conversation with the dementia patient. The monitoring unit monitors the patient's condition using the camera 42 and microphone 38B of the smart device 14. The acquisition unit acquires vital signs such as heart rate and body temperature using sensors in the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12. The detection unit detects abnormalities using the specific processing unit 290 of the data processing device 12. The notification unit notifies the caregiver using the control unit 46A of the smart device 14. The provision unit provides advice based on the collected data using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the communication unit, monitoring unit, acquisition unit, analysis unit, detection unit, notification unit, and provision unit, described above, is realized by, for example, at least one of the smart glasses 214 and the data processing device 12. For example, the communication unit is realized by the control unit 46A of the smart glasses 214 and uses a generative AI to have a natural conversation with the dementia patient. The monitoring unit monitors the patient's condition using the camera 42 and microphone 238 of the smart glasses 214. The acquisition unit acquires vital signs such as heart rate and body temperature using the sensors of the smart glasses 214. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12. The detection unit detects abnormalities using the specific processing unit 290 of the data processing device 12. The notification unit notifies the caregiver using the control unit 46A of the smart glasses 214. The provision unit provides advice based on the collected data using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the communication unit, monitoring unit, acquisition unit, analysis unit, detection unit, notification unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the communication unit is realized by the control unit 46A of the headset-type terminal 314 and uses a generative AI to have a natural conversation with the dementia patient. The monitoring unit monitors the patient's condition using the camera 42 and microphone 238 of the headset-type terminal 314. The acquisition unit acquires vital signs such as heart rate and body temperature using the sensors of the headset-type terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12. The detection unit detects abnormalities using the specific processing unit 290 of the data processing device 12. The notification unit notifies the caregiver using the control unit 46A of the headset-type terminal 314. The provision unit provides advice based on the collected data using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the communication unit, monitoring unit, acquisition unit, analysis unit, detection unit, notification unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the communication unit is realized by the control unit 46A of the robot 414 and uses a generative AI to have a natural conversation with the dementia patient. The monitoring unit monitors the patient's condition using the camera 42 and microphone 238 of the robot 414. The acquisition unit acquires vital signs such as heart rate and body temperature using sensors of the robot 414. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12. The detection unit detects abnormalities using the specific processing unit 290 of the data processing device 12. The notification unit notifies the caregiver using the control unit 46A of the robot 414. The provision unit provides advice based on the collected data using the specific processing unit 290 of the data processing device 12.

[0135] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0136] The care support system may further include an environmental data acquisition unit. The environmental data acquisition unit acquires environmental data surrounding the patient. For example, it may periodically measure and collect data such as room temperature, humidity, and illuminance. The environmental data acquisition unit may also estimate the patient's emotions and adjust the frequency of environmental data acquisition based on the estimated emotions. For example, if the patient is feeling anxious, it may increase the frequency of environmental data acquisition to quickly grasp changes in the environment. Alternatively, if the patient is relaxed, it may decrease the frequency of environmental data acquisition to respect the patient's privacy. In this way, environmental data surrounding the patient may be acquired and appropriate environmental management may be performed.

[0137] The care support system can further include a voice analysis unit. The voice analysis unit analyzes the content of the patient's speech and detects abnormalities. For example, it can detect changes in the patient's speech rate or volume and detect abnormalities early. The voice analysis unit can also estimate the patient's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the patient is excited, the accuracy of the voice analysis can be increased to perform a more detailed analysis. Also, if the patient is relaxed, the accuracy of the voice analysis can be relaxed to respect the patient's privacy. This allows the content of the patient's speech to be analyzed and abnormalities to be detected early.

[0138] The care support system may further include a location tracking unit. The location tracking unit tracks the patient's location information in real time. For example, it can obtain location information when the patient moves around the home and detect abnormal movement patterns. The location tracking unit can also estimate the patient's emotions and adjust the tracking accuracy based on the estimated emotions. For example, if the patient feels anxious, it can increase the tracking accuracy to obtain detailed location information. Also, if the patient feels relaxed, it can relax the tracking accuracy to respect the patient's privacy. This allows the patient's location information to be tracked in real time and abnormalities to be detected early.

[0139] The care support system can further include a diet management unit. The diet management unit records the patient's dietary content and manages nutritional balance. For example, it can record the content of meals eaten by the patient and analyze the nutritional balance. The diet management unit can also estimate the patient's emotions and suggest meals based on the estimated emotions. For example, if the patient is feeling anxious, it can suggest a meal that will help the patient relax. Also, if the patient is relaxed, it can suggest a normal meal. This makes it possible to manage the patient's dietary content and maintain an appropriate nutritional balance.

[0140] The care support system may further include an exercise management unit. The exercise management unit records the patient's exercise status and provides an appropriate exercise plan. For example, it can record the details of the exercise performed by the patient and analyze the amount of exercise. The exercise management unit can also estimate the patient's emotions and adjust the exercise plan based on the estimated emotions. For example, if the patient is feeling anxious, it can suggest exercise that will help them relax. Also, if the patient is relaxed, it can provide a regular exercise plan. In this way, it is possible to manage the patient's exercise status and provide an appropriate exercise plan.

[0141] The care support system can further include a medication management unit. The medication management unit records the patient's medication status and performs appropriate medication management. For example, it can record the type and amount of medication taken by the patient and manage the medication schedule. The medication management unit can also estimate the patient's emotions and adjust the timing of medication based on the estimated emotions. For example, if the patient is feeling anxious, it can suggest taking medication at a time when the patient is relaxed. Also, if the patient is relaxed, it can suggest taking medication at a normal time. This makes it possible to manage the patient's medication status and perform appropriate medication management.

[0142] The care support system may further include a sleep management unit. The sleep management unit records the patient's sleep status and provides an appropriate sleep environment. For example, it can record the patient's sleep time and quality and analyze sleep patterns. The sleep management unit can also estimate the patient's emotions and adjust the sleep environment based on the estimated emotions. For example, if the patient is feeling anxious, it can provide a relaxing environment. If the patient is relaxed, it can provide a normal sleep environment. In this way, the patient's sleep status can be managed and an appropriate sleep environment can be provided.

[0143] The care support system can further include a music therapy unit. The music therapy unit provides appropriate music according to the patient's emotions and state. For example, if the patient is feeling anxious, it can provide relaxing music. If the patient is relaxed, it can provide music to maintain the patient's mood. Furthermore, if the patient is excited, it can provide music to calm the patient. In this way, it is possible to provide appropriate music according to the patient's emotions and state, and to achieve psychological stability.

[0144] The care support system can further include an art therapy unit. The art therapy unit provides appropriate art activities according to the patient's emotions and condition. For example, if the patient is feeling anxious, it can suggest art activities that will help them relax. If the patient is relaxed, it can provide art activities that will bring out their creativity. Furthermore, if the patient is excited, it can also suggest art activities to calm them down. In this way, it is possible to provide appropriate art activities according to the patient's emotions and condition, and to achieve psychological stability.

[0145] The care support system can further include a virtual reality (VR) unit. The VR unit provides appropriate VR content according to the patient's emotions and state. For example, if the patient is feeling anxious, it can provide relaxing VR content. If the patient is relaxed, it can provide enjoyable VR content. Furthermore, if the patient is excited, it can provide VR content to calm them down. In this way, it is possible to provide appropriate VR content according to the patient's emotions and state, and to achieve psychological stability.

[0146] The processing flow of the second embodiment will be briefly explained below.

[0147] Step 1: The communication department uses generative AI to have natural conversations with dementia patients. For example, when a patient asks, "What day is it today?", the generative AI provides an appropriate answer. The generative AI can also estimate the patient's emotions and select conversation topics based on the estimated emotions. Step 2: The monitoring unit monitors the patient's condition using a camera and microphone. For example, the monitoring unit monitors in real time how the patient walks around the room and what they are talking about. The monitoring unit can also estimate the patient's emotions and adjust the frequency of monitoring based on the estimated emotions. Step 3: The acquisition unit periodically measures vital signs, such as heart rate and body temperature, and collects data. For example, the acquisition unit measures heart rate and body temperature to constantly monitor the patient's health condition. The acquisition unit can also estimate the patient's emotions and adjust the frequency of vital sign measurements based on the estimated emotions. Step 4: The analysis unit uses ICT technology to analyze the collected data. For example, the analysis unit analyzes changes in the patient's behavioral patterns and health status and provides appropriate advice to caregivers. The analysis unit can also estimate the patient's emotions and determine the priority of analysis based on the estimated emotions.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0169] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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 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.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0185] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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 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.

[0199] 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.

[0200] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0206] 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."

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] [Explanation of symbols]

[0220] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A communication unit that uses generative AI to have natural conversations with dementia patients; a monitoring unit that monitors the patient's condition using a camera or microphone; an acquisition unit for acquiring physical data of a patient; an analysis unit that analyzes the collected data; Equipped with A system characterized by:

2. Equipped with a detection unit that detects abnormalities 2. The system of claim 1.

3. Equipped with a notification department to notify caregivers 2. The system of claim 1.

4. Equipped with a section that provides advice based on collected data 2. The system of claim 1.

5. The communication unit Use generative AI to have natural conversations 2. The system of claim 1.

6. The monitoring unit Real-time monitoring of the patient's condition using a camera or microphone 2. The system of claim 1.

7. The acquisition unit Regularly measuring vital signs such as heart rate or temperature 2. The system of claim 1.

8. The analysis unit Analyzing collected data using ICT technology 2. The system of claim 1.

9. The communication unit Estimate the patient's emotions and select conversation topics based on the estimated emotions.

2. The system of claim 1.

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