system

Abstract

The system according to this embodiment aims to monitor the condition of patients and those receiving care, and to manage schedules and optimize operations. [Solution] The system according to the embodiment comprises a monitoring unit, an input unit, a management unit, a reminder unit, and an advice unit. The monitoring unit monitors the condition of patients or care recipients. The input unit inputs data acquired by the monitoring unit. The management unit manages schedules based on the data entered by the input unit. The reminder unit provides reminders based on the schedule managed by the management unit. The advice unit advises on optimizing and improving operations based on the information provided by the reminder unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 conventional technology, the states of patients and care recipients have not been sufficiently monitored efficiently, and schedule management and business optimization have not been fully carried out, leaving room for improvement.

[0005] The system according to the embodiment aims to monitor the states of patients and care recipients and perform schedule management and business optimization.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, an input unit, a management unit, a reminder unit, and an advice unit. The monitoring unit monitors the condition of patients or care recipients. The input unit inputs data acquired by the monitoring unit. The management unit manages schedules based on the data entered by the input unit. The reminder unit provides reminders based on the schedule managed by the management unit. The advice unit advises on optimizing and improving operations based on the information provided by the reminder unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the condition of patients and those receiving care, and can perform schedule management and optimize operations. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The medical and nursing care support system according to an embodiment of the present invention is a system that monitors the condition of patients and those receiving care, performs data entry, schedule management, provides reminders, and advises on optimizing and improving operations. This system improves the quality of medical and nursing care services by monitoring the condition of patients and those receiving care, performing data entry, schedule management, providing reminders, and advising on optimizing and improving operations. For example, the medical and nursing care support system monitors the condition of patients and those receiving care in real time. For example, the medical and nursing care support system automatically enters data on patients and those receiving care. For example, the medical and nursing care support system automatically manages schedules. For example, the medical and nursing care support system automatically provides reminders. For example, the medical and nursing care support system automatically advises on optimizing and improving operations. The medical and nursing care support system utilizes generative AI and analyzes data on patients and those receiving care using natural language processing technology and machine learning. The medical and nursing care support system uses a large-scale language model (LLM) to understand the content of tasks from text and voice input and provides appropriate advice and performs tasks on behalf of others. The medical and nursing care support system incorporates computer vision technology to support fall prevention and anomaly detection. Furthermore, it utilizes conversational AI to answer staff questions and perform simple troubleshooting. This system aims to reduce the burden on medical and nursing care staff and contribute to the well-being of those receiving services. The goal is to improve the quality of medical and nursing care services through efficiency and automation. By monitoring the condition of patients and those receiving care, inputting data, managing schedules, providing reminders, and offering advice on optimizing and improving operations, the medical and nursing care support system can enhance the quality of medical and nursing care services.

[0029] The medical and nursing care support system according to this embodiment comprises a monitoring unit, an input unit, a management unit, a reminder unit, and an advice unit. The monitoring unit monitors the condition of patients and those receiving care. The monitoring unit, for example, uses sensors to monitor the vital signs of patients and those receiving care in real time. The monitoring unit can also monitor the movements of patients and those receiving care using cameras, for example. The monitoring unit can also monitor changes in the voice of patients and those receiving care using voice recognition technology, for example. The input unit inputs data acquired by the monitoring unit. The input unit can, for example, automatically input data from sensors. The input unit can also, for example, manually input data. The input unit can also, for example, input data using voice input. The management unit manages schedules based on the data input by the input unit. The management unit can, for example, automatically create schedules for patients and those receiving care. The management unit can also, for example, automatically reflect schedule changes. The management unit can also, for example, set schedule reminders. The reminder unit provides reminders based on the schedule managed by the management unit. The reminder unit provides reminders, for example, by voice. The reminder unit can also provide reminders, for example, by text message. The reminder unit can also provide reminders, for example, by alarm sound. The advice unit provides advice on optimizing and improving operations based on the information provided by the reminder unit. The advice unit provides advice on improving the efficiency of operations, for example. The advice unit can also advise on the optimal allocation of resources, for example. The advice unit can also propose improvements to operations, for example. As a result, the medical and nursing care support system according to this embodiment can improve the quality of medical and nursing care services by monitoring the condition of patients and those receiving care, inputting data, managing schedules, providing reminders, and providing advice on optimizing and improving operations.

[0030] The monitoring unit monitors the condition of patients and those receiving care. For example, the monitoring unit uses sensors to monitor the vital signs of patients and those receiving care in real time. Specifically, sensors for measuring vital signs such as heart rate, blood pressure, body temperature, and respiratory rate are attached to patients and those receiving care. These sensors transmit data wirelessly and provide it to a central monitoring system in real time. The monitoring unit can also monitor the movements of patients and those receiving care using cameras. Cameras are installed on the ceiling or walls of the room to constantly monitor the movements of patients and those receiving care. This allows for early detection of falls and abnormal behavior, enabling a rapid response. The monitoring unit can also monitor changes in the voice of patients and those receiving care using speech recognition technology. Speech recognition technology detects changes in the tone and pattern of the patient's or care's voice, enabling early detection of abnormalities. For example, detecting tremors in the voice or signs of difficulty breathing allows for early medical intervention. In this way, the monitoring unit can monitor the condition of patients and those receiving care from multiple angles and detect abnormalities early. Furthermore, the monitoring unit centrally manages this data and can notify healthcare professionals and caregivers as needed. For example, if an abnormality is detected, the monitoring system automatically issues an alert and notifies healthcare professionals and caregivers. This enables a rapid response and ensures the safety of patients and those receiving care.

[0031] The input unit receives data acquired by the monitoring unit. For example, the input unit automatically inputs data from sensors. Specifically, data transmitted from sensors is automatically saved to the database by the input unit. This eliminates the need for manual data entry and ensures data accuracy. The input unit can also accept manual data entry. Healthcare workers and caregivers can manually enter data as needed. For example, they can manually enter patient symptoms, treatment details, and care progress. The input unit can also accept data using voice input. Voice input technology allows healthcare workers and caregivers to enter data without using their hands. This improves work efficiency and reduces the effort required for data entry. Furthermore, the input unit centrally manages the entered data and can link with other systems and departments as needed. For example, entered data can be saved to a cloud server and accessed by the management and reminder units. Adjusting the frequency and accuracy of data entry allows for flexible responses to specific situations and conditions. This enables the input unit to efficiently and effectively input data, improving the overall system performance.

[0032] The management department manages schedules based on data entered by the input department. For example, the management department automatically creates schedules for patients and those receiving care. Specifically, it automatically incorporates patient appointment schedules, treatment plans, and care plans for those receiving care into the schedule. This allows healthcare professionals and caregivers to manage their schedules efficiently. The management department can also automatically reflect schedule changes. For example, if an appointment is canceled or a treatment plan is changed, the management department automatically updates the schedule and notifies the relevant parties. This allows for a quick response to schedule changes. The management department can also set schedule reminders. Reminders are provided via voice, text messages, alarms, etc., to prevent healthcare professionals and caregivers from forgetting important appointments. This allows the management department to manage schedules efficiently and improve the quality of medical and care services. Furthermore, the management department can save schedule history and refer to past data. This allows for reviewing past consultation and treatment history and using it to inform future planning. The management department can also analyze schedules and propose ways to improve work efficiency and implement improvements. This allows the administrative department to not only improve the quality of medical and nursing care through schedule management, but also contribute to increased operational efficiency.

[0033] The reminder unit provides reminders based on schedules managed by the management unit. The reminder unit can provide reminders, for example, via voice. Specifically, it uses a voice assistant to notify patients and care recipients of scheduled appointments via voice. This allows reminders to be provided to patients and care recipients with visual impairments. The reminder unit can also provide reminders via text message. Text messages are sent to smartphones or tablets to notify patients and care recipients of appointments. This provides visually verifiable reminders. The reminder unit can also provide reminders via alarm sounds. Alarm sounds ring at designated times to inform patients and care recipients of appointments. This provides auditory verifiable reminders. Furthermore, the reminder unit can customize the content and delivery method of reminders. For example, the audio and text content of reminders can be changed according to the preferences of patients and care recipients. Furthermore, by adjusting the frequency and timing of reminder delivery, it is possible to provide reminders tailored to individual needs. This allows the reminder department to provide appropriate reminders to patients and those receiving care, supporting adherence to schedules.

[0034] The Advice Department provides advice on optimizing and improving operations based on information provided by the Reminder Department. For example, the Advice Department provides advice on improving operational efficiency. Specifically, it proposes to healthcare professionals and caregivers to review work priorities and procedures. This can improve operational efficiency and enhance the quality of medical and nursing care. The Advice Department can also advise on the optimal allocation of resources. For example, it can optimize the placement of medical equipment and care supplies so that they can be quickly used when needed. This can improve operational efficiency and ensure the safety of patients and those receiving care. The Advice Department can also propose measures to improve operations. For example, it can propose reviewing work processes and introducing new technologies to improve operational efficiency and quality. In this way, the Advice Department can support the optimization and improvement of operations in medical and nursing care settings. Furthermore, the Advice Department can also propose long-term operational improvement measures by utilizing historical data and statistical information. For example, it can analyze historical operational data to identify operational bottlenecks and areas for improvement. In this way, the Advice Department can provide specific improvement measures tailored to the needs of the field, thereby improving the quality of medical and nursing care.

[0035] The medical and nursing care support system includes a section that uses computer vision technology to prevent falls and detect anomalies. Using computer vision technology improves the accuracy of fall prevention and anomaly detection. For example, computer vision technology can be used to monitor the movements of patients and those receiving care in real time and predict the risk of falls. For example, computer vision technology can be used to detect abnormal movements of patients and those receiving care, enabling early intervention. For example, computer vision technology can be used to issue alerts to prevent falls in patients and those receiving care. Thus, using computer vision technology improves the accuracy of fall prevention and anomaly detection.

[0036] The medical and nursing care support system includes a section that uses conversational AI to answer staff questions and troubleshoot problems. Using conversational AI streamlines the process of answering staff questions and troubleshooting. For example, conversational AI can quickly answer questions from staff. For example, conversational AI can quickly resolve problems faced by staff. For example, conversational AI can quickly provide staff with the information they need. In short, using conversational AI streamlines the process of answering staff questions and troubleshooting.

[0037] The monitoring unit can monitor the condition of patients and those receiving care in real time. For example, the monitoring unit can use sensors to monitor the vital signs of patients and those receiving care in real time. For example, the monitoring unit can use cameras to monitor the movements of patients and those receiving care in real time. For example, the monitoring unit can use voice recognition technology to monitor changes in the voice of patients and those receiving care in real time. This allows for a rapid response by monitoring the condition of patients and those receiving care in real time.

[0038] The input unit can automatically input data from patients and those receiving care. For example, it can automatically input data from sensors. The input unit can also accept manual data input. Furthermore, it can accept data input using voice input. This automation of data entry reduces input errors and enables efficient data management.

[0039] The management department can automatically manage schedules. For example, it can automatically create schedules for patients and those receiving care. The management department can also automatically reflect schedule changes. For example, it can set schedule reminders. This automation of schedule management enables efficient schedule management.

[0040] The reminder function can automatically provide reminders. For example, it can provide reminders via voice. It can also provide reminders via text message. Furthermore, it can provide reminders via alarm sound. This allows you to manage important appointments without forgetting them through automatic reminder delivery.

[0041] The advisory unit can automatically provide advice on optimizing and improving business operations. For example, it can provide advice on improving operational efficiency. It can also advise on the optimal allocation of resources. Furthermore, it can propose improvements to business operations. This automated advice on optimizing and improving operations enables efficient business management.

[0042] The monitoring unit can improve the accuracy of anomaly detection by referring to the patient's or care recipient's past health data during monitoring. For example, the monitoring unit can refer to past heart rate data to detect abnormal heart rate fluctuations. For example, the monitoring unit can refer to past blood pressure data to detect abnormal blood pressure fluctuations. For example, the monitoring unit can refer to past body temperature data to detect abnormal body temperature fluctuations. In this way, the accuracy of anomaly detection is improved by referring to past health data.

[0043] The monitoring unit can predict abnormalities based on the lifestyle patterns of patients and those receiving care during monitoring. For example, the monitoring unit can analyze a patient's sleep patterns and predict abnormal sleep fluctuations. For example, the monitoring unit can analyze a patient's eating patterns and predict abnormal eating fluctuations. For example, the monitoring unit can analyze a patient's exercise patterns and predict abnormal exercise fluctuations. This allows for early detection of abnormalities by predicting them based on lifestyle patterns.

[0044] The monitoring unit can detect anomalies while considering the geographical location information of patients and those receiving care. For example, the monitoring unit performs normal monitoring when the patient is at home. The monitoring unit can also increase the frequency of anomaly detection when the patient is out. For example, the monitoring unit can notify medical staff when the patient is in a hospital. This improves the accuracy of anomaly detection by considering geographical location information.

[0045] The monitoring unit can analyze the social media activity of patients and care recipients during monitoring to detect signs of abnormality. For example, the monitoring unit can analyze the content of a patient's posts to detect abnormal emotional fluctuations. For example, the monitoring unit can analyze the frequency of a patient's posts to detect abnormal activity fluctuations. For example, the monitoring unit can analyze interactions with a patient's followers to detect abnormal communication fluctuations. In this way, signs of abnormality can be detected early by analyzing social media activity.

[0046] The input unit can improve the accuracy of data entry by referring to past data of patients or care recipients during data entry. For example, the input unit can refer to past heart rate data to input an accurate heart rate. For example, the input unit can refer to past blood pressure data to input an accurate blood pressure. For example, the input unit can refer to past body temperature data to input an accurate body temperature. In this way, the accuracy of data entry is improved by referring to past data.

[0047] The input unit can customize the input content based on the patient's or care recipient's lifestyle patterns during data entry. For example, the input unit can input sleep data considering the patient's sleep patterns. For example, the input unit can input meal data considering the patient's eating patterns. For example, the input unit can input exercise data considering the patient's exercise patterns. By customizing the input content based on lifestyle patterns, more accurate data entry becomes possible.

[0048] The input unit can adjust the input content while considering the geographical location information of the patient or care recipient. For example, if the patient is at home, the input unit performs normal data entry. If the patient is out, for example, the input unit can simplify the input content. If the patient is in a hospital, for example, the input unit can prioritize inputting data necessary for medical staff. This improves the accuracy of the input content by considering geographical location information.

[0049] The input unit can analyze the social media activity of patients and care recipients during data entry to supplement the input data. For example, the input unit can analyze the content of a patient's posts to supplement emotional data. The input unit can also analyze the frequency of a patient's posts to supplement activity data. For example, the input unit can analyze interactions with a patient's followers to supplement communication data. This allows for more accurate data entry by supplementing input content through the analysis of social media activity.

[0050] The management department can propose the optimal schedule by referring to the past schedule history of patients and those receiving care when managing schedules. For example, the management department can refer to past schedule history and propose the optimal schedule. For example, the management department can also propose a less burdensome schedule based on past schedule history. For example, the management department can analyze past schedule history and propose an efficient schedule. In this way, the optimal schedule can be proposed by referring to past schedule history.

[0051] The management department can customize schedules based on the lifestyle patterns of patients and those receiving care. For example, the management department can propose an optimal schedule considering the patient's sleep patterns. It can also propose an optimal schedule considering the patient's eating patterns. Furthermore, it can propose an optimal schedule considering the patient's exercise patterns. This allows for more appropriate schedule management by customizing schedules based on lifestyle patterns.

[0052] The management department can adjust schedules by considering the geographical location of patients and those receiving care. For example, if a patient is at home, the management department can maintain the normal schedule. If a patient is out, for example, the management department can simplify the schedule. If a patient is in the hospital, for example, the management department can prioritize scheduling activities necessary for medical staff. This improves the accuracy of the schedule by considering geographical location.

[0053] The management department can supplement schedules by analyzing the social media activity of patients and those receiving care. For example, the management department can analyze the content of patients' posts to supplement sentiment data. The management department can also analyze the frequency of patients' posts to supplement activity data. The management department can also analyze interactions with patients' followers to supplement communication data. This allows for more accurate schedule management by supplementing schedules through the analysis of social media activity.

[0054] The reminder function can improve notification accuracy by referring to the patient's or care recipient's past reminder history when providing reminders. For example, the reminder function can refer to past reminder history and suggest the optimal notification timing. For example, the reminder function can prioritize important reminders based on past reminder history. For example, the reminder function can analyze past reminder history and suggest efficient notification timing. As a result, the accuracy of notifications is improved by referring to past reminder history.

[0055] The reminder function can customize notification content based on the patient's or caregiver's lifestyle patterns when providing reminders. For example, the reminder function can suggest the optimal notification timing by considering the patient's sleep patterns. It can also suggest the optimal notification content by considering the patient's eating patterns. It can also suggest the optimal notification content by considering the patient's exercise patterns. By customizing notification content based on lifestyle patterns, it can provide more appropriate reminders.

[0056] The reminder function can adjust notification content by considering the geographical location of the patient or care recipient when providing reminders. For example, if the patient is at home, the reminder function will provide standard notification content. If the patient is out, for example, the reminder function can also simplify the notification content. If the patient is in a hospital, for example, the reminder function can also provide necessary notification content for medical staff. This improves the accuracy of notification content by considering geographical location information.

[0057] The reminder function can analyze the social media activity of patients and care recipients to supplement the notification content when providing reminders. For example, the reminder function can analyze the content of the patient's posts to supplement sentiment data. It can also analyze the frequency of the patient's posts to supplement activity data. It can also analyze interactions with the patient's followers to supplement communication data. By analyzing social media activity, the system can supplement notification content and provide more accurate reminders.

[0058] The advice unit can improve the accuracy of its advice by referring to past data of the patient or care recipient when providing advice. For example, the advice unit can refer to past health data to provide optimal advice. For example, the advice unit can refer to past lifestyle patterns to provide optimal advice. For example, the advice unit can refer to past reminder history to provide optimal advice. In this way, the accuracy of advice is improved by referring to past data.

[0059] The advice unit can customize the advice it provides based on the patient's or care recipient's lifestyle patterns. For example, it can provide optimal advice by considering the patient's sleep patterns. It can also provide optimal advice by considering the patient's eating patterns. It can also provide optimal advice by considering the patient's exercise patterns. By customizing the advice based on lifestyle patterns, it can provide more appropriate advice.

[0060] The advice unit can adjust the content of advice given by considering the geographical location information of the patient or care recipient. For example, if the patient is at home, the advice unit will provide standard advice. If the patient is out, for example, the advice unit can simplify the content of the advice. If the patient is in a hospital, for example, the advice unit can provide necessary advice to medical staff. This improves the accuracy of the advice by considering geographical location information.

[0061] The advice department can supplement advice provided by analyzing the social media activity of patients and those receiving care. For example, the advice department can analyze the content of patients' posts to supplement emotional data. The advice department can also analyze the frequency of patients' posts to supplement activity data. The advice department can also analyze interactions with patients' followers to supplement communication data. By analyzing social media activity, the advice department can supplement advice and provide more accurate advice.

[0062] The fall prevention and anomaly detection unit can improve the accuracy of fall prevention and anomaly detection by referring to past data of the patient or care recipient. For example, the fall prevention and anomaly detection unit can improve the accuracy of fall prevention by referring to past fall history. For example, the fall prevention and anomaly detection unit can also improve the accuracy of anomaly detection by referring to past anomaly detection history. For example, the fall prevention and anomaly detection unit can also improve the accuracy of fall prevention and anomaly detection by referring to past health data. In this way, the accuracy of fall prevention and anomaly detection is improved by referring to past data.

[0063] The fall prevention and anomaly detection unit can perform fall prevention and anomaly detection while considering the geographical location information of the patient or care recipient. For example, if the patient is at home, the fall prevention and anomaly detection unit will perform normal fall prevention and anomaly detection. For example, if the patient is out, the fall prevention and anomaly detection unit can increase the frequency of fall prevention and anomaly detection. For example, if the patient is in a hospital, the fall prevention and anomaly detection unit can also notify medical staff. This improves the accuracy of fall prevention and anomaly detection by considering geographical location information.

[0064] The Question Response and Troubleshooting Department can improve the accuracy of its responses by referring to past data of patients and care recipients when responding to questions or troubleshooting. For example, the Question Response and Troubleshooting Department can refer to past question history to provide the most appropriate response. For example, the Question Response and Troubleshooting Department can also refer to past troubleshooting history to provide the most appropriate response. For example, the Question Response and Troubleshooting Department can also refer to past health data to provide the most appropriate response. As a result, the accuracy of question responses and troubleshooting is improved by referring to past data.

[0065] The Question Response and Troubleshooting Department can take into account the geographical location of patients and those receiving care when responding to questions and troubleshooting. For example, if the patient is at home, the Question Response and Troubleshooting Department will perform standard question response and troubleshooting. If the patient is out, for example, the Question Response and Troubleshooting Department can also simplify the response. If the patient is in a hospital, for example, the Question Response and Troubleshooting Department can also provide necessary support to medical staff. This improves the accuracy of question response and troubleshooting by considering geographical location information.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The monitoring unit can not only monitor the condition of patients and those receiving care, but also detect changes in the environment. For example, it can detect changes in room temperature and humidity and automatically adjust air conditioners and humidifiers to maintain patient comfort. For example, it can adjust the brightness of lighting to provide an optimal environment according to the patient's sleep patterns. For example, it can monitor noise levels and activate noise cancellation functions as needed. In this way, by responding to changes in the environment, the comfort of patients and those receiving care can be improved.

[0068] Conversational AI can provide personalized advice tailored to the individual needs of patients and those receiving care. For example, it can offer appropriate diet and exercise advice based on the patient's health condition. For example, it can suggest stress management and relaxation methods based on the patient's lifestyle. For example, it can suggest recreational activities based on the patient's hobbies and interests. By providing personalized advice tailored to individual needs, it can improve the quality of life for patients and those receiving care.

[0069] The input unit can not only automatically input patient and care recipient data, but also include a feedback function to verify data accuracy. For example, if an anomaly is detected in the input data, it can notify medical staff and request verification. For example, it can also include a function to automatically correct data input errors. For example, it can save the data input history so that it can be reviewed and corrected later. By improving the accuracy of data input, the quality of medical and nursing care can be improved.

[0070] The monitoring unit not only improves the accuracy of anomaly detection by referring to the past health data of patients and those receiving care, but can also automatically propose countermeasures when an anomaly is detected. For example, if an abnormal heart rate is detected, it will notify medical staff and propose measures to stabilize the heart rate. For example, if an abnormal blood pressure is detected, it can notify medical staff and propose measures to stabilize the blood pressure. For example, if an abnormal body temperature is detected, it can notify medical staff and propose measures to stabilize the body temperature. This ensures the safety of patients and those receiving care by quickly proposing countermeasures when an anomaly is detected.

[0071] The monitoring unit can not only predict abnormalities based on the lifestyle patterns of patients and those receiving care, but also propose proactive measures against the predicted abnormalities. For example, if an abnormal sleep pattern is predicted, it can notify medical staff and propose measures to improve sleep quality. For example, if an abnormal eating pattern is predicted, it can notify medical staff and propose measures to improve the diet. For example, if an abnormal exercise pattern is predicted, it can notify medical staff and propose measures to improve exercise. In this way, by proposing proactive measures against predicted abnormalities, the health of patients and those receiving care can be maintained.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The monitoring unit monitors the condition of the patient or care recipient. For example, it can monitor vital signs in real time using sensors, monitor movements using cameras, or monitor changes in voice using voice recognition technology. Step 2: The input unit receives the data acquired by the monitoring unit. For example, data from sensors can be automatically entered, manually entered, or entered using voice input. Step 3: The management unit manages schedules based on the data entered by the input unit. For example, it can automatically create schedules for patients and care recipients, automatically reflect schedule changes, and set schedule reminders. Step 4: The reminder unit provides reminders based on the schedule managed by the management unit. For example, it can provide reminders by voice, text message, or alarm sound. Step 5: The Advice Department provides advice on optimizing and improving operations based on the information provided by the Reminder Department. For example, they can offer advice on streamlining operations, advising on the optimal allocation of resources, and proposing improvements to operations.

[0074] (Example of form 2) The medical and nursing care support system according to an embodiment of the present invention is a system that monitors the condition of patients and those receiving care, performs data entry, schedule management, provides reminders, and advises on optimizing and improving operations. This system improves the quality of medical and nursing care services by monitoring the condition of patients and those receiving care, performing data entry, schedule management, providing reminders, and advising on optimizing and improving operations. For example, the medical and nursing care support system monitors the condition of patients and those receiving care in real time. For example, the medical and nursing care support system automatically enters data on patients and those receiving care. For example, the medical and nursing care support system automatically manages schedules. For example, the medical and nursing care support system automatically provides reminders. For example, the medical and nursing care support system automatically advises on optimizing and improving operations. The medical and nursing care support system utilizes generative AI and analyzes data on patients and those receiving care using natural language processing technology and machine learning. The medical and nursing care support system uses a large-scale language model (LLM) to understand the content of tasks from text and voice input and provides appropriate advice and performs tasks on behalf of others. The medical and nursing care support system incorporates computer vision technology to support fall prevention and anomaly detection. Furthermore, it utilizes conversational AI to answer staff questions and perform simple troubleshooting. This system aims to reduce the burden on medical and nursing care staff and contribute to the well-being of those receiving services. The goal is to improve the quality of medical and nursing care services through efficiency and automation. By monitoring the condition of patients and those receiving care, inputting data, managing schedules, providing reminders, and offering advice on optimizing and improving operations, the medical and nursing care support system can enhance the quality of medical and nursing care services.

[0075] The medical and nursing care support system according to this embodiment comprises a monitoring unit, an input unit, a management unit, a reminder unit, and an advice unit. The monitoring unit monitors the condition of patients and those receiving care. The monitoring unit, for example, uses sensors to monitor the vital signs of patients and those receiving care in real time. The monitoring unit can also monitor the movements of patients and those receiving care using cameras, for example. The monitoring unit can also monitor changes in the voice of patients and those receiving care using voice recognition technology, for example. The input unit inputs data acquired by the monitoring unit. The input unit can, for example, automatically input data from sensors. The input unit can also, for example, manually input data. The input unit can also, for example, input data using voice input. The management unit manages schedules based on the data input by the input unit. The management unit can, for example, automatically create schedules for patients and those receiving care. The management unit can also, for example, automatically reflect schedule changes. The management unit can also, for example, set schedule reminders. The reminder unit provides reminders based on the schedule managed by the management unit. The reminder unit provides reminders, for example, by voice. The reminder unit can also provide reminders, for example, by text message. The reminder unit can also provide reminders, for example, by alarm sound. The advice unit provides advice on optimizing and improving operations based on the information provided by the reminder unit. The advice unit provides advice on improving the efficiency of operations, for example. The advice unit can also advise on the optimal allocation of resources, for example. The advice unit can also propose improvements to operations, for example. As a result, the medical and nursing care support system according to this embodiment can improve the quality of medical and nursing care services by monitoring the condition of patients and those receiving care, inputting data, managing schedules, providing reminders, and providing advice on optimizing and improving operations.

[0076] The monitoring unit monitors the condition of patients and those receiving care. For example, the monitoring unit uses sensors to monitor the vital signs of patients and those receiving care in real time. Specifically, sensors for measuring vital signs such as heart rate, blood pressure, body temperature, and respiratory rate are attached to patients and those receiving care. These sensors transmit data wirelessly and provide it to a central monitoring system in real time. The monitoring unit can also monitor the movements of patients and those receiving care using cameras. Cameras are installed on the ceiling or walls of the room to constantly monitor the movements of patients and those receiving care. This allows for early detection of falls and abnormal behavior, enabling a rapid response. The monitoring unit can also monitor changes in the voice of patients and those receiving care using speech recognition technology. Speech recognition technology detects changes in the tone and pattern of the patient's or care's voice, enabling early detection of abnormalities. For example, detecting tremors in the voice or signs of difficulty breathing allows for early medical intervention. In this way, the monitoring unit can monitor the condition of patients and those receiving care from multiple angles and detect abnormalities early. Furthermore, the monitoring unit centrally manages this data and can notify healthcare professionals and caregivers as needed. For example, if an abnormality is detected, the monitoring system automatically issues an alert and notifies healthcare professionals and caregivers. This enables a rapid response and ensures the safety of patients and those receiving care.

[0077] The input unit receives data acquired by the monitoring unit. For example, the input unit automatically inputs data from sensors. Specifically, data transmitted from sensors is automatically saved to the database by the input unit. This eliminates the need for manual data entry and ensures data accuracy. The input unit can also accept manual data entry. Healthcare workers and caregivers can manually enter data as needed. For example, they can manually enter patient symptoms, treatment details, and care progress. The input unit can also accept data using voice input. Voice input technology allows healthcare workers and caregivers to enter data without using their hands. This improves work efficiency and reduces the effort required for data entry. Furthermore, the input unit centrally manages the entered data and can link with other systems and departments as needed. For example, entered data can be saved to a cloud server and accessed by the management and reminder units. Adjusting the frequency and accuracy of data entry allows for flexible responses to specific situations and conditions. This enables the input unit to efficiently and effectively input data, improving the overall system performance.

[0078] The management department manages schedules based on data entered by the input department. For example, the management department automatically creates schedules for patients and those receiving care. Specifically, it automatically incorporates patient appointment schedules, treatment plans, and care plans for those receiving care into the schedule. This allows healthcare professionals and caregivers to manage their schedules efficiently. The management department can also automatically reflect schedule changes. For example, if an appointment is canceled or a treatment plan is changed, the management department automatically updates the schedule and notifies the relevant parties. This allows for a quick response to schedule changes. The management department can also set schedule reminders. Reminders are provided via voice, text messages, alarms, etc., to prevent healthcare professionals and caregivers from forgetting important appointments. This allows the management department to manage schedules efficiently and improve the quality of medical and care services. Furthermore, the management department can save schedule history and refer to past data. This allows for reviewing past consultation and treatment history and using it to inform future planning. The management department can also analyze schedules and propose ways to improve work efficiency and implement improvements. This allows the administrative department to not only improve the quality of medical and nursing care through schedule management, but also contribute to increased operational efficiency.

[0079] The reminder unit provides reminders based on schedules managed by the management unit. The reminder unit can provide reminders, for example, via voice. Specifically, it uses a voice assistant to notify patients and care recipients of scheduled appointments via voice. This allows reminders to be provided to patients and care recipients with visual impairments. The reminder unit can also provide reminders via text message. Text messages are sent to smartphones or tablets to notify patients and care recipients of appointments. This provides visually verifiable reminders. The reminder unit can also provide reminders via alarm sounds. Alarm sounds ring at designated times to inform patients and care recipients of appointments. This provides auditory verifiable reminders. Furthermore, the reminder unit can customize the content and delivery method of reminders. For example, the audio and text content of reminders can be changed according to the preferences of patients and care recipients. Furthermore, by adjusting the frequency and timing of reminder delivery, it is possible to provide reminders tailored to individual needs. This allows the reminder department to provide appropriate reminders to patients and those receiving care, supporting adherence to schedules.

[0080] The Advice Department provides advice on optimizing and improving operations based on information provided by the Reminder Department. For example, the Advice Department provides advice on improving operational efficiency. Specifically, it proposes to healthcare professionals and caregivers to review work priorities and procedures. This can improve operational efficiency and enhance the quality of medical and nursing care. The Advice Department can also advise on the optimal allocation of resources. For example, it can optimize the placement of medical equipment and care supplies so that they can be quickly used when needed. This can improve operational efficiency and ensure the safety of patients and those receiving care. The Advice Department can also propose measures to improve operations. For example, it can propose reviewing work processes and introducing new technologies to improve operational efficiency and quality. In this way, the Advice Department can support the optimization and improvement of operations in medical and nursing care settings. Furthermore, the Advice Department can also propose long-term operational improvement measures by utilizing historical data and statistical information. For example, it can analyze historical operational data to identify operational bottlenecks and areas for improvement. In this way, the Advice Department can provide specific improvement measures tailored to the needs of the field, thereby improving the quality of medical and nursing care.

[0081] The medical and nursing care support system includes a section that uses computer vision technology to prevent falls and detect anomalies. Using computer vision technology improves the accuracy of fall prevention and anomaly detection. For example, computer vision technology can be used to monitor the movements of patients and those receiving care in real time and predict the risk of falls. For example, computer vision technology can be used to detect abnormal movements of patients and those receiving care, enabling early intervention. For example, computer vision technology can be used to issue alerts to prevent falls in patients and those receiving care. Thus, using computer vision technology improves the accuracy of fall prevention and anomaly detection.

[0082] The medical and nursing care support system includes a section that uses conversational AI to answer staff questions and troubleshoot problems. Using conversational AI streamlines the process of answering staff questions and troubleshooting. For example, conversational AI can quickly answer questions from staff. For example, conversational AI can quickly resolve problems faced by staff. For example, conversational AI can quickly provide staff with the information they need. In short, using conversational AI streamlines the process of answering staff questions and troubleshooting.

[0083] The monitoring unit can monitor the condition of patients and those receiving care in real time. For example, the monitoring unit can use sensors to monitor the vital signs of patients and those receiving care in real time. For example, the monitoring unit can use cameras to monitor the movements of patients and those receiving care in real time. For example, the monitoring unit can use voice recognition technology to monitor changes in the voice of patients and those receiving care in real time. This allows for a rapid response by monitoring the condition of patients and those receiving care in real time.

[0084] The input unit can automatically input data from patients and those receiving care. For example, it can automatically input data from sensors. The input unit can also accept manual data input. Furthermore, it can accept data input using voice input. This automation of data entry reduces input errors and enables efficient data management.

[0085] The management department can automatically manage schedules. For example, it can automatically create schedules for patients and those receiving care. The management department can also automatically reflect schedule changes. For example, it can set schedule reminders. This automation of schedule management enables efficient schedule management.

[0086] The reminder function can automatically provide reminders. For example, it can provide reminders via voice. It can also provide reminders via text message. Furthermore, it can provide reminders via alarm sound. This allows you to manage important appointments without forgetting them through automatic reminder delivery.

[0087] The advisory unit can automatically provide advice on optimizing and improving business operations. For example, it can provide advice on improving operational efficiency. It can also advise on the optimal allocation of resources. Furthermore, it can propose improvements to business operations. This automated advice on optimizing and improving operations enables efficient business management.

[0088] The monitoring unit can estimate the emotions of patients and care recipients and adjust the monitoring frequency based on the estimated emotions. For example, if a patient is feeling anxious, the monitoring unit can increase the monitoring frequency to check their condition in real time. For example, if a patient is relaxed, the monitoring unit can also decrease the monitoring frequency and only check when necessary. For example, if a patient is feeling stressed, the monitoring unit can adjust the monitoring frequency appropriately to reduce the burden. In this way, adjusting the monitoring frequency based on emotions reduces the burden on patients and care recipients. 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.

[0089] The monitoring unit can improve the accuracy of anomaly detection by referring to the patient's or care recipient's past health data during monitoring. For example, the monitoring unit can refer to past heart rate data to detect abnormal heart rate fluctuations. For example, the monitoring unit can refer to past blood pressure data to detect abnormal blood pressure fluctuations. For example, the monitoring unit can refer to past body temperature data to detect abnormal body temperature fluctuations. In this way, the accuracy of anomaly detection is improved by referring to past health data.

[0090] The monitoring unit can predict abnormalities based on the lifestyle patterns of patients and those receiving care during monitoring. For example, the monitoring unit can analyze a patient's sleep patterns and predict abnormal sleep fluctuations. For example, the monitoring unit can analyze a patient's eating patterns and predict abnormal eating fluctuations. For example, the monitoring unit can analyze a patient's exercise patterns and predict abnormal exercise fluctuations. This allows for early detection of abnormalities by predicting them based on lifestyle patterns.

[0091] The monitoring unit can estimate the emotions of patients and care recipients and adjust the notification method of monitoring results based on the estimated emotions. For example, if the patient is feeling anxious, the monitoring unit can notify in a gentle tone. For example, if the patient is relaxed, the monitoring unit can notify in a normal tone. For example, if the patient is stressed, the monitoring unit can notify in a concise tone. In this way, appropriate notifications can be made to patients and care recipients by adjusting the notification method based on emotions. 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.

[0092] The monitoring unit can detect anomalies while considering the geographical location information of patients and those receiving care. For example, the monitoring unit performs normal monitoring when the patient is at home. The monitoring unit can also increase the frequency of anomaly detection when the patient is out. For example, the monitoring unit can notify medical staff when the patient is in a hospital. This improves the accuracy of anomaly detection by considering geographical location information.

[0093] The monitoring unit can analyze the social media activity of patients and care recipients during monitoring to detect signs of abnormality. For example, the monitoring unit can analyze the content of a patient's posts to detect abnormal emotional fluctuations. For example, the monitoring unit can analyze the frequency of a patient's posts to detect abnormal activity fluctuations. For example, the monitoring unit can analyze interactions with a patient's followers to detect abnormal communication fluctuations. In this way, signs of abnormality can be detected early by analyzing social media activity.

[0094] The input unit can estimate the emotions of patients or care recipients and adjust the timing of data entry based on the estimated emotions. For example, if the patient is relaxed, the input unit will enter data at the normal timing. For example, if the patient is stressed, the input unit can delay the timing of data entry. For example, if the patient is in a hurry, the input unit can speed up the timing of data entry. This reduces the burden on patients and care recipients by adjusting the timing of data entry based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The input unit can improve the accuracy of data entry by referring to past data of patients or care recipients during data entry. For example, the input unit can refer to past heart rate data to input an accurate heart rate. For example, the input unit can refer to past blood pressure data to input an accurate blood pressure. For example, the input unit can refer to past body temperature data to input an accurate body temperature. In this way, the accuracy of data entry is improved by referring to past data.

[0096] The input unit can customize the input content based on the patient's or care recipient's lifestyle patterns during data entry. For example, the input unit can input sleep data considering the patient's sleep patterns. For example, the input unit can input meal data considering the patient's eating patterns. For example, the input unit can input exercise data considering the patient's exercise patterns. By customizing the input content based on lifestyle patterns, more accurate data entry becomes possible.

[0097] The input unit can estimate the emotions of patients or care recipients and prioritize input data based on the estimated emotions. For example, if a patient is feeling anxious, the input unit will prioritize inputting important data. If a patient is relaxed, the input unit can also perform normal data input. If a patient is stressed, the input unit can also prioritize inputting simple data. This allows for the priority of inputting important data by prioritizing input data based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The input unit can adjust the input content while considering the geographical location information of the patient or care recipient. For example, if the patient is at home, the input unit performs normal data entry. If the patient is out, for example, the input unit can simplify the input content. If the patient is in a hospital, for example, the input unit can prioritize inputting data necessary for medical staff. This improves the accuracy of the input content by considering geographical location information.

[0099] The input unit can analyze the social media activity of patients and care recipients during data entry to supplement the input data. For example, the input unit can analyze the content of a patient's posts to supplement emotional data. The input unit can also analyze the frequency of a patient's posts to supplement activity data. For example, the input unit can analyze interactions with a patient's followers to supplement communication data. This allows for more accurate data entry by supplementing input content through the analysis of social media activity.

[0100] The management department can estimate the emotions of patients and care recipients and adjust schedule priorities based on those estimated emotions. For example, if a patient is feeling anxious, the management department can prioritize important appointments in the schedule. For example, if a patient is relaxed, the management department can maintain the normal schedule. For example, if a patient is feeling stressed, the management department can prioritize less burdensome appointments in the schedule. This reduces the burden on patients and care recipients by adjusting schedule priorities based on emotions. 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.

[0101] The management department can propose the optimal schedule by referring to the past schedule history of patients and those receiving care when managing schedules. For example, the management department can refer to past schedule history and propose the optimal schedule. For example, the management department can also propose a less burdensome schedule based on past schedule history. For example, the management department can analyze past schedule history and propose an efficient schedule. In this way, the optimal schedule can be proposed by referring to past schedule history.

[0102] The management department can customize schedules based on the lifestyle patterns of patients and those receiving care. For example, the management department can propose an optimal schedule considering the patient's sleep patterns. It can also propose an optimal schedule considering the patient's eating patterns. Furthermore, it can propose an optimal schedule considering the patient's exercise patterns. This allows for more appropriate schedule management by customizing schedules based on lifestyle patterns.

[0103] The management department can estimate the emotions of patients and care recipients and adjust the notification method for schedules based on the estimated emotions. For example, if a patient is feeling anxious, the management department can send notifications in a gentle tone. For example, if a patient is relaxed, the management department can send notifications in a normal tone. For example, if a patient is stressed, the management department can send concise notifications. This allows for appropriate notifications to be sent to patients and care recipients by adjusting the notification method based on emotions. 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.

[0104] The management department can adjust schedules by considering the geographical location of patients and those receiving care. For example, if a patient is at home, the management department can maintain the normal schedule. If a patient is out, for example, the management department can simplify the schedule. If a patient is in the hospital, for example, the management department can prioritize scheduling activities necessary for medical staff. This improves the accuracy of the schedule by considering geographical location.

[0105] The management department can supplement schedules by analyzing the social media activity of patients and those receiving care. For example, the management department can analyze the content of patients' posts to supplement sentiment data. The management department can also analyze the frequency of patients' posts to supplement activity data. The management department can also analyze interactions with patients' followers to supplement communication data. This allows for more accurate schedule management by supplementing schedules through the analysis of social media activity.

[0106] The reminder unit can estimate the emotions of patients or care recipients and adjust the timing of reminder notifications based on the estimated emotions. For example, if a patient is relaxed, the reminder unit will provide a reminder at the normal timing. For example, if a patient is stressed, the reminder unit can delay the notification timing. For example, if a patient is in a hurry, the reminder unit can speed up the notification timing. In this way, by adjusting the notification timing based on emotions, reminders can be provided to patients or care recipients at the appropriate time. 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.

[0107] The reminder function can improve notification accuracy by referring to the patient's or care recipient's past reminder history when providing reminders. For example, the reminder function can refer to past reminder history and suggest the optimal notification timing. For example, the reminder function can prioritize important reminders based on past reminder history. For example, the reminder function can analyze past reminder history and suggest efficient notification timing. As a result, the accuracy of notifications is improved by referring to past reminder history.

[0108] The reminder function can customize notification content based on the patient's or caregiver's lifestyle patterns when providing reminders. For example, the reminder function can suggest the optimal notification timing by considering the patient's sleep patterns. It can also suggest the optimal notification content by considering the patient's eating patterns. It can also suggest the optimal notification content by considering the patient's exercise patterns. By customizing notification content based on lifestyle patterns, it can provide more appropriate reminders.

[0109] The reminder unit can estimate the emotions of patients or care recipients and adjust the notification method of reminders based on the estimated emotions. For example, if a patient is feeling anxious, the reminder unit will send a notification in a gentle tone. For example, if a patient is relaxed, the reminder unit can send a notification in a normal tone. For example, if a patient is stressed, the reminder unit can send a concise notification. In this way, by adjusting the notification method based on emotions, appropriate notifications can be sent to patients or care recipients. 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.

[0110] The reminder function can adjust notification content by considering the geographical location of the patient or care recipient when providing reminders. For example, if the patient is at home, the reminder function will provide standard notification content. If the patient is out, for example, the reminder function can also simplify the notification content. If the patient is in a hospital, for example, the reminder function can also provide necessary notification content for medical staff. This improves the accuracy of notification content by considering geographical location information.

[0111] The reminder function can analyze the social media activity of patients and care recipients to supplement the notification content when providing reminders. For example, the reminder function can analyze the content of the patient's posts to supplement sentiment data. It can also analyze the frequency of the patient's posts to supplement activity data. It can also analyze interactions with the patient's followers to supplement communication data. By analyzing social media activity, the system can supplement notification content and provide more accurate reminders.

[0112] The advice unit can estimate the emotions of patients and those receiving care, and adjust the way advice is expressed based on the estimated emotions. For example, if a patient is feeling anxious, the advice unit will give advice in a gentle tone. For example, if a patient is relaxed, the advice unit can give advice in a normal tone. For example, if a patient is feeling stressed, the advice unit can give concise advice. In this way, by adjusting the way advice is expressed based on emotions, appropriate advice can be provided to patients and those receiving care. 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.

[0113] The advice unit can improve the accuracy of its advice by referring to past data of the patient or care recipient when providing advice. For example, the advice unit can refer to past health data to provide optimal advice. For example, the advice unit can refer to past lifestyle patterns to provide optimal advice. For example, the advice unit can refer to past reminder history to provide optimal advice. In this way, the accuracy of advice is improved by referring to past data.

[0114] The advice unit can customize the advice it provides based on the patient's or care recipient's lifestyle patterns. For example, it can provide optimal advice by considering the patient's sleep patterns. It can also provide optimal advice by considering the patient's eating patterns. It can also provide optimal advice by considering the patient's exercise patterns. By customizing the advice based on lifestyle patterns, it can provide more appropriate advice.

[0115] The advice unit can estimate the emotions of patients or those receiving care and prioritize advice based on those estimated emotions. For example, if a patient is feeling anxious, the advice unit will prioritize providing important advice. For example, if a patient is relaxed, the advice unit may provide standard advice. For example, if a patient is stressed, the advice unit may prioritize providing simple advice. This ensures that important advice is prioritized by prioritizing advice based on emotions. 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.

[0116] The advice unit can adjust the content of advice given by considering the geographical location information of the patient or care recipient. For example, if the patient is at home, the advice unit will provide standard advice. If the patient is out, for example, the advice unit can simplify the content of the advice. If the patient is in a hospital, for example, the advice unit can provide necessary advice to medical staff. This improves the accuracy of the advice by considering geographical location information.

[0117] The advice department can supplement advice provided by analyzing the social media activity of patients and those receiving care. For example, the advice department can analyze the content of patients' posts to supplement emotional data. The advice department can also analyze the frequency of patients' posts to supplement activity data. The advice department can also analyze interactions with patients' followers to supplement communication data. By analyzing social media activity, the advice department can supplement advice and provide more accurate advice.

[0118] The fall prevention and anomaly detection unit can estimate the emotions of patients and care recipients and adjust the frequency of fall prevention and anomaly detection based on the estimated emotions. For example, if a patient is feeling anxious, the fall prevention and anomaly detection unit can increase the frequency of fall prevention and anomaly detection. For example, if a patient is relaxed, the fall prevention and anomaly detection unit can decrease the frequency of fall prevention and anomaly detection. For example, if a patient is feeling stressed, the fall prevention and anomaly detection unit can appropriately adjust the frequency of fall prevention and anomaly detection. In this way, by adjusting the frequency of fall prevention and anomaly detection based on emotions, the burden on patients and care recipients is reduced. 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.

[0119] The fall prevention and anomaly detection unit can improve the accuracy of fall prevention and anomaly detection by referring to past data of the patient or care recipient. For example, the fall prevention and anomaly detection unit can improve the accuracy of fall prevention by referring to past fall history. For example, the fall prevention and anomaly detection unit can also improve the accuracy of anomaly detection by referring to past anomaly detection history. For example, the fall prevention and anomaly detection unit can also improve the accuracy of fall prevention and anomaly detection by referring to past health data. In this way, the accuracy of fall prevention and anomaly detection is improved by referring to past data.

[0120] The fall prevention and anomaly detection unit can estimate the emotions of patients and care recipients and adjust the notification method for fall prevention and anomaly detection based on the estimated emotions. For example, if a patient is feeling anxious, the fall prevention and anomaly detection unit will notify in a gentle tone. For example, if a patient is relaxed, the fall prevention and anomaly detection unit can notify in a normal tone. For example, if a patient is feeling stressed, the fall prevention and anomaly detection unit can notify in a concise tone. In this way, by adjusting the notification method based on emotions, appropriate notifications can be provided to patients and care recipients. 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.

[0121] The fall prevention and anomaly detection unit can perform fall prevention and anomaly detection while considering the geographical location information of the patient or care recipient. For example, if the patient is at home, the fall prevention and anomaly detection unit will perform normal fall prevention and anomaly detection. For example, if the patient is out, the fall prevention and anomaly detection unit can increase the frequency of fall prevention and anomaly detection. For example, if the patient is in a hospital, the fall prevention and anomaly detection unit can also notify medical staff. This improves the accuracy of fall prevention and anomaly detection by considering geographical location information.

[0122] The question-answering and troubleshooting unit can estimate the emotions of patients and care recipients and adjust its question-answering and troubleshooting methods based on the estimated emotions. For example, if a patient is feeling anxious, the question-answering and troubleshooting unit will respond in a gentle tone. For example, if a patient is relaxed, the question-answering and troubleshooting unit can respond in a normal tone. For example, if a patient is stressed, the question-answering and troubleshooting unit can respond concisely. In this way, appropriate responses can be provided to patients and care recipients by adjusting question-answering and troubleshooting methods based on emotions. 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.

[0123] The Question Response and Troubleshooting Department can improve the accuracy of its responses by referring to past data of patients and care recipients when responding to questions or troubleshooting. For example, the Question Response and Troubleshooting Department can refer to past question history to provide the most appropriate response. For example, the Question Response and Troubleshooting Department can also refer to past troubleshooting history to provide the most appropriate response. For example, the Question Response and Troubleshooting Department can also refer to past health data to provide the most appropriate response. As a result, the accuracy of question responses and troubleshooting is improved by referring to past data.

[0124] The question-answering and troubleshooting unit can estimate the emotions of patients and those receiving care, and determine the priority of question-answering and troubleshooting based on the estimated emotions. For example, if a patient is feeling anxious, the question-answering and troubleshooting unit will prioritize important questions and issues. For example, if a patient is relaxed, the question-answering and troubleshooting unit can also handle ordinary questions and issues. For example, if a patient is feeling stressed, the question-answering and troubleshooting unit can also prioritize simple questions and issues. This allows for prioritizing important responses by determining the priority of question-answering and troubleshooting based on emotions. 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.

[0125] The Question Response and Troubleshooting Department can take into account the geographical location of patients and those receiving care when responding to questions and troubleshooting. For example, if the patient is at home, the Question Response and Troubleshooting Department will perform standard question response and troubleshooting. If the patient is out, for example, the Question Response and Troubleshooting Department can also simplify the response. If the patient is in a hospital, for example, the Question Response and Troubleshooting Department can also provide necessary support to medical staff. This improves the accuracy of question response and troubleshooting by considering geographical location information.

[0126] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0127] The monitoring unit can not only monitor the condition of patients and those receiving care, but also detect changes in the environment. For example, it can detect changes in room temperature and humidity and automatically adjust air conditioners and humidifiers to maintain patient comfort. For example, it can adjust the brightness of lighting to provide an optimal environment according to the patient's sleep patterns. For example, it can monitor noise levels and activate noise cancellation functions as needed. In this way, by responding to changes in the environment, the comfort of patients and those receiving care can be improved.

[0128] By using computer vision technology, it is possible to analyze the facial expressions of patients and those receiving care and detect changes in their emotions. For example, if a patient smiles, it can be determined that they are relaxed, and the frequency of monitoring can be reduced. For example, if a patient frowns, it can be determined that they are experiencing pain or discomfort, and medical staff can be notified. For example, if a patient is crying, it can be determined that they need emotional support, and a counselor can be contacted. In this way, changes in emotions can be detected through facial expression analysis, and appropriate responses can be taken.

[0129] Conversational AI can provide personalized advice tailored to the individual needs of patients and those receiving care. For example, it can offer appropriate diet and exercise advice based on the patient's health condition. For example, it can suggest stress management and relaxation methods based on the patient's lifestyle. For example, it can suggest recreational activities based on the patient's hobbies and interests. By providing personalized advice tailored to individual needs, it can improve the quality of life for patients and those receiving care.

[0130] The monitoring unit can estimate the emotions of patients and care recipients and adjust the monitoring frequency based on the estimated emotions. For example, if a patient is feeling anxious, the monitoring frequency can be increased to check their condition in real time. For example, if a patient is relaxed, the monitoring frequency can be reduced to check only when necessary. For example, if a patient is feeling stressed, the monitoring frequency can be appropriately adjusted to reduce their burden. In this way, adjusting the monitoring frequency based on emotions reduces the burden on patients and care recipients.

[0131] The input unit can not only automatically input patient and care recipient data, but also include a feedback function to verify data accuracy. For example, if an anomaly is detected in the input data, it can notify medical staff and request verification. For example, it can also include a function to automatically correct data input errors. For example, it can save the data input history so that it can be reviewed and corrected later. By improving the accuracy of data input, the quality of medical and nursing care can be improved.

[0132] The management system not only automatically manages schedules but can also estimate the emotions of patients and care recipients and adjust schedule priorities based on those estimated emotions. For example, if a patient is feeling anxious, important appointments will be prioritized in the schedule. For example, if a patient is relaxed, the normal schedule can be maintained. For example, if a patient is feeling stressed, less burdensome appointments can be prioritized in the schedule. In this way, the burden on patients and care recipients is reduced by adjusting schedule priorities based on emotions.

[0133] The reminder function not only automatically provides reminders, but can also estimate the emotions of patients and care recipients and adjust the notification method based on those emotions. For example, if a patient is feeling anxious, the notification can be sent in a gentle tone. If a patient is relaxed, the notification can be sent in a normal tone. If a patient is stressed, the notification can be sent in a concise tone. By adjusting the notification method based on emotions, appropriate notifications can be delivered to patients and care recipients.

[0134] The advice function not only automatically provides advice on optimizing and improving operations, but can also estimate the emotions of patients and those receiving care, and adjust the way advice is expressed based on those estimated emotions. For example, if a patient is feeling anxious, it will give advice in a gentle tone. For example, if a patient is relaxed, it can give advice in a normal tone. For example, if a patient is feeling stressed, it can give advice in a concise tone. In this way, by adjusting the way advice is expressed based on emotions, it can provide appropriate advice to patients and those receiving care.

[0135] The monitoring unit not only improves the accuracy of anomaly detection by referring to the past health data of patients and those receiving care, but can also automatically propose countermeasures when an anomaly is detected. For example, if an abnormal heart rate is detected, it will notify medical staff and propose measures to stabilize the heart rate. For example, if an abnormal blood pressure is detected, it can notify medical staff and propose measures to stabilize the blood pressure. For example, if an abnormal body temperature is detected, it can notify medical staff and propose measures to stabilize the body temperature. This ensures the safety of patients and those receiving care by quickly proposing countermeasures when an anomaly is detected.

[0136] The monitoring unit can not only predict abnormalities based on the lifestyle patterns of patients and those receiving care, but also propose proactive measures against the predicted abnormalities. For example, if an abnormal sleep pattern is predicted, it can notify medical staff and propose measures to improve sleep quality. For example, if an abnormal eating pattern is predicted, it can notify medical staff and propose measures to improve the diet. For example, if an abnormal exercise pattern is predicted, it can notify medical staff and propose measures to improve exercise. In this way, by proposing proactive measures against predicted abnormalities, the health of patients and those receiving care can be maintained.

[0137] The following briefly describes the processing flow for example form 2.

[0138] Step 1: The monitoring unit monitors the condition of the patient or care recipient. For example, it can monitor vital signs in real time using sensors, monitor movements using cameras, or monitor changes in voice using voice recognition technology. Step 2: The input unit receives the data acquired by the monitoring unit. For example, data from sensors can be automatically entered, manually entered, or entered using voice input. Step 3: The management unit manages schedules based on the data entered by the input unit. For example, it can automatically create schedules for patients and care recipients, automatically reflect schedule changes, and set schedule reminders. Step 4: The reminder unit provides reminders based on the schedule managed by the management unit. For example, it can provide reminders by voice, text message, or alarm sound. Step 5: The Advice Department provides advice on optimizing and improving operations based on the information provided by the Reminder Department. For example, they can offer advice on streamlining operations, advising on the optimal allocation of resources, and proposing improvements to operations.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the monitoring unit, input unit, management unit, reminder unit, advice unit, fall prevention unit, and conversational AI unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors the condition of patients or care recipients using the camera 42 and sensors of the smart device 14 and acquires data by the control unit 46A. The input unit automatically inputs data from sensors by the control unit 46A of the smart device 14. The management unit manages schedules by the specific processing unit 290 of the data processing unit 12. The reminder unit provides reminders by the control unit 46A of the smart device 14. The advice unit advises on optimizing and improving operations by the specific processing unit 290 of the data processing unit 12. The fall prevention unit performs fall prevention and anomaly detection using computer vision technology with the camera 42 of the smart device 14. The conversational AI unit answers questions from staff and troubleshoots by 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.

[0143] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

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

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the monitoring unit, input unit, management unit, reminder unit, advice unit, fall prevention unit, and conversational AI unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors the condition of patients or care recipients using the camera 42 and sensors of the smart glasses 214 and acquires data by the control unit 46A. The input unit automatically inputs data from sensors by the control unit 46A of the smart glasses 214. The management unit manages schedules by the specific processing unit 290 of the data processing unit 12. The reminder unit provides reminders by the control unit 46A of the smart glasses 214. The advice unit advises on optimizing and improving operations by the specific processing unit 290 of the data processing unit 12. The fall prevention unit performs fall prevention and anomaly detection using computer vision technology with the camera 42 of the smart glasses 214. The interactive AI unit, for example, uses the specific processing unit 290 of the data processing device 12 to answer questions from staff and troubleshoot problems. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the monitoring unit, input unit, management unit, reminder unit, advice unit, fall prevention unit, and interactive AI unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors the condition of patients or care recipients using the camera 42 and sensors of the headset terminal 314 and acquires data by the control unit 46A. The input unit automatically inputs data from sensors by the control unit 46A of the headset terminal 314. The management unit manages schedules by the specific processing unit 290 of the data processing unit 12. The reminder unit provides reminders by the control unit 46A of the headset terminal 314. The advice unit advises on optimizing and improving operations by the specific processing unit 290 of the data processing unit 12. The fall prevention unit performs fall prevention and anomaly detection using computer vision technology with the camera 42 of the headset terminal 314. The interactive AI unit, for example, uses the specific processing unit 290 of the data processing device 12 to answer questions from staff and troubleshoot problems. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0175] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0180] 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).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] Each of the multiple elements described above, including the monitoring unit, input unit, management unit, reminder unit, advice unit, fall prevention unit, and conversational AI unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit monitors the condition of patients or care recipients using the camera 42 and sensors of the robot 414 and acquires data by the control unit 46A. The input unit automatically inputs data from sensors by the control unit 46A of the robot 414. The management unit manages schedules by the specific processing unit 290 of the data processing unit 12, for example. The reminder unit provides reminders by the control unit 46A of the robot 414, for example. The advice unit advises on optimizing and improving operations by the specific processing unit 290 of the data processing unit 12, for example. The fall prevention unit performs fall prevention and anomaly detection using computer vision technology with the camera 42 of the robot 414, for example. The conversational AI unit answers questions from staff and troubleshoots by the specific processing unit 290 of the data processing unit 12, for example. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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."

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] (Note 1) The monitoring department monitors the condition of patients and those receiving care, An input unit for inputting data acquired by the monitoring unit, A management unit that performs schedule management based on the data entered by the input unit, A reminder unit provides reminders based on the schedule managed by the aforementioned management unit, The system includes an advice unit that provides advice on optimizing and improving operations based on the information provided by the reminder unit. A system characterized by the following features. (Note 2) It is equipped with a unit that uses computer vision technology to prevent falls and detect anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 3) The company has a department that uses conversational AI to answer questions from staff and troubleshoot problems. The system described in Appendix 1, characterized by the features described herein. (Note 4) The monitoring unit, Monitor the condition of patients and those receiving care in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned input unit is Automatically inputs data for patients and those receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, Manage your schedule automatically The system described in Appendix 1, characterized by the features described herein. (Note 7) The reminder unit is, Automatically provides reminders The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned advice section, Automatically provides advice on optimizing and improving business operations. The system described in Appendix 1, characterized by the features described herein. (Note 9) The monitoring unit, The system estimates the emotions of patients and care recipients and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The monitoring unit, During monitoring, we improve the accuracy of anomaly detection by referring to the patient's or care recipient's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The monitoring unit, During monitoring, abnormalities are predicted based on the lifestyle patterns of the patient or care recipient. The system described in Appendix 1, characterized by the features described herein. (Note 12) The monitoring unit, The system estimates the emotions of patients and care recipients, and adjusts the notification method of monitoring results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The monitoring unit, During monitoring, anomaly detection is performed while considering the geographical location information of patients and those receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 14) The monitoring unit, During monitoring, the social media activity of patients and care recipients is analyzed to detect signs of abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned input unit is The system estimates the emotions of patients and those receiving care, and adjusts the timing of data entry based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned input unit is When entering data, we improve the accuracy of the data entry by referring to past data of patients and care recipients. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned input unit is When entering data, the input content is customized based on the lifestyle patterns of the patient or care recipient. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned input unit is The system estimates the emotions of patients and those receiving care, and prioritizes input data based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned input unit is When entering data, the input content is adjusted to take into account the geographical location information of patients and those receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned input unit is During data entry, the system analyzes the social media activity of patients and those receiving care to supplement the entered information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, The system estimates the emotions of patients and those receiving care, and adjusts schedule priorities based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, When managing schedules, the system suggests the optimal schedule by referring to the patient's or care recipient's past schedule history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, When managing schedules, customize them based on the patient's or care recipient's lifestyle patterns. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, The system estimates the emotions of patients and care recipients and adjusts the schedule notification method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, When managing schedules, adjust them while taking into account the geographical location of patients and those receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, When managing schedules, analyze the social media activity of patients and those receiving care to supplement the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 27) The reminder unit is, It estimates the emotions of patients and care recipients and adjusts the timing of reminder notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The reminder unit is, When providing reminders, we improve the accuracy of notifications by referring to the patient's or care recipient's past reminder history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The reminder unit is, When providing reminders, the notification content is customized based on the patient's or caregiver's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 30) The reminder unit is, It estimates the emotions of patients and care recipients and adjusts the way reminders are notified based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The reminder unit is, When sending reminders, the notification content will be adjusted to take into account the geographical location of the patient or care recipient. The system described in Appendix 1, characterized by the features described herein. (Note 32) The reminder unit is, When providing reminders, analyze the social media activity of patients and care recipients to supplement the notification content. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned advice section, The system estimates the emotions of patients and those receiving care, and adjusts the way advice is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned advice section, When providing advice, we refer to past data of patients and those receiving care to improve the accuracy of the advice. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned advice section, When providing advice, customize the advice based on the patient's or care recipient's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned advice section, The system estimates the emotions of patients and those receiving care, and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned advice section, When providing advice, we adjust the content of the advice to take into account the geographical location of the patient or care recipient. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned advice section, When providing advice, analyze the social media activity of patients and those receiving care to supplement the advice provided. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned fall prevention and abnormality detection unit is The system estimates the emotions of patients and those receiving care, and adjusts the frequency of fall prevention and abnormality detection based on these estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned fall prevention and abnormality detection unit is To improve the accuracy of fall prevention and abnormality detection, past data of patients and those receiving care is referenced. The system described in Appendix 2, characterized by the features described herein. (Note 41) The aforementioned fall prevention and abnormality detection unit is The system estimates the emotions of patients and those receiving care, and adjusts the notification methods for fall prevention and abnormality detection based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 42) The aforementioned fall prevention and abnormality detection unit is When preventing falls or detecting abnormalities, the system takes into account the geographical location information of the patient or care recipient. The system described in Appendix 2, characterized by the features described herein. (Note 43) The aforementioned question handling and troubleshooting department is: The system estimates the emotions of patients and those receiving care, and adjusts questioning and troubleshooting methods based on these estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 44) The aforementioned question handling and troubleshooting department is: When answering questions or troubleshooting, referencing past data of patients or care recipients improves the accuracy of responses. The system described in Appendix 3, characterized by the features described herein. (Note 45) The aforementioned question handling and troubleshooting department is: The system estimates the emotions of patients and those receiving care, and prioritizes questions and troubleshooting based on these estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 46) The aforementioned question handling and troubleshooting department is: When answering questions or troubleshooting, take into account the geographical location of the patient or care recipient. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0211] 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. The monitoring department monitors the condition of patients and those receiving care, An input unit for inputting data acquired by the monitoring unit, A management unit that performs schedule management based on the data entered by the input unit, A reminder unit provides reminders based on the schedule managed by the aforementioned management unit, The system includes an advice unit that provides advice on optimizing and improving operations based on the information provided by the reminder unit. A system characterized by the following features.

2. It is equipped with a unit that uses computer vision technology to prevent falls and detect anomalies. The system according to feature 1.

3. The company has a department that uses interactive AI to answer staff questions and troubleshoot problems. The system according to feature 1.

4. The monitoring unit, Monitor the condition of patients and those receiving care in real time. The system according to feature 1.

5. The aforementioned input unit is Automatically inputs data for patients and those receiving care. The system according to feature 1.

6. The aforementioned management department, Manage your schedule automatically The system according to feature 1.

7. The reminder unit is, Automatically provides reminders The system according to feature 1.

8. The aforementioned advice section, Automatically provides advice on optimizing and improving business operations. The system according to feature 1.

9. The monitoring unit, The system estimates the emotions of patients and care recipients and adjusts the monitoring frequency based on the estimated emotions. The system according to feature 1.

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