System

Abstract

To provide a system capable of efficiently monitoring and detecting user vital data and abnormal operations in a care business.SOLUTION: A system according to an embodiment comprises a vital check unit, an interaction unit, an abnormality detection unit, a notification unit, and an operation detection unit. The vital check unit collects vital data. The interaction unit interacts with a user on the basis of the data collected by the vital check unit. The abnormality detection unit detects an abnormality on the basis of the information obtained by the interaction unit. The notification unit notifies the user of the abnormality detected by the abnormality detection unit. The operation detection unit detects an abnormal operation using a sensor technique.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to efficiently monitor and detect a user's vital signs and abnormal behavior during care work.

[0005] The system according to the embodiment aims to efficiently monitor and detect vital data and abnormal movements of a user in care work. [Means for solving the problem]

[0006] The system according to the embodiment includes a vital sign check unit, a dialogue unit, an abnormality detection unit, a notification unit, and a motion detection unit. The vital sign check unit collects vital sign data. The dialogue unit dialogues with a user based on the data collected by the vital sign check unit. The abnormality detection unit detects an abnormality based on information obtained by the dialogue unit. The notification unit notifies the user of the abnormality detected by the abnormality detection unit. The motion detection unit detects abnormal motion using sensor technology. Effect of the Invention

[0007] The system according to the embodiment can efficiently monitor and detect vital data and abnormal behavior of a user in care work. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The nursing care work efficiency system according to the embodiment of the present invention is a system that automates the check of a user's vital signs and dialogue, and detects and notifies the user of an abnormality. This system automates the check of a user's vital signs, and AI dialogues with the user to detect and notify the user of an abnormality. In addition, abnormal behavior is caught using sensor technology. This reduces the burden on staff and realizes a comfortable daily life for the user. For example, the nursing care work efficiency system automates the check of a user's vital signs. For example, a wearable device or a bed sensor is used to monitor the user's vital data such as heart rate, blood pressure, and body temperature in real time. This can eliminate the need for staff to manually check the vital signs. Next, in the nursing care work efficiency system, AI dialogues with the user and detects and notifies the user of an abnormality. For example, AI asks the user a question such as "How are you feeling?" and analyzes the user's response. If an abnormality is detected, a notification is sent to the staff. This allows changes in the user's physical condition to be detected early and a prompt response to be made possible. In addition, the nursing care work efficiency system catches abnormal behavior using sensor technology. For example, if a user falls, the sensor detects the movement and the AI ​​recognizes it as an abnormality. This allows staff to respond quickly. This system reduces the burden on staff. By automating vital signs checks and dialogue, staff can focus on other important tasks by reducing manual tasks. It also realizes a comfortable daily life for users. Users can live with peace of mind because they are constantly being monitored and will be quickly responded to if an abnormality occurs. In this way, by utilizing cutting-edge IoT and AI technology, the efficiency of nursing care work is improved and an innovative platform that paves the way for the future of the nursing care industry is realized. As a result, the nursing care work efficiency system reduces the burden on staff and realizes a comfortable daily life for users.

[0029] The nursing care work efficiency improvement system according to the embodiment includes a vital check unit, a dialogue unit, an abnormality detection unit, a notification unit, and a motion detection unit. The vital check unit collects vital data of a user. The vital data of a user includes, for example, heart rate, blood pressure, body temperature, and the like, but is not limited to such examples. The vital check unit collects vital data using, for example, a wearable device or a bed sensor. The wearable device includes a smart watch or a fitness tracker. The bed sensor includes a pressure sensor or a body movement sensor. For example, the vital check unit monitors the user's heart rate in real time using a smart watch. In addition, the vital check unit can detect the user's body movement using the bed sensor and detect abnormalities early. The dialogue unit uses AI to have a dialogue with the user. For example, the dialogue unit uses natural language processing technology to ask a question to the user and analyze the response. For example, the dialogue unit uses AI to ask a question such as "How are you feeling?" and analyzes the user's response. In addition, the dialogue unit can detect abnormalities based on the user's response. For example, when the user responds with "my head hurts," the dialogue unit detects an abnormality and notifies the staff. The abnormality detection unit uses AI to analyze the user's response and detects an abnormality. For example, the abnormality detection unit uses a machine learning algorithm to analyze the user's response and detects an abnormality. For example, the abnormality detection unit detects an abnormality using an AI model that inputs the user's response data and outputs an abnormality. The notification unit sends a notification to the staff when an abnormality is detected. The notification unit notifies the staff by means of, for example, email or alert. For example, the notification unit sends an email to the staff when an abnormality is detected. The notification unit can also notify the staff of the abnormality by issuing an alert. The motion detection unit detects abnormal motion of the user using sensor technology. The motion detection unit detects abnormal motion using, for example, an acceleration sensor or a gyro sensor. For example, when the user falls, the motion detection unit detects the motion using an acceleration sensor and recognizes it as an abnormality. As a result, the nursing care work efficiency system according to the embodiment can reduce the burden on the staff and realize a comfortable daily life for the user.Some or all of the above-mentioned processing in the motion detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the motion detection unit may input data acquired by a sensor to the generation AI and cause the generation AI to detect abnormal motion.

[0030] The vital check unit collects vital data of the user. The vital data of the user includes, but is not limited to, for example, heart rate, blood pressure, and body temperature. The vital check unit collects vital data using, for example, a wearable device or a bed sensor. The wearable device includes a smart watch and a fitness tracker. These devices are worn on the wrist or chest of the user and monitor data such as heart rate, blood pressure, and body temperature in real time. For example, a smart watch measures heart rate using an optical heart rate sensor and transmits data to a central system via Bluetooth (registered trademark). The fitness tracker has a built-in acceleration sensor and gyro sensor, detects the user's movement, and calculates the amount of activity and calories burned. The bed sensor includes a pressure sensor and a body movement sensor. The pressure sensor measures the body pressure distribution when the user is lying on the bed and detects changes in body position and body movement. The body movement sensor detects minute movements and monitors the user's breathing and heart rate fluctuations. For example, the bed sensor can detect pressure changes when the user turns over in bed and issue an alert if abnormal body movements occur. This allows the vital signs check unit to monitor the user's health condition in real time and respond quickly if an abnormality occurs. Furthermore, the vital signs check unit stores the collected data on a cloud server so that medical staff and caregivers can access it remotely. This allows the user's health condition to be continuously monitored and appropriate intervention can be performed if necessary.

[0031] The dialogue unit uses AI to have a dialogue with the user. For example, the dialogue unit uses natural language processing technology to ask questions to the user and analyze the response. Specifically, the dialogue unit converts the user's speech into text data using voice recognition technology and analyzes the content using natural language processing technology. For example, the dialogue unit uses AI to ask a question such as "How are you feeling?" and analyzes the user's response. If the user responds "I'm a little tired today," the dialogue unit analyzes the content and evaluates the cause and degree of fatigue. The dialogue unit can also detect abnormalities based on the user's response. For example, if the user responds "I have a headache," the dialogue unit detects an abnormality and notifies a staff member. The dialogue unit can analyze not only the content of the user's speech, but also changes in the tone of voice and speaking style to evaluate the user's emotional state and stress level. This allows the dialogue unit to comprehensively grasp the user's health and psychological state and take appropriate measures. Furthermore, the dialogue unit can conduct dialogue according to the user's individual needs and preferences based on the user's past dialogue history. For example, if the user has previously said, "I like going for walks," the dialogue unit can make a suggestion such as, "The weather is nice today, so why don't we go for a walk?" This allows the dialogue unit to build a relationship of trust with the user and support a comfortable daily life.

[0032] The anomaly detection unit uses AI to analyze the user's response and detect anomalies. For example, the anomaly detection unit uses a machine learning algorithm to analyze the user's response and detect anomalies. Specifically, the anomaly detection unit detects anomalies using an AI model that takes the user's response data as input and outputs anomalies. For example, the anomaly detection unit analyzes the user's speech content, tone of voice, and changes in speaking style to detect abnormal patterns. If the user utters something like "My chest hurts," the anomaly detection unit analyzes the content and evaluates the possibility of a heart problem. The anomaly detection unit can also analyze the user's vital data and movement data to comprehensively detect anomalies. For example, if the user's heart rate suddenly increases and at the same time there is an utterance such as "I'm having trouble breathing," the anomaly detection unit integrates and analyzes these data to evaluate the possibility of an emergency. The anomaly detection unit can improve the accuracy of anomaly detection by utilizing past data and statistical information. For example, by using an AI model that has learned past abnormal cases, it is possible to detect anomalies with higher accuracy. This allows the anomaly detection unit to monitor the user's health condition in real time and respond quickly when an abnormality occurs. Furthermore, the anomaly detection unit can suggest appropriate countermeasures depending on the type and urgency of the detected abnormality. For example, in the case of a mild abnormality, a notification is sent to the user urging them to take a rest, and in the case of a severe abnormality, an emergency notification is sent to medical staff. This allows the anomaly detection unit to play an important role in protecting the safety and health of the user.

[0033] The notification unit sends a notification to staff when an abnormality is detected. The notification unit notifies staff by means of, for example, email or alert. Specifically, the notification unit sends an email to staff when an abnormality is detected. The email contains detailed information about the abnormality and countermeasures, so that staff can respond quickly. The notification unit can also send an alert to inform staff of the abnormality. The alert is sent to the staff's smartphone or tablet, and alerts the staff by sound or vibration. Furthermore, the notification unit can set the priority of the notification according to the type and urgency of the abnormality. For example, in the case of a minor abnormality, only email notification is sent, and in the case of a major abnormality, alert notification is also sent. This allows the notification unit to support staff so that they can respond quickly and appropriately. Furthermore, the notification unit can collect feedback from staff and continuously improve the accuracy and effectiveness of the notification content. For example, by staff providing feedback after responding, the notification unit can optimize the accuracy of anomaly detection and the timing of notification. Furthermore, the notification unit can reliably transmit information using multiple communication means. For example, important information can be delivered reliably not only by smartphone notifications but also by using voice calls, SMS, emails, etc. This allows the notification department to quickly and reliably provide abnormality information to staff, and play an important role in protecting the safety and health of users.

[0034] The motion detection unit detects abnormal motion of the user using sensor technology. The motion detection unit detects abnormal motion using, for example, an acceleration sensor or a gyro sensor. Specifically, when the user falls, the motion detection unit detects the motion using the acceleration sensor and recognizes it as abnormal. The acceleration sensor measures the acceleration of the user's motion and detects an abnormality when there is a sudden change. For example, when the user falls, the acceleration sensor detects a sudden change in acceleration and recognizes it as abnormal. In addition, the gyro sensor measures the rotational motion of the user and detects abnormal motion. For example, when the user suddenly rotates, the gyro sensor detects the motion and recognizes it as abnormal. This allows the motion detection unit to detect abnormal motion of the user in real time and respond quickly. Furthermore, the motion detection unit inputs data acquired by the sensor to the generation AI and allows the generation AI to detect abnormal motion. The generation AI can perform more accurate abnormality detection by analyzing data from the sensor and learning patterns of abnormal motion. For example, the generative AI can learn past fall data and detect early signs of a fall. This allows the motion detection unit to ensure the safety of the user and take prompt and appropriate action. Furthermore, the motion detection unit stores data on detected abnormal motions on a cloud server so that medical staff and caregivers can access it remotely. This allows the motion detection unit to continuously monitor the user's abnormal motions and take appropriate interventions as necessary.

[0035] The vital check unit can collect vital data using a wearable device or a bed sensor. The vital check unit collects vital data using, for example, a wearable device. The wearable device includes a smart watch and a fitness tracker. For example, the vital check unit monitors the user's heart rate in real time using a smart watch. The vital check unit can also collect vital data using a bed sensor. The bed sensor includes a pressure sensor and a body movement sensor. For example, the vital check unit detects the user's body movement using the bed sensor and detects abnormalities early. As a result, the use of the wearable device or the bed sensor makes the collection of vital data more efficient. A part or all of the above-mentioned processing in the vital check unit may be performed using, for example, AI, or may be performed without using AI. For example, the vital check unit can input data acquired by the wearable device to the generation AI and cause the generation AI to execute an analysis of the vital data.

[0036] The dialogue unit can ask a question to the user by AI and analyze the response. The dialogue unit, for example, uses AI to ask a question to the user and analyze the response. The dialogue unit uses natural language processing technology to ask a question to the user. For example, the dialogue unit uses AI to ask a question such as "How are you feeling?" and analyzes the user's response. The dialogue unit can also detect an abnormality based on the user's response. For example, if the user responds "I have a headache," the dialogue unit detects an abnormality and notifies a staff member. In this way, by using AI, dialogue with the user can be automated and performed efficiently. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the dialogue unit can input the user's response data to the generation AI and have the generation AI execute the analysis of the response.

[0037] The anomaly detection unit can analyze the user's response using AI and detect anomalies. The anomaly detection unit, for example, uses AI to analyze the user's response and detects anomalies. The anomaly detection unit uses a machine learning algorithm to analyze the user's response and detects anomalies. For example, the anomaly detection unit detects anomalies using an AI model that receives user response data as input and outputs an anomaly. As a result, the use of AI improves the accuracy of anomaly detection. A part or all of the above-mentioned processing in the anomaly detection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the anomaly detection unit can input user response data to the generation AI and cause the generation AI to detect anomalies.

[0038] The notification unit can send a notification to a staff member when an abnormality is detected. For example, the notification unit sends a notification to a staff member when an abnormality is detected. The notification unit notifies by means of email, alert, or the like. For example, the notification unit sends an email to a staff member when an abnormality is detected. The notification unit can also send an alert to notify the staff member of the abnormality. This allows a notification to be sent to the staff member promptly when an abnormality is detected. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the abnormality detected by the anomaly detection unit to a generation AI and cause the generation AI to generate a notification.

[0039] The motion detection unit can detect an abnormal motion of a user using sensor technology. The motion detection unit detects an abnormal motion of a user using, for example, sensor technology. The motion detection unit detects an abnormal motion using an acceleration sensor or a gyro sensor. For example, when a user falls, the motion detection unit detects the motion using an acceleration sensor and recognizes it as an abnormality. This makes it possible to detect an abnormal motion by using sensor technology. A part or all of the above-mentioned processing in the motion detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the motion detection unit can input data acquired by a sensor to a generation AI and cause the generation AI to detect an abnormal motion.

[0040] The vital check unit can analyze the user's past vital data and apply an algorithm for early detection of abnormality signs. The vital check unit, for example, analyzes the user's past vital data and applies an algorithm for early detection of abnormality signs. The vital check unit analyzes the past vital data using time series analysis or an abnormality detection algorithm. For example, the vital check unit analyzes past heart rate data and applies an algorithm for detecting abnormal patterns. The vital check unit can also analyze past blood pressure data and apply an algorithm for detecting abnormal fluctuations. Furthermore, the vital check unit can also analyze past body temperature data and apply an algorithm for detecting abnormal increases and decreases. In this way, by analyzing past data, abnormality signs can be detected early. A part or all of the above-mentioned processing in the vital check unit may be performed, for example, using AI, or may be performed without using AI. For example, the vital check unit can input past vital data to the generation AI and cause the generation AI to execute detection of abnormality signs.

[0041] The vital check unit can select an appropriate timing based on the user's life rhythm when collecting vital data. For example, the vital check unit selects an appropriate timing based on the user's life rhythm when collecting vital data. The vital check unit adjusts the collection timing of the vital data in consideration of the user's life rhythm. For example, the vital check unit starts collecting vital data according to the time the user wakes up. The vital check unit can also collect vital data before and after the user eats a meal to evaluate the influence of the meal. Furthermore, the vital check unit can collect vital data before the user goes to bed and detect abnormalities at night early. As a result, more accurate data can be obtained by selecting the timing of data collection based on the user's life rhythm. Part or all of the above-mentioned processing in the vital check unit may be performed, for example, using AI, or may be performed without using AI. For example, the vital check unit can input life rhythm data to the generation AI and cause the generation AI to select the collection timing.

[0042] The vital check unit can collect the user's diet and exercise data at the same time as collecting the vital data, and evaluate the overall health condition. The vital check unit, for example, collects the user's diet and exercise data at the same time as collecting the vital data, and evaluates the overall health condition. The vital check unit collects the user's diet and exercise data, and integrates it with the vital data to perform an evaluation. For example, the vital check unit collects vital data and dietary content before and after the user eats a meal, and evaluates the impact of the meal. The vital check unit can also collect vital data and exercise content after the user exercises, and evaluate the impact of the exercise. Furthermore, the vital check unit can also comprehensively collect data on the user's daily life and evaluate the health condition. This makes it possible to evaluate the overall health condition by collecting diet and exercise data at the same time. Some or all of the above-mentioned processing in the vital check unit may be performed, for example, using AI, or may be performed without using AI. For example, the vital check unit can input diet and exercise data to the generation AI, and cause the generation AI to execute an evaluation of the overall health condition.

[0043] The vital check unit can correct the data based on the user's environmental data when collecting the vital data. For example, the vital check unit corrects the data based on the user's environmental data when collecting the vital data. The vital check unit collects temperature data and humidity data of the user's residential environment and corrects the vital data. For example, the vital check unit collects temperature data of the user's residential environment and corrects the vital data. The vital check unit can also collect humidity data of the user's residential environment and correct the vital data. Furthermore, the vital check unit can also collect air pressure data of the user's residential environment and correct the vital data. As a result, more accurate vital data can be obtained by correcting the data in consideration of the environmental data. A part or all of the above-mentioned processing in the vital check unit may be performed using, for example, AI, or may be performed without using AI. For example, the vital check unit can input environmental data to the generation AI and cause the generation AI to execute the correction of the data.

[0044] The dialogue unit can generate a more appropriate question by referring to the user's past response history during dialogue. The dialogue unit can generate a more appropriate question by referring to the user's past response history during dialogue, for example. The dialogue unit analyzes the user's past response history and generates a related question. For example, the dialogue unit generates a related question based on a question answered by the user in the past. The dialogue unit can also generate a question by selecting a topic that is likely to interest the user from the user's past response history. Furthermore, the dialogue unit can analyze the user's past response history and generate an optimal question. As a result, a more appropriate question can be generated by referring to the past response history. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit can input past response history data to a generation AI and cause the generation AI to generate a question.

[0045] The dialogue unit can apply different dialogue scenarios depending on the health condition of the user during dialogue. For example, the dialogue unit applies different dialogue scenarios depending on the health condition of the user during dialogue. The dialogue unit selects a dialogue scenario taking into account the health condition of the user. For example, when the user complains of poor health, the dialogue unit prioritizes questions about health. In addition, when the user is relaxed, the dialogue unit can also prioritize questions about hobbies and interests. Furthermore, when the user is feeling stressed, the dialogue unit can also apply a dialogue scenario for relaxing the user. This enables more appropriate dialogue by applying a dialogue scenario according to the health condition. Part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input health condition data to the generation AI and cause the generation AI to select a dialogue scenario.

[0046] The dialogue unit can customize the question content by incorporating the opinions of the user's family and caregiver during dialogue. For example, the dialogue unit customizes the question content by incorporating the opinions of the user's family and caregiver during dialogue. The dialogue unit generates the question content based on the opinions of the user's family and caregiver. For example, the dialogue unit generates a question suitable for the user based on information provided by the user's family. The dialogue unit can also generate a question regarding the health condition based on information provided by the user's caregiver. Furthermore, the dialogue unit can also customize the dialogue content by reflecting the opinions of the user's family and caregiver. As a result, more appropriate question content is generated by incorporating the opinions of the family and caregiver. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input opinion data of the family and caregiver to the generation AI and cause the generation AI to customize the question content.

[0047] The dialogue unit can provide a topic for relaxing the user based on the user's hobbies and interests during dialogue. The dialogue unit, for example, provides a topic for relaxing the user based on the user's hobbies and interests during dialogue. The dialogue unit generates a topic based on the user's hobbies and interests. For example, the dialogue unit provides a topic related to music that the user likes. The dialogue unit can also provide a topic related to sports in which the user is interested. Furthermore, the dialogue unit can also provide a topic related to handicrafts that the user enjoys as a hobby. This allows the user to relax by providing a topic based on the hobbies and interests. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit can input hobby and interest data to the generation AI and cause the generation AI to generate a topic.

[0048] The abnormality detection unit can identify an abnormality pattern by referring to the user's past health data when an abnormality is detected. For example, the abnormality detection unit identifies an abnormality pattern by referring to the user's past health data when an abnormality is detected. The abnormality detection unit analyzes the user's past health data and identifies an abnormality pattern. For example, the abnormality detection unit identifies an abnormal pattern by referring to past heart rate data. The abnormality detection unit can also identify abnormal fluctuations by referring to past blood pressure data. Furthermore, the abnormality detection unit can also identify abnormal increases and decreases by referring to past body temperature data. This makes it easier to identify an abnormality pattern by referring to past health data. A part or all of the above-mentioned processing in the abnormality detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the abnormality detection unit can input past health data to the generation AI and cause the generation AI to identify an abnormality pattern.

[0049] The anomaly detection unit can improve the accuracy of anomaly detection by considering the user's living environment and activity level when detecting an anomaly. For example, the anomaly detection unit improves the accuracy of anomaly detection by considering the user's living environment and activity level when detecting an anomaly. The anomaly detection unit improves the accuracy of anomaly detection by referring to the user's living environment and activity level. For example, the anomaly detection unit improves the accuracy of anomaly detection by referring to temperature data of the user's living environment. The anomaly detection unit can also improve the accuracy of anomaly detection by referring to the user's activity level. Furthermore, the anomaly detection unit can also improve the accuracy of anomaly detection by referring to the user's life rhythm. As a result, the accuracy of anomaly detection is improved by considering the living environment and activity level. A part or all of the above-mentioned processing in the anomaly detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the anomaly detection unit can input the living environment and activity level data to the generation AI and cause the generation AI to execute the improvement of the accuracy of anomaly detection.

[0050] The abnormality detection unit can also notify the user's family and caregivers of the abnormality when an abnormality is detected. For example, the abnormality detection unit also notifies the user's family and caregivers of the abnormality when an abnormality is detected. The abnormality detection unit notifies the user's family and caregivers that an abnormality has been detected. For example, the abnormality detection unit notifies the user's family that an abnormality has been detected. The abnormality detection unit can also notify the user's caregiver that an abnormality has been detected. Furthermore, the abnormality detection unit can also notify the user's family and caregivers of detailed information about the abnormality. This allows for a quick response by notifying the family and caregivers. A part or all of the above-mentioned processing in the abnormality detection unit may be performed, for example, using AI or may be performed without using AI. For example, the abnormality detection unit can input the abnormality detection data to a generation AI and cause the generation AI to generate a notification.

[0051] The anomaly detection unit can propose different countermeasures according to the user's health condition when an anomaly is detected. For example, the anomaly detection unit proposes different countermeasures according to the user's health condition when an anomaly is detected. The anomaly detection unit proposes countermeasures taking into account the user's health condition. For example, the anomaly detection unit proposes a doctor's examination when the user complains of poor health. In addition, the anomaly detection unit can also suggest a relaxation method when the user feels stressed. Furthermore, the anomaly detection unit can also suggest light exercise when the user is relaxed. This allows for more appropriate responses by proposing countermeasures according to the health condition. Part or all of the above-mentioned processing in the anomaly detection unit may be performed, for example, using AI or may be performed without using AI. For example, the anomaly detection unit can input health condition data to the generation AI and cause the generation AI to execute the countermeasure proposal.

[0052] The notification unit can select a more appropriate notification method by referring to the user's past abnormality history at the time of notification. For example, the notification unit selects a more appropriate notification method by referring to the user's past abnormality history at the time of notification. The notification unit analyzes the user's past abnormality history and selects the optimal notification method. For example, the notification unit selects the optimal notification method based on the notification history received by the user in the past. The notification unit can also select a notification method appropriate for when a quick response is required from the user's past abnormality history. Furthermore, the notification unit can analyze the user's past abnormality history and select the most effective notification method. As a result, a more appropriate notification method can be selected by referring to the past abnormality history. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input past abnormality history data to the generation AI and cause the generation AI to select the notification method.

[0053] The notification unit can optimize the timing of notification based on the user's life rhythm at the time of notification. For example, the notification unit optimizes the timing of notification based on the user's life rhythm at the time of notification. The notification unit adjusts the timing of notification taking into account the user's life rhythm. For example, the notification unit notifies the user at the time the user wakes up. The notification unit can also notify the user before or after eating a meal, taking into account the influence of the meal. Furthermore, the notification unit can notify the user before going to bed, and detect abnormalities at night early. This enables more appropriate notification by optimizing the timing of notification based on the life rhythm. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input life rhythm data to the generation AI and cause the generation AI to optimize the notification timing.

[0054] The notification unit can also notify the user's family and caregivers at the time of notification, thereby strengthening the cooperation. The notification unit can also notify the user's family and caregivers at the time of notification, for example, thereby strengthening the cooperation. The notification unit notifies the user's family and caregivers. For example, the notification unit notifies the user's family that an abnormality has been detected. The notification unit can also notify the user's caregiver that an abnormality has been detected. Furthermore, the notification unit can also notify the user's family and caregivers of detailed information about the abnormality. As a result, by notifying the family and caregivers, cooperation is strengthened. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input notification data to a generation AI and cause the generation AI to generate a notification.

[0055] The notification unit can select the optimal notification method in consideration of the device information of the user at the time of notification. For example, the notification unit selects the optimal notification method in consideration of the device information of the user at the time of notification. The notification unit refers to the device information of the user and selects the optimal notification method. For example, the notification unit performs a push notification when the user is using a smartphone. In addition, the notification unit can also perform an email notification when the user is using a tablet. Furthermore, the notification unit can also perform a vibration notification when the user is using a smartwatch. This makes it possible to select the optimal notification method by considering the device information. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the device information to the generation AI and cause the generation AI to select the notification method.

[0056] The motion detection unit can identify an abnormal pattern by referring to the user's past motion data when detecting a motion. For example, the motion detection unit can identify an abnormal pattern by referring to the user's past motion data when detecting a motion. The motion detection unit analyzes the user's past motion data to identify an abnormal pattern. For example, the motion detection unit can identify an abnormal pattern by referring to past fall data. The motion detection unit can also identify an abnormal variation by referring to past walking data. Furthermore, the motion detection unit can also identify an abnormal rise or fall by referring to past motion data. This makes it easier to identify an abnormal pattern by referring to past motion data. A part or all of the above-mentioned processing in the motion detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the motion detection unit can input past motion data to the generation AI and cause the generation AI to identify an abnormal pattern.

[0057] The motion detection unit can improve the detection accuracy by considering the user's living environment and activity level when detecting the motion. For example, the motion detection unit improves the detection accuracy by considering the user's living environment and activity level when detecting the motion. The motion detection unit improves the detection accuracy by referring to the user's living environment and activity level. For example, the motion detection unit improves the accuracy of the motion detection by referring to the temperature data of the user's living environment. The motion detection unit can also improve the accuracy of the motion detection by referring to the user's activity level. Furthermore, the motion detection unit can also improve the accuracy of the motion detection by referring to the user's life rhythm. Thereby, the detection accuracy is improved by considering the living environment and activity level. A part or all of the above-mentioned processing in the motion detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the motion detection unit can input the living environment and activity level data to the generation AI and cause the generation AI to execute the improvement of the detection accuracy.

[0058] The motion detection unit can also notify the user's family and caregivers of the abnormality when detecting the motion. For example, the motion detection unit also notifies the user's family and caregivers of the abnormality when detecting the motion. The motion detection unit notifies the user's family and caregivers that the abnormality has been detected. For example, the motion detection unit notifies the user's family that the abnormality has been detected. The motion detection unit can also notify the user's caregiver that the abnormality has been detected. Furthermore, the motion detection unit can also notify the user's family and caregivers of detailed information about the abnormality. This allows for a quick response by notifying the family and caregivers. A part or all of the above-mentioned processing in the motion detection unit may be performed, for example, using AI or may be performed without using AI. For example, the motion detection unit can input the abnormality detection data to the generation AI and cause the generation AI to generate a notification.

[0059] The motion detection unit can propose different countermeasures according to the user's health condition when detecting a motion. For example, the motion detection unit proposes different countermeasures according to the user's health condition when detecting a motion. The motion detection unit proposes countermeasures taking into account the user's health condition. For example, the motion detection unit proposes a doctor's examination when the user complains of poor physical condition. The motion detection unit can also suggest a relaxation method when the user feels stressed. Furthermore, the motion detection unit can also suggest light exercise when the user is relaxed. This allows for more appropriate responses by proposing countermeasures according to the health condition. Part or all of the above-mentioned processing in the motion detection unit may be performed, for example, using AI or may be performed without using AI. For example, the motion detection unit can input health condition data to the generation AI and cause the generation AI to execute the countermeasure proposal.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The nursing care work efficiency system can further include a dietary management unit that monitors the user's dietary intake. The dietary management unit records the user's dietary intake and evaluates the nutritional balance. For example, the dietary management unit automatically calculates the calories and nutrients of the meals consumed by the user and integrates them with vital data to evaluate the health condition. The dietary management unit can also issue an alert and notify staff if the user's dietary intake is insufficient. Furthermore, the dietary management unit can compare the user's dietary intake with past data to detect abnormalities early. This makes the user's nutritional management more efficient and contributes to maintaining a health condition.

[0062] The vital check unit can monitor the user's sleep pattern and evaluate the quality of sleep. For example, the vital check unit can detect the depth and interruption of sleep using the user's heart rate and body movement data. The vital check unit can also analyze the user's sleep data and issue an alert if the quality of sleep is declining. Furthermore, the vital check unit can compare the user's sleep data with past data and detect abnormal patterns early. This allows the user's sleep state to be understood and can be used to manage health.

[0063] The nursing care work efficiency system can further include an exercise management unit that monitors the amount of exercise performed by the user. The exercise management unit records the amount of exercise performed by the user and evaluates the health condition. For example, the exercise management unit automatically records the type and time of exercise performed by the user and evaluates the health condition by integrating this with vital data. The exercise management unit can also send an alert and notify staff if the user's amount of exercise is insufficient. Furthermore, the exercise management unit can compare the amount of exercise performed by the user with past data and detect abnormalities early. This makes the user's exercise management more efficient and contributes to maintaining a health condition.

[0064] The vital signs check unit can monitor the user's stress level and manage stress. For example, the vital signs check unit can evaluate the stress level using the user's heart rate and blood pressure data. The vital signs check unit can also send an alert and notify staff when the user's stress level is high. Furthermore, the vital signs check unit can compare the user's stress level with past data and detect abnormal patterns early on. This makes the user's stress management more efficient and contributes to maintaining a healthy state.

[0065] The dialogue unit can refer to the user's past dialogue history and generate more appropriate questions. For example, the dialogue unit generates related questions based on questions the user has answered in the past. The dialogue unit can also select topics that are likely to interest the user from the user's past dialogue history and generate questions. Furthermore, the dialogue unit can analyze the user's past dialogue history and generate optimal questions. In this way, more appropriate questions can be generated by referring to the past dialogue history.

[0066] When an abnormality is detected, the abnormality detection unit can identify an abnormal pattern by referring to the user's past health data. For example, the abnormality detection unit can identify an abnormal pattern by referring to past heart rate data. The abnormality detection unit can also identify abnormal fluctuations by referring to past blood pressure data. Furthermore, the abnormality detection unit can also identify abnormal increases or decreases by referring to past body temperature data. This makes it easier to identify an abnormal pattern by referring to past health data.

[0067] The process flow of the first embodiment will be briefly described below.

[0068] Step 1: The vital check unit collects the user's vital data. The vital data includes heart rate, blood pressure, body temperature, etc. The vital check unit collects data using a wearable device or a bed sensor. For example, a smart watch or fitness tracker is used to monitor the heart rate in real time, and a bed sensor is used to detect body movement. Step 2: The dialogue unit uses AI to engage in dialogue with the user. The dialogue unit uses natural language processing technology to ask questions to the user and analyze the response. For example, it asks a question such as "How are you feeling?" and analyzes the user's response. Step 3: The anomaly detection unit uses AI to analyze the user's response and detect anomalies. The anomaly detection unit uses an AI model that uses a machine learning algorithm to analyze the response data and output anomalies. Step 4: The notification unit notifies staff if an abnormality is detected. The notification unit notifies staff by means of email, alerts, etc. For example, if an abnormality is detected, an email is sent to staff and an alert is issued to inform them of the abnormality. Step 5: The motion detection unit detects abnormal motion of the user using sensor technology. The motion detection unit detects abnormal motion using an acceleration sensor or a gyro sensor. For example, if the user falls, the motion is detected by the acceleration sensor and recognized as an abnormality.

[0069] (Example 2) The nursing care work efficiency system according to the embodiment of the present invention is a system that automates the check of a user's vital signs and dialogue, and detects and notifies the user of an abnormality. This system automates the check of a user's vital signs, and AI dialogues with the user to detect and notify the user of an abnormality. In addition, abnormal behavior is caught using sensor technology. This reduces the burden on staff and realizes a comfortable daily life for the user. For example, the nursing care work efficiency system automates the check of a user's vital signs. For example, a wearable device or a bed sensor is used to monitor the user's vital data such as heart rate, blood pressure, and body temperature in real time. This can eliminate the need for staff to manually check the vital signs. Next, in the nursing care work efficiency system, AI dialogues with the user and detects and notifies the user of an abnormality. For example, AI asks the user a question such as "How are you feeling?" and analyzes the user's response. If an abnormality is detected, a notification is sent to the staff. This allows changes in the user's physical condition to be detected early and a prompt response to be made possible. In addition, the nursing care work efficiency system catches abnormal behavior using sensor technology. For example, if a user falls, the sensor detects the movement and the AI ​​recognizes it as an abnormality. This allows staff to respond quickly. This system reduces the burden on staff. By automating vital signs checks and dialogue, staff can focus on other important tasks by reducing manual tasks. It also realizes a comfortable daily life for users. Users can live with peace of mind because they are constantly being monitored and will be quickly responded to if an abnormality occurs. In this way, by utilizing cutting-edge IoT and AI technology, the efficiency of nursing care work is improved and an innovative platform that paves the way for the future of the nursing care industry is realized. As a result, the nursing care work efficiency system reduces the burden on staff and realizes a comfortable daily life for users.

[0070] The nursing care work efficiency improvement system according to the embodiment includes a vital check unit, a dialogue unit, an abnormality detection unit, a notification unit, and a motion detection unit. The vital check unit collects vital data of a user. The vital data of a user includes, for example, heart rate, blood pressure, body temperature, and the like, but is not limited to such examples. The vital check unit collects vital data using, for example, a wearable device or a bed sensor. The wearable device includes a smart watch or a fitness tracker. The bed sensor includes a pressure sensor or a body movement sensor. For example, the vital check unit monitors the user's heart rate in real time using a smart watch. In addition, the vital check unit can detect the user's body movement using the bed sensor and detect abnormalities early. The dialogue unit uses AI to have a dialogue with the user. For example, the dialogue unit uses natural language processing technology to ask a question to the user and analyze the response. For example, the dialogue unit uses AI to ask a question such as "How are you feeling?" and analyzes the user's response. In addition, the dialogue unit can detect abnormalities based on the user's response. For example, when the user responds with "my head hurts," the dialogue unit detects an abnormality and notifies the staff. The abnormality detection unit uses AI to analyze the user's response and detects an abnormality. For example, the abnormality detection unit uses a machine learning algorithm to analyze the user's response and detects an abnormality. For example, the abnormality detection unit detects an abnormality using an AI model that inputs the user's response data and outputs an abnormality. The notification unit sends a notification to the staff when an abnormality is detected. The notification unit notifies the staff by means of, for example, email or alert. For example, the notification unit sends an email to the staff when an abnormality is detected. The notification unit can also notify the staff of the abnormality by issuing an alert. The motion detection unit detects abnormal motion of the user using sensor technology. The motion detection unit detects abnormal motion using, for example, an acceleration sensor or a gyro sensor. For example, when the user falls, the motion detection unit detects the motion using an acceleration sensor and recognizes it as an abnormality. As a result, the nursing care work efficiency system according to the embodiment can reduce the burden on the staff and realize a comfortable daily life for the user.Some or all of the above-mentioned processing in the motion detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the motion detection unit may input data acquired by a sensor to the generation AI and cause the generation AI to detect abnormal motion.

[0071] The vital check unit collects vital data of the user. The vital data of the user includes, but is not limited to, for example, heart rate, blood pressure, and body temperature. The vital check unit collects vital data using, for example, a wearable device or a bed sensor. The wearable device includes a smart watch and a fitness tracker. These devices are worn on the wrist or chest of the user and monitor data such as heart rate, blood pressure, and body temperature in real time. For example, a smart watch measures heart rate using an optical heart rate sensor and transmits data to a central system via Bluetooth. The fitness tracker has a built-in acceleration sensor and gyro sensor, detects the user's movement, and calculates the amount of activity and calories burned. The bed sensor includes a pressure sensor and a body movement sensor. The pressure sensor measures the body pressure distribution when the user is lying on the bed and detects changes in body position and body movement. The body movement sensor detects minute movements and monitors the user's breathing and heart rate fluctuations. For example, the bed sensor can detect pressure changes when the user turns over in bed and issue an alert if abnormal body movements occur. This allows the vital signs check unit to monitor the user's health condition in real time and respond quickly if an abnormality occurs. Furthermore, the vital signs check unit stores the collected data on a cloud server so that medical staff and caregivers can access it remotely. This allows the user's health condition to be continuously monitored and appropriate intervention can be performed if necessary.

[0072] The dialogue unit uses AI to have a dialogue with the user. For example, the dialogue unit uses natural language processing technology to ask questions to the user and analyze the response. Specifically, the dialogue unit converts the user's speech into text data using voice recognition technology and analyzes the content using natural language processing technology. For example, the dialogue unit uses AI to ask a question such as "How are you feeling?" and analyzes the user's response. If the user responds "I'm a little tired today," the dialogue unit analyzes the content and evaluates the cause and degree of fatigue. The dialogue unit can also detect abnormalities based on the user's response. For example, if the user responds "I have a headache," the dialogue unit detects an abnormality and notifies a staff member. The dialogue unit can analyze not only the content of the user's speech, but also changes in the tone of voice and speaking style to evaluate the user's emotional state and stress level. This allows the dialogue unit to comprehensively grasp the user's health and psychological state and take appropriate measures. Furthermore, the dialogue unit can conduct dialogue according to the user's individual needs and preferences based on the user's past dialogue history. For example, if the user has previously said, "I like going for walks," the dialogue unit can make a suggestion such as, "The weather is nice today, so why don't we go for a walk?" This allows the dialogue unit to build a relationship of trust with the user and support a comfortable daily life.

[0073] The anomaly detection unit uses AI to analyze the user's response and detect anomalies. For example, the anomaly detection unit uses a machine learning algorithm to analyze the user's response and detect anomalies. Specifically, the anomaly detection unit detects anomalies using an AI model that takes the user's response data as input and outputs anomalies. For example, the anomaly detection unit analyzes the user's speech content, tone of voice, and changes in speaking style to detect abnormal patterns. If the user utters something like "My chest hurts," the anomaly detection unit analyzes the content and evaluates the possibility of a heart problem. The anomaly detection unit can also analyze the user's vital data and movement data to comprehensively detect anomalies. For example, if the user's heart rate suddenly increases and at the same time there is an utterance such as "I'm having trouble breathing," the anomaly detection unit integrates and analyzes these data to evaluate the possibility of an emergency. The anomaly detection unit can improve the accuracy of anomaly detection by utilizing past data and statistical information. For example, by using an AI model that has learned past abnormal cases, it is possible to detect anomalies with higher accuracy. This allows the anomaly detection unit to monitor the user's health condition in real time and respond quickly when an abnormality occurs. Furthermore, the anomaly detection unit can suggest appropriate countermeasures depending on the type and urgency of the detected abnormality. For example, in the case of a mild abnormality, a notification is sent to the user urging them to take a rest, and in the case of a severe abnormality, an emergency notification is sent to medical staff. This allows the anomaly detection unit to play an important role in protecting the safety and health of the user.

[0074] The notification unit sends a notification to staff when an abnormality is detected. The notification unit notifies staff by means of, for example, email or alert. Specifically, the notification unit sends an email to staff when an abnormality is detected. The email contains detailed information about the abnormality and countermeasures, so that staff can respond quickly. The notification unit can also send an alert to inform staff of the abnormality. The alert is sent to the staff's smartphone or tablet, and alerts the staff by sound or vibration. Furthermore, the notification unit can set the priority of the notification according to the type and urgency of the abnormality. For example, in the case of a minor abnormality, only email notification is sent, and in the case of a major abnormality, alert notification is also sent. This allows the notification unit to support staff so that they can respond quickly and appropriately. Furthermore, the notification unit can collect feedback from staff and continuously improve the accuracy and effectiveness of the notification content. For example, by staff providing feedback after responding, the notification unit can optimize the accuracy of anomaly detection and the timing of notification. Furthermore, the notification unit can reliably transmit information using multiple communication means. For example, important information can be delivered reliably not only by smartphone notifications but also by using voice calls, SMS, emails, etc. This allows the notification department to quickly and reliably provide abnormality information to staff, and play an important role in protecting the safety and health of users.

[0075] The motion detection unit detects abnormal motion of the user using sensor technology. The motion detection unit detects abnormal motion using, for example, an acceleration sensor or a gyro sensor. Specifically, when the user falls, the motion detection unit detects the motion using the acceleration sensor and recognizes it as abnormal. The acceleration sensor measures the acceleration of the user's motion and detects an abnormality when there is a sudden change. For example, when the user falls, the acceleration sensor detects a sudden change in acceleration and recognizes it as abnormal. In addition, the gyro sensor measures the rotational motion of the user and detects abnormal motion. For example, when the user suddenly rotates, the gyro sensor detects the motion and recognizes it as abnormal. This allows the motion detection unit to detect abnormal motion of the user in real time and respond quickly. Furthermore, the motion detection unit inputs data acquired by the sensor to the generation AI and allows the generation AI to detect abnormal motion. The generation AI can perform more accurate abnormality detection by analyzing data from the sensor and learning patterns of abnormal motion. For example, the generative AI can learn past fall data and detect early signs of a fall. This allows the motion detection unit to ensure the safety of the user and take prompt and appropriate action. Furthermore, the motion detection unit stores data on detected abnormal motions on a cloud server so that medical staff and caregivers can access it remotely. This allows the motion detection unit to continuously monitor the user's abnormal motions and take appropriate interventions as necessary.

[0076] The vital check unit can collect vital data using a wearable device or a bed sensor. The vital check unit collects vital data using, for example, a wearable device. The wearable device includes a smart watch and a fitness tracker. For example, the vital check unit monitors the user's heart rate in real time using a smart watch. The vital check unit can also collect vital data using a bed sensor. The bed sensor includes a pressure sensor and a body movement sensor. For example, the vital check unit detects the user's body movement using the bed sensor and detects abnormalities early. As a result, the use of the wearable device or the bed sensor makes the collection of vital data more efficient. A part or all of the above-mentioned processing in the vital check unit may be performed using, for example, AI, or may be performed without using AI. For example, the vital check unit can input data acquired by the wearable device to the generation AI and cause the generation AI to execute an analysis of the vital data.

[0077] The dialogue unit can ask a question to the user by AI and analyze the response. The dialogue unit, for example, uses AI to ask a question to the user and analyze the response. The dialogue unit uses natural language processing technology to ask a question to the user. For example, the dialogue unit uses AI to ask a question such as "How are you feeling?" and analyzes the user's response. The dialogue unit can also detect an abnormality based on the user's response. For example, if the user responds "I have a headache," the dialogue unit detects an abnormality and notifies a staff member. In this way, by using AI, dialogue with the user can be automated and performed efficiently. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the dialogue unit can input the user's response data to the generation AI and have the generation AI execute the analysis of the response.

[0078] The anomaly detection unit can analyze the user's response using AI and detect anomalies. The anomaly detection unit, for example, uses AI to analyze the user's response and detects anomalies. The anomaly detection unit uses a machine learning algorithm to analyze the user's response and detects anomalies. For example, the anomaly detection unit detects anomalies using an AI model that receives user response data as input and outputs an anomaly. As a result, the use of AI improves the accuracy of anomaly detection. A part or all of the above-mentioned processing in the anomaly detection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the anomaly detection unit can input user response data to the generation AI and cause the generation AI to detect anomalies.

[0079] The notification unit can send a notification to a staff member when an abnormality is detected. For example, the notification unit sends a notification to a staff member when an abnormality is detected. The notification unit notifies by means of email, alert, or the like. For example, the notification unit sends an email to a staff member when an abnormality is detected. The notification unit can also send an alert to notify the staff member of the abnormality. This allows a notification to be sent to the staff member promptly when an abnormality is detected. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the abnormality detected by the anomaly detection unit to a generation AI and cause the generation AI to generate a notification.

[0080] The motion detection unit can detect an abnormal motion of a user using sensor technology. The motion detection unit detects an abnormal motion of a user using, for example, sensor technology. The motion detection unit detects an abnormal motion using an acceleration sensor or a gyro sensor. For example, when a user falls, the motion detection unit detects the motion using an acceleration sensor and recognizes it as an abnormality. This makes it possible to detect an abnormal motion by using sensor technology. A part or all of the above-mentioned processing in the motion detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the motion detection unit can input data acquired by a sensor to a generation AI and cause the generation AI to detect an abnormal motion.

[0081] The vital check unit can estimate the user's emotions and adjust the frequency of collection of vital data based on the estimated user's emotions. The vital check unit, for example, estimates the user's emotions and adjusts the frequency of collection of vital data based on the estimated user's emotions. The vital check unit estimates the user's emotions using an emotion estimation algorithm. For example, the vital check unit estimates the user's emotions using facial expression recognition technology. The vital check unit can also estimate the user's emotions using voice analysis technology. For example, the vital check unit estimates emotions using an AI model that inputs the user's facial expression data and outputs emotions. Furthermore, the vital check unit adjusts the frequency of collection of vital data based on the estimated emotions. For example, when the user is feeling stressed, the frequency of collection of vital data is increased to aim for early detection of abnormalities. Also, when the user is relaxed, the frequency of collection of vital data can be reduced to reduce the burden on the user. This allows more appropriate data collection by adjusting the frequency of collection of vital data according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the vital sign check unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the vital sign check unit may input emotion data estimated by an emotion estimation algorithm to the generation AI, and cause the generation AI to adjust the frequency of collecting vital sign data.

[0082] The vital check unit can analyze the user's past vital data and apply an algorithm for early detection of abnormality signs. The vital check unit, for example, analyzes the user's past vital data and applies an algorithm for early detection of abnormality signs. The vital check unit analyzes the past vital data using time series analysis or an abnormality detection algorithm. For example, the vital check unit analyzes past heart rate data and applies an algorithm for detecting abnormal patterns. The vital check unit can also analyze past blood pressure data and apply an algorithm for detecting abnormal fluctuations. Furthermore, the vital check unit can also analyze past body temperature data and apply an algorithm for detecting abnormal increases and decreases. In this way, by analyzing past data, abnormality signs can be detected early. A part or all of the above-mentioned processing in the vital check unit may be performed, for example, using AI, or may be performed without using AI. For example, the vital check unit can input past vital data to the generation AI and cause the generation AI to execute detection of abnormality signs.

[0083] The vital check unit can select an appropriate timing based on the user's life rhythm when collecting vital data. For example, the vital check unit selects an appropriate timing based on the user's life rhythm when collecting vital data. The vital check unit adjusts the collection timing of the vital data in consideration of the user's life rhythm. For example, the vital check unit starts collecting vital data according to the time the user wakes up. The vital check unit can also collect vital data before and after the user eats a meal to evaluate the influence of the meal. Furthermore, the vital check unit can collect vital data before the user goes to bed and detect abnormalities at night early. As a result, more accurate data can be obtained by selecting the timing of data collection based on the user's life rhythm. Part or all of the above-mentioned processing in the vital check unit may be performed, for example, using AI, or may be performed without using AI. For example, the vital check unit can input life rhythm data to the generation AI and cause the generation AI to select the collection timing.

[0084] The vital check unit can estimate the user's emotion and select the type of vital data to be collected based on the estimated user's emotion. The vital check unit, for example, estimates the user's emotion and selects the type of vital data to be collected based on the estimated user's emotion. The vital check unit estimates the user's emotion using an emotion estimation algorithm. For example, the vital check unit estimates the user's emotion using facial expression recognition technology. The vital check unit can also estimate the user's emotion using voice analysis technology. Furthermore, the vital check unit selects the type of vital data to be collected based on the estimated emotion. For example, when the user is stressed, data on heart rate and blood pressure are preferentially collected. Also, when the user is relaxed, data on body temperature and respiratory rate can be preferentially collected. This enables more appropriate data collection by selecting the type of data to be collected according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the vital sign check unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the vital sign check unit may input emotion data estimated by an emotion estimation algorithm to the generation AI, and cause the generation AI to select the type of vital sign data to be collected.

[0085] The vital check unit can collect the user's diet and exercise data at the same time as collecting the vital data, and evaluate the overall health condition. The vital check unit, for example, collects the user's diet and exercise data at the same time as collecting the vital data, and evaluates the overall health condition. The vital check unit collects the user's diet and exercise data, and integrates it with the vital data to perform an evaluation. For example, the vital check unit collects vital data and dietary content before and after the user eats a meal, and evaluates the impact of the meal. The vital check unit can also collect vital data and exercise content after the user exercises, and evaluate the impact of the exercise. Furthermore, the vital check unit can also comprehensively collect data on the user's daily life and evaluate the health condition. This makes it possible to evaluate the overall health condition by collecting diet and exercise data at the same time. Some or all of the above-mentioned processing in the vital check unit may be performed, for example, using AI, or may be performed without using AI. For example, the vital check unit can input diet and exercise data to the generation AI, and cause the generation AI to execute an evaluation of the overall health condition.

[0086] The vital check unit can correct the data based on the user's environmental data when collecting the vital data. For example, the vital check unit corrects the data based on the user's environmental data when collecting the vital data. The vital check unit collects temperature data and humidity data of the user's residential environment and corrects the vital data. For example, the vital check unit collects temperature data of the user's residential environment and corrects the vital data. The vital check unit can also collect humidity data of the user's residential environment and correct the vital data. Furthermore, the vital check unit can also collect air pressure data of the user's residential environment and correct the vital data. As a result, more accurate vital data can be obtained by correcting the data in consideration of the environmental data. A part or all of the above-mentioned processing in the vital check unit may be performed using, for example, AI, or may be performed without using AI. For example, the vital check unit can input environmental data to the generation AI and cause the generation AI to execute the correction of the data.

[0087] The dialogue unit can estimate the user's emotion and adjust the tone and content of the dialogue based on the estimated user's emotion. The dialogue unit, for example, estimates the user's emotion and adjusts the tone and content of the dialogue based on the estimated user's emotion. The dialogue unit estimates the user's emotion using an emotion estimation algorithm. For example, the dialogue unit estimates the user's emotion using facial expression recognition technology. The dialogue unit can also estimate the user's emotion using voice analysis technology. Furthermore, the dialogue unit adjusts the tone and content of the dialogue based on the estimated emotion. For example, when the user is nervous, the dialogue is conducted in a calm tone. Also, when the user is relaxed, the dialogue can be conducted in a bright tone. This enables a more appropriate dialogue by adjusting the tone and content of the dialogue according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit can input emotion data estimated by an emotion estimation algorithm into the generation AI and have the generation AI adjust the tone and content of the dialogue.

[0088] The dialogue unit can generate a more appropriate question by referring to the user's past response history during dialogue. The dialogue unit can generate a more appropriate question by referring to the user's past response history during dialogue, for example. The dialogue unit analyzes the user's past response history and generates a related question. For example, the dialogue unit generates a related question based on a question answered by the user in the past. The dialogue unit can also generate a question by selecting a topic that is likely to interest the user from the user's past response history. Furthermore, the dialogue unit can analyze the user's past response history and generate an optimal question. As a result, a more appropriate question can be generated by referring to the past response history. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit can input past response history data to a generation AI and cause the generation AI to generate a question.

[0089] The dialogue unit can apply different dialogue scenarios depending on the health condition of the user during dialogue. For example, the dialogue unit applies different dialogue scenarios depending on the health condition of the user during dialogue. The dialogue unit selects a dialogue scenario taking into account the health condition of the user. For example, when the user complains of poor health, the dialogue unit prioritizes questions about health. In addition, when the user is relaxed, the dialogue unit can also prioritize questions about hobbies and interests. Furthermore, when the user is feeling stressed, the dialogue unit can also apply a dialogue scenario for relaxing the user. This enables more appropriate dialogue by applying a dialogue scenario according to the health condition. Part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input health condition data to the generation AI and cause the generation AI to select a dialogue scenario.

[0090] The dialogue unit can estimate the user's emotion and adjust the frequency of dialogue based on the estimated user's emotion. The dialogue unit, for example, estimates the user's emotion and adjusts the frequency of dialogue based on the estimated user's emotion. The dialogue unit estimates the user's emotion using an emotion estimation algorithm. For example, the dialogue unit estimates the user's emotion using facial expression recognition technology. The dialogue unit can also estimate the user's emotion using voice analysis technology. Furthermore, the dialogue unit adjusts the frequency of dialogue based on the estimated emotion. For example, when the user is stressed, the frequency of dialogue is increased to provide a sense of security. Also, when the user is relaxed, the frequency of dialogue can be reduced to respect the user's free time. This enables more appropriate dialogue by adjusting the frequency of dialogue according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit can input emotion data estimated by an emotion estimation algorithm to the generation AI and have the generation AI adjust the frequency of dialogue.

[0091] The dialogue unit can customize the question content by incorporating the opinions of the user's family and caregiver during dialogue. For example, the dialogue unit customizes the question content by incorporating the opinions of the user's family and caregiver during dialogue. The dialogue unit generates the question content based on the opinions of the user's family and caregiver. For example, the dialogue unit generates a question suitable for the user based on information provided by the user's family. The dialogue unit can also generate a question regarding the health condition based on information provided by the user's caregiver. Furthermore, the dialogue unit can also customize the dialogue content by reflecting the opinions of the user's family and caregiver. As a result, more appropriate question content is generated by incorporating the opinions of the family and caregiver. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input opinion data of the family and caregiver to the generation AI and cause the generation AI to customize the question content.

[0092] The dialogue unit can provide a topic for relaxing the user based on the user's hobbies and interests during dialogue. The dialogue unit, for example, provides a topic for relaxing the user based on the user's hobbies and interests during dialogue. The dialogue unit generates a topic based on the user's hobbies and interests. For example, the dialogue unit provides a topic related to music that the user likes. The dialogue unit can also provide a topic related to sports in which the user is interested. Furthermore, the dialogue unit can also provide a topic related to handicrafts that the user enjoys as a hobby. This allows the user to relax by providing a topic based on the hobbies and interests. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit can input hobby and interest data to the generation AI and cause the generation AI to generate a topic.

[0093] The anomaly detection unit can estimate the user's emotion and adjust the anomaly detection criteria based on the estimated user's emotion. The anomaly detection unit, for example, estimates the user's emotion and adjusts the anomaly detection criteria based on the estimated user's emotion. The anomaly detection unit estimates the user's emotion using an emotion estimation algorithm. For example, the anomaly detection unit estimates the user's emotion using facial expression recognition technology. The anomaly detection unit can also estimate the user's emotion using voice analysis technology. Furthermore, the anomaly detection unit adjusts the anomaly detection criteria based on the estimated emotion. For example, when the user is stressed, the anomaly detection criteria is set to be strict. Also, when the user is relaxed, the anomaly detection criteria can be set to be loose. This allows for more appropriate anomaly detection by adjusting the anomaly detection criteria according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the anomaly detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the anomaly detection unit may input emotion data estimated by an emotion estimation algorithm to the generation AI, and cause the generation AI to adjust the anomaly detection criteria.

[0094] The abnormality detection unit can identify an abnormality pattern by referring to the user's past health data when an abnormality is detected. For example, the abnormality detection unit identifies an abnormality pattern by referring to the user's past health data when an abnormality is detected. The abnormality detection unit analyzes the user's past health data and identifies an abnormality pattern. For example, the abnormality detection unit identifies an abnormal pattern by referring to past heart rate data. The abnormality detection unit can also identify abnormal fluctuations by referring to past blood pressure data. Furthermore, the abnormality detection unit can also identify abnormal increases and decreases by referring to past body temperature data. This makes it easier to identify an abnormality pattern by referring to past health data. A part or all of the above-mentioned processing in the abnormality detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the abnormality detection unit can input past health data to the generation AI and cause the generation AI to identify an abnormality pattern.

[0095] The anomaly detection unit can improve the accuracy of anomaly detection by considering the user's living environment and activity level when detecting an anomaly. For example, the anomaly detection unit improves the accuracy of anomaly detection by considering the user's living environment and activity level when detecting an anomaly. The anomaly detection unit improves the accuracy of anomaly detection by referring to the user's living environment and activity level. For example, the anomaly detection unit improves the accuracy of anomaly detection by referring to temperature data of the user's living environment. The anomaly detection unit can also improve the accuracy of anomaly detection by referring to the user's activity level. Furthermore, the anomaly detection unit can also improve the accuracy of anomaly detection by referring to the user's life rhythm. As a result, the accuracy of anomaly detection is improved by considering the living environment and activity level. A part or all of the above-mentioned processing in the anomaly detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the anomaly detection unit can input the living environment and activity level data to the generation AI and cause the generation AI to execute the improvement of the accuracy of anomaly detection.

[0096] The anomaly detection unit can estimate the user's emotion and select the method of notifying the anomaly based on the estimated user's emotion. The anomaly detection unit, for example, estimates the user's emotion and selects the method of notifying the anomaly based on the estimated user's emotion. The anomaly detection unit estimates the user's emotion using an emotion estimation algorithm. For example, the anomaly detection unit estimates the user's emotion using facial expression recognition technology. The anomaly detection unit can also estimate the user's emotion using voice analysis technology. Furthermore, the anomaly detection unit selects the method of notifying the anomaly based on the estimated emotion. For example, if the user is stressed, the notification can be made in a gentle tone. Also, if the user is relaxed, the notification can be made in a bright tone. This allows for more appropriate notification by selecting the notification method according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the anomaly detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the anomaly detection unit can input emotion data estimated by an emotion estimation algorithm to the generation AI and have the generation AI select the notification method.

[0097] The abnormality detection unit can also notify the user's family and caregivers of the abnormality when an abnormality is detected. For example, the abnormality detection unit also notifies the user's family and caregivers of the abnormality when an abnormality is detected. The abnormality detection unit notifies the user's family and caregivers that an abnormality has been detected. For example, the abnormality detection unit notifies the user's family that an abnormality has been detected. The abnormality detection unit can also notify the user's caregiver that an abnormality has been detected. Furthermore, the abnormality detection unit can also notify the user's family and caregivers of detailed information about the abnormality. This allows for a quick response by notifying the family and caregivers. A part or all of the above-mentioned processing in the abnormality detection unit may be performed, for example, using AI or may be performed without using AI. For example, the abnormality detection unit can input the abnormality detection data to a generation AI and cause the generation AI to generate a notification.

[0098] The anomaly detection unit can propose different countermeasures according to the user's health condition when an anomaly is detected. For example, the anomaly detection unit proposes different countermeasures according to the user's health condition when an anomaly is detected. The anomaly detection unit proposes countermeasures taking into account the user's health condition. For example, the anomaly detection unit proposes a doctor's examination when the user complains of poor health. In addition, the anomaly detection unit can also suggest a relaxation method when the user feels stressed. Furthermore, the anomaly detection unit can also suggest light exercise when the user is relaxed. This allows for more appropriate responses by proposing countermeasures according to the health condition. Part or all of the above-mentioned processing in the anomaly detection unit may be performed, for example, using AI or may be performed without using AI. For example, the anomaly detection unit can input health condition data to the generation AI and cause the generation AI to execute the countermeasure proposal.

[0099] The notification unit can estimate the user's emotion and adjust the content and timing of the notification based on the estimated user's emotion. The notification unit, for example, estimates the user's emotion and adjusts the content and timing of the notification based on the estimated user's emotion. The notification unit estimates the user's emotion using an emotion estimation algorithm. For example, the notification unit estimates the user's emotion using facial expression recognition technology. The notification unit can also estimate the user's emotion using voice analysis technology. Furthermore, the notification unit adjusts the content and timing of the notification based on the estimated emotion. For example, when the user is stressed, the notification content is simplified and the timing is carefully selected. Also, when the user is relaxed, detailed notification content can be provided and the timing can be flexibly adjusted. This enables more appropriate notification by adjusting the content and timing of the notification according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit may input emotion data estimated by an emotion estimation algorithm to the generation AI, and cause the generation AI to adjust the content and timing of the notification.

[0100] The notification unit can select a more appropriate notification method by referring to the user's past abnormality history at the time of notification. For example, the notification unit selects a more appropriate notification method by referring to the user's past abnormality history at the time of notification. The notification unit analyzes the user's past abnormality history and selects the optimal notification method. For example, the notification unit selects the optimal notification method based on the notification history received by the user in the past. The notification unit can also select a notification method appropriate for when a quick response is required from the user's past abnormality history. Furthermore, the notification unit can analyze the user's past abnormality history and select the most effective notification method. As a result, a more appropriate notification method can be selected by referring to the past abnormality history. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input past abnormality history data to the generation AI and cause the generation AI to select the notification method.

[0101] The notification unit can optimize the timing of notification based on the user's life rhythm at the time of notification. For example, the notification unit optimizes the timing of notification based on the user's life rhythm at the time of notification. The notification unit adjusts the timing of notification taking into account the user's life rhythm. For example, the notification unit notifies the user at the time the user wakes up. The notification unit can also notify the user before or after eating a meal, taking into account the influence of the meal. Furthermore, the notification unit can notify the user before going to bed, and detect abnormalities at night early. This enables more appropriate notification by optimizing the timing of notification based on the life rhythm. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input life rhythm data to the generation AI and cause the generation AI to optimize the notification timing.

[0102] The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. The notification unit, for example, estimates the user's emotion and determines the priority of notifications based on the estimated user's emotion. The notification unit estimates the user's emotion using an emotion estimation algorithm. For example, the notification unit estimates the user's emotion using facial expression recognition technology. The notification unit can also estimate the user's emotion using voice analysis technology. Furthermore, the notification unit determines the priority of notifications based on the estimated emotion. For example, when the user is stressed, important notifications are given priority. Also, when the user is relaxed, normal notifications can be given priority. This enables more appropriate notifications by determining the priority of notifications according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input emotion data estimated by an emotion estimation algorithm to the generation AI and have the generation AI determine the priority of notifications.

[0103] The notification unit can also notify the user's family and caregivers at the time of notification, thereby strengthening the cooperation. The notification unit can also notify the user's family and caregivers at the time of notification, for example, thereby strengthening the cooperation. The notification unit notifies the user's family and caregivers. For example, the notification unit notifies the user's family that an abnormality has been detected. The notification unit can also notify the user's caregiver that an abnormality has been detected. Furthermore, the notification unit can also notify the user's family and caregivers of detailed information about the abnormality. As a result, by notifying the family and caregivers, cooperation is strengthened. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input notification data to a generation AI and cause the generation AI to generate a notification.

[0104] The notification unit can select the optimal notification method in consideration of the device information of the user at the time of notification. For example, the notification unit selects the optimal notification method in consideration of the device information of the user at the time of notification. The notification unit refers to the device information of the user and selects the optimal notification method. For example, the notification unit performs a push notification when the user is using a smartphone. In addition, the notification unit can also perform an email notification when the user is using a tablet. Furthermore, the notification unit can also perform a vibration notification when the user is using a smartwatch. This makes it possible to select the optimal notification method by considering the device information. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the device information to the generation AI and cause the generation AI to select the notification method.

[0105] The motion detection unit can estimate the user's emotion and adjust the sensitivity of the motion detection based on the estimated user's emotion. The motion detection unit, for example, estimates the user's emotion and adjusts the sensitivity of the motion detection based on the estimated user's emotion. The motion detection unit estimates the user's emotion using an emotion estimation algorithm. For example, the motion detection unit estimates the user's emotion using facial expression recognition technology. The motion detection unit can also estimate the user's emotion using voice analysis technology. Furthermore, the motion detection unit adjusts the sensitivity of the motion detection based on the estimated emotion. For example, when the user is stressed, the sensitivity of the motion detection is increased to detect abnormalities early. Also, when the user is relaxed, the sensitivity of the motion detection can be lowered to reduce false positives. This allows more appropriate motion detection by adjusting the sensitivity of the motion detection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the motion detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the motion detection unit may input emotion data estimated by an emotion estimation algorithm to the generation AI, and cause the generation AI to adjust the sensitivity of the motion detection.

[0106] The motion detection unit can identify an abnormal pattern by referring to the user's past motion data when detecting a motion. For example, the motion detection unit can identify an abnormal pattern by referring to the user's past motion data when detecting a motion. The motion detection unit analyzes the user's past motion data to identify an abnormal pattern. For example, the motion detection unit can identify an abnormal pattern by referring to past fall data. The motion detection unit can also identify an abnormal variation by referring to past walking data. Furthermore, the motion detection unit can also identify an abnormal rise or fall by referring to past motion data. This makes it easier to identify an abnormal pattern by referring to past motion data. A part or all of the above-mentioned processing in the motion detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the motion detection unit can input past motion data to the generation AI and cause the generation AI to identify an abnormal pattern.

[0107] The motion detection unit can improve the detection accuracy by considering the user's living environment and activity level when detecting the motion. For example, the motion detection unit improves the detection accuracy by considering the user's living environment and activity level when detecting the motion. The motion detection unit improves the detection accuracy by referring to the user's living environment and activity level. For example, the motion detection unit improves the accuracy of the motion detection by referring to the temperature data of the user's living environment. The motion detection unit can also improve the accuracy of the motion detection by referring to the user's activity level. Furthermore, the motion detection unit can also improve the accuracy of the motion detection by referring to the user's life rhythm. Thereby, the detection accuracy is improved by considering the living environment and activity level. A part or all of the above-mentioned processing in the motion detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the motion detection unit can input the living environment and activity level data to the generation AI and cause the generation AI to execute the improvement of the detection accuracy.

[0108] The motion detection unit can estimate the user's emotion and select a notification method for motion detection based on the estimated user's emotion. The motion detection unit, for example, estimates the user's emotion and selects a notification method for motion detection based on the estimated user's emotion. The motion detection unit estimates the user's emotion using an emotion estimation algorithm. For example, the motion detection unit estimates the user's emotion using facial expression recognition technology. The motion detection unit can also estimate the user's emotion using voice analysis technology. Furthermore, the motion detection unit selects a notification method for motion detection based on the estimated emotion. For example, if the user is stressed, the notification is made in a gentle tone. Also, if the user is relaxed, the notification can be made in a bright tone. This allows for more appropriate notification by selecting a notification method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the motion detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the motion detection unit may input emotion data estimated by an emotion estimation algorithm to a generation AI, and cause the generation AI to select a notification method.

[0109] The motion detection unit can also notify the user's family and caregivers of the abnormality when detecting the motion. For example, the motion detection unit also notifies the user's family and caregivers of the abnormality when detecting the motion. The motion detection unit notifies the user's family and caregivers that the abnormality has been detected. For example, the motion detection unit notifies the user's family that the abnormality has been detected. The motion detection unit can also notify the user's caregiver that the abnormality has been detected. Furthermore, the motion detection unit can also notify the user's family and caregivers of detailed information about the abnormality. This allows for a quick response by notifying the family and caregivers. A part or all of the above-mentioned processing in the motion detection unit may be performed, for example, using AI or may be performed without using AI. For example, the motion detection unit can input the abnormality detection data to the generation AI and cause the generation AI to generate a notification.

[0110] The motion detection unit can propose different countermeasures according to the user's health condition when detecting a motion. For example, the motion detection unit proposes different countermeasures according to the user's health condition when detecting a motion. The motion detection unit proposes countermeasures taking into account the user's health condition. For example, the motion detection unit proposes a doctor's examination when the user complains of poor physical condition. The motion detection unit can also suggest a relaxation method when the user feels stressed. Furthermore, the motion detection unit can also suggest light exercise when the user is relaxed. This allows for more appropriate responses by proposing countermeasures according to the health condition. Part or all of the above-mentioned processing in the motion detection unit may be performed, for example, using AI or may be performed without using AI. For example, the motion detection unit can input health condition data to the generation AI and cause the generation AI to execute the countermeasure proposal.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The nursing care work efficiency system can further include a dietary management unit that monitors the user's dietary intake. The dietary management unit records the user's dietary intake and evaluates the nutritional balance. For example, the dietary management unit automatically calculates the calories and nutrients of the meals consumed by the user and integrates them with vital data to evaluate the health condition. The dietary management unit can also issue an alert and notify staff if the user's dietary intake is insufficient. Furthermore, the dietary management unit can compare the user's dietary intake with past data to detect abnormalities early. This makes the user's nutritional management more efficient and contributes to maintaining a health condition.

[0113] The vital check unit can monitor the user's sleep pattern and evaluate the quality of sleep. For example, the vital check unit can detect the depth and interruption of sleep using the user's heart rate and body movement data. The vital check unit can also analyze the user's sleep data and issue an alert if the quality of sleep is declining. Furthermore, the vital check unit can compare the user's sleep data with past data and detect abnormal patterns early. This allows the user's sleep state to be understood and can be used to manage health.

[0114] The dialogue unit can estimate the user's emotions and adjust the tone and content of the dialogue based on the estimated user's emotions. For example, the dialogue unit can conduct the dialogue in a calm tone when the user is feeling stressed, or in a bright tone when the user is relaxed. Furthermore, the dialogue unit can adjust the content of questions according to the user's emotions to provide a more appropriate dialogue. This enables a dialogue that takes the user's emotions into consideration, and reduces the user's psychological burden.

[0115] The anomaly detection unit can estimate the user's emotions and adjust the anomaly detection criteria based on the estimated user's emotions. For example, the anomaly detection unit can set stricter anomaly detection criteria when the user is stressed. Also, the anomaly detection unit can set looser anomaly detection criteria when the user is relaxed. Furthermore, the anomaly detection unit can adjust the anomaly detection algorithm according to the user's emotions to perform more appropriate anomaly detection. This makes it possible to detect anomalies according to the user's emotions, and is expected to allow for rapid response.

[0116] The notification unit can estimate the user's emotions and adjust the content and timing of notifications based on the estimated user's emotions. For example, when the user is feeling stressed, the notification unit can simplify the content of the notification and carefully select the timing. When the user is relaxed, the notification unit can provide detailed notification content and flexibly adjust the timing. Furthermore, the notification unit can determine the priority of notifications according to the user's emotions and prioritize important notifications. This makes it possible to provide notifications that take the user's emotions into consideration, and more appropriate responses can be expected.

[0117] The motion detection unit can estimate the user's emotions and adjust the sensitivity of motion detection based on the estimated user's emotions. For example, when the user is feeling stressed, the motion detection unit can increase the sensitivity of motion detection to detect abnormalities early. Also, when the user is relaxed, the motion detection unit can decrease the sensitivity of motion detection to reduce false positives. Furthermore, the motion detection unit can adjust the motion detection algorithm according to the user's emotions to perform more appropriate motion detection. This enables motion detection according to the user's emotions, and a quick response can be expected.

[0118] The nursing care work efficiency system can further include an exercise management unit that monitors the amount of exercise performed by the user. The exercise management unit records the amount of exercise performed by the user and evaluates the health condition. For example, the exercise management unit automatically records the type and time of exercise performed by the user and evaluates the health condition by integrating this with vital data. The exercise management unit can also send an alert and notify staff if the user's amount of exercise is insufficient. Furthermore, the exercise management unit can compare the amount of exercise performed by the user with past data and detect abnormalities early. This makes the user's exercise management more efficient and contributes to maintaining a health condition.

[0119] The vital signs check unit can monitor the user's stress level and manage stress. For example, the vital signs check unit can evaluate the stress level using the user's heart rate and blood pressure data. The vital signs check unit can also send an alert and notify staff when the user's stress level is high. Furthermore, the vital signs check unit can compare the user's stress level with past data and detect abnormal patterns early on. This makes the user's stress management more efficient and contributes to maintaining a healthy state.

[0120] The dialogue unit can refer to the user's past dialogue history and generate more appropriate questions. For example, the dialogue unit generates related questions based on questions the user has answered in the past. The dialogue unit can also select topics that are likely to interest the user from the user's past dialogue history and generate questions. Furthermore, the dialogue unit can analyze the user's past dialogue history and generate optimal questions. In this way, more appropriate questions can be generated by referring to the past dialogue history.

[0121] When an abnormality is detected, the abnormality detection unit can identify an abnormal pattern by referring to the user's past health data. For example, the abnormality detection unit can identify an abnormal pattern by referring to past heart rate data. The abnormality detection unit can also identify abnormal fluctuations by referring to past blood pressure data. Furthermore, the abnormality detection unit can also identify abnormal increases or decreases by referring to past body temperature data. This makes it easier to identify an abnormal pattern by referring to past health data.

[0122] The process flow of the second embodiment will be briefly described below.

[0123] Step 1: The vital check unit collects the user's vital data. The vital data includes heart rate, blood pressure, body temperature, etc. The vital check unit collects data using a wearable device or a bed sensor. For example, a smart watch or fitness tracker is used to monitor the heart rate in real time, and a bed sensor is used to detect body movement. Step 2: The dialogue unit uses AI to engage in dialogue with the user. The dialogue unit uses natural language processing technology to ask questions to the user and analyze the response. For example, it asks a question such as "How are you feeling?" and analyzes the user's response. Step 3: The anomaly detection unit uses AI to analyze the user's response and detect anomalies. The anomaly detection unit uses an AI model that uses a machine learning algorithm to analyze the response data and output anomalies. Step 4: The notification unit notifies staff if an abnormality is detected. The notification unit notifies staff by means of email, alerts, etc. For example, if an abnormality is detected, an email is sent to staff and an alert is issued to inform them of the abnormality. Step 5: The motion detection unit detects abnormal motion of the user using sensor technology. The motion detection unit detects abnormal motion using an acceleration sensor or a gyro sensor. For example, if the user falls, the motion is detected by the acceleration sensor and recognized as an abnormality.

[0124] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0126] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0127] Each of the plurality of elements including the above-mentioned vital check unit, dialogue unit, abnormality detection unit, notification unit, and motion detection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the vital check unit is realized using a wearable device or a bed sensor of the smart device 14. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and an AI dialogues with the user. The abnormality detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and an AI analyzes the user's response to detect an abnormality. The notification unit is realized, for example, by the control unit 46A of the smart device 14, and sends a notification to a staff member when an abnormality is detected. The motion detection unit is realized, for example, by using the sensor technology of the smart device 14, and detects an abnormal motion of the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0135] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0138] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0140] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements including the above-mentioned vital check unit, dialogue unit, abnormality detection unit, notification unit, and motion detection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the vital check unit is realized using a wearable device or a bed sensor of the smart glasses 214. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI dialogues with the user. The abnormality detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the user's response to detect an abnormality. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214, and sends a notification to a staff member when an abnormality is detected. The motion detection unit is realized, for example, by using the sensor technology of the smart glasses 214, and detects abnormal motion of the user. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0146] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0151] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0154] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0159] Each of the plurality of elements including the above-mentioned vital check unit, dialogue unit, abnormality detection unit, notification unit, and motion detection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the vital check unit is realized using a wearable device or a bed sensor of the headset type terminal 314. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI dialogues with the user. The abnormality detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the user's response to detect an abnormality. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314, and sends a notification to a staff member when an abnormality is detected. The motion detection unit is realized, for example, by using the sensor technology of the headset type terminal 314, and detects abnormal motion of the user. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0167] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0168] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0169] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0170] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0171] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0172] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0176] Each of the plurality of elements including the above-mentioned vitals check unit, dialogue unit, abnormality detection unit, notification unit, and motion detection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the vitals check unit is realized using a wearable device or a bed sensor of the robot 414. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and an AI dialogues with the user. The abnormality detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and an AI analyzes the user's response and detects an abnormality. The notification unit is realized, for example, by the control unit 46A of the robot 414, and sends a notification to a staff member when an abnormality is detected. The motion detection unit is realized, for example, by using the sensor technology of the robot 414, and detects an abnormal motion of the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0177] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0178] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0179] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0180] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0181] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0182] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0185] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0186] In addition, 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 upon request from the data processing device 12.

[0187] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0188] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0189] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0190] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0191] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0192] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0193] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0194] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0195] (Appendix 1) a vital check unit that collects vital data; a dialogue unit that dialogues with a user based on the data collected by the vital sign check unit; an anomaly detection unit that detects an anomaly based on information obtained by the dialogue unit; a notification unit that notifies an abnormality detected by the abnormality detection unit; A motion detection unit that detects abnormal motion using sensor technology. A system characterized by: (Appendix 2) The vital sign check unit includes: Collect vital data using wearable devices or bed sensors 2. The system of claim 1, (Appendix 3) The dialogue unit includes: AI asks questions to users and analyzes their responses 2. The system of claim 1, (Appendix 4) The abnormality detection unit is AI analyzes user responses and detects anomalies 2. The system of claim 1, (Appendix 5) The notification unit is Notify personnel if an abnormality is detected 2. The system of claim 1, (Appendix 6) The motion detection unit is Detecting abnormal user behavior using sensor technology 2. The system of claim 1, (Appendix 7) The vital sign check unit includes: To estimate a user's emotion and adjust a frequency of vital data collection based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The vital sign check unit includes: Analyzes the user's past vital data and applies algorithms to detect early signs of abnormalities 2. The system of claim 1, (Appendix 9) The vital sign check unit includes: When collecting vital data, select the appropriate timing based on the user's daily rhythm 2. The system of claim 1, (Appendix 10) The vital sign check unit includes: A user's emotion is estimated, and the type of vital data to be collected is selected based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The vital sign check unit includes: When collecting vital data, the user's diet and exercise data are also collected to assess their overall health condition. 2. The system of claim 1, (Appendix 12) The vital sign check unit includes: When collecting vital data, the data is corrected based on the user's environmental data. 2. The system of claim 1, (Appendix 13) The dialogue unit includes: Infer user emotions and adjust the tone and content of the dialogue based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The dialogue unit includes: During a dialogue, the system generates more appropriate questions by referring to the user's past response history. 2. The system of claim 1, (Appendix 15) The dialogue unit includes: During dialogue, different dialogue scenarios are applied depending on the user's health condition. 2. The system of claim 1, (Appendix 16) The dialogue unit includes: To estimate a user's emotion and adjust the frequency of interactions based on the estimated user's emotion. 2. The system of claim 1, (Appendix 17) The dialogue unit includes: During the dialogue, questions are customized based on the user's family and caregivers' opinions. 2. The system of claim 1, (Appendix 18) The dialogue unit includes: During conversation, provide topics to relax the user based on their hobbies and interests 2. The system of claim 1, (Appendix 19) The abnormality detection unit is Inferring user emotions and adjusting anomaly detection criteria based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The abnormality detection unit is When an anomaly is detected, the user's past health data is referenced to identify the abnormal pattern. 2. The system of claim 1, (Appendix 21) The abnormality detection unit is Improve the accuracy of anomaly detection by taking into account the user's living environment and activity level 2. The system of claim 1, (Appendix 22) The abnormality detection unit is The user's emotions are estimated, and a method of notifying an anomaly is selected based on the estimated user's emotions. 2. The system of claim 1, (Appendix 23) The abnormality detection unit is When an abnormality is detected, the user's family and caregivers are also notified of the abnormality. 2. The system of claim 1, (Appendix 24) The abnormality detection unit is When an anomaly is detected, different countermeasures are proposed depending on the user's health condition. 2. The system of claim 1, (Appendix 25) The notification unit is Infer user emotions and adjust the content and timing of notifications based on the estimated user emotions 2. The system of claim 1, (Appendix 26) The notification unit is When notifying, the system selects the most appropriate notification method by referring to the user's past abnormality history. 2. The system of claim 1, (Appendix 27) The notification unit is Optimize the timing of notifications based on the user's daily routine 2. The system of claim 1, (Appendix 28) The notification unit is Infer user emotions and prioritize notifications based on the estimated user emotions 2. The system of claim 1, (Appendix 29) The notification unit is When a notification is sent, the user's family or caregiver will also be notified to strengthen collaboration. 2. The system of claim 1, (Appendix 30) The notification unit is When notifying users, the optimal notification method is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 31) The motion detection unit is Estimate the user's emotion and adjust the sensitivity of motion detection based on the estimated user's emotion. 2. The system of claim 1, (Appendix 32) The motion detection unit is When motion is detected, the system references the user's past motion data to identify abnormal patterns 2. The system of claim 1, (Appendix 33) The motion detection unit is Improves accuracy of motion detection by taking into account the user's living environment and activity level 2. The system of claim 1, (Appendix 34) The motion detection unit is A user's emotion is estimated, and a notification method for motion detection is selected based on the estimated user's emotion. 2. The system of claim 1, (Appendix 35) The motion detection unit is When motion is detected, the user's family and caregivers are notified of the abnormality. 2. The system of claim 1, (Appendix 36) The motion detection unit is When motion is detected, different countermeasures are proposed depending on the user's health condition. 2. The system of claim 1, [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a vital check unit that collects vital data; a dialogue unit that dialogues with a user based on the data collected by the vital sign check unit; an anomaly detection unit that detects an anomaly based on information obtained by the dialogue unit; a notification unit that notifies an abnormality detected by the abnormality detection unit; A motion detection unit that detects abnormal motion using sensor technology. A system characterized by:

2. The vital sign check unit includes: Collect vital data using wearable devices or bed sensors 2. The system of claim 1 .

3. The dialogue unit includes: AI asks questions to users and analyzes their responses 2. The system of claim 1 .

4. The abnormality detection unit is AI analyzes user responses and detects anomalies 2. The system of claim 1 .

5. The notification unit is Notify personnel if an abnormality is detected 2. The system of claim 1 .

6. The motion detection unit is Detecting abnormal user behavior using sensor technology 2. The system of claim 1 .

7. The vital sign check unit includes: To estimate a user's emotion and adjust a frequency of vital data collection based on the estimated user's emotion.

2. The system of claim 1 .

8. The vital sign check unit includes: Analyzes the user's past vital signs and applies algorithms to detect early signs of abnormalities 2. The system of claim 1 .

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