system

Abstract

The system according to this embodiment aims to provide an environment in which dementia patients can live independently and their families can provide appropriate support. [Solution] The system according to the embodiment comprises a collection unit, a learning unit, a support unit, a management unit, an assistance unit, and a care unit. The collection unit collects data on dementia patients. The learning unit learns from the data collected by the collection unit and provides optimal support to individual patients. The support unit provides support for daily life based on the support content obtained by the learning unit. The management unit performs health management based on the support content provided by the support unit. The assistance unit provides communication support based on the health management content provided by the management unit. The care unit provides care support based on the communication support content provided by the assistance unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that the burden on family members and caregivers increased with the increase in the number of dementia patients, and social costs increased.

[0005] The system according to the embodiment aims to provide an environment in which dementia patients can live an independent life and family members can provide appropriate support.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a learning unit, a support unit, a management unit, an assistance unit, and a care unit. The data collection unit collects data on dementia patients. The learning unit learns from the data collected by the data collection unit and provides optimal support to individual patients. The support unit provides support for daily life based on the support content obtained by the learning unit. The management unit performs health management based on the support content provided by the support unit. The assistance unit provides communication support based on the health management content provided by the management unit. The care unit provides care support based on the communication support content provided by the assistance unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an environment in which dementia patients can live independently and their families can provide appropriate support. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system for supporting dementia patients and their families. This AI agent system learns from data collected from dementia patients around the world and provides optimal support for individual patients. The AI ​​agent system provides four main functions: support for daily life, health management, communication support, and care support. This enables dementia patients to live independently and reduces the burden on families and care providers. For example, the AI ​​agent system provides support for daily life such as medication reminders, meal preparation assistance, and location information notifications. Health management includes monitoring of body temperature and blood pressure, anomaly detection algorithms, and notification functions. Communication support includes voice recognition technology and appropriate word suggestion functions. Care support includes creating care plans, supporting the efficiency of care work, and providing real-time data. This aims to improve the quality of life (QOL) of dementia patients.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, a learning unit, a support unit, a management unit, an assistance unit, and a care unit. The data collection unit collects data from dementia patients. The data collection unit can, for example, collect data based on the patient's health status, living situation, and individual needs. The data collection unit can, for example, collect data to monitor the patient's health status and detect abnormalities. The data collection unit can also collect data related to the patient's living situation and use it to support daily life. Furthermore, the data collection unit can collect data based on the patient's individual needs and collect data to provide optimal support. The learning unit learns from the data collected by the data collection unit and provides optimal support to individual patients. The learning unit can, for example, analyze the collected data using AI and learn the optimal support content based on the patient's health status and living situation. The learning unit can, for example, analyze the patient's health status using AI and learn algorithms for detecting abnormalities. Furthermore, the learning unit can learn support content that is useful for supporting daily life based on the patient's living situation. Furthermore, the learning unit can learn the optimal support content based on the patient's individual needs. The support unit provides support for daily life based on the support provided by the learning unit. For example, the support unit can provide medication reminders. For example, the support unit can remind patients to take their medication at the appropriate time to prevent them from forgetting. The support unit can also provide assistance with meal preparation. For example, the support unit can suggest menus and provide cooking support when patients are preparing meals. Furthermore, the support unit can provide location information notifications. For example, the support unit can obtain the patient's location information and notify family members or caregivers. The management unit manages health based on the support provided by the support unit. For example, the management unit can monitor body temperature and blood pressure. For example, the management unit can periodically measure the patient's body temperature and blood pressure and detect abnormalities. Furthermore, the management unit can provide anomaly detection algorithms. For example, the management unit can provide an algorithm for detecting abnormalities using AI and notify if an abnormality is detected. Furthermore, the management unit can provide a notification function.The management department notifies patients and their families, for example, when an abnormality is detected. The support department provides communication support based on the health management information provided by the management department. The support department can provide, for example, speech recognition technology. The support department can recognize the patient's voice and suggest appropriate words. The support department can also provide an appropriate word suggestion function. The support department can suggest appropriate words when the patient is communicating. The care department provides care support based on the communication support information provided by the support department. The care department can create care plans, for example. The care department creates care plans based on the patient's health condition and living situation. The care department can also support the efficiency of care work. The care department manages the progress of care work and prioritizes tasks, for example. Furthermore, the care department can provide real-time data. The care department provides data on the patient's health condition and living situation in real time. As a result, the AI ​​agent system according to this embodiment can improve the quality of life of dementia patients.

[0030] The data collection unit collects data from dementia patients. For example, it can collect data based on the patient's health status, lifestyle, and individual needs. Specifically, to monitor the patient's health, it uses wearable devices and home sensors to collect vital data such as heart rate, blood pressure, body temperature, and activity level. This data is transmitted in real time to a central database, and an alert is immediately issued if an abnormality is detected. Furthermore, data related to the patient's lifestyle is recorded using smart home devices and motion sensors to track daily behavior patterns and changes in the living environment. For example, data such as the number of times the refrigerator is opened and closed, television viewing time, and room movement history can be collected to support daily life. In addition, the data collection unit collects data based on the patient's individual needs to provide optimal support. For example, it can collect information on the patient's dietary preferences, allergies, medication schedule, hobbies, and interests to provide individually customized support. This allows the data collection unit to collect a wide range of data on the patient's health status, lifestyle, and individual needs, improving the overall accuracy and effectiveness of the system. Moreover, the data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the learning and support departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The learning unit learns from the data collected by the collection unit and provides optimal support to individual patients. For example, the learning unit can analyze the collected data using AI and learn the optimal support based on the patient's health status and lifestyle. Specifically, it uses machine learning algorithms to analyze the patient's health data and learn patterns for detecting abnormalities. For example, it can detect abnormal fluctuations in heart rate and blood pressure, enabling early detection of abnormalities. It can also learn support that is useful for supporting daily life based on the patient's lifestyle. For example, it can analyze the patient's behavioral patterns to understand meal timing and exercise habits and provide appropriate advice. Furthermore, the learning unit can learn the optimal support based on the patient's individual needs. For example, it can suggest meal menus or manage medication schedules based on the patient's dietary preferences and allergy information. In this way, the learning unit can accumulate knowledge to provide optimal support to individual patients based on the collected data, improving the accuracy and effectiveness of the entire system. In addition, the learning unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past health data, it can predict fluctuations in specific symptoms or risks and formulate future countermeasures. Furthermore, the learning unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the learning unit to not only monitor the situation in real time but also handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The support department provides support for daily life based on the support information obtained from the learning department. For example, the support department can provide medication reminders. Specifically, it can use smartphones and wearable devices to notify patients of the time to take their medication and prevent them from forgetting. The support department can also provide assistance with meal preparation. For example, it can suggest menus and provide cooking support when patients prepare meals. This includes suggesting menus that consider nutritional balance, guiding cooking procedures, and listing necessary ingredients. Furthermore, the support department can provide location information notifications. For example, it can obtain the patient's location information and notify family members or caregivers to ensure the patient's safety. This includes a function that uses GPS to track the patient's current location in real time and issues alerts if abnormal movement or prolonged inactivity is detected. In this way, the support department can support the patient's daily life from various angles and improve their quality of life (QOL). In addition, the support department can collect patient feedback and continuously improve the accuracy and effectiveness of the support provided. For example, the support department collects feedback on how patients felt about the support they received and what improvements they think could be made, and incorporates this feedback into future support. Furthermore, the support department can reliably transmit information using multiple communication methods. For instance, they use not only smartphone notifications but also voice calls, SMS, and email to ensure important information is delivered reliably. This allows the support department to provide prompt and reliable support to patients, improving their quality of life.

[0033] The management department performs health management based on the support provided by the support department. For example, the management department can monitor body temperature and blood pressure. Specifically, it uses wearable devices and home sensors to regularly measure patients' body temperature and blood pressure and detect abnormalities. This data is transmitted in real time to a central database, and an alert is immediately issued if an abnormality is detected. The management department can also provide anomaly detection algorithms. For example, it can provide an algorithm using AI to detect abnormalities and notify when an abnormality is detected. This allows the management department to continuously monitor the patient's health status and detect abnormalities early. Furthermore, the management department can provide notification functions. For example, it can notify patients and their families when an abnormality is detected. This includes a function that uses smartphones and wearable devices to immediately notify when an abnormality is detected. This allows the management department to continuously monitor the patient's health status and respond quickly when an abnormality occurs. In addition, the management department can utilize historical data and statistical information to perform long-term health management and risk assessment. For example, based on past health data, fluctuations in specific symptoms or risks can be predicted, and future countermeasures can be planned. Furthermore, the management department can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the management department to not only monitor the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0034] The support department provides communication support based on the health management information provided by the management department. For example, the support department can provide speech recognition technology. Specifically, it uses natural language processing technology to recognize the patient's voice and suggest appropriate words. This allows for the suggestion of appropriate words when the patient communicates, supporting smoother conversations. The support department can also provide an appropriate word suggestion function. For example, if a patient cannot recall a specific word, it can suggest related words or phrases to facilitate communication. Furthermore, the support department can use speech recognition technology to analyze the patient's utterances and understand their emotions and intentions. This allows for the understanding of the patient's emotional state and needs, enabling appropriate responses. For example, if a patient is feeling anxious or stressed, the support department can provide advice on relaxation or suggest activities to change their mood. This allows the support department to support the patient's communication from multiple angles and improve their quality of life (QOL). Additionally, the support department can collect patient feedback to continuously improve the accuracy and effectiveness of the support provided. For example, it can collect feedback on how patients felt about the support provided and what improvements could be made, and incorporate this into future support. Furthermore, the support department can reliably transmit information using multiple communication methods. For example, in addition to smartphone notifications, they can use voice calls, SMS, and email in combination to ensure that important information is delivered reliably. This allows the support department to provide support to patients quickly and reliably, improving the quality of communication.

[0035] The Caregiving Department provides care support based on the communication support provided by the Support Department. For example, the Caregiving Department can create care plans. Specifically, it creates individually customized care plans based on the patient's health condition and living situation. This includes daily care schedules, rehabilitation programs, and management of meals and medication. The Caregiving Department can also support the efficiency of caregiving tasks. For example, it manages the progress of caregiving tasks and prioritizes tasks to help caregivers perform their duties efficiently. Furthermore, the Caregiving Department can provide real-time data. For example, it can provide real-time data on the patient's health condition and living situation to enable caregivers to respond quickly. This includes a function that monitors the patient's current condition in real time based on data collected from wearable devices and home sensors, and immediately notifies if an abnormality is detected. This allows the Caregiving Department to continuously understand the patient's health condition and living situation and provide appropriate care support. Additionally, the Caregiving Department can collect patient feedback and continuously improve the accuracy and effectiveness of care plans and support. For example, it can revise care plans and improve support based on feedback from patients and their families. Furthermore, the caregiving department can reliably transmit information using multiple communication methods. For example, in addition to smartphone notifications, they can use voice calls, SMS, and email in combination to ensure that important information is delivered reliably. This allows the caregiving department to provide prompt and reliable care support to patients, thereby improving their quality of life (QOL).

[0036] The support unit can provide medication reminders, meal preparation assistance, and location notifications. For example, the support unit can provide medication reminders. For example, the support unit can remind patients to take their medication at the appropriate time to prevent them from forgetting. The support unit can also provide meal preparation assistance. For example, the support unit can suggest menus and provide cooking support when patients are preparing meals. Furthermore, the support unit can provide location notifications. For example, the support unit can obtain the patient's location information and notify family members or caregivers. In this way, the support unit can support the patient's daily life. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, when providing medication reminders, the support unit can use AI to analyze the patient's medication history and determine the optimal reminder timing.

[0037] The management unit can provide monitoring of body temperature and blood pressure, anomaly detection algorithms, and notification functions. For example, the management unit can monitor body temperature and blood pressure. For example, the management unit can periodically measure the patient's body temperature and blood pressure and detect abnormalities. The management unit can also provide anomaly detection algorithms. For example, the management unit can provide an algorithm for detecting abnormalities using AI and notify when an abnormality is detected. Furthermore, the management unit can provide notification functions. For example, the management unit can notify the patient or their family when an abnormality is detected. This allows the management unit to support the patient's health management. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, when monitoring body temperature and blood pressure, the management unit can use AI to analyze the measurement data and detect abnormalities.

[0038] The support unit can provide speech recognition technology and appropriate word suggestion functions. For example, the support unit provides speech recognition technology. For example, the support unit recognizes the patient's voice and suggests appropriate words. The support unit can also provide an appropriate word suggestion function. For example, the support unit suggests appropriate words when the patient is communicating. In this way, the support unit can support the patient's communication. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, when providing speech recognition technology, the support unit can use AI to analyze the patient's voice data and suggest appropriate words.

[0039] The caregiving department can create care plans, support the efficiency of caregiving operations, and provide real-time data. For example, the caregiving department can create care plans. For example, the caregiving department can create care plans based on the patient's health condition and living situation. The caregiving department can also support the efficiency of caregiving operations. For example, the caregiving department can manage the progress of caregiving operations and prioritize tasks. Furthermore, the caregiving department can provide real-time data. For example, the caregiving department can provide data on the patient's health condition and living situation in real time. This allows the caregiving department to streamline care support. Some or all of the above processes in the caregiving department may be performed using AI, for example, or not using AI. For example, when creating care plans, the caregiving department can use AI to analyze patient data and create an optimal care plan.

[0040] The data collection unit can analyze the patient's past data collection history and select the optimal collection method. For example, the data collection unit can identify the time of day when the patient is most cooperative based on past data collection history and collect data during that time. For example, the data collection unit can collect data in an environment where the patient is most relaxed, based on past data collection history. For example, the data collection unit can analyze past data collection history and collect data in a way that causes the patient the least stress. In this way, the data collection unit can select the optimal collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0041] The data collection unit can filter data based on the patient's current health status and living situation during data collection. For example, if the patient's health is poor, the data collection unit may limit the types of data collected. For example, the data collection unit may change the priority of data to be collected according to the patient's living situation. For example, if the patient's health is good, the data collection unit may collect more detailed data. This allows the data collection unit to collect more appropriate data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input data on the patient's health status and living situation into a generating AI and have the generating AI perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location information during data collection. For example, if the patient is at home, the data collection unit will prioritize the collection of data related to their living situation at home. For example, if the patient is out, the data collection unit will prioritize the collection of data related to their health condition while out. For example, if the patient is in a hospital, the data collection unit will prioritize the collection of data related to the results of their medical examination at the hospital. This enables the data collection unit to collect more appropriate data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0043] The data collection unit can analyze the patient's social media activity and collect relevant data during data collection. For example, if the patient is very active on social media, the data collection unit will collect data related to that activity. For example, if the patient is not very active on social media, the data collection unit will prioritize other data collection methods. For example, the data collection unit will collect information about the patient's health status from the patient's social media activity. This allows the data collection unit to collect relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0044] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and adjust the parameters of the learning algorithm. For example, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. In this way, the learning unit can improve the accuracy of learning. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0045] The learning unit can update its learning content in response to changes in the patient's health condition during the learning process. For example, if the patient's health condition deteriorates, the learning unit will modify the learning content to address the issue. For example, if the patient's health condition improves, the learning unit will update and optimize the learning content. For example, the learning unit will periodically review the learning content in response to changes in the patient's health condition. This enables the learning unit to perform more appropriate learning. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input patient health data into a generating AI and have the generating AI perform the updating of the learning content.

[0046] The learning unit can weight the learning data based on the patient's lifestyle during the learning process. For example, the learning unit weights important data based on the patient's lifestyle. For example, the learning unit determines the priority of the learning data, taking the patient's lifestyle into consideration. For example, the learning unit adjusts the weighting of the learning data according to the patient's lifestyle. This enables the learning unit to learn more appropriately. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input patient lifestyle data into a generating AI and have the generating AI perform the weighting of the learning data.

[0047] The learning unit can improve its learning content by incorporating feedback from the patient's family during the learning process. For example, the learning unit improves the learning content based on feedback from the patient's family. For example, the learning unit adjusts its learning algorithm by incorporating opinions from the patient's family. For example, the learning unit optimizes the learning content by reflecting feedback from the patient's family. In this way, the learning unit can improve its learning content. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input feedback data from the patient's family into a generating AI and have the generating AI perform improvements to the learning content.

[0048] The support unit can select the optimal support method by referring to the patient's past behavioral history during support. For example, the support unit selects the optimal support method based on the patient's past behavioral history. For example, the support unit analyzes the patient's past behavioral history and adjusts the support method. For example, the support unit optimizes the support content by referring to the patient's past behavioral history. This allows the support unit to select the optimal support method. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the patient's past behavioral history data into a generating AI and have the generating AI select the optimal support method.

[0049] The support unit can customize the support provided based on the patient's current health condition. For example, if the patient's health condition is poor, the support unit will provide support related to maintaining health. For example, if the patient's health condition is good, the support unit will provide support related to promoting health. The support unit customizes the support content according to the patient's health condition. This allows the support unit to provide more appropriate support. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input patient health condition data into a generating AI and have the generating AI perform the customization of the support content.

[0050] The support unit can select the optimal support method by considering the patient's geographical location information during support. For example, if the patient is at home, the support unit will provide support methods for use at home. For example, if the patient is out, the support unit will provide support methods for use while out. For example, if the patient is in a hospital, the support unit will provide support methods for use at the hospital. This enables the support unit to provide more appropriate support. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the patient's geographical location information into a generating AI and have the generating AI select the optimal support method.

[0051] The support unit can improve its support by incorporating feedback from the patient's family during support. For example, the support unit improves the support based on feedback from the patient's family. For example, the support unit adjusts the support method by incorporating opinions from the patient's family. For example, the support unit optimizes the support by reflecting feedback from the patient's family. In this way, the support unit can improve the support. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input feedback data from the patient's family into a generating AI and have the generating AI perform improvements to the support.

[0052] The management department can select the optimal management method by referring to the patient's past health data during health management. For example, the management department selects the optimal health management method based on the patient's past health data. For example, the management department analyzes the patient's past health data and adjusts the health management method. For example, the management department optimizes the health management content by referring to the patient's past health data. In this way, the management department can select the optimal management method. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the patient's past health data into a generating AI and have the generating AI perform the selection of the optimal management method.

[0053] The management department can customize the management content based on the patient's current health status during health management. For example, if the patient's health status is poor, the management department will provide management content related to maintaining health. For example, if the patient's health status is good, the management department will provide management content related to promoting health. For example, the management department will customize the management content according to the patient's health status. This enables the management department to provide more appropriate health management. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input patient health status data into a generating AI and have the generating AI perform the customization of the management content.

[0054] The management department can select the optimal management method when managing a patient's health, taking into account the patient's geographical location. For example, if the patient is at home, the management department can provide a health management method for use at home. For example, if the patient is out, the management department can provide a health management method for use while away from home. For example, if the patient is in a hospital, the management department can provide a health management method for use in a hospital. This enables the management department to provide more appropriate health management. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input the patient's geographical location information into a generating AI and have the generating AI select the optimal management method.

[0055] The management department can improve its management practices by incorporating feedback from patients' families during health management. For example, the management department can improve management practices based on feedback from patients' families. For example, the management department can adjust management methods by incorporating opinions from patients' families. For example, the management department can optimize management practices by reflecting feedback from patients' families. In this way, the management department can improve its management practices. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input feedback data from patients' families into a generating AI and have the generating AI implement improvements to the management practices.

[0056] The support unit can select the optimal support method by referring to the patient's past communication history when providing communication support. For example, the support unit selects the optimal support method based on the patient's past communication history. For example, the support unit analyzes the patient's past communication history and adjusts the support method. For example, the support unit optimizes the support content by referring to the patient's past communication history. This allows the support unit to select the optimal support method. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the patient's past communication history data into a generating AI and have the generating AI select the optimal support method.

[0057] The support unit can customize the support provided during communication assistance based on the patient's current health condition. For example, if the patient's health condition is poor, the support unit will provide support related to maintaining health. For example, if the patient's health condition is good, the support unit will provide support related to promoting health. The support unit customizes the support content according to the patient's health condition. This enables the support unit to provide more appropriate communication assistance. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input patient health condition data into a generating AI and have the generating AI perform the customization of the support content.

[0058] The support unit can select the optimal support method when providing communication support, taking into account the patient's geographical location. For example, if the patient is at home, the support unit can provide communication support methods at home. For example, if the patient is out, the support unit can provide communication support methods at their destination. For example, if the patient is in a hospital, the support unit can provide communication support methods at the hospital. This enables the support unit to provide more appropriate communication support. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the patient's geographical location information into a generating AI and have the generating AI select the optimal support method.

[0059] The support department can improve its support by incorporating feedback from the patient's family during communication support. For example, the support department improves the support based on feedback from the patient's family. For example, the support department adjusts the support method by incorporating opinions from the patient's family. For example, the support department optimizes the support by reflecting feedback from the patient's family. In this way, the support department can improve the support. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input feedback data from the patient's family into a generating AI and have the generating AI execute improvements to the support.

[0060] The caregiving department can select the optimal support method by referring to the patient's past care history when providing care support. For example, the caregiving department selects the optimal support method based on the patient's past care history. For example, the caregiving department analyzes the patient's past care history and adjusts the support method. For example, the caregiving department optimizes the support content by referring to the patient's past care history. This allows the caregiving department to select the optimal support method. Some or all of the above processes in the caregiving department may be performed using AI, for example, or not using AI. For example, the caregiving department can input the patient's past care history data into a generating AI and have the generating AI perform the selection of the optimal support method.

[0061] The caregiving department can customize the support provided during caregiving based on the patient's current health condition. For example, if the patient's health is poor, the caregiving department will provide support related to maintaining health. For example, if the patient's health is good, the caregiving department will provide support related to promoting health. The caregiving department customizes the support according to the patient's health condition. This enables the caregiving department to provide more appropriate care. Some or all of the above processes in the caregiving department may be performed using AI, for example, or not using AI. For example, the caregiving department can input patient health data into a generating AI and have the generating AI perform the customization of the support.

[0062] The caregiving department can select the most appropriate support method when providing care, taking into account the patient's geographical location. For example, if the patient is at home, the caregiving department can provide care support methods at home. For example, if the patient is out, the caregiving department can provide care support methods at the patient's location. For example, if the patient is in a hospital, the caregiving department can provide care support methods at the hospital. This enables the caregiving department to provide more appropriate care support. Some or all of the above processing in the caregiving department may be performed using AI, for example, or without AI. For example, the caregiving department can input the patient's geographical location information into a generating AI and have the generating AI select the most appropriate support method.

[0063] The caregiving department can improve its support by incorporating feedback from the patient's family during caregiving support. For example, the caregiving department improves support based on feedback from the patient's family. For example, the caregiving department adjusts support methods by incorporating opinions from the patient's family. For example, the caregiving department optimizes support by reflecting feedback from the patient's family. In this way, the caregiving department can improve its support. Some or all of the above processes in the caregiving department may be performed using AI, for example, or not using AI. For example, the caregiving department can input feedback data from the patient's family into a generating AI and have the generating AI execute improvements to the support.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The AI ​​agent system can further suggest individualized recreational activities based on the patient's hobbies and interests. For example, the data collection unit collects data on the patient's hobbies and interests, and the learning unit analyzes this data to learn the optimal recreational activities. The support unit suggests recreational activities obtained by the learning unit and provides activities that the patient can enjoy. The management unit monitors the implementation status of recreational activities and makes adjustments as needed. This can improve the patient's quality of life.

[0066] The AI ​​agent system can further monitor the patient's sleep patterns and provide an optimal sleep environment. For example, the data collection unit collects the patient's sleep data, and the learning unit analyzes this data to learn the optimal sleep environment. The support unit proposes the sleep environment obtained by the learning unit, helping the patient sleep comfortably. The management unit monitors the implementation of the sleep environment and makes adjustments as needed. This can improve the patient's health.

[0067] The AI ​​agent system can further propose individualized meal plans based on the patient's dietary preferences. For example, the data collection unit collects data on the patient's dietary preferences, and the learning unit analyzes this data to learn the optimal meal plan. The support unit proposes the meal plan obtained by the learning unit and provides meals that the patient can enjoy. The management unit monitors the implementation status of the meal plan and makes adjustments as needed. This can improve the patient's nutritional status.

[0068] The AI ​​agent system can further propose individualized exercise programs based on the patient's exercise habits. For example, the data collection unit collects data on the patient's exercise habits, and the learning unit analyzes this data to learn the optimal exercise program. The support unit proposes the exercise program obtained by the learning unit, providing exercises that the patient can continue without difficulty. The management unit monitors the implementation status of the exercise program and makes adjustments as needed. This can improve the patient's physical fitness.

[0069] The AI ​​agent system can further propose activities to promote patients' social interaction. For example, the data collection unit collects data on patients' social interactions, and the learning unit analyzes this data to learn optimal interaction activities. The support unit proposes interaction activities obtained from the learning unit, providing activities that are easy for patients to participate in. The management unit monitors the implementation status of the interaction activities and makes adjustments as needed. This can reduce patients' feelings of isolation and improve their mental health.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The data collection unit collects data from dementia patients. The data collection unit can collect data based on, for example, the patient's health status, living situation, and individual needs. The data collection unit collects data to monitor the patient's health status and detect abnormalities. The data collection unit can also collect data on the patient's living situation to help support their daily life. Furthermore, the data collection unit can collect data based on the patient's individual needs to provide optimal support. Step 2: The learning unit learns from the data collected by the collection unit and provides optimal support for each individual patient. The learning unit analyzes the collected data using AI and learns the optimal support content based on the patient's health status and living situation. The learning unit learns algorithms to analyze the patient's health status and detect abnormalities. In addition, the learning unit learns support content that is useful for supporting daily life based on the patient's living situation. Furthermore, the learning unit can learn the optimal support content based on the individual needs of the patient. Step 3: The support team provides support for daily life based on the support received from the learning team. The support team can provide medication reminders. The support team reminds patients when they need to take their medication to prevent them from forgetting. The support team can also provide assistance with meal preparation. The support team offers menu suggestions and cooking support when patients prepare meals. Furthermore, the support team can provide location notifications. The support team obtains the patient's location information and notifies family members and caregivers. Step 4: The management department performs health management based on the support provided by the support department. The management department can monitor body temperature and blood pressure. The management department regularly measures the patient's body temperature and blood pressure and detects abnormalities. The management department can also provide an abnormality detection algorithm. The management department provides an algorithm for detecting abnormalities using AI and notifies when an abnormality is detected. Furthermore, the management department can provide a notification function. The management department notifies the patient and their family when an abnormality is detected. Step 5: The support department provides communication support based on the health management information provided by the management department. The support department can provide speech recognition technology. The support department recognizes the patient's voice and suggests appropriate words. The support department can also provide an appropriate word suggestion function. The support department suggests appropriate words when the patient is communicating. Step 6: The care department provides care support based on the communication support provided by the support department. The care department can create care plans. The care department creates care plans based on the patient's health condition and living situation. The care department can also support the efficiency of care work. The care department manages the progress of care work and prioritizes tasks. Furthermore, the care department can provide real-time data. The care department provides data on the patient's health condition and living situation in real time.

[0072] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system for supporting dementia patients and their families. This AI agent system learns from data collected from dementia patients around the world and provides optimal support for individual patients. The AI ​​agent system provides four main functions: support for daily life, health management, communication support, and care support. This enables dementia patients to live independently and reduces the burden on families and care providers. For example, the AI ​​agent system provides support for daily life such as medication reminders, meal preparation assistance, and location information notifications. Health management includes monitoring of body temperature and blood pressure, anomaly detection algorithms, and notification functions. Communication support includes voice recognition technology and appropriate word suggestion functions. Care support includes creating care plans, supporting the efficiency of care work, and providing real-time data. This aims to improve the quality of life (QOL) of dementia patients.

[0073] The AI ​​agent system according to this embodiment comprises a data collection unit, a learning unit, a support unit, a management unit, an assistance unit, and a care unit. The data collection unit collects data from dementia patients. The data collection unit can, for example, collect data based on the patient's health status, living situation, and individual needs. The data collection unit can, for example, collect data to monitor the patient's health status and detect abnormalities. The data collection unit can also collect data related to the patient's living situation and use it to support daily life. Furthermore, the data collection unit can collect data based on the patient's individual needs and collect data to provide optimal support. The learning unit learns from the data collected by the data collection unit and provides optimal support to individual patients. The learning unit can, for example, analyze the collected data using AI and learn the optimal support content based on the patient's health status and living situation. The learning unit can, for example, analyze the patient's health status using AI and learn algorithms for detecting abnormalities. Furthermore, the learning unit can learn support content that is useful for supporting daily life based on the patient's living situation. Furthermore, the learning unit can learn the optimal support content based on the patient's individual needs. The support unit provides support for daily life based on the support provided by the learning unit. For example, the support unit can provide medication reminders. For example, the support unit can remind patients to take their medication at the appropriate time to prevent them from forgetting. The support unit can also provide assistance with meal preparation. For example, the support unit can suggest menus and provide cooking support when patients are preparing meals. Furthermore, the support unit can provide location information notifications. For example, the support unit can obtain the patient's location information and notify family members or caregivers. The management unit manages health based on the support provided by the support unit. For example, the management unit can monitor body temperature and blood pressure. For example, the management unit can periodically measure the patient's body temperature and blood pressure and detect abnormalities. Furthermore, the management unit can provide anomaly detection algorithms. For example, the management unit can provide an algorithm for detecting abnormalities using AI and notify if an abnormality is detected. Furthermore, the management unit can provide a notification function.The management department notifies patients and their families, for example, when an abnormality is detected. The support department provides communication support based on the health management information provided by the management department. The support department can provide, for example, speech recognition technology. The support department can recognize the patient's voice and suggest appropriate words. The support department can also provide an appropriate word suggestion function. The support department can suggest appropriate words when the patient is communicating. The care department provides care support based on the communication support information provided by the support department. The care department can create care plans, for example. The care department creates care plans based on the patient's health condition and living situation. The care department can also support the efficiency of care work. The care department manages the progress of care work and prioritizes tasks, for example. Furthermore, the care department can provide real-time data. The care department provides data on the patient's health condition and living situation in real time. As a result, the AI ​​agent system according to this embodiment can improve the quality of life of dementia patients.

[0074] The data collection unit collects data from dementia patients. For example, it can collect data based on the patient's health status, lifestyle, and individual needs. Specifically, to monitor the patient's health, it uses wearable devices and home sensors to collect vital data such as heart rate, blood pressure, body temperature, and activity level. This data is transmitted in real time to a central database, and an alert is immediately issued if an abnormality is detected. Furthermore, data related to the patient's lifestyle is recorded using smart home devices and motion sensors to track daily behavior patterns and changes in the living environment. For example, data such as the number of times the refrigerator is opened and closed, television viewing time, and room movement history can be collected to support daily life. In addition, the data collection unit collects data based on the patient's individual needs to provide optimal support. For example, it can collect information on the patient's dietary preferences, allergies, medication schedule, hobbies, and interests to provide individually customized support. This allows the data collection unit to collect a wide range of data on the patient's health status, lifestyle, and individual needs, improving the overall accuracy and effectiveness of the system. Moreover, the data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the learning and support departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0075] The learning unit learns from the data collected by the collection unit and provides optimal support to individual patients. For example, the learning unit can analyze the collected data using AI and learn the optimal support based on the patient's health status and lifestyle. Specifically, it uses machine learning algorithms to analyze the patient's health data and learn patterns for detecting abnormalities. For example, it can detect abnormal fluctuations in heart rate and blood pressure, enabling early detection of abnormalities. It can also learn support that is useful for supporting daily life based on the patient's lifestyle. For example, it can analyze the patient's behavioral patterns to understand meal timing and exercise habits and provide appropriate advice. Furthermore, the learning unit can learn the optimal support based on the patient's individual needs. For example, it can suggest meal menus or manage medication schedules based on the patient's dietary preferences and allergy information. In this way, the learning unit can accumulate knowledge to provide optimal support to individual patients based on the collected data, improving the accuracy and effectiveness of the entire system. In addition, the learning unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past health data, it can predict fluctuations in specific symptoms or risks and formulate future countermeasures. Furthermore, the learning unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the learning unit to not only monitor the situation in real time but also handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0076] The support department provides support for daily life based on the support information obtained from the learning department. For example, the support department can provide medication reminders. Specifically, it can use smartphones and wearable devices to notify patients of the time to take their medication and prevent them from forgetting. The support department can also provide assistance with meal preparation. For example, it can suggest menus and provide cooking support when patients prepare meals. This includes suggesting menus that consider nutritional balance, guiding cooking procedures, and listing necessary ingredients. Furthermore, the support department can provide location information notifications. For example, it can obtain the patient's location information and notify family members or caregivers to ensure the patient's safety. This includes a function that uses GPS to track the patient's current location in real time and issues alerts if abnormal movement or prolonged inactivity is detected. In this way, the support department can support the patient's daily life from various angles and improve their quality of life (QOL). In addition, the support department can collect patient feedback and continuously improve the accuracy and effectiveness of the support provided. For example, the support department collects feedback on how patients felt about the support they received and what improvements they think could be made, and incorporates this feedback into future support. Furthermore, the support department can reliably transmit information using multiple communication methods. For instance, they use not only smartphone notifications but also voice calls, SMS, and email to ensure important information is delivered reliably. This allows the support department to provide prompt and reliable support to patients, improving their quality of life.

[0077] The management department performs health management based on the support provided by the support department. For example, the management department can monitor body temperature and blood pressure. Specifically, it uses wearable devices and home sensors to regularly measure patients' body temperature and blood pressure and detect abnormalities. This data is transmitted in real time to a central database, and an alert is immediately issued if an abnormality is detected. The management department can also provide anomaly detection algorithms. For example, it can provide an algorithm using AI to detect abnormalities and notify when an abnormality is detected. This allows the management department to continuously monitor the patient's health status and detect abnormalities early. Furthermore, the management department can provide notification functions. For example, it can notify patients and their families when an abnormality is detected. This includes a function that uses smartphones and wearable devices to immediately notify when an abnormality is detected. This allows the management department to continuously monitor the patient's health status and respond quickly when an abnormality occurs. In addition, the management department can utilize historical data and statistical information to perform long-term health management and risk assessment. For example, based on past health data, fluctuations in specific symptoms or risks can be predicted, and future countermeasures can be planned. Furthermore, the management department can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the management department to not only monitor the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0078] The support department provides communication support based on the health management information provided by the management department. For example, the support department can provide speech recognition technology. Specifically, it uses natural language processing technology to recognize the patient's voice and suggest appropriate words. This allows for the suggestion of appropriate words when the patient communicates, supporting smoother conversations. The support department can also provide an appropriate word suggestion function. For example, if a patient cannot recall a specific word, it can suggest related words or phrases to facilitate communication. Furthermore, the support department can use speech recognition technology to analyze the patient's utterances and understand their emotions and intentions. This allows for the understanding of the patient's emotional state and needs, enabling appropriate responses. For example, if a patient is feeling anxious or stressed, the support department can provide advice on relaxation or suggest activities to change their mood. This allows the support department to support the patient's communication from multiple angles and improve their quality of life (QOL). Additionally, the support department can collect patient feedback to continuously improve the accuracy and effectiveness of the support provided. For example, it can collect feedback on how patients felt about the support provided and what improvements could be made, and incorporate this into future support. Furthermore, the support department can reliably transmit information using multiple communication methods. For example, in addition to smartphone notifications, they can use voice calls, SMS, and email in combination to ensure that important information is delivered reliably. This allows the support department to provide support to patients quickly and reliably, improving the quality of communication.

[0079] The Caregiving Department provides care support based on the communication support provided by the Support Department. For example, the Caregiving Department can create care plans. Specifically, it creates individually customized care plans based on the patient's health condition and living situation. This includes daily care schedules, rehabilitation programs, and management of meals and medication. The Caregiving Department can also support the efficiency of caregiving tasks. For example, it manages the progress of caregiving tasks and prioritizes tasks to help caregivers perform their duties efficiently. Furthermore, the Caregiving Department can provide real-time data. For example, it can provide real-time data on the patient's health condition and living situation to enable caregivers to respond quickly. This includes a function that monitors the patient's current condition in real time based on data collected from wearable devices and home sensors, and immediately notifies if an abnormality is detected. This allows the Caregiving Department to continuously understand the patient's health condition and living situation and provide appropriate care support. Additionally, the Caregiving Department can collect patient feedback and continuously improve the accuracy and effectiveness of care plans and support. For example, it can revise care plans and improve support based on feedback from patients and their families. Furthermore, the caregiving department can reliably transmit information using multiple communication methods. For example, in addition to smartphone notifications, they can use voice calls, SMS, and email in combination to ensure that important information is delivered reliably. This allows the caregiving department to provide prompt and reliable care support to patients, thereby improving their quality of life (QOL).

[0080] The support unit can provide medication reminders, meal preparation assistance, and location notifications. For example, the support unit can provide medication reminders. For example, the support unit can remind patients to take their medication at the appropriate time to prevent them from forgetting. The support unit can also provide meal preparation assistance. For example, the support unit can suggest menus and provide cooking support when patients are preparing meals. Furthermore, the support unit can provide location notifications. For example, the support unit can obtain the patient's location information and notify family members or caregivers. In this way, the support unit can support the patient's daily life. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, when providing medication reminders, the support unit can use AI to analyze the patient's medication history and determine the optimal reminder timing.

[0081] The management unit can provide monitoring of body temperature and blood pressure, anomaly detection algorithms, and notification functions. For example, the management unit can monitor body temperature and blood pressure. For example, the management unit can periodically measure the patient's body temperature and blood pressure and detect abnormalities. The management unit can also provide anomaly detection algorithms. For example, the management unit can provide an algorithm for detecting abnormalities using AI and notify when an abnormality is detected. Furthermore, the management unit can provide notification functions. For example, the management unit can notify the patient or their family when an abnormality is detected. This allows the management unit to support the patient's health management. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, when monitoring body temperature and blood pressure, the management unit can use AI to analyze the measurement data and detect abnormalities.

[0082] The support unit can provide speech recognition technology and appropriate word suggestion functions. For example, the support unit provides speech recognition technology. For example, the support unit recognizes the patient's voice and suggests appropriate words. The support unit can also provide an appropriate word suggestion function. For example, the support unit suggests appropriate words when the patient is communicating. In this way, the support unit can support the patient's communication. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, when providing speech recognition technology, the support unit can use AI to analyze the patient's voice data and suggest appropriate words.

[0083] The caregiving department can create care plans, support the efficiency of caregiving operations, and provide real-time data. For example, the caregiving department can create care plans. For example, the caregiving department can create care plans based on the patient's health condition and living situation. The caregiving department can also support the efficiency of caregiving operations. For example, the caregiving department can manage the progress of caregiving operations and prioritize tasks. Furthermore, the caregiving department can provide real-time data. For example, the caregiving department can provide data on the patient's health condition and living situation in real time. This allows the caregiving department to streamline care support. Some or all of the above processes in the caregiving department may be performed using AI, for example, or not using AI. For example, when creating care plans, the caregiving department can use AI to analyze patient data and create an optimal care plan.

[0084] The data collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the patient is stressed, the data collection unit may reduce the frequency of data collection and collect data when the patient is relaxed. For example, if the patient is anxious, the data collection unit may change the collection timing from night to day. For example, if the patient is calm, the data collection unit may increase the collection frequency to collect more detailed data. This allows the data collection unit to collect more appropriate data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit may input patient emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The data collection unit can analyze the patient's past data collection history and select the optimal collection method. For example, the data collection unit can identify the time of day when the patient is most cooperative based on past data collection history and collect data during that time. For example, the data collection unit can collect data in an environment where the patient is most relaxed, based on past data collection history. For example, the data collection unit can analyze past data collection history and collect data in a way that causes the patient the least stress. In this way, the data collection unit can select the optimal collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0086] The data collection unit can filter data based on the patient's current health status and living situation during data collection. For example, if the patient's health is poor, the data collection unit may limit the types of data collected. For example, the data collection unit may change the priority of data to be collected according to the patient's living situation. For example, if the patient's health is good, the data collection unit may collect more detailed data. This allows the data collection unit to collect more appropriate data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input data on the patient's health status and living situation into a generating AI and have the generating AI perform the filtering.

[0087] The data collection unit can estimate the patient's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the patient is stressed, the data collection unit will prioritize collecting stress-related data. For example, if the patient is relaxed, the data collection unit will prioritize collecting health-related data. For example, if the patient is anxious, the data collection unit will prioritize collecting anxiety-related data. This allows the data collection unit to prioritize collecting more important data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input patient emotion data into a generative AI and have the generative AI determine the priority of the data.

[0088] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location information during data collection. For example, if the patient is at home, the data collection unit will prioritize the collection of data related to their living situation at home. For example, if the patient is out, the data collection unit will prioritize the collection of data related to their health condition while out. For example, if the patient is in a hospital, the data collection unit will prioritize the collection of data related to the results of their medical examination at the hospital. This enables the data collection unit to collect more appropriate data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0089] The data collection unit can analyze the patient's social media activity and collect relevant data during data collection. For example, if the patient is very active on social media, the data collection unit will collect data related to that activity. For example, if the patient is not very active on social media, the data collection unit will prioritize other data collection methods. For example, the data collection unit will collect information about the patient's health status from the patient's social media activity. This allows the data collection unit to collect relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0090] The learning unit can estimate the patient's emotions and select training data based on the estimated emotions. For example, if the patient is stressed, the learning unit will prioritize learning data related to stress reduction. For example, if the patient is relaxed, the learning unit will prioritize learning data related to health maintenance. For example, if the patient is anxious, the learning unit will prioritize learning data related to anxiety reduction. This allows the learning unit to learn more appropriately. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input patient emotion data into the generative AI and have the generative AI perform the selection of training data.

[0091] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and adjust the parameters of the learning algorithm. For example, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. In this way, the learning unit can improve the accuracy of learning. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0092] The learning unit can update its learning content in response to changes in the patient's health condition during the learning process. For example, if the patient's health condition deteriorates, the learning unit will modify the learning content to address the issue. For example, if the patient's health condition improves, the learning unit will update and optimize the learning content. For example, the learning unit will periodically review the learning content in response to changes in the patient's health condition. This enables the learning unit to perform more appropriate learning. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input patient health data into a generating AI and have the generating AI perform the updating of the learning content.

[0093] The learning unit can estimate the patient's emotions and adjust the learning frequency based on the estimated emotions. For example, if the patient is stressed, the learning unit reduces the learning frequency. For example, if the patient is relaxed, the learning unit increases the learning frequency. For example, if the patient is anxious, the learning unit adjusts the learning frequency. This allows the learning unit to learn more appropriately. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input patient emotion data into a generative AI and have the generative AI adjust the learning frequency.

[0094] The learning unit can weight the learning data based on the patient's lifestyle during the learning process. For example, the learning unit weights important data based on the patient's lifestyle. For example, the learning unit determines the priority of the learning data, taking the patient's lifestyle into consideration. For example, the learning unit adjusts the weighting of the learning data according to the patient's lifestyle. This enables the learning unit to learn more appropriately. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input patient lifestyle data into a generating AI and have the generating AI perform the weighting of the learning data.

[0095] The learning unit can improve its learning content by incorporating feedback from the patient's family during the learning process. For example, the learning unit improves the learning content based on feedback from the patient's family. For example, the learning unit adjusts its learning algorithm by incorporating opinions from the patient's family. For example, the learning unit optimizes the learning content by reflecting feedback from the patient's family. In this way, the learning unit can improve its learning content. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input feedback data from the patient's family into a generating AI and have the generating AI perform improvements to the learning content.

[0096] The support unit can estimate the patient's emotions and adjust the support content based on the estimated emotions. For example, if the patient is stressed, the support unit can provide support that helps them relax. For example, if the patient is relaxed, the support unit can provide support that helps maintain their health. For example, if the patient is anxious, the support unit can provide support that helps reduce anxiety. This allows the support unit to provide more appropriate support. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input patient emotion data into the generative AI and have the generative AI adjust the support content.

[0097] The support unit can select the optimal support method by referring to the patient's past behavioral history during support. For example, the support unit selects the optimal support method based on the patient's past behavioral history. For example, the support unit analyzes the patient's past behavioral history and adjusts the support method. For example, the support unit optimizes the support content by referring to the patient's past behavioral history. This allows the support unit to select the optimal support method. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the patient's past behavioral history data into a generating AI and have the generating AI select the optimal support method.

[0098] The support unit can customize the support provided based on the patient's current health condition. For example, if the patient's health condition is poor, the support unit will provide support related to maintaining health. For example, if the patient's health condition is good, the support unit will provide support related to promoting health. The support unit customizes the support content according to the patient's health condition. This allows the support unit to provide more appropriate support. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input patient health condition data into a generating AI and have the generating AI perform the customization of the support content.

[0099] The support unit can estimate the patient's emotions and determine the priority of support based on the estimated emotions. For example, if the patient is stressed, the support unit will prioritize support related to stress reduction. For example, if the patient is relaxed, the support unit will prioritize support related to maintaining health. For example, if the patient is anxious, the support unit will prioritize support related to anxiety reduction. This allows the support unit to prioritize the most important support. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input patient emotion data into a generative AI and have the generative AI determine the priority of support.

[0100] The support unit can select the optimal support method by considering the patient's geographical location information during support. For example, if the patient is at home, the support unit will provide support methods for use at home. For example, if the patient is out, the support unit will provide support methods for use while out. For example, if the patient is in a hospital, the support unit will provide support methods for use at the hospital. This enables the support unit to provide more appropriate support. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the patient's geographical location information into a generating AI and have the generating AI select the optimal support method.

[0101] The support unit can improve its support by incorporating feedback from the patient's family during support. For example, the support unit improves the support based on feedback from the patient's family. For example, the support unit adjusts the support method by incorporating opinions from the patient's family. For example, the support unit optimizes the support by reflecting feedback from the patient's family. In this way, the support unit can improve the support. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input feedback data from the patient's family into a generating AI and have the generating AI perform improvements to the support.

[0102] The management department can estimate the patient's emotions and adjust the health management methods based on the estimated emotions. For example, if the patient is stressed, the management department can provide health management methods related to stress reduction. For example, if the patient is relaxed, the management department can provide health management methods related to maintaining health. For example, if the patient is anxious, the management department can provide health management methods related to anxiety reduction. This enables the management department to provide more appropriate health management. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI, for example, or not using AI. For example, the management department can input patient emotion data into a generative AI and have the generative AI adjust the health management methods.

[0103] The management department can select the optimal management method by referring to the patient's past health data during health management. For example, the management department selects the optimal health management method based on the patient's past health data. For example, the management department analyzes the patient's past health data and adjusts the health management method. For example, the management department optimizes the health management content by referring to the patient's past health data. In this way, the management department can select the optimal management method. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the patient's past health data into a generating AI and have the generating AI perform the selection of the optimal management method.

[0104] The management department can customize the management content based on the patient's current health status during health management. For example, if the patient's health status is poor, the management department will provide management content related to maintaining health. For example, if the patient's health status is good, the management department will provide management content related to promoting health. For example, the management department will customize the management content according to the patient's health status. This enables the management department to provide more appropriate health management. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input patient health status data into a generating AI and have the generating AI perform the customization of the management content.

[0105] The management department can estimate a patient's emotions and determine the priority of health management based on the estimated emotions. For example, if a patient is stressed, the management department will prioritize stress reduction-related health management. For example, if a patient is relaxed, the management department will prioritize health maintenance-related health management. For example, if a patient is anxious, the management department will prioritize anxiety reduction-related health management. This allows the management department to prioritize providing more important health management. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input patient emotion data into a generative AI and have the generative AI determine the priority of health management.

[0106] The management department can select the optimal management method when managing a patient's health, taking into account the patient's geographical location. For example, if the patient is at home, the management department can provide a health management method for use at home. For example, if the patient is out, the management department can provide a health management method for use while away from home. For example, if the patient is in a hospital, the management department can provide a health management method for use in a hospital. This enables the management department to provide more appropriate health management. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input the patient's geographical location information into a generating AI and have the generating AI select the optimal management method.

[0107] The management department can improve its management practices by incorporating feedback from patients' families during health management. For example, the management department can improve management practices based on feedback from patients' families. For example, the management department can adjust management methods by incorporating opinions from patients' families. For example, the management department can optimize management practices by reflecting feedback from patients' families. In this way, the management department can improve its management practices. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input feedback data from patients' families into a generating AI and have the generating AI implement improvements to the management practices.

[0108] The support unit can estimate the patient's emotions and adjust its communication support methods based on the estimated emotions. For example, if the patient is stressed, the support unit can provide relaxing communication methods. For example, if the patient is relaxed, the support unit can provide communication methods related to maintaining health. For example, if the patient is anxious, the support unit can provide communication methods related to reducing anxiety. This enables the support unit to provide more appropriate communication support. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input patient emotion data into a generative AI and have the generative AI adjust the communication support methods.

[0109] The support unit can select the optimal support method by referring to the patient's past communication history when providing communication support. For example, the support unit selects the optimal support method based on the patient's past communication history. For example, the support unit analyzes the patient's past communication history and adjusts the support method. For example, the support unit optimizes the support content by referring to the patient's past communication history. This allows the support unit to select the optimal support method. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the patient's past communication history data into a generating AI and have the generating AI select the optimal support method.

[0110] The support unit can customize the support provided during communication assistance based on the patient's current health condition. For example, if the patient's health condition is poor, the support unit will provide support related to maintaining health. For example, if the patient's health condition is good, the support unit will provide support related to promoting health. The support unit customizes the support content according to the patient's health condition. This enables the support unit to provide more appropriate communication assistance. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input patient health condition data into a generating AI and have the generating AI perform the customization of the support content.

[0111] The support unit can estimate the patient's emotions and determine the priority of communication support based on the estimated emotions. For example, if the patient is stressed, the support unit will prioritize communication support related to stress reduction. For example, if the patient is relaxed, the support unit will prioritize communication support related to maintaining health. For example, if the patient is anxious, the support unit will prioritize communication support related to anxiety reduction. This allows the support unit to prioritize the most important support. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input patient emotion data into a generative AI and have the generative AI determine the priority of communication support.

[0112] The support unit can select the optimal support method when providing communication support, taking into account the patient's geographical location. For example, if the patient is at home, the support unit can provide communication support methods at home. For example, if the patient is out, the support unit can provide communication support methods at their destination. For example, if the patient is in a hospital, the support unit can provide communication support methods at the hospital. This enables the support unit to provide more appropriate communication support. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the patient's geographical location information into a generating AI and have the generating AI select the optimal support method.

[0113] The support department can improve its support by incorporating feedback from the patient's family during communication support. For example, the support department improves the support based on feedback from the patient's family. For example, the support department adjusts the support method by incorporating opinions from the patient's family. For example, the support department optimizes the support by reflecting feedback from the patient's family. In this way, the support department can improve the support. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input feedback data from the patient's family into a generating AI and have the generating AI execute improvements to the support.

[0114] The care department can estimate the patient's emotions and adjust the care support methods based on the estimated emotions. For example, if the patient is stressed, the care department can provide relaxing care support methods. For example, if the patient is relaxed, the care department can provide care support methods related to maintaining health. For example, if the patient is anxious, the care department can provide care support methods related to reducing anxiety. This enables the care department to provide more appropriate care support. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the care department may be performed using AI, for example, or not using AI. For example, the care department can input patient emotion data into a generative AI and have the generative AI adjust the care support methods.

[0115] The caregiving department can select the optimal support method by referring to the patient's past care history when providing care support. For example, the caregiving department selects the optimal support method based on the patient's past care history. For example, the caregiving department analyzes the patient's past care history and adjusts the support method. For example, the caregiving department optimizes the support content by referring to the patient's past care history. This allows the caregiving department to select the optimal support method. Some or all of the above processes in the caregiving department may be performed using AI, for example, or not using AI. For example, the caregiving department can input the patient's past care history data into a generating AI and have the generating AI perform the selection of the optimal support method.

[0116] The caregiving department can customize the support provided during caregiving based on the patient's current health condition. For example, if the patient's health is poor, the caregiving department will provide support related to maintaining health. For example, if the patient's health is good, the caregiving department will provide support related to promoting health. The caregiving department customizes the support according to the patient's health condition. This enables the caregiving department to provide more appropriate care. Some or all of the above processes in the caregiving department may be performed using AI, for example, or not using AI. For example, the caregiving department can input patient health data into a generating AI and have the generating AI perform the customization of the support.

[0117] The care department can estimate the patient's emotions and determine the priority of care support based on the estimated emotions. For example, if the patient is stressed, the care department will prioritize care support related to stress reduction. For example, if the patient is relaxed, the care department will prioritize care support related to maintaining health. For example, if the patient is anxious, the care department will prioritize care support related to anxiety reduction. This allows the care department to prioritize the most important support. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the care department may be performed using AI, or not using AI. For example, the care department can input patient emotion data into a generative AI and have the generative AI determine the priority of care support.

[0118] The caregiving department can select the most appropriate support method when providing care, taking into account the patient's geographical location. For example, if the patient is at home, the caregiving department can provide care support methods at home. For example, if the patient is out, the caregiving department can provide care support methods at the patient's location. For example, if the patient is in a hospital, the caregiving department can provide care support methods at the hospital. This enables the caregiving department to provide more appropriate care support. Some or all of the above processing in the caregiving department may be performed using AI, for example, or without AI. For example, the caregiving department can input the patient's geographical location information into a generating AI and have the generating AI select the most appropriate support method.

[0119] The caregiving department can improve its support by incorporating feedback from the patient's family during caregiving support. For example, the caregiving department improves support based on feedback from the patient's family. For example, the caregiving department adjusts support methods by incorporating opinions from the patient's family. For example, the caregiving department optimizes support by reflecting feedback from the patient's family. In this way, the caregiving department can improve its support. Some or all of the above processes in the caregiving department may be performed using AI, for example, or not using AI. For example, the caregiving department can input feedback data from the patient's family into a generating AI and have the generating AI execute improvements to the support.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The AI ​​agent system can further suggest individualized recreational activities based on the patient's hobbies and interests. For example, the data collection unit collects data on the patient's hobbies and interests, and the learning unit analyzes this data to learn the optimal recreational activities. The support unit suggests recreational activities obtained by the learning unit and provides activities that the patient can enjoy. The management unit monitors the implementation status of recreational activities and makes adjustments as needed. This can improve the patient's quality of life.

[0122] The AI ​​agent system can further monitor the patient's sleep patterns and provide an optimal sleep environment. For example, the data collection unit collects the patient's sleep data, and the learning unit analyzes this data to learn the optimal sleep environment. The support unit proposes the sleep environment obtained by the learning unit, helping the patient sleep comfortably. The management unit monitors the implementation of the sleep environment and makes adjustments as needed. This can improve the patient's health.

[0123] The AI ​​agent system can further propose individualized meal plans based on the patient's dietary preferences. For example, the data collection unit collects data on the patient's dietary preferences, and the learning unit analyzes this data to learn the optimal meal plan. The support unit proposes the meal plan obtained by the learning unit and provides meals that the patient can enjoy. The management unit monitors the implementation status of the meal plan and makes adjustments as needed. This can improve the patient's nutritional status.

[0124] The AI ​​agent system can further propose individualized exercise programs based on the patient's exercise habits. For example, the data collection unit collects data on the patient's exercise habits, and the learning unit analyzes this data to learn the optimal exercise program. The support unit proposes the exercise program obtained by the learning unit, providing exercises that the patient can continue without difficulty. The management unit monitors the implementation status of the exercise program and makes adjustments as needed. This can improve the patient's physical fitness.

[0125] The AI ​​agent system can further propose activities to promote patients' social interaction. For example, the data collection unit collects data on patients' social interactions, and the learning unit analyzes this data to learn optimal interaction activities. The support unit proposes interaction activities obtained from the learning unit, providing activities that are easy for patients to participate in. The management unit monitors the implementation status of the interaction activities and makes adjustments as needed. This can reduce patients' feelings of isolation and improve their mental health.

[0126] The AI ​​agent system can further estimate the patient's emotions and provide music therapy based on those emotions. For example, the data collection unit collects the patient's emotional data, and the learning unit analyzes this data to learn the optimal music therapy. The support unit provides music therapy suggestions derived from the learning unit and offers music that helps the patient relax. The management unit monitors the implementation of the music therapy and makes adjustments as needed. This can reduce the patient's stress and improve their mental health.

[0127] The AI ​​agent system can further estimate the patient's emotions and provide aromatherapy based on those emotions. For example, the data collection unit collects the patient's emotional data, and the learning unit analyzes this data to learn the optimal aromatherapy. The support unit suggests aromatherapy based on the learning unit's findings and provides the patient with a relaxing scent. The management unit monitors the implementation of the aromatherapy and makes adjustments as needed. This can reduce the patient's stress and improve their mental health.

[0128] The AI ​​agent system can further estimate the patient's emotions and provide pet therapy based on those estimated emotions. For example, the data collection unit collects the patient's emotional data, and the learning unit analyzes this data to learn the optimal pet therapy. The support unit proposes pet therapy based on the learning unit's findings, providing the patient with a relaxing interaction with a pet. The management unit monitors the implementation of pet therapy and makes adjustments as needed. This can reduce patient stress and improve their mental health.

[0129] The AI ​​agent system can further estimate the patient's emotions and provide art therapy based on those estimated emotions. For example, the data collection unit collects the patient's emotional data, and the learning unit analyzes this data to learn the optimal art therapy. The support unit proposes art therapy based on the learning unit's findings and provides art activities that help the patient relax. The management unit monitors the implementation of art therapy and makes adjustments as needed. This can reduce the patient's stress and improve their mental health.

[0130] The AI ​​agent system can further estimate the patient's emotions and provide gardening therapy based on those estimated emotions. For example, the data collection unit collects the patient's emotional data, and the learning unit analyzes this data to learn the optimal gardening therapy. The support unit proposes gardening therapy based on the learning unit's findings and provides gardening activities that allow the patient to relax. The management unit monitors the implementation of the gardening therapy and makes adjustments as needed. This can reduce the patient's stress and improve their mental health.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The data collection unit collects data from dementia patients. The data collection unit can collect data based on, for example, the patient's health status, living situation, and individual needs. The data collection unit collects data to monitor the patient's health status and detect abnormalities. The data collection unit can also collect data on the patient's living situation to help support their daily life. Furthermore, the data collection unit can collect data based on the patient's individual needs to provide optimal support. Step 2: The learning unit learns from the data collected by the collection unit and provides optimal support for each individual patient. The learning unit analyzes the collected data using AI and learns the optimal support content based on the patient's health status and living situation. The learning unit learns algorithms to analyze the patient's health status and detect abnormalities. In addition, the learning unit learns support content that is useful for supporting daily life based on the patient's living situation. Furthermore, the learning unit can learn the optimal support content based on the individual needs of the patient. Step 3: The support team provides support for daily life based on the support received from the learning team. The support team can provide medication reminders. The support team reminds patients when they need to take their medication to prevent them from forgetting. The support team can also provide assistance with meal preparation. The support team offers menu suggestions and cooking support when patients prepare meals. Furthermore, the support team can provide location notifications. The support team obtains the patient's location information and notifies family members and caregivers. Step 4: The management department performs health management based on the support provided by the support department. The management department can monitor body temperature and blood pressure. The management department regularly measures the patient's body temperature and blood pressure and detects abnormalities. The management department can also provide an abnormality detection algorithm. The management department provides an algorithm for detecting abnormalities using AI and notifies when an abnormality is detected. Furthermore, the management department can provide a notification function. The management department notifies the patient and their family when an abnormality is detected. Step 5: The support department provides communication support based on the health management information provided by the management department. The support department can provide speech recognition technology. The support department recognizes the patient's voice and suggests appropriate words. The support department can also provide an appropriate word suggestion function. The support department suggests appropriate words when the patient is communicating. Step 6: The care department provides care support based on the communication support provided by the support department. The care department can create care plans. The care department creates care plans based on the patient's health condition and living situation. The care department can also support the efficiency of care work. The care department manages the progress of care work and prioritizes tasks. Furthermore, the care department can provide real-time data. The care department provides data on the patient's health condition and living situation in real time.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0135] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the data collection unit, learning unit, support unit, management unit, assistance unit, and care unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit monitors the patient's health and living conditions using the camera 42 and sensors of the smart device 14 and collects data using the specific processing unit 290 of the data processing unit 12. The learning unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and learns the optimal support content. The support unit provides, for example, medication reminders and meal preparation assistance using the control unit 46A of the smart device 14. The management unit monitors body temperature and blood pressure using the specific processing unit 290 of the data processing unit 12 and detects abnormalities. The assistance unit provides, for example, voice recognition technology using the control unit 46A of the smart device 14 and suggests appropriate words. The care unit provides, for example, assistance in creating care plans and improving the efficiency of care work using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the data collection unit, learning unit, support unit, management unit, assistance unit, and care unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit monitors the patient's health and living conditions using the camera 42 and sensors of the smart glasses 214 and collects data using the specific processing unit 290 of the data processing unit 12. The learning unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and learns the optimal support content. The support unit provides, for example, medication reminders and meal preparation assistance using the control unit 46A of the smart glasses 214. The management unit monitors body temperature and blood pressure using the specific processing unit 290 of the data processing unit 12 and detects abnormalities. The assistance unit provides, for example, voice recognition technology using the control unit 46A of the smart glasses 214 and suggests appropriate words. The care unit provides, for example, assistance in creating care plans and improving the efficiency of care work using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0157] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the data collection unit, learning unit, support unit, management unit, assistance unit, and care unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit monitors the patient's health and living conditions using the camera 42 and sensors of the headset terminal 314 and collects data using the specific processing unit 290 of the data processing unit 12. The learning unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and learns the optimal support content. The support unit provides, for example, medication reminders and meal preparation assistance using the control unit 46A of the headset terminal 314. The management unit monitors body temperature and blood pressure using the specific processing unit 290 of the data processing unit 12 and detects abnormalities. The assistance unit provides, for example, voice recognition technology using the control unit 46A of the headset terminal 314 and suggests appropriate words. The care unit provides, for example, assistance in creating care plans and improving the efficiency of care work using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0173] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0175] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0176] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0177] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0178] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0179] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0180] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0185] Each of the multiple elements described above, including the collection unit, learning unit, support unit, management unit, assistance unit, and care unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit monitors the patient's health and living conditions using the camera 42 and sensors of the robot 414 and collects data using the specific processing unit 290 of the data processing unit 12. The learning unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 and learns the optimal support content. The support unit provides, for example, medication reminders and meal preparation assistance using the control unit 46A of the robot 414. The management unit monitors body temperature and blood pressure using the specific processing unit 290 of the data processing unit 12 and detects abnormalities. The assistance unit provides, for example, voice recognition technology using the control unit 46A of the robot 414 and suggests appropriate words. The care unit provides, for example, assistance in creating care plans and improving the efficiency of care work using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0186] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0188] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0189] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0190] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0194] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0195] 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.

[0196] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0197] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0198] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0199] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0201] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0202] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0203] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0204] (Note 1) A data collection unit that collects data on dementia patients, A learning unit learns from the data collected by the aforementioned collection unit and provides optimal support for each individual patient, A support unit provides support for daily life based on the support content obtained by the aforementioned learning unit, The Management Department, which manages health based on the support provided by the aforementioned Support Department, The support department provides communication support based on the health management information provided by the aforementioned management department, The system comprises a caregiving department that provides caregiving support based on the communication support content provided by the aforementioned support department. A system characterized by the following features. (Note 2) The aforementioned support unit is It provides medication reminders, assistance with meal preparation, and location notifications. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned management department, It provides body temperature and blood pressure monitoring, anomaly detection algorithms, and notification functions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit, It provides speech recognition technology and a function to suggest appropriate words. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned nursing care department, We provide services such as creating care plans, supporting the efficiency of caregiving operations, and providing real-time data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the patient's past data collection history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the patient's current health status and living situation. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, analyze patients' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, The system estimates the patient's emotions and selects training data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, During the learning process, the learning content is updated according to changes in the patient's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning unit, The system estimates the patient's emotions and adjusts the learning frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, During training, the training data is weighted based on the patient's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, During the learning process, we incorporate feedback from patients' families to improve the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned support unit is The system estimates the patient's emotions and adjusts the support provided based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned support unit is During support, the optimal support method is selected by referring to the patient's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned support unit is During support, customize the support provided based on the patient's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned support unit is The system estimates the patient's emotions and prioritizes support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned support unit is When providing support, the most suitable support method is selected considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned support unit is During support, we incorporate feedback from the patient's family to improve the support provided. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, The system estimates the patient's emotions and adjusts health management methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, When managing a patient's health, the optimal management method is selected by referring to the patient's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, During health management, customize the management plan based on the patient's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, The system estimates the patient's emotions and determines the priority of health management based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, When managing a patient's health, the optimal management method should be selected considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, When managing a patient's health, we incorporate feedback from their family to improve the management plan. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit, We estimate the patient's emotions and adjust communication support methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned support unit, When providing communication support, the optimal support method is selected by referring to the patient's past communication history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned support unit, When providing communication support, customize the support content based on the patient's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned support unit, The system estimates the patient's emotions and prioritizes communication support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit, When providing communication support, the most appropriate support method is selected by considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned support unit, When providing communication support, we incorporate feedback from the patient's family to improve the support provided. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned nursing care department, The system estimates the patient's emotions and adjusts care support methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned nursing care department, When providing care support, the most appropriate support method is selected by referring to the patient's past care history. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned nursing care department, When providing care support, customize the support content based on the patient's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned nursing care department, The system estimates the patient's emotions and determines the priority of care support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned nursing care department, When providing care support, the most appropriate support method is selected by considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned nursing care department, When providing care support, we incorporate feedback from the patient's family to improve the support provided. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data on dementia patients, A learning unit learns from the data collected by the aforementioned collection unit and provides optimal support for each individual patient, A support unit provides support for daily life based on the support content obtained by the aforementioned learning unit, The Management Department, which manages health based on the support provided by the aforementioned Support Department, The support department provides communication support based on the health management information provided by the aforementioned management department, The system comprises a caregiving department that provides caregiving support based on the communication support content provided by the aforementioned support department. A system characterized by the following features.

2. The aforementioned support unit is It provides medication reminders, assistance with meal preparation, and location notifications. The system according to feature 1.

3. The aforementioned management department, It provides body temperature and blood pressure monitoring, anomaly detection algorithms, and notification functions. The system according to feature 1.

4. The aforementioned support unit, It provides speech recognition technology and a function to suggest appropriate words. The system according to feature 1.

5. The aforementioned nursing care department, We provide services such as creating care plans, supporting the efficiency of caregiving operations, and providing real-time data. The system according to feature 1.

6. The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the patient's past data collection history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on the patient's current health status and living situation. The system according to feature 1.

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