system

Abstract

The system according to this embodiment aims to monitor the health status of elderly people in real time and to detect and respond to abnormalities at an early stage. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a detection unit, and a response unit. The data collection unit monitors the health status of elderly people in real time. The analysis unit analyzes the data collected by the data collection unit. The detection unit detects abnormalities based on the data analyzed by the analysis unit. The response unit takes action for early treatment or accident prevention based on the abnormalities detected by the detection unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the health status of the elderly has not been sufficiently monitored in real time, and abnormalities have not been detected and addressed early enough, leaving room for improvement.

[0005] The system according to the embodiment aims to monitor the health status of the elderly in real time and detect and address abnormalities early.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a detection unit, and a response unit. The data collection unit monitors the health status of elderly people in real time. The analysis unit analyzes the data collected by the data collection unit. The detection unit detects abnormalities based on the data analyzed by the analysis unit. The response unit takes action to provide early treatment or prevent accidents based on the abnormalities detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the health status of elderly people in real time and detect and respond to abnormalities at an early stage. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An elderly support system according to an embodiment of the present invention is a system that uses IoT devices and robots to grasp the health status of elderly people in real time and realize early treatment and accident prevention in the event of an abnormality. In this elderly support system, IoT devices and robots monitor the health status of elderly people in real time and collect data. Next, AI analyzes the collected data and detects abnormalities. If an abnormality is detected, appropriate measures are taken for early treatment and accident prevention. In addition, in daily life, AI supports the independent living of elderly people and provides personalized care. For example, AI can converse with elderly people to reduce feelings of loneliness. Furthermore, AI proposes and implements an optimized care plan using predictive analytics. This improves the quality of life for elderly people and realizes increased sales of products and accumulation of data through entry into the care market, reduction of the financial burden on the country, and increased happiness for users. For example, AI evaluates the health status of elderly people and manages care services as needed. For example, if AI evaluates the health status of elderly people and detects an abnormality, it notifies care staff to encourage early response. Also, AI converses with elderly people and reduces feelings of loneliness by providing reminders and suggestions for daily life. Furthermore, the AI ​​proposes and implements care plans tailored to the individual needs and lifestyles of elderly individuals. This alleviates the problem of labor shortages, and by having the AI ​​perform some of the tasks of care staff, high-quality care can be provided. In addition, feelings of loneliness among the elderly will be reduced, and they will be able to cope with fluctuations in their daily lives. For example, the AI ​​will monitor the health status of elderly individuals in real time, and if an abnormality is detected, early treatment or accident prevention measures will be taken. Moreover, by having the AI ​​interact with elderly individuals and reducing feelings of loneliness, the quality of life for the elderly will be improved. In this way, the elderly support system can grasp the health status of elderly individuals in real time and realize early treatment and accident prevention when abnormalities occur.

[0029] The elderly support system according to this embodiment comprises a data collection unit, an analysis unit, a detection unit, and a response unit. The data collection unit monitors the health status of the elderly person in real time. The data collection unit can collect health data such as heart rate, blood pressure, and body temperature. The data collection unit can monitor heart rate using a wearable device, for example. The data collection unit can also measure blood pressure using a blood pressure monitor. Furthermore, the data collection unit can measure body temperature using a thermometer. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using AI, for example, to evaluate the health status of the elderly person. The analysis unit analyzes the data using machine learning algorithms, for example. The analysis unit can also analyze the data using deep learning. Furthermore, the analysis unit can also analyze the data using statistical analysis. The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit detects anomalies using AI, for example. The detection unit detects anomalies using an anomaly detection algorithm, for example. Furthermore, the detection unit can also detect anomalies using a rule-based system. Furthermore, the detection unit can also detect abnormalities by setting a threshold for abnormal values. The response unit takes action for early treatment or accident prevention based on the abnormality detected by the detection unit. For example, the response unit notifies care staff when an abnormality is detected. For example, the response unit notifies care staff by issuing an alert. The response unit can also automatically call an ambulance when an abnormality is detected. Furthermore, the response unit can issue a voice warning to the elderly person when an abnormality is detected. As a result, the elderly support system according to this embodiment can grasp the health status of the elderly person in real time and realize early treatment and accident prevention in the event of an abnormality.

[0030] The data collection unit monitors the health status of elderly individuals in real time. The unit can collect health data such as heart rate, blood pressure, and body temperature. Specifically, the unit monitors heart rate using wearable devices. These devices include wristbands and chest straps, and incorporate high-precision sensors. Heart rate sensors use optical or electrical technology to detect heart rate fluctuations in real time. This allows the unit to continuously monitor the elderly person's heart rate and immediately detect any abnormal fluctuations. The unit can also measure blood pressure using a blood pressure monitor. Blood pressure monitors come in upper arm and wrist types, and these devices automatically measure blood pressure and transmit the data to the data collection unit. Furthermore, the unit can measure body temperature using a thermometer. Thermometers include ear and forehead contact types, as well as non-contact infrared thermometers, and these devices measure body temperature quickly and accurately. The data collection unit centrally manages the data obtained from these devices and transmits it to a central database in real time. This allows the data collection unit to comprehensively monitor the health status of elderly individuals and contribute to the early detection of abnormalities. Furthermore, by adjusting the frequency and accuracy of data collection, the data collection unit can flexibly respond to specific situations and conditions. For example, if a sudden change in heart rate or blood pressure is detected, the data collection unit can increase the data collection frequency and perform more detailed monitoring. In this way, the data collection unit can efficiently and effectively collect data and support the health management of elderly individuals.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the data and evaluate the health status of elderly individuals. Specifically, the analysis unit uses machine learning algorithms to analyze the data. Machine learning algorithms can learn from large amounts of health data and extract patterns and trends. For example, they can detect abnormal patterns that deviate from the normal range based on heart rate and blood pressure data. The analysis unit can also analyze data using deep learning. Deep learning uses multi-layered neural networks to extract features from complex data and perform highly accurate analysis. This allows the analysis unit to evaluate the health status of elderly individuals in detail and contribute to the early detection of abnormalities. Furthermore, the analysis unit can also analyze data using statistical analysis. Statistical analysis analyzes the distribution and correlation of data to identify outliers and trends. For example, it can evaluate how abnormal the current health status is compared to past data. This allows the analysis unit to analyze the collected data from multiple perspectives and comprehensively evaluate the health status of elderly individuals. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term health risk assessments and trend analyses. For example, based on past health data, it is possible to predict fluctuations in health risks during specific seasons or time periods and formulate future countermeasures. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term health management and risk assessment, enabling comprehensive support for the health of the elderly.

[0032] The detection unit detects anomalies based on data analyzed by the analysis unit. The detection unit can, for example, use AI to detect anomalies. Specifically, the detection unit uses an anomaly detection algorithm to detect anomalies. An anomaly detection algorithm learns normal data patterns and can identify abnormal data that deviates from them. For example, if heart rate or blood pressure data exceeds the normal range, it can be detected as an anomaly. The detection unit can also detect anomalies using a rule-based system. A rule-based system detects anomalies based on pre-set rules and thresholds. For example, if heart rate exceeds a certain range or blood pressure fluctuates rapidly, it can be detected as an anomaly. Furthermore, the detection unit can also detect anomalies by setting an anomaly threshold. The threshold is set based on past data and the opinions of medical professionals, and data exceeding this threshold is treated as an anomaly. This allows the detection unit to detect anomalies with high accuracy and speed, enabling early response. Furthermore, the detection unit can combine multiple anomaly detection algorithms to improve the accuracy of anomaly detection. For example, using a machine learning algorithm in combination with a rule-based system can improve the accuracy and reliability of anomaly detection. This allows the detection unit to continuously monitor the health status of elderly individuals, enabling early detection of abnormalities and prompt response.

[0033] The response unit takes action to provide early treatment and prevent accidents based on abnormalities detected by the detection unit. For example, the response unit notifies care staff when an abnormality is detected. Specifically, the response unit notifies care staff by issuing an alert. The alert is reliably transmitted using multiple means, such as voice notifications, vibration notifications, and smartphone push notifications. The response unit can also automatically call an ambulance when an abnormality is detected. The ambulance call is automatically notified to a pre-set emergency contact, enabling a quick response. Furthermore, the response unit can issue a voice warning to the elderly when an abnormality is detected. Voice warnings are an important means for the elderly to immediately recognize the abnormality and take appropriate action. This enables the response unit to detect abnormalities early and respond quickly, ensuring the safety of the elderly. In addition, the response unit can pre-set response procedures when an abnormality is detected, enabling a quick and effective response. For example, the response procedure when an abnormality is detected can be set to first notify care staff, and then, if necessary, call an ambulance. This allows the response unit to respond quickly and reliably to abnormal situations, ensuring the safety of the elderly. Furthermore, the response unit records the history of responses when abnormalities are detected, which can be used later to evaluate the effectiveness of the responses and identify areas for improvement. This enables the response unit to continuously improve the accuracy and effectiveness of its responses, supporting the health management of the elderly.

[0034] The dialogue unit can interact with elderly people and alleviate their feelings of loneliness. The dialogue unit can, for example, use AI to interact with elderly people. The dialogue unit can, for example, use natural language processing technology to interact with elderly people. The dialogue unit can also understand what elderly people are saying using speech recognition technology. Furthermore, the dialogue unit can respond to elderly people using speech synthesis technology. For example, the dialogue unit can provide elderly people with reminders for their daily lives. For example, the dialogue unit can remind them when to take their medication. It can also remind them when to eat. Furthermore, it can also remind them when to exercise. In this way, the dialogue unit can alleviate feelings of loneliness in elderly people.

[0035] The proposal department can propose care plans tailored to the individual needs and lifestyles of elderly people. For example, the proposal department can analyze the needs of elderly people using AI. For example, the proposal department can analyze the needs of elderly people using machine learning algorithms. Furthermore, the proposal department can analyze the needs of elderly people using deep learning. In addition, the proposal department can analyze the needs of elderly people using statistical analysis. For example, the proposal department can propose care plans considering the health status, lifestyle, hobbies, and preferences of elderly people. For example, the proposal department can propose care plans based on the health status of elderly people. Furthermore, the proposal department can propose care plans based on the lifestyle of elderly people. In addition, the proposal department can propose care plans based on the hobbies and preferences of elderly people. This allows the proposal department to provide the most suitable care plan for elderly people.

[0036] The analysis unit can analyze collected data and assess the health status of elderly individuals. For example, the analysis unit can analyze data using AI. For example, the analysis unit can analyze data using machine learning algorithms. Furthermore, the analysis unit can analyze data using deep learning. In addition, the analysis unit can analyze data using statistical analysis. For example, the analysis unit analyzes health checkup results and daily health data. For example, the analysis unit can assess the health status of elderly individuals based on health checkup results. Furthermore, the analysis unit can assess the health status of elderly individuals based on daily health data. This allows the analysis unit to assess the health status of elderly individuals and take appropriate action.

[0037] The response unit can notify care staff when an abnormality is detected. For example, the response unit can use AI to detect abnormalities and notify care staff. For example, the response unit can issue an alert to notify care staff. The response unit can also automatically call an ambulance when an abnormality is detected. Furthermore, the response unit can issue a voice warning to the elderly when an abnormality is detected. This enables the response unit to respond quickly when an abnormality is detected.

[0038] The proposal department can propose optimized care plans using predictive analytics. For example, the proposal department can perform predictive analytics using AI. For example, the proposal department can perform predictive analytics using machine learning algorithms. Furthermore, the proposal department can also perform predictive analytics using deep learning. In addition, the proposal department can perform predictive analytics using statistical analysis. For example, the proposal department can predict future health conditions based on the elderly person's past health data and propose an optimal care plan. For example, the proposal department can predict future risks based on the elderly person's current health condition and propose a care plan to mitigate those risks. Furthermore, the proposal department can predict future health conditions based on the elderly person's lifestyle and propose a care plan to improve their lifestyle. In this way, the proposal department can provide an optimal care plan based on predictive analytics.

[0039] The data collection unit can analyze past health data of elderly individuals and select the optimal collection method. For example, the unit can use AI to analyze past health data. It can also use machine learning algorithms to analyze past health data. Furthermore, it can use deep learning to analyze past health data. In addition, it can use statistical analysis to analyze past health data. For example, the unit can analyze patterns of fluctuations in the health status of elderly individuals from past data and determine the optimal collection timing. For example, the unit can identify a tendency for health status to deteriorate during specific time periods from the elderly's past data and focus data collection during those times. The unit can also customize collection methods for specific health indicators based on the elderly's past data. This allows the unit to efficiently collect data by selecting the optimal collection method based on past data.

[0040] The data collection unit can filter health data based on the elderly person's current activity level. For example, the data collection unit can use AI to analyze the elderly person's activity level and filter the data. For example, the data collection unit can use machine learning algorithms to analyze the elderly person's activity level. The data collection unit can also use deep learning to analyze the elderly person's activity level. Furthermore, the data collection unit can use statistical analysis to analyze the elderly person's activity level. For example, if the elderly person is exercising, the data collection unit will prioritize collecting exercise-related data. For example, if the elderly person is resting, the data collection unit will focus on collecting data such as heart rate and blood pressure. Also, if the elderly person is eating, the data collection unit can collect meal-related data and filter out other data. This allows the data collection unit to collect data according to the elderly person's activity level.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of elderly individuals when collecting health data. For example, the data collection unit can use AI to analyze the geographical location information of elderly individuals and collect highly relevant data. For example, the data collection unit can use machine learning algorithms to analyze the geographical location information of elderly individuals. The data collection unit can also use deep learning to analyze the geographical location information of elderly individuals. Furthermore, the data collection unit can also use statistical analysis to analyze the geographical location information of elderly individuals. For example, if an elderly individual is out, the data collection unit will prioritize the collection of data related to the environment at their destination. For example, if an elderly individual is at home, the data collection unit will prioritize the collection of data related to the environment inside their home. In addition, if an elderly individual is in a specific facility, the data collection unit can prioritize the collection of data related to the characteristics of that facility. As a result, the data collection unit can collect highly relevant data based on the geographical location information of elderly individuals, enabling more accurate health management.

[0042] The data collection unit can analyze the social media activities of older adults and collect relevant data when collecting health data. For example, the unit can use AI to analyze older adults' social media activities. For example, the unit can use machine learning algorithms to analyze older adults' social media activities. Furthermore, the unit can use deep learning to analyze older adults' social media activities. In addition, the unit can use statistical analysis to analyze older adults' social media activities. For example, if an older adult is experiencing stress on social media, the unit can collect stress-related data. For example, if an older adult is relaxing on social media, the unit can collect overall health data. Furthermore, if an older adult is experiencing anxiety on social media, the unit can collect data that may be causing that anxiety. This allows the data collection unit to enable more comprehensive health management by collecting relevant data based on older adults' social media activities.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can use AI to evaluate the importance of health data and adjust the level of detail of the analysis. For example, the analysis unit can use machine learning algorithms to evaluate the importance of health data. The analysis unit can also use deep learning to evaluate the importance of health data. Furthermore, the analysis unit can also evaluate the importance of health data using statistical analysis. For example, the analysis unit can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. For example, the analysis unit can perform a multifaceted analysis on high-importance data and a single analysis on low-importance data. The analysis unit can also perform frequent analyses on high-importance data and periodic analyses on low-importance data. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis according to the importance of the health data.

[0044] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can classify health data categories using AI and apply an appropriate analysis algorithm. For example, the analysis unit can classify health data categories using machine learning algorithms. The analysis unit can also classify health data categories using deep learning. Furthermore, the analysis unit can classify health data categories using statistical analysis. For example, the analysis unit can apply a time series analysis algorithm to heart rate data. For example, the analysis unit can apply a statistical analysis algorithm to blood pressure data. The analysis unit can also apply an anomaly detection algorithm to body temperature data. This enables the analysis unit to perform analysis according to the category of health data.

[0045] The analysis unit can determine the priority of analysis based on the timing of health data collection during the analysis process. For example, the analysis unit can use AI to evaluate the timing of health data collection and determine the priority of analysis. For example, the analysis unit can use machine learning algorithms to evaluate the timing of health data collection. The analysis unit can also use deep learning to evaluate the timing of health data collection. Furthermore, the analysis unit can use statistical analysis to evaluate the timing of health data collection. For example, the analysis unit prioritizes the analysis of recently collected data. For example, the analysis unit prioritizes the analysis of data collected during a specific time period. The analysis unit can also prioritize the analysis of data that shows abnormalities when compared to past data. This enables efficient analysis by allowing the analysis unit to determine the priority of analysis based on the timing of health data collection.

[0046] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit can use AI to evaluate the relevance of health data and adjust the order of analysis. For example, the analysis unit can use machine learning algorithms to evaluate the relevance of health data. The analysis unit can also use deep learning to evaluate the relevance of health data. Furthermore, the analysis unit can also use statistical analysis to evaluate the relevance of health data. For example, the analysis unit can prioritize the analysis of data with high correlation. For example, the analysis unit can prioritize the analysis of data in which anomalies have been detected. The analysis unit can also prioritize the analysis of data that has a significant impact on the health status of elderly people. As a result, the analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of health data.

[0047] The detection unit can improve detection accuracy by considering the interrelationships of health data during detection. For example, the detection unit can use AI to evaluate the interrelationships of health data and improve detection accuracy. For example, the detection unit can use machine learning algorithms to evaluate the interrelationships of health data. The detection unit can also use deep learning to evaluate the interrelationships of health data. Furthermore, the detection unit can also use statistical analysis to evaluate the interrelationships of health data. For example, the detection unit can detect abnormalities by considering the correlation between heart rate and blood pressure. For example, the detection unit can detect abnormalities by considering the correlation between body temperature and respiratory rate. The detection unit can also detect abnormalities by considering the correlation between blood glucose levels and weight. In this way, the detection unit improves detection accuracy by considering the interrelationships of health data.

[0048] The detection unit can perform detection while considering the attribute information of the health data submitter. For example, the detection unit can use AI to evaluate the attribute information of the health data submitter and perform detection. For example, the detection unit can use machine learning algorithms to evaluate the attribute information of the health data submitter. The detection unit can also use deep learning to evaluate the attribute information of the health data submitter. Furthermore, the detection unit can also use statistical analysis to evaluate the attribute information of the health data submitter. For example, the detection unit can detect anomalies by considering the age of elderly people. For example, the detection unit can detect anomalies by considering the gender of elderly people. The detection unit can also detect anomalies by considering the medical history of elderly people. As a result, the detection unit can perform more appropriate anomaly detection by considering the attribute information of the health data submitter.

[0049] The detection unit can perform detection while considering the geographical distribution of health data. For example, the detection unit can use AI to evaluate the geographical distribution of health data and perform detection. For example, the detection unit can use machine learning algorithms to evaluate the geographical distribution of health data. The detection unit can also use deep learning to evaluate the geographical distribution of health data. Furthermore, the detection unit can also use statistical analysis to evaluate the geographical distribution of health data. For example, if an elderly person is in a specific area, the detection unit will detect an anomaly by considering the characteristics of that area. For example, if an elderly person is on the move, the detection unit will detect an anomaly by considering the environment of their destination. Also, if an elderly person is at home, the detection unit can detect an anomaly by considering the environment of their home. As a result, by considering the geographical distribution of health data, the detection unit can perform more accurate anomaly detection.

[0050] The detection unit can improve detection accuracy by referring to relevant literature on health data during detection. For example, the detection unit can use AI to refer to relevant literature on health data and improve detection accuracy. For example, the detection unit can use machine learning algorithms to refer to relevant literature on health data. Furthermore, the detection unit can also use deep learning to refer to relevant literature on health data. In addition, the detection unit can use statistical analysis to refer to relevant literature on health data. For example, the detection unit can detect anomalies by referring to the latest medical papers. For example, the detection unit can detect anomalies by referring to past research data. Furthermore, the detection unit can also detect anomalies by referring to the results of relevant clinical trials. As a result, the detection unit improves detection accuracy by referring to relevant literature.

[0051] The response unit can select the optimal response method by referring to past response data when responding. For example, the response unit can analyze past response data using AI and select the optimal response method. For example, the response unit can analyze past response data using machine learning algorithms. The response unit can also analyze past response data using deep learning. Furthermore, the response unit can analyze past response data using statistical analysis. For example, the response unit can prioritize selecting response methods that have been effective in the past. For example, the response unit can select a response method suitable for a specific situation from past response data. The response unit can also analyze past response data and select the most effective response method. In this way, the response unit can select the optimal response method by referring to past response data.

[0052] The response unit can customize its response methods based on the elderly person's current living situation when responding. For example, the response unit can use AI to evaluate the elderly person's living situation and customize the response methods. For example, the response unit can use machine learning algorithms to evaluate the elderly person's living situation. The response unit can also use deep learning to evaluate the elderly person's living situation. Furthermore, the response unit can also use statistical analysis to evaluate the elderly person's living situation. For example, if the elderly person is at home, the response unit can provide response methods that can be done at home. For example, if the elderly person is out, the response unit can provide response methods that can be done while out. Also, if the elderly person is in a facility, the response unit can provide response methods tailored to the characteristics of that facility. As a result, the response unit can provide more appropriate responses by providing response methods that are tailored to the elderly person's living situation.

[0053] The response unit can select the optimal response method when responding to an elderly person, taking into account their geographical location. For example, the response unit can use AI to evaluate the elderly person's geographical location and select the optimal response method. For example, the response unit can use machine learning algorithms to evaluate the elderly person's geographical location. The response unit can also use deep learning to evaluate the elderly person's geographical location. Furthermore, the response unit can use statistical analysis to evaluate the elderly person's geographical location. For example, if the elderly person is at home, the response unit can provide a response method that can be done at home. For example, if the elderly person is out, the response unit can provide a response method that can be done at their destination. Also, if the elderly person is in a facility, the response unit can provide a response method tailored to the characteristics of that facility. As a result, the response unit can provide a more appropriate response by selecting the optimal response method based on the elderly person's geographical location.

[0054] The response unit can analyze the social media activities of elderly individuals and propose appropriate responses when responding to their situations. For example, the response unit can use AI to analyze the social media activities of elderly individuals and propose appropriate responses. For example, the response unit can use machine learning algorithms to analyze the social media activities of elderly individuals. The response unit can also use deep learning to analyze the social media activities of elderly individuals. Furthermore, the response unit can also use statistical analysis to analyze the social media activities of elderly individuals. For example, if an elderly person is experiencing stress from social media, the response unit can propose measures to reduce stress. For example, if an elderly person is relaxing from social media, the response unit can propose measures for overall health management. The response unit can also propose measures to reduce anxiety if an elderly person is experiencing anxiety from social media. As a result, the response unit can provide more appropriate responses by proposing measures based on the social media activities of elderly individuals.

[0055] The dialogue unit can provide optimal dialogue content by referring to the elderly person's past dialogue history during a conversation. For example, the dialogue unit can use AI to analyze the elderly person's past dialogue history and provide optimal dialogue content. For example, the dialogue unit can use machine learning algorithms to analyze the elderly person's past dialogue history. Furthermore, the dialogue unit can also use deep learning to analyze the elderly person's past dialogue history. In addition, the dialogue unit can use statistical analysis to analyze the elderly person's past dialogue history. For example, the dialogue unit can prioritize providing dialogue content that has been effective in the past. For example, the dialogue unit can provide dialogue content suitable for a specific situation based on past dialogue history. Furthermore, the dialogue unit can analyze past dialogue history and provide the most effective dialogue content. In this way, the dialogue unit can provide optimal dialogue content by referring to past dialogue history.

[0056] The dialogue unit can provide optimal dialogue content during conversations, taking into account the elderly person's device information. For example, the dialogue unit can use AI to evaluate the elderly person's device information and provide optimal dialogue content. For example, the dialogue unit can use machine learning algorithms to evaluate the elderly person's device information. The dialogue unit can also use deep learning to evaluate the elderly person's device information. Furthermore, the dialogue unit can use statistical analysis to evaluate the elderly person's device information. For example, if the elderly person is using a smartphone, the dialogue unit will provide dialogue content that is adapted to the screen size. For example, if the elderly person is using a tablet, the dialogue unit will provide dialogue content optimized for a larger screen. Also, if the elderly person is using a smartwatch, the dialogue unit can provide concise and highly visible dialogue content. In this way, the dialogue unit can provide more appropriate dialogue by providing optimal dialogue content based on the elderly person's device information.

[0057] The proposal department can provide optimal suggestions by referring to the elderly person's past care plan history when making a proposal. For example, the proposal department can use AI to analyze the elderly person's past care plan history and provide optimal suggestions. For example, the proposal department can use machine learning algorithms to analyze the elderly person's past care plan history. Furthermore, the proposal department can also use deep learning to analyze the elderly person's past care plan history. In addition, the proposal department can use statistical analysis to analyze the elderly person's past care plan history. For example, the proposal department can prioritize suggesting care plans that have been effective in the past. For example, the proposal department can suggest a care plan suitable for a specific situation based on past care plan history. Furthermore, the proposal department can analyze past care plan history and suggest the most effective care plan. In this way, the proposal department can provide optimal care plans by referring to past care plan history.

[0058] The proposal department can customize care plans based on the elderly person's current living situation when making a proposal. For example, the proposal department can use AI to evaluate the elderly person's living situation and customize the care plan. For example, the proposal department can use machine learning algorithms to evaluate the elderly person's living situation. The proposal department can also use deep learning to evaluate the elderly person's living situation. Furthermore, the proposal department can also use statistical analysis to evaluate the elderly person's living situation. For example, if the elderly person is at home, the proposal department will propose a care plan that can be done at home. For example, if the elderly person is out, the proposal department will propose a care plan that can be done at their destination. Also, if the elderly person is in a facility, the proposal department can propose a care plan tailored to the characteristics of that facility. In this way, the proposal department can provide more appropriate care by offering care plans that are tailored to the elderly person's living situation.

[0059] The proposal department can provide optimal care plans by considering the geographical location information of elderly individuals when making proposals. For example, the proposal department can use AI to evaluate the geographical location information of elderly individuals and provide optimal care plans. For example, the proposal department can use machine learning algorithms to evaluate the geographical location information of elderly individuals. Furthermore, the proposal department can also use deep learning to evaluate the geographical location information of elderly individuals. In addition, the proposal department can use statistical analysis to evaluate the geographical location information of elderly individuals. For example, if an elderly person is at home, the proposal department can provide care plans that can be carried out at home. For example, if an elderly person is out, the proposal department can provide care plans that can be carried out at their destination. Furthermore, if an elderly person is in a facility, the proposal department can provide care plans tailored to the characteristics of that facility. In this way, the proposal department can provide more appropriate care by providing optimal care plans based on the geographical location information of elderly individuals.

[0060] The proposal department can analyze the social media activities of elderly individuals and propose care plans based on those activities. For example, the proposal department can use AI to analyze the social media activities of elderly individuals and propose care plans. For example, the proposal department can use machine learning algorithms to analyze the social media activities of elderly individuals. The proposal department can also use deep learning to analyze the social media activities of elderly individuals. Furthermore, the proposal department can use statistical analysis to analyze the social media activities of elderly individuals. For example, if an elderly individual is experiencing stress from social media, the proposal department can propose a care plan to reduce stress. For example, if an elderly individual is relaxing from social media, the proposal department can propose a care plan for overall health management. The proposal department can also propose a care plan to reduce anxiety if an elderly individual is experiencing anxiety from social media. This allows the proposal department to provide more appropriate care by proposing care plans based on the social media activities of elderly individuals.

[0061] The proposal department can propose optimized care plans using predictive analytics at the time of proposal. For example, the proposal department can perform predictive analytics using AI and propose the optimal care plan. For example, the proposal department can perform predictive analytics using machine learning algorithms. Furthermore, the proposal department can also perform predictive analytics using deep learning. In addition, the proposal department can perform predictive analytics using statistical analysis. For example, the proposal department can predict the future health status of elderly people based on their past health data and propose the optimal care plan. For example, the proposal department can predict future risks based on the current health status of elderly people and propose care plans to mitigate those risks. Furthermore, the proposal department can predict future health status based on the lifestyle habits of elderly people and propose care plans to improve their lifestyle habits. As a result, the proposal department can provide more appropriate care by offering optimal care plans based on predictive analytics.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The elderly support system can also be equipped with an environmental monitoring unit. This unit monitors the elderly person's living environment in real time, collecting data such as temperature, humidity, and air quality. For example, the unit can automatically adjust the air conditioner if the room temperature is too high. It can also activate a humidifier if the humidity is too low. Furthermore, it can issue an alert to encourage ventilation if the air quality deteriorates. This ensures that the elderly person's living environment remains comfortable at all times, reducing health risks.

[0064] The elderly support system can also include a reminder function. This function manages the elderly person's daily schedule and reminds them of important tasks. For example, it can notify them of medication times, as well as regular exercise and meal times. Furthermore, it can provide reminders for medical appointments and contact with family members. This makes health management easier for the elderly person, ensuring they don't forget important daily tasks.

[0065] The elderly support system can also include a communication section. This section facilitates communication between the elderly, their families, and care staff. For example, it can provide video call functionality, allowing them to talk face-to-face with family members in remote locations. It can also send and receive text and voice messages. Furthermore, the communication section can include an emergency call function for quick contact in emergencies. This ensures that the elderly can live with peace of mind and avoid isolation.

[0066] The elderly support system can also include an entertainment section. This section has functions to provide enjoyment to the lives of the elderly. For example, it can stream music, movies, and television programs. It can also provide intellectual activities such as games and puzzles. Furthermore, the entertainment section can suggest content tailored to hobbies and interests. This enriches the lives of the elderly and improves their mental health.

[0067] The elderly support system can also include a feedback unit. This unit collects feedback from the elderly and care staff, and uses it to improve the system. For example, the feedback unit can conduct regular surveys to gather opinions on the system's usability and functionality. It can also accept real-time feedback. Furthermore, the feedback unit can analyze the collected feedback and incorporate it into system updates and the addition of new features. This ensures that the elderly support system is always provided in an optimal state that meets the user's needs.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit monitors the health status of the elderly person in real time. The data collection unit can collect health data such as heart rate, blood pressure, and body temperature. For example, it can monitor heart rate using a wearable device, measure blood pressure using a blood pressure monitor, and measure body temperature using a thermometer. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI, machine learning algorithms, deep learning, and statistical analysis to analyze the data and evaluate the health status of the elderly. Step 3: The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit detects anomalies using AI, anomaly detection algorithms, rule-based systems, and threshold settings for anomaly values. Step 4: The response unit takes action to provide early treatment or prevent accidents based on the abnormality detected by the detection unit. When an abnormality is detected, the response unit notifies care staff, issues an alert, automatically calls an ambulance, and issues a voice warning to the elderly person.

[0070] (Example of form 2) An elderly support system according to an embodiment of the present invention is a system that uses IoT devices and robots to grasp the health status of elderly people in real time and realize early treatment and accident prevention in the event of an abnormality. In this elderly support system, IoT devices and robots monitor the health status of elderly people in real time and collect data. Next, AI analyzes the collected data and detects abnormalities. If an abnormality is detected, appropriate measures are taken for early treatment and accident prevention. In addition, in daily life, AI supports the independent living of elderly people and provides personalized care. For example, AI can converse with elderly people to reduce feelings of loneliness. Furthermore, AI proposes and implements an optimized care plan using predictive analytics. This improves the quality of life for elderly people and realizes increased sales of products and accumulation of data through entry into the care market, reduction of the financial burden on the country, and increased happiness for users. For example, AI evaluates the health status of elderly people and manages care services as needed. For example, if AI evaluates the health status of elderly people and detects an abnormality, it notifies care staff to encourage early response. Also, AI converses with elderly people and reduces feelings of loneliness by providing reminders and suggestions for daily life. Furthermore, the AI ​​proposes and implements care plans tailored to the individual needs and lifestyles of elderly individuals. This alleviates the problem of labor shortages, and by having the AI ​​perform some of the tasks of care staff, high-quality care can be provided. In addition, feelings of loneliness among the elderly will be reduced, and they will be able to cope with fluctuations in their daily lives. For example, the AI ​​will monitor the health status of elderly individuals in real time, and if an abnormality is detected, early treatment or accident prevention measures will be taken. Moreover, by having the AI ​​interact with elderly individuals and reducing feelings of loneliness, the quality of life for the elderly will be improved. In this way, the elderly support system can grasp the health status of elderly individuals in real time and realize early treatment and accident prevention when abnormalities occur.

[0071] The elderly support system according to this embodiment comprises a data collection unit, an analysis unit, a detection unit, and a response unit. The data collection unit monitors the health status of the elderly person in real time. The data collection unit can collect health data such as heart rate, blood pressure, and body temperature. The data collection unit can monitor heart rate using a wearable device, for example. The data collection unit can also measure blood pressure using a blood pressure monitor. Furthermore, the data collection unit can measure body temperature using a thermometer. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using AI, for example, to evaluate the health status of the elderly person. The analysis unit analyzes the data using machine learning algorithms, for example. The analysis unit can also analyze the data using deep learning. Furthermore, the analysis unit can also analyze the data using statistical analysis. The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit detects anomalies using AI, for example. The detection unit detects anomalies using an anomaly detection algorithm, for example. Furthermore, the detection unit can also detect anomalies using a rule-based system. Furthermore, the detection unit can also detect abnormalities by setting a threshold for abnormal values. The response unit takes action for early treatment or accident prevention based on the abnormality detected by the detection unit. For example, the response unit notifies care staff when an abnormality is detected. For example, the response unit notifies care staff by issuing an alert. The response unit can also automatically call an ambulance when an abnormality is detected. Furthermore, the response unit can issue a voice warning to the elderly person when an abnormality is detected. As a result, the elderly support system according to this embodiment can grasp the health status of the elderly person in real time and realize early treatment and accident prevention in the event of an abnormality.

[0072] The data collection unit monitors the health status of elderly individuals in real time. The unit can collect health data such as heart rate, blood pressure, and body temperature. Specifically, the unit monitors heart rate using wearable devices. These devices include wristbands and chest straps, and incorporate high-precision sensors. Heart rate sensors use optical or electrical technology to detect heart rate fluctuations in real time. This allows the unit to continuously monitor the elderly person's heart rate and immediately detect any abnormal fluctuations. The unit can also measure blood pressure using a blood pressure monitor. Blood pressure monitors come in upper arm and wrist types, and these devices automatically measure blood pressure and transmit the data to the data collection unit. Furthermore, the unit can measure body temperature using a thermometer. Thermometers include ear and forehead contact types, as well as non-contact infrared thermometers, and these devices measure body temperature quickly and accurately. The data collection unit centrally manages the data obtained from these devices and transmits it to a central database in real time. This allows the data collection unit to comprehensively monitor the health status of elderly individuals and contribute to the early detection of abnormalities. Furthermore, by adjusting the frequency and accuracy of data collection, the data collection unit can flexibly respond to specific situations and conditions. For example, if a sudden change in heart rate or blood pressure is detected, the data collection unit can increase the data collection frequency and perform more detailed monitoring. In this way, the data collection unit can efficiently and effectively collect data and support the health management of elderly individuals.

[0073] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the data and evaluate the health status of elderly individuals. Specifically, the analysis unit uses machine learning algorithms to analyze the data. Machine learning algorithms can learn from large amounts of health data and extract patterns and trends. For example, they can detect abnormal patterns that deviate from the normal range based on heart rate and blood pressure data. The analysis unit can also analyze data using deep learning. Deep learning uses multi-layered neural networks to extract features from complex data and perform highly accurate analysis. This allows the analysis unit to evaluate the health status of elderly individuals in detail and contribute to the early detection of abnormalities. Furthermore, the analysis unit can also analyze data using statistical analysis. Statistical analysis analyzes the distribution and correlation of data to identify outliers and trends. For example, it can evaluate how abnormal the current health status is compared to past data. This allows the analysis unit to analyze the collected data from multiple perspectives and comprehensively evaluate the health status of elderly individuals. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term health risk assessments and trend analyses. For example, based on past health data, it is possible to predict fluctuations in health risks during specific seasons or time periods and formulate future countermeasures. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term health management and risk assessment, enabling comprehensive support for the health of the elderly.

[0074] The detection unit detects anomalies based on data analyzed by the analysis unit. The detection unit can, for example, use AI to detect anomalies. Specifically, the detection unit uses an anomaly detection algorithm to detect anomalies. An anomaly detection algorithm learns normal data patterns and can identify abnormal data that deviates from them. For example, if heart rate or blood pressure data exceeds the normal range, it can be detected as an anomaly. The detection unit can also detect anomalies using a rule-based system. A rule-based system detects anomalies based on pre-set rules and thresholds. For example, if heart rate exceeds a certain range or blood pressure fluctuates rapidly, it can be detected as an anomaly. Furthermore, the detection unit can also detect anomalies by setting an anomaly threshold. The threshold is set based on past data and the opinions of medical professionals, and data exceeding this threshold is treated as an anomaly. This allows the detection unit to detect anomalies with high accuracy and speed, enabling early response. Furthermore, the detection unit can combine multiple anomaly detection algorithms to improve the accuracy of anomaly detection. For example, using a machine learning algorithm in combination with a rule-based system can improve the accuracy and reliability of anomaly detection. This allows the detection unit to continuously monitor the health status of elderly individuals, enabling early detection of abnormalities and prompt response.

[0075] The response unit takes action to provide early treatment and prevent accidents based on abnormalities detected by the detection unit. For example, the response unit notifies care staff when an abnormality is detected. Specifically, the response unit notifies care staff by issuing an alert. The alert is reliably transmitted using multiple means, such as voice notifications, vibration notifications, and smartphone push notifications. The response unit can also automatically call an ambulance when an abnormality is detected. The ambulance call is automatically notified to a pre-set emergency contact, enabling a quick response. Furthermore, the response unit can issue a voice warning to the elderly when an abnormality is detected. Voice warnings are an important means for the elderly to immediately recognize the abnormality and take appropriate action. This enables the response unit to detect abnormalities early and respond quickly, ensuring the safety of the elderly. In addition, the response unit can pre-set response procedures when an abnormality is detected, enabling a quick and effective response. For example, the response procedure when an abnormality is detected can be set to first notify care staff, and then, if necessary, call an ambulance. This allows the response unit to respond quickly and reliably to abnormal situations, ensuring the safety of the elderly. Furthermore, the response unit records the history of responses when abnormalities are detected, which can be used later to evaluate the effectiveness of the responses and identify areas for improvement. This enables the response unit to continuously improve the accuracy and effectiveness of its responses, supporting the health management of the elderly.

[0076] The dialogue unit can interact with elderly people and alleviate their feelings of loneliness. The dialogue unit can, for example, use AI to interact with elderly people. The dialogue unit can, for example, use natural language processing technology to interact with elderly people. The dialogue unit can also understand what elderly people are saying using speech recognition technology. Furthermore, the dialogue unit can respond to elderly people using speech synthesis technology. For example, the dialogue unit can provide elderly people with reminders for their daily lives. For example, the dialogue unit can remind them when to take their medication. It can also remind them when to eat. Furthermore, it can also remind them when to exercise. In this way, the dialogue unit can alleviate feelings of loneliness in elderly people.

[0077] The proposal department can propose care plans tailored to the individual needs and lifestyles of elderly people. For example, the proposal department can analyze the needs of elderly people using AI. For example, the proposal department can analyze the needs of elderly people using machine learning algorithms. Furthermore, the proposal department can analyze the needs of elderly people using deep learning. In addition, the proposal department can analyze the needs of elderly people using statistical analysis. For example, the proposal department can propose care plans considering the health status, lifestyle, hobbies, and preferences of elderly people. For example, the proposal department can propose care plans based on the health status of elderly people. Furthermore, the proposal department can propose care plans based on the lifestyle of elderly people. In addition, the proposal department can propose care plans based on the hobbies and preferences of elderly people. This allows the proposal department to provide the most suitable care plan for elderly people.

[0078] The analysis unit can analyze collected data and assess the health status of elderly individuals. For example, the analysis unit can analyze data using AI. For example, the analysis unit can analyze data using machine learning algorithms. Furthermore, the analysis unit can analyze data using deep learning. In addition, the analysis unit can analyze data using statistical analysis. For example, the analysis unit analyzes health checkup results and daily health data. For example, the analysis unit can assess the health status of elderly individuals based on health checkup results. Furthermore, the analysis unit can assess the health status of elderly individuals based on daily health data. This allows the analysis unit to assess the health status of elderly individuals and take appropriate action.

[0079] The response unit can notify care staff when an abnormality is detected. For example, the response unit can use AI to detect abnormalities and notify care staff. For example, the response unit can issue an alert to notify care staff. The response unit can also automatically call an ambulance when an abnormality is detected. Furthermore, the response unit can issue a voice warning to the elderly when an abnormality is detected. This enables the response unit to respond quickly when an abnormality is detected.

[0080] The proposal department can propose optimized care plans using predictive analytics. For example, the proposal department can perform predictive analytics using AI. For example, the proposal department can perform predictive analytics using machine learning algorithms. Furthermore, the proposal department can also perform predictive analytics using deep learning. In addition, the proposal department can perform predictive analytics using statistical analysis. For example, the proposal department can predict future health conditions based on the elderly person's past health data and propose an optimal care plan. For example, the proposal department can predict future risks based on the elderly person's current health condition and propose a care plan to mitigate those risks. Furthermore, the proposal department can predict future health conditions based on the elderly person's lifestyle and propose a care plan to improve their lifestyle. In this way, the proposal department can provide an optimal care plan based on predictive analytics.

[0081] The data collection unit can estimate the emotions of elderly individuals and adjust the frequency of health data collection based on the estimated emotions. For example, the data collection unit estimates the emotions of elderly individuals using emotion estimation functions, such as an emotion engine or generative AI. Alternatively, it can estimate emotions using facial recognition technology. Furthermore, it can estimate emotions using voice analysis technology. In addition, it can estimate emotions using biometric data. For example, if an elderly person is stressed, the data collection unit reduces the collection frequency to alleviate the burden. If an elderly person is relaxed, for example, the data collection unit increases the collection frequency to obtain more detailed data. Also, if an elderly person is anxious, the data collection unit can appropriately adjust the collection frequency to provide a sense of security. In this way, the data collection unit can reduce its burden by adjusting the collection frequency according to the emotions of elderly individuals.

[0082] The data collection unit can analyze past health data of elderly individuals and select the optimal collection method. For example, the unit can use AI to analyze past health data. It can also use machine learning algorithms to analyze past health data. Furthermore, it can use deep learning to analyze past health data. In addition, it can use statistical analysis to analyze past health data. For example, the unit can analyze patterns of fluctuations in the health status of elderly individuals from past data and determine the optimal collection timing. For example, the unit can identify a tendency for health status to deteriorate during specific time periods from the elderly's past data and focus data collection during those times. The unit can also customize collection methods for specific health indicators based on the elderly's past data. This allows the unit to efficiently collect data by selecting the optimal collection method based on past data.

[0083] The data collection unit can filter health data based on the elderly person's current activity level. For example, the data collection unit can use AI to analyze the elderly person's activity level and filter the data. For example, the data collection unit can use machine learning algorithms to analyze the elderly person's activity level. The data collection unit can also use deep learning to analyze the elderly person's activity level. Furthermore, the data collection unit can use statistical analysis to analyze the elderly person's activity level. For example, if the elderly person is exercising, the data collection unit will prioritize collecting exercise-related data. For example, if the elderly person is resting, the data collection unit will focus on collecting data such as heart rate and blood pressure. Also, if the elderly person is eating, the data collection unit can collect meal-related data and filter out other data. This allows the data collection unit to collect data according to the elderly person's activity level.

[0084] The data collection unit can estimate the emotions of elderly individuals and prioritize the data to be collected based on those estimated emotions. For example, the unit can estimate the emotions of elderly individuals using emotion estimation functions, such as an emotion engine or generative AI. Alternatively, it can estimate emotions using facial recognition technology. Furthermore, it can estimate emotions using voice analysis technology. In addition, it can estimate emotions using biometric data. For example, if an elderly individual is stressed, the unit prioritizes collecting stress-related data. If an elderly individual is relaxed, the unit collects overall health data in a balanced manner. Also, if an elderly individual is anxious, the unit can prioritize collecting data that may be causing anxiety. This allows the data collection unit to prioritize important data by prioritizing data according to the elderly individual's emotions.

[0085] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of elderly individuals when collecting health data. For example, the data collection unit can use AI to analyze the geographical location information of elderly individuals and collect highly relevant data. For example, the data collection unit can use machine learning algorithms to analyze the geographical location information of elderly individuals. The data collection unit can also use deep learning to analyze the geographical location information of elderly individuals. Furthermore, the data collection unit can also use statistical analysis to analyze the geographical location information of elderly individuals. For example, if an elderly individual is out, the data collection unit will prioritize the collection of data related to the environment at their destination. For example, if an elderly individual is at home, the data collection unit will prioritize the collection of data related to the environment inside their home. In addition, if an elderly individual is in a specific facility, the data collection unit can prioritize the collection of data related to the characteristics of that facility. As a result, the data collection unit can collect highly relevant data based on the geographical location information of elderly individuals, enabling more accurate health management.

[0086] The data collection unit can analyze the social media activities of older adults and collect relevant data when collecting health data. For example, the unit can use AI to analyze older adults' social media activities. For example, the unit can use machine learning algorithms to analyze older adults' social media activities. Furthermore, the unit can use deep learning to analyze older adults' social media activities. In addition, the unit can use statistical analysis to analyze older adults' social media activities. For example, if an older adult is experiencing stress on social media, the unit can collect stress-related data. For example, if an older adult is relaxing on social media, the unit can collect overall health data. Furthermore, if an older adult is experiencing anxiety on social media, the unit can collect data that may be causing that anxiety. This allows the data collection unit to enable more comprehensive health management by collecting relevant data based on older adults' social media activities.

[0087] The analysis unit can estimate the emotions of elderly individuals and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit estimates the emotions of elderly individuals using emotion estimation functions, such as an emotion engine or generative AI. For example, the analysis unit estimates the emotions of elderly individuals using facial recognition technology. Furthermore, the analysis unit can also estimate the emotions of elderly individuals using voice analysis technology. In addition, the analysis unit can estimate the emotions of elderly individuals using biometric data. For example, if an elderly person is experiencing stress, the analysis unit provides simple and easily understandable analysis results. For example, if an elderly person is relaxed, the analysis unit provides detailed analysis results. Furthermore, if an elderly person is experiencing anxiety, the analysis unit can provide analysis results to alleviate that anxiety. This allows the analysis unit to provide more appropriate analysis results by adjusting the presentation of the analysis according to the emotions of the elderly individual.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can use AI to evaluate the importance of health data and adjust the level of detail of the analysis. For example, the analysis unit can use machine learning algorithms to evaluate the importance of health data. The analysis unit can also use deep learning to evaluate the importance of health data. Furthermore, the analysis unit can also evaluate the importance of health data using statistical analysis. For example, the analysis unit can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. For example, the analysis unit can perform a multifaceted analysis on high-importance data and a single analysis on low-importance data. The analysis unit can also perform frequent analyses on high-importance data and periodic analyses on low-importance data. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis according to the importance of the health data.

[0089] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can classify health data categories using AI and apply an appropriate analysis algorithm. For example, the analysis unit can classify health data categories using machine learning algorithms. The analysis unit can also classify health data categories using deep learning. Furthermore, the analysis unit can classify health data categories using statistical analysis. For example, the analysis unit can apply a time series analysis algorithm to heart rate data. For example, the analysis unit can apply a statistical analysis algorithm to blood pressure data. The analysis unit can also apply an anomaly detection algorithm to body temperature data. This enables the analysis unit to perform analysis according to the category of health data.

[0090] The analysis unit can estimate the emotions of elderly individuals and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit estimates the emotions of elderly individuals using emotion estimation functions, such as an emotion engine or generative AI. The analysis unit can also estimate the emotions of elderly individuals using facial recognition technology. Furthermore, the analysis unit can estimate the emotions of elderly individuals using voice analysis technology. In addition, the analysis unit can estimate the emotions of elderly individuals using biometric data. For example, if an elderly person is feeling stressed, the analysis unit provides a short, concise analysis result. If an elderly person is relaxed, the analysis unit provides a detailed analysis result. Furthermore, if an elderly person is feeling anxious, the analysis unit can provide analysis results to alleviate that anxiety. This allows the analysis unit to provide more appropriate analysis results by adjusting the length of the analysis according to the emotions of the elderly person.

[0091] The analysis unit can determine the priority of analysis based on the timing of health data collection during the analysis process. For example, the analysis unit can use AI to evaluate the timing of health data collection and determine the priority of analysis. For example, the analysis unit can use machine learning algorithms to evaluate the timing of health data collection. The analysis unit can also use deep learning to evaluate the timing of health data collection. Furthermore, the analysis unit can use statistical analysis to evaluate the timing of health data collection. For example, the analysis unit prioritizes the analysis of recently collected data. For example, the analysis unit prioritizes the analysis of data collected during a specific time period. The analysis unit can also prioritize the analysis of data that shows abnormalities when compared to past data. This enables efficient analysis by allowing the analysis unit to determine the priority of analysis based on the timing of health data collection.

[0092] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit can use AI to evaluate the relevance of health data and adjust the order of analysis. For example, the analysis unit can use machine learning algorithms to evaluate the relevance of health data. The analysis unit can also use deep learning to evaluate the relevance of health data. Furthermore, the analysis unit can also use statistical analysis to evaluate the relevance of health data. For example, the analysis unit can prioritize the analysis of data with high correlation. For example, the analysis unit can prioritize the analysis of data in which anomalies have been detected. The analysis unit can also prioritize the analysis of data that has a significant impact on the health status of elderly people. As a result, the analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of health data.

[0093] The detection unit can estimate the emotions of elderly individuals and adjust the anomaly detection criteria based on the estimated emotions. For example, the detection unit estimates the emotions of elderly individuals using an emotion estimation function, such as an emotion engine or generative AI. Alternatively, it can estimate the emotions of elderly individuals using facial recognition technology. Furthermore, the detection unit can estimate the emotions of elderly individuals using voice analysis technology. In addition, it can estimate the emotions of elderly individuals using biometric data. For example, the detection unit may relax the anomaly detection criteria if the elderly individual is experiencing stress. Conversely, it may tighten the anomaly detection criteria if the elderly individual is relaxed. It can also appropriately adjust the anomaly detection criteria if the elderly individual is experiencing anxiety. This allows the detection unit to perform more accurate anomaly detection by adjusting the criteria according to the elderly individual's emotions.

[0094] The detection unit can improve detection accuracy by considering the interrelationships of health data during detection. For example, the detection unit can use AI to evaluate the interrelationships of health data and improve detection accuracy. For example, the detection unit can use machine learning algorithms to evaluate the interrelationships of health data. The detection unit can also use deep learning to evaluate the interrelationships of health data. Furthermore, the detection unit can also use statistical analysis to evaluate the interrelationships of health data. For example, the detection unit can detect abnormalities by considering the correlation between heart rate and blood pressure. For example, the detection unit can detect abnormalities by considering the correlation between body temperature and respiratory rate. The detection unit can also detect abnormalities by considering the correlation between blood glucose levels and weight. In this way, the detection unit improves detection accuracy by considering the interrelationships of health data.

[0095] The detection unit can perform detection while considering the attribute information of the health data submitter. For example, the detection unit can use AI to evaluate the attribute information of the health data submitter and perform detection. For example, the detection unit can use machine learning algorithms to evaluate the attribute information of the health data submitter. The detection unit can also use deep learning to evaluate the attribute information of the health data submitter. Furthermore, the detection unit can also use statistical analysis to evaluate the attribute information of the health data submitter. For example, the detection unit can detect anomalies by considering the age of elderly people. For example, the detection unit can detect anomalies by considering the gender of elderly people. The detection unit can also detect anomalies by considering the medical history of elderly people. As a result, the detection unit can perform more appropriate anomaly detection by considering the attribute information of the health data submitter.

[0096] The detection unit can estimate the emotions of elderly individuals and adjust the display order of detection results based on the estimated emotions. For example, the detection unit estimates the emotions of elderly individuals using an emotion estimation function, such as an emotion engine or generative AI. Alternatively, it can estimate emotions using facial recognition technology. Furthermore, it can estimate emotions using voice analysis technology. In addition, it can estimate emotions using biometric data. For example, if an elderly person is experiencing stress, the detection unit prioritizes displaying important detection results. If an elderly person is relaxed, the detection unit displays all detection results in a balanced manner. Also, if an elderly person is feeling anxious, the detection unit can prioritize displaying detection results that help alleviate anxiety. This allows the detection unit to prioritize the display of important information by adjusting the display order of detection results according to the elderly person's emotions.

[0097] The detection unit can perform detection while considering the geographical distribution of health data. For example, the detection unit can use AI to evaluate the geographical distribution of health data and perform detection. For example, the detection unit can use machine learning algorithms to evaluate the geographical distribution of health data. The detection unit can also use deep learning to evaluate the geographical distribution of health data. Furthermore, the detection unit can also use statistical analysis to evaluate the geographical distribution of health data. For example, if an elderly person is in a specific area, the detection unit will detect an anomaly by considering the characteristics of that area. For example, if an elderly person is on the move, the detection unit will detect an anomaly by considering the environment of their destination. Also, if an elderly person is at home, the detection unit can detect an anomaly by considering the environment of their home. As a result, by considering the geographical distribution of health data, the detection unit can perform more accurate anomaly detection.

[0098] The detection unit can improve detection accuracy by referring to relevant literature on health data during detection. For example, the detection unit can use AI to refer to relevant literature on health data and improve detection accuracy. For example, the detection unit can use machine learning algorithms to refer to relevant literature on health data. Furthermore, the detection unit can also use deep learning to refer to relevant literature on health data. In addition, the detection unit can use statistical analysis to refer to relevant literature on health data. For example, the detection unit can detect anomalies by referring to the latest medical papers. For example, the detection unit can detect anomalies by referring to past research data. Furthermore, the detection unit can also detect anomalies by referring to the results of relevant clinical trials. As a result, the detection unit improves detection accuracy by referring to relevant literature.

[0099] The response unit can estimate the emotions of elderly people and adjust its response method based on the estimated emotions. For example, the response unit estimates the emotions of elderly people using an emotion estimation function, such as an emotion engine or generative AI. For example, the response unit estimates the emotions of elderly people using facial recognition technology. The response unit can also estimate the emotions of elderly people using voice analysis technology. Furthermore, the response unit can also estimate the emotions of elderly people using biometric data. For example, if an elderly person is feeling stressed, the response unit provides a response method that helps them relax. For example, if an elderly person is relaxed, the response unit provides a detailed response method. Also, if an elderly person is feeling anxious, the response unit can provide a response method to alleviate that anxiety. In this way, the response unit can provide a more appropriate response by adjusting its response method according to the emotions of elderly people.

[0100] The response unit can select the optimal response method by referring to past response data when responding. For example, the response unit can analyze past response data using AI and select the optimal response method. For example, the response unit can analyze past response data using machine learning algorithms. The response unit can also analyze past response data using deep learning. Furthermore, the response unit can analyze past response data using statistical analysis. For example, the response unit can prioritize selecting response methods that have been effective in the past. For example, the response unit can select a response method suitable for a specific situation from past response data. The response unit can also analyze past response data and select the most effective response method. In this way, the response unit can select the optimal response method by referring to past response data.

[0101] The response unit can customize its response methods based on the elderly person's current living situation when responding. For example, the response unit can use AI to evaluate the elderly person's living situation and customize the response methods. For example, the response unit can use machine learning algorithms to evaluate the elderly person's living situation. The response unit can also use deep learning to evaluate the elderly person's living situation. Furthermore, the response unit can also use statistical analysis to evaluate the elderly person's living situation. For example, if the elderly person is at home, the response unit can provide response methods that can be done at home. For example, if the elderly person is out, the response unit can provide response methods that can be done while out. Also, if the elderly person is in a facility, the response unit can provide response methods tailored to the characteristics of that facility. As a result, the response unit can provide more appropriate responses by providing response methods that are tailored to the elderly person's living situation.

[0102] The response unit can estimate the emotions of elderly people and determine the priority of responses based on the estimated emotions. The response unit estimates the emotions of elderly people using emotion estimation functions, such as using an emotion engine or generative AI. The response unit estimates the emotions of elderly people using, for example, facial recognition technology. The response unit can also estimate the emotions of elderly people using voice analysis technology. Furthermore, the response unit can estimate the emotions of elderly people using biometric data. For example, if the response unit is stressed, it will prioritize responses to reduce stress. If the response unit is relaxed, it will prioritize overall health management. The response unit can also prioritize responses to reduce anxiety if the elderly person is anxious. In this way, the response unit can prioritize important responses by determining the priority of responses according to the emotions of elderly people.

[0103] The response unit can select the optimal response method when responding to an elderly person, taking into account their geographical location. For example, the response unit can use AI to evaluate the elderly person's geographical location and select the optimal response method. For example, the response unit can use machine learning algorithms to evaluate the elderly person's geographical location. The response unit can also use deep learning to evaluate the elderly person's geographical location. Furthermore, the response unit can use statistical analysis to evaluate the elderly person's geographical location. For example, if the elderly person is at home, the response unit can provide a response method that can be done at home. For example, if the elderly person is out, the response unit can provide a response method that can be done at their destination. Also, if the elderly person is in a facility, the response unit can provide a response method tailored to the characteristics of that facility. As a result, the response unit can provide a more appropriate response by selecting the optimal response method based on the elderly person's geographical location.

[0104] The response unit can analyze the social media activities of elderly individuals and propose appropriate responses when responding to their situations. For example, the response unit can use AI to analyze the social media activities of elderly individuals and propose appropriate responses. For example, the response unit can use machine learning algorithms to analyze the social media activities of elderly individuals. The response unit can also use deep learning to analyze the social media activities of elderly individuals. Furthermore, the response unit can also use statistical analysis to analyze the social media activities of elderly individuals. For example, if an elderly person is experiencing stress from social media, the response unit can propose measures to reduce stress. For example, if an elderly person is relaxing from social media, the response unit can propose measures for overall health management. The response unit can also propose measures to reduce anxiety if an elderly person is experiencing anxiety from social media. As a result, the response unit can provide more appropriate responses by proposing measures based on the social media activities of elderly individuals.

[0105] The dialogue unit can estimate the emotions of elderly people and adjust the content of the dialogue based on the estimated emotions. For example, the dialogue unit estimates the emotions of elderly people using emotion estimation functions, such as an emotion engine or generative AI. For example, the dialogue unit estimates the emotions of elderly people using facial recognition technology. The dialogue unit can also estimate the emotions of elderly people using voice analysis technology. Furthermore, the dialogue unit can also estimate the emotions of elderly people using biometric data. For example, if an elderly person is feeling stressed, the dialogue unit will provide dialogue content that helps them relax. For example, if an elderly person is relaxed, the dialogue unit will provide detailed dialogue content. Also, if an elderly person is feeling anxious, the dialogue unit can provide dialogue content that helps alleviate anxiety. In this way, the dialogue unit can provide more appropriate dialogue by adjusting the content of the dialogue according to the emotions of elderly people.

[0106] The dialogue unit can provide optimal dialogue content by referring to the elderly person's past dialogue history during a conversation. For example, the dialogue unit can use AI to analyze the elderly person's past dialogue history and provide optimal dialogue content. For example, the dialogue unit can use machine learning algorithms to analyze the elderly person's past dialogue history. Furthermore, the dialogue unit can also use deep learning to analyze the elderly person's past dialogue history. In addition, the dialogue unit can use statistical analysis to analyze the elderly person's past dialogue history. For example, the dialogue unit can prioritize providing dialogue content that has been effective in the past. For example, the dialogue unit can provide dialogue content suitable for a specific situation based on past dialogue history. Furthermore, the dialogue unit can analyze past dialogue history and provide the most effective dialogue content. In this way, the dialogue unit can provide optimal dialogue content by referring to past dialogue history.

[0107] The dialogue unit can estimate the emotions of elderly individuals and determine the priority of conversations based on those estimated emotions. For example, the dialogue unit estimates the emotions of elderly individuals using emotion estimation functions, such as an emotion engine or generative AI. It can also estimate emotions using facial recognition technology, voice analysis technology, or biometric data. For instance, if an elderly person is stressed, the dialogue unit prioritizes stress-reducing conversations. If an elderly person is relaxed, it prioritizes conversations about overall health management. Furthermore, if an elderly person is anxious, it can prioritize anxiety-reducing conversations. This allows the dialogue unit to prioritize important conversations by determining the priority of conversations according to the elderly person's emotions.

[0108] The dialogue unit can provide optimal dialogue content during conversations, taking into account the elderly person's device information. For example, the dialogue unit can use AI to evaluate the elderly person's device information and provide optimal dialogue content. For example, the dialogue unit can use machine learning algorithms to evaluate the elderly person's device information. The dialogue unit can also use deep learning to evaluate the elderly person's device information. Furthermore, the dialogue unit can use statistical analysis to evaluate the elderly person's device information. For example, if the elderly person is using a smartphone, the dialogue unit will provide dialogue content that is adapted to the screen size. For example, if the elderly person is using a tablet, the dialogue unit will provide dialogue content optimized for a larger screen. Also, if the elderly person is using a smartwatch, the dialogue unit can provide concise and highly visible dialogue content. In this way, the dialogue unit can provide more appropriate dialogue by providing optimal dialogue content based on the elderly person's device information.

[0109] The proposal unit can estimate the emotions of elderly individuals and adjust the method of proposing care plans based on the estimated emotions. For example, the proposal unit can estimate the emotions of elderly individuals using emotion estimation functions, such as an emotion engine or generative AI. For example, the proposal unit can estimate the emotions of elderly individuals using facial recognition technology. Furthermore, the proposal unit can estimate the emotions of elderly individuals using voice analysis technology. In addition, the proposal unit can estimate the emotions of elderly individuals using biometric data. For example, if an elderly individual is experiencing stress, the proposal unit will propose a stress-reducing care plan. For example, if an elderly individual is relaxed, the proposal unit will propose a comprehensive health management care plan. Furthermore, if an elderly individual is experiencing anxiety, the proposal unit can propose an anxiety-reducing care plan. This allows the proposal unit to provide more appropriate care plans by adjusting the method of proposing care plans according to the emotions of elderly individuals.

[0110] The proposal department can provide optimal suggestions by referring to the elderly person's past care plan history when making a proposal. For example, the proposal department can use AI to analyze the elderly person's past care plan history and provide optimal suggestions. For example, the proposal department can use machine learning algorithms to analyze the elderly person's past care plan history. Furthermore, the proposal department can also use deep learning to analyze the elderly person's past care plan history. In addition, the proposal department can use statistical analysis to analyze the elderly person's past care plan history. For example, the proposal department can prioritize suggesting care plans that have been effective in the past. For example, the proposal department can suggest a care plan suitable for a specific situation based on past care plan history. Furthermore, the proposal department can analyze past care plan history and suggest the most effective care plan. In this way, the proposal department can provide optimal care plans by referring to past care plan history.

[0111] The proposal department can customize care plans based on the elderly person's current living situation when making a proposal. For example, the proposal department can use AI to evaluate the elderly person's living situation and customize the care plan. For example, the proposal department can use machine learning algorithms to evaluate the elderly person's living situation. The proposal department can also use deep learning to evaluate the elderly person's living situation. Furthermore, the proposal department can also use statistical analysis to evaluate the elderly person's living situation. For example, if the elderly person is at home, the proposal department will propose a care plan that can be done at home. For example, if the elderly person is out, the proposal department will propose a care plan that can be done at their destination. Also, if the elderly person is in a facility, the proposal department can propose a care plan tailored to the characteristics of that facility. In this way, the proposal department can provide more appropriate care by offering care plans that are tailored to the elderly person's living situation.

[0112] The proposal unit can estimate the emotions of elderly individuals and determine the priority of care plans based on those estimated emotions. For example, the proposal unit estimates the emotions of elderly individuals using emotion estimation functions, such as an emotion engine or generative AI. For example, the proposal unit can estimate the emotions of elderly individuals using facial recognition technology. Furthermore, the proposal unit can estimate the emotions of elderly individuals using voice analysis technology. In addition, the proposal unit can estimate the emotions of elderly individuals using biometric data. For example, if an elderly individual is experiencing stress, the proposal unit will prioritize stress reduction care plans. For example, if an elderly individual is relaxed, the proposal unit will prioritize overall health management care plans. Furthermore, if an elderly individual is experiencing anxiety, the proposal unit can prioritize anxiety reduction care plans. This allows the proposal unit to prioritize important care by determining the priority of care plans according to the elderly individual's emotions.

[0113] The proposal department can provide optimal care plans by considering the geographical location information of elderly individuals when making proposals. For example, the proposal department can use AI to evaluate the geographical location information of elderly individuals and provide optimal care plans. For example, the proposal department can use machine learning algorithms to evaluate the geographical location information of elderly individuals. Furthermore, the proposal department can also use deep learning to evaluate the geographical location information of elderly individuals. In addition, the proposal department can use statistical analysis to evaluate the geographical location information of elderly individuals. For example, if an elderly person is at home, the proposal department can provide care plans that can be carried out at home. For example, if an elderly person is out, the proposal department can provide care plans that can be carried out at their destination. Furthermore, if an elderly person is in a facility, the proposal department can provide care plans tailored to the characteristics of that facility. In this way, the proposal department can provide more appropriate care by providing optimal care plans based on the geographical location information of elderly individuals.

[0114] The proposal department can analyze the social media activities of elderly individuals and propose care plans based on those activities. For example, the proposal department can use AI to analyze the social media activities of elderly individuals and propose care plans. For example, the proposal department can use machine learning algorithms to analyze the social media activities of elderly individuals. The proposal department can also use deep learning to analyze the social media activities of elderly individuals. Furthermore, the proposal department can use statistical analysis to analyze the social media activities of elderly individuals. For example, if an elderly individual is experiencing stress from social media, the proposal department can propose a care plan to reduce stress. For example, if an elderly individual is relaxing from social media, the proposal department can propose a care plan for overall health management. The proposal department can also propose a care plan to reduce anxiety if an elderly individual is experiencing anxiety from social media. This allows the proposal department to provide more appropriate care by proposing care plans based on the social media activities of elderly individuals.

[0115] The proposal department can propose optimized care plans using predictive analytics at the time of proposal. For example, the proposal department can perform predictive analytics using AI and propose the optimal care plan. For example, the proposal department can perform predictive analytics using machine learning algorithms. Furthermore, the proposal department can also perform predictive analytics using deep learning. In addition, the proposal department can perform predictive analytics using statistical analysis. For example, the proposal department can predict the future health status of elderly people based on their past health data and propose the optimal care plan. For example, the proposal department can predict future risks based on the current health status of elderly people and propose care plans to mitigate those risks. Furthermore, the proposal department can predict future health status based on the lifestyle habits of elderly people and propose care plans to improve their lifestyle habits. As a result, the proposal department can provide more appropriate care by offering optimal care plans based on predictive analytics.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The elderly support system can also be equipped with an environmental monitoring unit. This unit monitors the elderly person's living environment in real time, collecting data such as temperature, humidity, and air quality. For example, the unit can automatically adjust the air conditioner if the room temperature is too high. It can also activate a humidifier if the humidity is too low. Furthermore, it can issue an alert to encourage ventilation if the air quality deteriorates. This ensures that the elderly person's living environment remains comfortable at all times, reducing health risks.

[0118] The elderly support system can also include a reminder function. This function manages the elderly person's daily schedule and reminds them of important tasks. For example, it can notify them of medication times, as well as regular exercise and meal times. Furthermore, it can provide reminders for medical appointments and contact with family members. This makes health management easier for the elderly person, ensuring they don't forget important daily tasks.

[0119] The elderly support system can also include a communication section. This section facilitates communication between the elderly, their families, and care staff. For example, it can provide video call functionality, allowing them to talk face-to-face with family members in remote locations. It can also send and receive text and voice messages. Furthermore, the communication section can include an emergency call function for quick contact in emergencies. This ensures that the elderly can live with peace of mind and avoid isolation.

[0120] The elderly support system can also include an entertainment section. This section has functions to provide enjoyment to the lives of the elderly. For example, it can stream music, movies, and television programs. It can also provide intellectual activities such as games and puzzles. Furthermore, the entertainment section can suggest content tailored to hobbies and interests. This enriches the lives of the elderly and improves their mental health.

[0121] The elderly support system can also include a feedback unit. This unit collects feedback from the elderly and care staff, and uses it to improve the system. For example, the feedback unit can conduct regular surveys to gather opinions on the system's usability and functionality. It can also accept real-time feedback. Furthermore, the feedback unit can analyze the collected feedback and incorporate it into system updates and the addition of new features. This ensures that the elderly support system is always provided in an optimal state that meets the user's needs.

[0122] The elderly support system can further utilize emotion estimation capabilities to provide music and video content tailored to the emotions of elderly individuals. For example, if an elderly person is feeling stressed, the system can provide relaxing music or videos. If they are relaxed, it can provide enjoyable music or videos. Furthermore, if they are feeling anxious, it can provide music or videos that promote a sense of security. This allows for the provision of entertainment that is tailored to the emotions of elderly individuals, thereby improving their mental health.

[0123] The elderly support system can further utilize emotion estimation capabilities to provide dialogue tailored to the elderly person's emotions. For example, if an elderly person is feeling stressed, the system can provide relaxing dialogue. If the elderly person is relaxed, it can provide more detailed dialogue. Furthermore, if an elderly person is feeling anxious, it can provide dialogue to alleviate that anxiety. This ensures that dialogue is tailored to the elderly person's emotions, improving their mental health.

[0124] The elderly support system can further utilize emotion estimation capabilities to propose care plans based on the elderly person's emotions. For example, if the system estimates that the elderly person is stressed, it can propose a care plan to reduce that stress. If the elderly person is relaxed, it can propose a care plan for overall health management. Furthermore, if the elderly person is anxious, it can propose a care plan to reduce that anxiety. This allows for the provision of care plans tailored to the elderly person's emotions, enabling more appropriate care.

[0125] The elderly support system can further provide reminders based on the elderly person's emotions using emotion estimation functionality. For example, if an elderly person is feeling stressed, the system can provide a relaxing reminder. If the elderly person is relaxed, it can also provide a more detailed reminder. Furthermore, if an elderly person is feeling anxious, it can provide a reminder to alleviate that anxiety. This provides reminders tailored to the elderly person's emotions, making it easier to manage their daily life.

[0126] The elderly support system can further collect feedback based on the elderly person's emotions using emotion estimation functionality. For example, if the elderly person is experiencing stress, the system can collect feedback to help reduce that stress. If the elderly person is relaxed, it can also collect feedback on overall health management. Furthermore, if the elderly person is feeling anxious, it can collect feedback to help reduce that anxiety. This allows for the collection of emotionally relevant feedback, which can then be used to improve the system.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The data collection unit monitors the health status of the elderly person in real time. The data collection unit can collect health data such as heart rate, blood pressure, and body temperature. For example, it can monitor heart rate using a wearable device, measure blood pressure using a blood pressure monitor, and measure body temperature using a thermometer. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI, machine learning algorithms, deep learning, and statistical analysis to analyze the data and evaluate the health status of the elderly. Step 3: The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit detects anomalies using AI, anomaly detection algorithms, rule-based systems, and threshold settings for anomaly values. Step 4: The response unit takes action to provide early treatment or prevent accidents based on the abnormality detected by the detection unit. When an abnormality is detected, the response unit notifies care staff, issues an alert, automatically calls an ambulance, and issues a voice warning to the elderly person.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, detection unit, and response unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects health data such as the heart rate, blood pressure, and body temperature of elderly people using the sensors of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12 and detects abnormalities based on the analyzed data. The response unit is implemented in the control unit 46A of the smart device 14 and notifies care staff when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the data collection unit, analysis unit, detection unit, and response unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects health data such as the heart rate, blood pressure, and body temperature of elderly people using the sensors of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12 and detects abnormalities based on the analyzed data. The response unit is implemented in the control unit 46A of the smart glasses 214 and notifies care staff when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, detection unit, and response unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects health data such as the heart rate, blood pressure, and body temperature of elderly people using the sensors of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12 and detects abnormalities based on the analyzed data. The response unit is implemented in the control unit 46A of the headset terminal 314 and notifies care staff when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the data collection unit, analysis unit, detection unit, and response unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects health data such as the heart rate, blood pressure, and body temperature of elderly people using the sensors of the robot 414. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using AI. The detection unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, and detects abnormalities based on the analyzed data. The response unit is implemented in, for example, the control unit 46A of the robot 414, and notifies the care staff when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0182] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0192] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) A data collection unit that monitors the health status of elderly people in real time, An analysis unit analyzes the data collected by the aforementioned collection unit, A detection unit that detects anomalies based on the data analyzed by the aforementioned analysis unit, A response unit that takes action for early treatment or accident prevention based on the abnormality detected by the aforementioned detection unit, Equipped with A system characterized by the following features. (Note 2) It features a dialogue section designed to engage in conversations with the elderly and alleviate feelings of loneliness. The system described in Appendix 1, characterized by the features described herein. (Note 3) The department has a proposal team that offers care plans tailored to the individual needs and lifestyles of elderly people. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The collected data is analyzed to assess the health status of the elderly. The system described in Appendix 1, characterized by the features described herein. (Note 5) The corresponding part is, If an abnormality is detected, the care staff will be notified. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose optimized care plans using predictive analytics. The system described in Appendix 3, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the emotions of older adults and adjusts the frequency of health data collection based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past health data of elderly individuals and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting health data, filtering is performed based on the current activity level of older adults. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the emotions of older adults and prioritizes the data to be collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting health data, prioritize the collection of highly relevant data by considering the geographical location information of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting health data, analyze the social media activity of older adults and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the emotions of elderly people and adjust the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the emotions of older adults and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit, The system estimates the emotions of elderly individuals and adjusts the criteria for detecting abnormalities based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit, When detection occurs, the accuracy of the detection is improved by considering the interrelationships of health data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The detection unit, When detection occurs, the data is performed while taking into account the attribute information of the person submitting the health data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit, The system estimates the emotions of elderly individuals and adjusts the display order of detection results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The detection unit, When detection is performed, the geographical distribution of health data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The detection unit, During detection, we improve detection accuracy by referring to relevant literature on health data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The corresponding part is, The system estimates the emotions of elderly individuals and adjusts its response methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The corresponding part is, When responding to an issue, the optimal response method is selected by referring to past response data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The corresponding part is, When responding, customize the response method based on the elderly person's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The corresponding part is, The system estimates the emotions of elderly individuals and determines the priority of responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The corresponding part is, When responding, the most appropriate response method will be selected considering the geographical location information of the elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 30) The corresponding part is, When responding to an inquiry, we analyze the social media activity of elderly individuals and propose appropriate response strategies. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned dialogue unit, The system estimates the emotions of elderly people and adjusts the content of the conversation based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned dialogue unit, During conversations, the system provides optimal dialogue content by referring to the elderly person's past conversation history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned dialogue unit, It estimates the emotions of elderly people and determines the priority of conversations based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned dialogue unit, During conversations, the system provides optimal dialogue content while taking into account the elderly person's device information. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned proposal section is, We estimate the emotions of elderly people and adjust the method of proposing care plans based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When making a proposal, we refer to the elderly person's past care plan history to provide the most suitable suggestion. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned proposal section is, When making a proposal, customize the care plan based on the elderly person's current living situation. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned proposal section is, The system estimates the emotions of elderly individuals and prioritizes care plans based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned proposal section is, When making a proposal, we provide the optimal care plan by taking into account the geographical location of the elderly person. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned proposal section is, When making a proposal, we analyze the social media activity of elderly individuals and propose a care plan based on that analysis. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned proposal section is, When making a proposal, we will use predictive analytics to suggest an optimized care plan. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that monitors the health status of elderly people in real time, An analysis unit analyzes the data collected by the aforementioned collection unit, A detection unit that detects anomalies based on the data analyzed by the aforementioned analysis unit, A response unit that takes action for early treatment or accident prevention based on the abnormality detected by the aforementioned detection unit, Equipped with A system characterized by the following features.

2. It features a dialogue section designed to engage in conversations with the elderly and alleviate feelings of loneliness. The system according to feature 1.

3. The department has a proposal team that offers care plans tailored to the individual needs and lifestyles of elderly people. The system according to feature 1.

4. The aforementioned analysis unit, The collected data is analyzed to assess the health status of the elderly. The system according to feature 1.

5. The corresponding part is, If an abnormality is detected, the care staff will be notified. The system according to feature 1.

6. The aforementioned proposal section is, We propose optimized care plans using predictive analytics. The system according to claim 3.

7. The aforementioned collection unit is The system estimates the emotions of older adults and adjusts the frequency of health data collection based on these estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past health data of elderly individuals and select the optimal data collection method. The system according to feature 1.

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