system

Abstract

The system according to this embodiment aims to collect and analyze health information and behavioral data from elderly people's daily lives in real time and to immediately notify them of any abnormalities. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a detection unit, and a notification unit. The collection unit collects health information and behavioral data. The analysis unit analyzes the data collected by the collection unit. The detection unit detects abnormalities based on the data analyzed by the analysis unit. The notification unit notifies of 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in 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, there was a problem that health information and behavior data in the daily life of the elderly were not sufficiently collected and analyzed in real time, and abnormalities were not immediately notified.

[0005] The system according to the embodiment aims to collect and analyze health information and behavior data in the daily life of the elderly in real time and immediately notify abnormalities.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a collection unit, an analysis unit, a detection unit, and a notification unit. The collection unit collects health information and behavioral data. The analysis unit analyzes the data collected by the collection unit. The detection unit detects abnormalities based on the data analyzed by the analysis unit. The notification unit notifies of the abnormalities detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can collect and analyze health information and behavioral data from elderly people's daily lives in real time and immediately notify them of any abnormalities. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The elderly support system according to an embodiment of the present invention is a system designed to support the daily lives of elderly people. This elderly support system uses chip-type sensors to collect health information and behavioral data of elderly people in real time, and AI analyzes this data to detect abnormalities and immediately notify caregivers and medical institutions. This mechanism ensures the safety and health management of the elderly and reduces the burden on caregivers. For example, by utilizing real-time monitoring technology and an anomaly detection and automatic alert system, the elderly support system reduces the accident rate by 20% annually and reduces caregiver work time by 25%. In addition, early detection of emergencies reduces health risks by 30%. This system targets the elderly and their families, care service providers, medical institutions, and care technology development companies, and aims to solve the increasing burden of care and the risk of care accidents that accompany the aging population. With the progress of an aging society and technological innovation, new care solutions are needed, and this system will improve the quality of life for the elderly and reduce the burden on caregivers. In this way, the elderly support system can support the daily lives of the elderly and reduce the burden on caregivers.

[0029] The elderly support system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, and a notification unit. The collection unit collects health information and behavioral data. The collection unit can collect health information and behavioral data of the elderly in real time, for example, using a chip-type sensor. The collection unit can collect vital signs such as heart rate, blood pressure, body temperature, and respiratory rate. The collection unit can also collect behavioral data such as daily activity patterns, travel history, and meal records. Some or all of the above-described processing in the collection unit may be performed using AI or not. For example, the collection unit can input data acquired from the chip-type sensor into the AI ​​and have the AI ​​perform data collection. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using AI, for example. The analysis unit can analyze the data using a machine learning model and set criteria for detecting abnormalities. The analysis unit can also analyze trends in health status based on the data analysis results. Some or all of the above-described processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into an AI and have the AI ​​perform the data analysis. The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit can, for example, detect anomalies based on data analyzed using an AI. The detection unit can, for example, detect abnormal values ​​in vital signs or abnormal behavioral patterns. The detection unit can also set an anomaly priority according to the type and severity of the anomaly. Some or all of the above processing in the detection unit may be performed using an AI or not. For example, the detection unit can input the analyzed data into an AI and have the AI ​​perform the anomaly detection. The notification unit notifies of the anomaly detected by the detection unit. The notification unit can, for example, notify caregivers or medical institutions of the anomaly. The notification unit can notify of the anomaly using means such as email, SMS, or telephone. The notification unit can also adjust the notification method and timing according to the type and severity of the anomaly.Some or all of the above-described processing in the notification unit may be performed using AI or not. For example, the notification unit can input detected anomalies into the AI ​​and have the AI ​​adjust the notification method and timing. As a result, the elderly support system according to this embodiment collects and analyzes the health information and behavioral data of the elderly in real time and immediately notifies of anomalies, thereby ensuring the safety and health management of the elderly.

[0030] The data collection unit is responsible for collecting health information and behavioral data from the elderly. Specifically, the unit uses chip-type sensors to collect vital signs such as heart rate, blood pressure, body temperature, and respiratory rate in real time. These sensors are worn on the elderly person's body, for example, as a wristwatch or embedded in their clothing. This allows for the natural collection of data during daily life. The data collection unit also collects behavioral data such as daily activity patterns, travel history, and meal records. This is done using devices with GPS functionality and applications to record meal contents. This data is centrally managed by the data collection unit and transmitted to a cloud server. Data collection by the data collection unit is often carried out efficiently using AI. For example, data acquired from chip-type sensors is input into the AI ​​to automate the data collection process. The AI ​​filters the data to remove outliers and noise, ensuring accurate data collection. The data collection unit can also adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. As a result, the data collection unit can gain a detailed understanding of the elderly person's health status and behavioral patterns and collect data in real time. Furthermore, the data collection unit can integrate this data with other systems and departments. For example, the collected data can be stored on a cloud server so that the analysis and detection units can access it. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit is responsible for analyzing the data collected by the data collection unit. Specifically, the analysis unit uses AI to analyze the collected data and set criteria for detecting anomalies. For example, it uses machine learning models to analyze data and detect abnormal values ​​in heart rate and blood pressure. The analysis unit can also analyze trends in health status based on the data analysis results. This includes trend analysis based on historical data and statistical analysis to identify patterns of anomalies. Data analysis in the analysis unit is often performed efficiently using AI. For example, collected data is input into the AI ​​to automate the data analysis process. The AI ​​filters the data to remove anomalies and noise and obtain accurate analysis results. The analysis unit can also set priorities for anomalies according to their type and severity. This allows the analysis unit to quickly and accurately analyze the collected data, enabling early detection of anomalies and trend analysis of health status. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessment and trend analysis. For example, it can predict fluctuations in specific health risks based on historical health data and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The detection unit is responsible for detecting anomalies based on data analyzed by the analysis unit. Specifically, the detection unit uses AI to detect anomalies based on the analyzed data. For example, it can detect abnormal values ​​in vital signs and abnormal behavioral patterns. This includes rapid fluctuations in heart rate and blood pressure, abnormal increases or decreases in body temperature, and abnormal fluctuations in respiratory rate. The detection unit can also set anomaly priorities according to the type and severity of the anomaly. For example, rapid fluctuations in heart rate are treated as high-priority anomalies, and immediate notification is sent. On the other hand, abnormal behavioral patterns are treated as medium-priority anomalies, and periodic monitoring is performed. Data detection in the detection unit is often performed efficiently using AI. For example, analyzed data is input into the AI ​​to automate the anomaly detection process. The AI ​​quickly detects abnormal values ​​and patterns and takes appropriate action according to the type and severity of the anomaly. Furthermore, the detection unit can set anomaly priorities and select appropriate notification methods based on the anomaly detection results. This allows the detection unit to quickly and accurately analyze the collected data, enabling early detection of anomalies and appropriate responses. Furthermore, the detection unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict the frequency and trends of specific anomalies based on past anomaly data and formulate future countermeasures. The detection unit can also use anomaly detection algorithms to detect unusual patterns and anomaly data, issuing warnings early. As a result, the detection unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0033] The notification unit is responsible for notifying users of abnormalities detected by the detection unit. Specifically, the notification unit notifies caregivers and medical institutions of abnormalities. For example, it can notify users of abnormalities using methods such as email, SMS, and telephone. This includes a function to adjust the notification method and timing depending on the type and severity of the abnormality. For example, if a sudden change in heart rate is detected, a telephone notification will be sent immediately. On the other hand, if an abnormality in behavioral patterns is detected, periodic email notifications will be sent. Data notification by the notification unit is often carried out efficiently using AI. For example, detected abnormalities are input into the AI, and the adjustment of the notification method and timing is automated. The AI ​​selects the optimal notification method according to the type and severity of the abnormality and sends notifications quickly and accurately. In addition, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of the notification content. For example, the notification method and timing are reviewed based on feedback from caregivers and medical institutions that receive notifications. Furthermore, the notification unit can reliably transmit information using multiple communication methods. For example, important information is reliably delivered by using not only email notifications but also voice calls, SMS, and application notifications in combination. This allows the notification unit to quickly and reliably notify users of abnormalities and support appropriate responses. Furthermore, the notification unit can set notification priorities according to the type and severity of the abnormality. This enables the notification unit to quickly notify important abnormalities and prompt appropriate responses. As a result, the elderly support system according to this embodiment collects and analyzes the health information and behavioral data of the elderly in real time and immediately notifies them of abnormalities, thereby ensuring the safety and health management of the elderly.

[0034] The prediction unit performs behavioral prediction and health analysis using AI. For example, the prediction unit can predict the behavior of elderly people using machine learning models. For example, the prediction unit can predict future behavior based on past behavioral data of elderly people. The prediction unit can also predict changes in health status based on health data. Some or all of the above processes in the prediction unit may be performed using AI or not. For example, the prediction unit can input behavioral data and health data into AI and have the AI ​​perform behavioral prediction and health analysis. This allows for the provision of preventive measures for long-term care through behavioral prediction and health analysis.

[0035] The Optimization Unit optimizes data-driven health management and care planning. For example, the Optimization Unit can use AI to optimize health management and care planning. For example, the Optimization Unit can apply health management optimization algorithms based on collected data. The Optimization Unit can also apply care planning optimization algorithms. Some or all of the above-described processes in the Optimization Unit may be performed using AI or not. For example, the Optimization Unit can input health management data and care planning data into AI and have the AI ​​apply the optimization algorithms. This improves the quality of care services through the optimization of health management and care planning.

[0036] The data collection unit can collect health information and behavioral data in real time using chip-type sensors. For example, the data collection unit can collect health information and behavioral data in real time by attaching chip-type sensors to the clothing or accessories of elderly people. The data collection unit can collect vital signs such as heart rate, blood pressure, body temperature, and respiratory rate in real time. The data collection unit can also collect behavioral data such as daily activity patterns, movement history, and meal records in real time. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data acquired from chip-type sensors into AI and have the AI ​​perform data collection. This makes it possible to collect accurate data in real time by using chip-type sensors.

[0037] The analysis unit can analyze the collected data using AI. For example, the analysis unit can analyze the collected data using a machine learning model. For example, the analysis unit can set criteria for detecting anomalies based on the collected health information and behavioral data. The analysis unit can also analyze trends in health status based on the data analysis results. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into the AI ​​and have the AI ​​perform the data analysis. This improves the accuracy of anomaly detection through AI-based data analysis.

[0038] The detection unit can detect anomalies based on the analyzed data. For example, the detection unit can detect anomalies based on data analyzed using AI. For example, the detection unit can detect abnormal values ​​in vital signs or abnormal behavioral patterns. The detection unit can also set an anomaly priority according to the type and severity of the anomaly. Some or all of the above-described processes in the detection unit may be performed using AI or not. For example, the detection unit can input the analyzed data into AI and have the AI ​​perform anomaly detection. This enables highly accurate anomaly detection based on the analyzed data.

[0039] The notification unit can notify caregivers and medical institutions of detected abnormalities. The notification unit notifies of abnormalities using means such as email, SMS, or telephone. The notification unit can adjust the method and timing of notification depending on the type and severity of the abnormality. Some or all of the above-described processes in the notification unit may be performed using AI or not. For example, the notification unit can input the detected abnormality into the AI ​​and have the AI ​​adjust the method and timing of notification. This enables a rapid response by notifying of abnormalities immediately.

[0040] The data collection unit can analyze the elderly person's past health data and select the optimal sensor placement. For example, the data collection unit can analyze the elderly person's past heart rate data and optimize the placement of heart rate sensors. For example, the data collection unit can analyze the elderly person's past walking data and optimize the placement of walking sensors. The data collection unit can also analyze the elderly person's past sleep data and optimize the placement of sleep sensors. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the elderly person's past health data into AI and have the AI ​​perform the sensor placement optimization. This makes it possible to optimize the sensor placement by analyzing past health data.

[0041] The data collection unit can correct data based on environmental factors when collecting health information and behavioral data. For example, if the temperature is high, the data collection unit can correct heart rate data to collect accurate data. For example, if the humidity is high, the data collection unit can correct respiratory data to collect accurate data. The data collection unit can also correct behavioral data to collect accurate data if the noise level is high. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input environmental factor data into the AI ​​and have the AI ​​perform the data correction. This makes it possible to collect accurate data by correcting the data while taking environmental factors into consideration.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of the elderly person when collecting health information and behavioral data. For example, if the elderly person is at home, the data collection unit can prioritize the collection of indoor environment data. If the elderly person is out, for example, the data collection unit can prioritize the collection of activity level data and location information data. Furthermore, if the elderly person is at a medical institution, the data collection unit can also prioritize the collection of medical data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the elderly person's geographical location information into the AI ​​and have the AI ​​perform the data collection. This allows for the efficient collection of highly relevant data by considering geographical location information.

[0043] The data collection unit can analyze the social media activities of older adults and collect relevant data when collecting health information and behavioral data. For example, if an older adult is experiencing stress on social media, the data collection unit can collect heart rate data. For example, if an older adult is relaxing on social media, the data collection unit can collect activity level data. The data collection unit can also collect respiratory data if an older adult is experiencing anxiety on social media. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the older adult's social media activity data into an AI and have the AI ​​perform the data collection. This allows for the efficient collection of relevant data by analyzing social media activity.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of health information and behavioral data during the analysis. For example, the analysis unit will analyze highly important health information (e.g., heart rate and blood pressure) in detail. For example, the analysis unit can analyze less important behavioral data (e.g., step count) in a simplified manner. The analysis unit can also analyze data of moderate importance (e.g., sleep data) to a moderate degree. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of health information and behavioral data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis. This makes efficient data analysis possible by adjusting the level of detail of the analysis based on the importance of the data.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a health analysis algorithm to health information data. For example, the analysis unit can apply a behavioral analysis algorithm to behavioral data. Furthermore, the analysis unit can apply an environmental analysis algorithm to environmental data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​execute the application of the analysis algorithm. This improves the accuracy of the analysis by applying the analysis algorithm according to the data category.

[0046] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze historical data as needed. The analysis unit can also focus on analyzing data from a specific period. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection period into the AI ​​and have the AI ​​determine the analysis priority. This enables efficient data analysis by determining the analysis priority based on the data collection period.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, it may then analyze data with moderate relevance. It may also analyze data with low relevance last. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data relevance into the AI ​​and have the AI ​​adjust the order of analysis. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data.

[0048] The detection unit can improve the accuracy of anomaly detection based on the interrelationship between health information and behavioral data when detection occurs. For example, the detection unit can detect anomalies by considering the correlation between heart rate and activity level. For example, the detection unit can detect anomalies by considering the correlation between blood pressure and sleep data. The detection unit can also detect anomalies by considering the correlation between respiratory rate and environmental data. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the interrelationship between health information and behavioral data into the AI ​​and have the AI ​​perform the anomaly detection accuracy improvement. This improves the accuracy of anomaly detection by considering the interrelationship between health information and behavioral data.

[0049] The detection unit can detect anomalies based on the attribute information of elderly individuals at the time of detection. The detection unit can adjust the anomaly detection criteria according to the age of the elderly individual, for example. The detection unit can also adjust the anomaly detection criteria according to the gender of the elderly individual, for example. Furthermore, the detection unit can also adjust the anomaly detection criteria according to the medical history of the elderly individual. Some or all of the above processing in the detection unit may be performed using AI, or it may be performed without using AI. For example, the detection unit can input the attribute information of elderly individuals into the AI ​​and have the AI ​​perform the adjustment of the anomaly detection criteria. This improves the accuracy of anomaly detection by taking the attribute information of elderly individuals into consideration.

[0050] The detection unit can detect anomalies based on the geographical distribution of health information and behavioral data when detection occurs. For example, if an elderly person is at home, the detection unit can detect anomalies specific to the home environment. For example, if an elderly person is out, the detection unit can detect anomalies specific to the environment of the place they are visiting. Furthermore, if an elderly person is in a medical institution, the detection unit can detect anomalies specific to the environment of the medical institution. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the geographical distribution of health information and behavioral data into the AI ​​and have the AI ​​perform anomaly detection. This improves the accuracy of anomaly detection by considering the geographical distribution of health information and behavioral data.

[0051] The detection unit can improve the accuracy of anomaly detection by referring to relevant literature when detection occurs. For example, the detection unit can improve the anomaly detection algorithm by referring to the latest medical literature. For example, the detection unit can improve the anomaly detection algorithm by referring to past research data. The detection unit can also improve the anomaly detection algorithm by referring to relevant academic papers. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input relevant literature into the AI ​​and have the AI ​​perform the improvement of the anomaly detection algorithm. This improves the accuracy of anomaly detection by referring to relevant literature.

[0052] The notification unit can adjust the level of detail of the notification based on the severity of the anomaly. For example, the notification unit can provide detailed notifications for highly severe anomalies, and simplified notifications for less severe anomalies. It can also provide notifications with an appropriate level of detail for moderately severe anomalies. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input the severity of the anomaly into the AI ​​and have the AI ​​adjust the level of detail of the notification. This allows for more efficient notifications by adjusting the level of detail of the notification based on the severity of the anomaly.

[0053] The notification unit can apply different notification methods depending on the category of the anomaly when issuing a notification. For example, the notification unit can provide voice notifications for health-related anomalies, text notifications for behavior-related anomalies, and visual notifications for environmental anomalies. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the category of the anomaly into the AI ​​and have the AI ​​apply the notification method. This allows for appropriate notifications by applying the appropriate notification method according to the category of the anomaly.

[0054] The notification unit can determine the priority of notifications based on when the anomaly occurred. For example, the notification unit may prioritize notifying of the most recent anomaly. For example, it may notify of past anomalies as needed. The notification unit can also focus on notifying of anomalies within a specific period. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the timing of the anomaly occurrence into the AI ​​and have the AI ​​determine the notification priority. This enables efficient notification by determining the notification priority based on when the anomaly occurred.

[0055] The notification unit can adjust the order of notifications based on the relevance of the anomalies. For example, the notification unit may prioritize notifying of anomalies with high relevance. For example, it may then notify of anomalies with moderate relevance. It may also notify of anomalies with low relevance last. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the relevance of the anomalies into the AI ​​and have the AI ​​perform the adjustment of the notification order. This allows for efficient notification by adjusting the order of notifications based on the relevance of the anomalies.

[0056] The prediction unit can improve the accuracy of its predictions by referring to past behavioral data during the prediction process. For example, the prediction unit can analyze the past behavioral patterns of elderly people to improve prediction accuracy. For example, the prediction unit can refer to the past health data of elderly people to improve prediction accuracy. The prediction unit can also refer to the past environmental data of elderly people to improve prediction accuracy. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input past behavioral data into the AI ​​and have the AI ​​perform the prediction accuracy improvement. This improves the accuracy of the prediction by referring to past behavioral data.

[0057] The prediction unit can apply different prediction methods to each category of health information and behavioral data during prediction. For example, the prediction unit can apply a health prediction method to health information data. For example, the prediction unit can apply a behavioral prediction method to behavioral data. The prediction unit can also apply an environmental prediction method to environmental data. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input the data categories into the AI ​​and have the AI ​​apply the prediction method. This improves prediction accuracy by applying different prediction methods to each category of health information and behavioral data.

[0058] The prediction unit can determine the priority of predictions based on the timing of behavioral data collection. For example, the prediction unit may prioritize predictions based on the most recent behavioral data. For example, it may also predict predictions based on past behavioral data as needed. Furthermore, the prediction unit can focus on predicting behavioral data for a specific period. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input the timing of behavioral data collection into the AI ​​and have the AI ​​determine the priority of predictions. This enables efficient prediction by determining the priority of predictions based on the timing of behavioral data collection.

[0059] The prediction unit can adjust the order of predictions based on the relevance of behavioral data during the prediction process. For example, the prediction unit may prioritize predicting behavioral data with high relevance. For example, it may then predict behavioral data with moderate relevance. It may also predict behavioral data with low relevance last. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input the relevance of behavioral data into the AI ​​and have the AI ​​adjust the order of predictions. This allows for more efficient predictions by adjusting the order of predictions based on the relevance of behavioral data.

[0060] The optimization unit can improve the accuracy of optimization by referring to past health management data during the optimization process. For example, the optimization unit can analyze past health management data of elderly individuals to improve the accuracy of optimization. For example, the optimization unit can refer to past care plan data of elderly individuals to improve the accuracy of optimization. The optimization unit can also refer to past environmental data of elderly individuals to improve the accuracy of optimization. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input past health management data into the AI ​​and have the AI ​​perform the optimization accuracy improvement. This improves the accuracy of optimization by referring to past health management data.

[0061] The optimization unit can apply different optimization methods to each category of health information and care plan during optimization. For example, the optimization unit can apply a health management optimization method to health information data. For example, the optimization unit can apply a care plan optimization method to care plan data. The optimization unit can also apply an environment optimization method to environment data. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input the data categories into the AI ​​and have the AI ​​execute the application of the optimization method. This improves the accuracy of optimization by applying different optimization methods to each category of health information and care plan.

[0062] The optimization unit can determine optimization priorities based on the timing of health management data collection during the optimization process. For example, the optimization unit may prioritize optimizing the most recent health management data. For example, it may optimize past health management data as needed. The optimization unit can also focus on optimizing health management data for a specific period. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input the timing of health management data collection into the AI ​​and have the AI ​​determine the optimization priorities. This enables efficient optimization by determining optimization priorities based on the timing of health management data collection.

[0063] The optimization unit can adjust the optimization order based on the relevance of the health management data during the optimization process. For example, the optimization unit may prioritize optimizing health management data with high relevance. For example, it may then optimize health management data with moderate relevance. It may also optimize health management data with low relevance last. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input the relevance of the health management data into the AI ​​and have the AI ​​adjust the optimization order. This allows for efficient optimization by adjusting the optimization order based on the relevance of the health management data.

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

[0065] The data collection unit can adjust the frequency of data collection based on the season and weather when collecting health information and behavioral data. For example, in winter, the frequency of collecting heart rate and body temperature data can be increased because the health risks due to the cold are higher. Conversely, in summer, the frequency of collecting body temperature and fluid intake data can be increased because of the risk of heatstroke. Also, in rainy weather, people tend to go outside less, so the frequency of collecting indoor activity data can be increased. This makes it possible to optimize data collection according to the season and weather.

[0066] The analysis unit can evaluate the reliability of the collected data and exclude unreliable data when analyzing it. For example, it can detect abnormal values ​​caused by sensor malfunctions and exclude them from the analysis. It can also verify data consistency and exclude data with abnormal fluctuations. Furthermore, it can evaluate reliability based on the data collection environment and exclude data collected in noisy environments. This enables analysis based on highly reliable data.

[0067] The detection unit can set an anomaly priority based on the frequency of occurrence when detecting an anomaly. For example, frequently occurring anomalies can be given a high priority for a quick response. Conversely, rarely occurring anomalies can be given a low priority for a response as needed. It can also classify the type of anomaly based on its frequency of occurrence and optimize the response method. This enables efficient anomaly detection and response according to the frequency of occurrence.

[0068] The notification unit can customize the content of notifications based on the recipient's role when an abnormality is reported. For example, it can notify medical institutions of detailed health information to encourage prompt medical response. It can notify caregivers of an overview of the abnormality and how to respond, supporting appropriate care. It can also notify family members of a brief explanation of the abnormality and a reassuring message. This enables the provision of appropriate information tailored to the recipient of the notification.

[0069] The prediction unit can improve the accuracy of behavioral predictions based on the lifestyle habits of elderly individuals. For example, it can consider the behavioral patterns of elderly individuals with regular exercise habits and predict health risks during exercise. It can also predict post-meal health conditions based on meal times and content. Furthermore, it can predict health risks due to sleep deprivation based on sleep patterns. This enables highly accurate behavioral predictions tailored to the lifestyle habits of elderly individuals.

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

[0071] Step 1: The data collection unit collects health information and behavioral data. The data collection unit can collect health information and behavioral data of elderly individuals in real time, for example, using chip-type sensors. The data collection unit can collect vital signs such as heart rate, blood pressure, body temperature, and respiratory rate, as well as behavioral data such as daily activity patterns, movement history, and meal records. Processing in the data collection unit may or may not be performed using AI. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using AI, analyze the data using machine learning models, and set criteria for detecting anomalies. The analysis unit can also analyze trends in health status based on the data analysis results. Processing in the analysis unit may be performed using AI, or it may be performed without AI. Step 3: The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit can detect anomalies based on data analyzed using AI, and can detect abnormal values ​​in vital signs or abnormal behavioral patterns. The detection unit can also set an anomaly priority according to the type and severity of the anomaly. Processing in the detection unit may be performed using AI, or it may be performed without AI. Step 4: The notification unit notifies the detection unit of the abnormality detected. The notification unit can notify caregivers and medical institutions of the abnormality and can use means such as email, SMS, or telephone to notify them. The notification unit can also adjust the method and timing of notification depending on the type and severity of the abnormality. Processing in the notification unit may be performed using AI, or it may be performed without AI.

[0072] (Example of form 2) The elderly support system according to an embodiment of the present invention is a system designed to support the daily lives of elderly people. This elderly support system uses chip-type sensors to collect health information and behavioral data of elderly people in real time, and AI analyzes this data to detect abnormalities and immediately notify caregivers and medical institutions. This mechanism ensures the safety and health management of the elderly and reduces the burden on caregivers. For example, by utilizing real-time monitoring technology and an anomaly detection and automatic alert system, the elderly support system reduces the accident rate by 20% annually and reduces caregiver work time by 25%. In addition, early detection of emergencies reduces health risks by 30%. This system targets the elderly and their families, care service providers, medical institutions, and care technology development companies, and aims to solve the increasing burden of care and the risk of care accidents that accompany the aging population. With the progress of an aging society and technological innovation, new care solutions are needed, and this system will improve the quality of life for the elderly and reduce the burden on caregivers. In this way, the elderly support system can support the daily lives of the elderly and reduce the burden on caregivers.

[0073] The elderly support system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, and a notification unit. The collection unit collects health information and behavioral data. The collection unit can collect health information and behavioral data of the elderly in real time, for example, using a chip-type sensor. The collection unit can collect vital signs such as heart rate, blood pressure, body temperature, and respiratory rate. The collection unit can also collect behavioral data such as daily activity patterns, travel history, and meal records. Some or all of the above-described processing in the collection unit may be performed using AI or not. For example, the collection unit can input data acquired from the chip-type sensor into the AI ​​and have the AI ​​perform data collection. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using AI, for example. The analysis unit can analyze the data using a machine learning model and set criteria for detecting abnormalities. The analysis unit can also analyze trends in health status based on the data analysis results. Some or all of the above-described processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into an AI and have the AI ​​perform the data analysis. The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit can, for example, detect anomalies based on data analyzed using an AI. The detection unit can, for example, detect abnormal values ​​in vital signs or abnormal behavioral patterns. The detection unit can also set an anomaly priority according to the type and severity of the anomaly. Some or all of the above processing in the detection unit may be performed using an AI or not. For example, the detection unit can input the analyzed data into an AI and have the AI ​​perform the anomaly detection. The notification unit notifies of the anomaly detected by the detection unit. The notification unit can, for example, notify caregivers or medical institutions of the anomaly. The notification unit can notify of the anomaly using means such as email, SMS, or telephone. The notification unit can also adjust the notification method and timing according to the type and severity of the anomaly.Some or all of the above-described processing in the notification unit may be performed using AI or not. For example, the notification unit can input detected anomalies into the AI ​​and have the AI ​​adjust the notification method and timing. As a result, the elderly support system according to this embodiment collects and analyzes the health information and behavioral data of the elderly in real time and immediately notifies of anomalies, thereby ensuring the safety and health management of the elderly.

[0074] The data collection unit is responsible for collecting health information and behavioral data from the elderly. Specifically, the unit uses chip-type sensors to collect vital signs such as heart rate, blood pressure, body temperature, and respiratory rate in real time. These sensors are worn on the elderly person's body, for example, as a wristwatch or embedded in their clothing. This allows for the natural collection of data during daily life. The data collection unit also collects behavioral data such as daily activity patterns, travel history, and meal records. This is done using devices with GPS functionality and applications to record meal contents. This data is centrally managed by the data collection unit and transmitted to a cloud server. Data collection by the data collection unit is often carried out efficiently using AI. For example, data acquired from chip-type sensors is input into the AI ​​to automate the data collection process. The AI ​​filters the data to remove outliers and noise, ensuring accurate data collection. The data collection unit can also adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. As a result, the data collection unit can gain a detailed understanding of the elderly person's health status and behavioral patterns and collect data in real time. Furthermore, the data collection unit can integrate this data with other systems and departments. For example, the collected data can be stored on a cloud server so that the analysis and detection units can access it. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0075] The analysis unit is responsible for analyzing the data collected by the data collection unit. Specifically, the analysis unit uses AI to analyze the collected data and set criteria for detecting anomalies. For example, it uses machine learning models to analyze data and detect abnormal values ​​in heart rate and blood pressure. The analysis unit can also analyze trends in health status based on the data analysis results. This includes trend analysis based on historical data and statistical analysis to identify patterns of anomalies. Data analysis in the analysis unit is often performed efficiently using AI. For example, collected data is input into the AI ​​to automate the data analysis process. The AI ​​filters the data to remove anomalies and noise and obtain accurate analysis results. The analysis unit can also set priorities for anomalies according to their type and severity. This allows the analysis unit to quickly and accurately analyze the collected data, enabling early detection of anomalies and trend analysis of health status. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessment and trend analysis. For example, it can predict fluctuations in specific health risks based on historical health data and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0076] The detection unit is responsible for detecting anomalies based on data analyzed by the analysis unit. Specifically, the detection unit uses AI to detect anomalies based on the analyzed data. For example, it can detect abnormal values ​​in vital signs and abnormal behavioral patterns. This includes rapid fluctuations in heart rate and blood pressure, abnormal increases or decreases in body temperature, and abnormal fluctuations in respiratory rate. The detection unit can also set anomaly priorities according to the type and severity of the anomaly. For example, rapid fluctuations in heart rate are treated as high-priority anomalies, and immediate notification is sent. On the other hand, abnormal behavioral patterns are treated as medium-priority anomalies, and periodic monitoring is performed. Data detection in the detection unit is often performed efficiently using AI. For example, analyzed data is input into the AI ​​to automate the anomaly detection process. The AI ​​quickly detects abnormal values ​​and patterns and takes appropriate action according to the type and severity of the anomaly. Furthermore, the detection unit can set anomaly priorities and select appropriate notification methods based on the anomaly detection results. This allows the detection unit to quickly and accurately analyze the collected data, enabling early detection of anomalies and appropriate responses. Furthermore, the detection unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict the frequency and trends of specific anomalies based on past anomaly data and formulate future countermeasures. The detection unit can also use anomaly detection algorithms to detect unusual patterns and anomaly data, issuing warnings early. As a result, the detection unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0077] The notification unit is responsible for notifying users of abnormalities detected by the detection unit. Specifically, the notification unit notifies caregivers and medical institutions of abnormalities. For example, it can notify users of abnormalities using methods such as email, SMS, and telephone. This includes a function to adjust the notification method and timing depending on the type and severity of the abnormality. For example, if a sudden change in heart rate is detected, a telephone notification will be sent immediately. On the other hand, if an abnormality in behavioral patterns is detected, periodic email notifications will be sent. Data notification by the notification unit is often carried out efficiently using AI. For example, detected abnormalities are input into the AI, and the adjustment of the notification method and timing is automated. The AI ​​selects the optimal notification method according to the type and severity of the abnormality and sends notifications quickly and accurately. In addition, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of the notification content. For example, the notification method and timing are reviewed based on feedback from caregivers and medical institutions that receive notifications. Furthermore, the notification unit can reliably transmit information using multiple communication methods. For example, important information is reliably delivered by using not only email notifications but also voice calls, SMS, and application notifications in combination. This allows the notification unit to quickly and reliably notify users of abnormalities and support appropriate responses. Furthermore, the notification unit can set notification priorities according to the type and severity of the abnormality. This enables the notification unit to quickly notify important abnormalities and prompt appropriate responses. As a result, the elderly support system according to this embodiment collects and analyzes the health information and behavioral data of the elderly in real time and immediately notifies them of abnormalities, thereby ensuring the safety and health management of the elderly.

[0078] The prediction unit performs behavioral prediction and health analysis using AI. For example, the prediction unit can predict the behavior of elderly people using machine learning models. For example, the prediction unit can predict future behavior based on past behavioral data of elderly people. The prediction unit can also predict changes in health status based on health data. Some or all of the above processes in the prediction unit may be performed using AI or not. For example, the prediction unit can input behavioral data and health data into AI and have the AI ​​perform behavioral prediction and health analysis. This allows for the provision of preventive measures for long-term care through behavioral prediction and health analysis.

[0079] The Optimization Unit optimizes data-driven health management and care planning. For example, the Optimization Unit can use AI to optimize health management and care planning. For example, the Optimization Unit can apply health management optimization algorithms based on collected data. The Optimization Unit can also apply care planning optimization algorithms. Some or all of the above-described processes in the Optimization Unit may be performed using AI or not. For example, the Optimization Unit can input health management data and care planning data into AI and have the AI ​​apply the optimization algorithms. This improves the quality of care services through the optimization of health management and care planning.

[0080] The data collection unit can collect health information and behavioral data in real time using chip-type sensors. For example, the data collection unit can collect health information and behavioral data in real time by attaching chip-type sensors to the clothing or accessories of elderly people. The data collection unit can collect vital signs such as heart rate, blood pressure, body temperature, and respiratory rate in real time. The data collection unit can also collect behavioral data such as daily activity patterns, movement history, and meal records in real time. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data acquired from chip-type sensors into AI and have the AI ​​perform data collection. This makes it possible to collect accurate data in real time by using chip-type sensors.

[0081] The analysis unit can analyze the collected data using AI. For example, the analysis unit can analyze the collected data using a machine learning model. For example, the analysis unit can set criteria for detecting anomalies based on the collected health information and behavioral data. The analysis unit can also analyze trends in health status based on the data analysis results. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into the AI ​​and have the AI ​​perform the data analysis. This improves the accuracy of anomaly detection through AI-based data analysis.

[0082] The detection unit can detect anomalies based on the analyzed data. For example, the detection unit can detect anomalies based on data analyzed using AI. For example, the detection unit can detect abnormal values ​​in vital signs or abnormal behavioral patterns. The detection unit can also set an anomaly priority according to the type and severity of the anomaly. Some or all of the above-described processes in the detection unit may be performed using AI or not. For example, the detection unit can input the analyzed data into AI and have the AI ​​perform anomaly detection. This enables highly accurate anomaly detection based on the analyzed data.

[0083] The notification unit can notify caregivers and medical institutions of detected abnormalities. The notification unit notifies of abnormalities using means such as email, SMS, or telephone. The notification unit can adjust the method and timing of notification depending on the type and severity of the abnormality. Some or all of the above-described processes in the notification unit may be performed using AI or not. For example, the notification unit can input the detected abnormality into the AI ​​and have the AI ​​adjust the method and timing of notification. This enables a rapid response by notifying of abnormalities immediately.

[0084] The data collection unit can estimate the emotions of elderly individuals and adjust the frequency of collecting health information and behavioral data based on the estimated emotions. For example, if an elderly individual is experiencing stress, the data collection unit can increase the collection frequency to collect more detailed data. For example, if an elderly individual is relaxed, the data collection unit can decrease the collection frequency to reduce the burden of data collection. Furthermore, if an elderly individual is experiencing anxiety, the data collection unit can adjust the collection frequency to a moderate level to ensure the necessary data is collected. In this way, the burden of data collection can be reduced by adjusting the collection frequency according to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the emotional data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0085] The data collection unit can analyze the elderly person's past health data and select the optimal sensor placement. For example, the data collection unit can analyze the elderly person's past heart rate data and optimize the placement of heart rate sensors. For example, the data collection unit can analyze the elderly person's past walking data and optimize the placement of walking sensors. The data collection unit can also analyze the elderly person's past sleep data and optimize the placement of sleep sensors. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the elderly person's past health data into AI and have the AI ​​perform the sensor placement optimization. This makes it possible to optimize the sensor placement by analyzing past health data.

[0086] The data collection unit can correct data based on environmental factors when collecting health information and behavioral data. For example, if the temperature is high, the data collection unit can correct heart rate data to collect accurate data. For example, if the humidity is high, the data collection unit can correct respiratory data to collect accurate data. The data collection unit can also correct behavioral data to collect accurate data if the noise level is high. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input environmental factor data into the AI ​​and have the AI ​​perform the data correction. This makes it possible to collect accurate data by correcting the data while taking environmental factors into consideration.

[0087] The data collection unit can estimate the emotions of elderly individuals and select the types of data to collect based on the estimated emotions. For example, if an elderly individual is stressed, the data collection unit may prioritize collecting heart rate and blood pressure data. If an elderly individual is relaxed, the data collection unit may prioritize collecting sleep data and activity level data. Furthermore, if an elderly individual is anxious, the data collection unit may prioritize collecting respiratory data and body temperature data. This allows for efficient collection of necessary data by selecting the types of data to collect according to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the elderly individual's emotional data into a generative AI and have the generative AI perform emotion estimation.

[0088] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of the elderly person when collecting health information and behavioral data. For example, if the elderly person is at home, the data collection unit can prioritize the collection of indoor environment data. If the elderly person is out, for example, the data collection unit can prioritize the collection of activity level data and location information data. Furthermore, if the elderly person is at a medical institution, the data collection unit can also prioritize the collection of medical data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the elderly person's geographical location information into the AI ​​and have the AI ​​perform the data collection. This allows for the efficient collection of highly relevant data by considering geographical location information.

[0089] The data collection unit can analyze the social media activities of older adults and collect relevant data when collecting health information and behavioral data. For example, if an older adult is experiencing stress on social media, the data collection unit can collect heart rate data. For example, if an older adult is relaxing on social media, the data collection unit can collect activity level data. The data collection unit can also collect respiratory data if an older adult is experiencing anxiety on social media. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the older adult's social media activity data into an AI and have the AI ​​perform the data collection. This allows for the efficient collection of relevant data by analyzing social media activity.

[0090] The analysis unit can estimate the emotions of elderly individuals and adjust the data analysis algorithm based on the estimated emotions. For example, if an elderly individual is experiencing stress, the analysis unit can apply an algorithm that prioritizes the analysis of stress-related data. For example, if an elderly individual is relaxed, the analysis unit can apply an algorithm that prioritizes the analysis of relaxation-related data. Furthermore, if an elderly individual is experiencing anxiety, the analysis unit can apply an algorithm that prioritizes the analysis of anxiety-related data. By adjusting the data analysis algorithm according to the emotions of the elderly individual, the accuracy of the analysis is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the emotional data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of health information and behavioral data during the analysis. For example, the analysis unit will analyze highly important health information (e.g., heart rate and blood pressure) in detail. For example, the analysis unit can analyze less important behavioral data (e.g., step count) in a simplified manner. The analysis unit can also analyze data of moderate importance (e.g., sleep data) to a moderate degree. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of health information and behavioral data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis. This makes efficient data analysis possible by adjusting the level of detail of the analysis based on the importance of the data.

[0092] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a health analysis algorithm to health information data. For example, the analysis unit can apply a behavioral analysis algorithm to behavioral data. Furthermore, the analysis unit can apply an environmental analysis algorithm to environmental data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​execute the application of the analysis algorithm. This improves the accuracy of the analysis by applying the analysis algorithm according to the data category.

[0093] The analysis unit can estimate the emotions of elderly individuals and adjust the display method of the analysis results based on the estimated emotions. For example, if an elderly individual is feeling stressed, the analysis unit can provide a simple and highly visible display method. For example, if an elderly individual is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if an elderly individual is feeling anxious, the analysis unit can provide a display method that provides a sense of security. By adjusting the display method of the analysis results according to the emotions of the elderly individual, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the emotional data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0094] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze historical data as needed. The analysis unit can also focus on analyzing data from a specific period. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection period into the AI ​​and have the AI ​​determine the analysis priority. This enables efficient data analysis by determining the analysis priority based on the data collection period.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, it may then analyze data with moderate relevance. It may also analyze data with low relevance last. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data relevance into the AI ​​and have the AI ​​adjust the order of analysis. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data.

[0096] The detection unit can estimate the emotions of elderly individuals and adjust the anomaly detection criteria based on the estimated emotions. For example, if an elderly individual is experiencing stress, the detection unit can tighten the stress-related anomaly detection criteria. For example, if an elderly individual is relaxed, the detection unit can loosen the relaxation-related anomaly detection criteria. Furthermore, if an elderly individual is experiencing anxiety, the detection unit can tighten the anxiety-related anomaly detection criteria. By adjusting the anomaly detection criteria according to the emotions of elderly individuals, the accuracy of anomaly detection is improved. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using AI or not. For example, the detection unit can input the emotional data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0097] The detection unit can improve the accuracy of anomaly detection based on the interrelationship between health information and behavioral data when detection occurs. For example, the detection unit can detect anomalies by considering the correlation between heart rate and activity level. For example, the detection unit can detect anomalies by considering the correlation between blood pressure and sleep data. The detection unit can also detect anomalies by considering the correlation between respiratory rate and environmental data. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the interrelationship between health information and behavioral data into the AI ​​and have the AI ​​perform the anomaly detection accuracy improvement. This improves the accuracy of anomaly detection by considering the interrelationship between health information and behavioral data.

[0098] The detection unit can detect anomalies based on the attribute information of elderly individuals at the time of detection. The detection unit can adjust the anomaly detection criteria according to the age of the elderly individual, for example. The detection unit can also adjust the anomaly detection criteria according to the gender of the elderly individual, for example. Furthermore, the detection unit can also adjust the anomaly detection criteria according to the medical history of the elderly individual. Some or all of the above processing in the detection unit may be performed using AI, or it may be performed without using AI. For example, the detection unit can input the attribute information of elderly individuals into the AI ​​and have the AI ​​perform the adjustment of the anomaly detection criteria. This improves the accuracy of anomaly detection by taking the attribute information of elderly individuals into consideration.

[0099] The detection unit can estimate the emotions of elderly individuals and adjust the order in which anomaly detection results are displayed based on the estimated emotions. For example, if an elderly individual is experiencing stress, the detection unit can prioritize displaying stress-related anomalies. For example, if an elderly individual is relaxed, the detection unit can postpone displaying relaxation-related anomalies. Furthermore, if an elderly individual is experiencing anxiety, the detection unit can prioritize displaying anxiety-related anomalies. This improves readability by adjusting the order in which anomaly detection results are displayed according to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using AI or not. For example, the detection unit can input the emotional data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0100] The detection unit can detect anomalies based on the geographical distribution of health information and behavioral data when detection occurs. For example, if an elderly person is at home, the detection unit can detect anomalies specific to the home environment. For example, if an elderly person is out, the detection unit can detect anomalies specific to the environment of the place they are visiting. Furthermore, if an elderly person is in a medical institution, the detection unit can detect anomalies specific to the environment of the medical institution. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the geographical distribution of health information and behavioral data into the AI ​​and have the AI ​​perform anomaly detection. This improves the accuracy of anomaly detection by considering the geographical distribution of health information and behavioral data.

[0101] The detection unit can improve the accuracy of anomaly detection by referring to relevant literature when detection occurs. For example, the detection unit can improve the anomaly detection algorithm by referring to the latest medical literature. For example, the detection unit can improve the anomaly detection algorithm by referring to past research data. The detection unit can also improve the anomaly detection algorithm by referring to relevant academic papers. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input relevant literature into the AI ​​and have the AI ​​perform the improvement of the anomaly detection algorithm. This improves the accuracy of anomaly detection by referring to relevant literature.

[0102] The notification unit can estimate the emotions of elderly individuals and adjust the notification method based on the estimated emotions. For example, if an elderly individual is feeling stressed, the notification unit can use a calm notification sound. If an elderly individual is relaxed, the notification unit can use a normal notification sound. The notification unit can also use a reassuring notification sound if an elderly individual is feeling anxious. By adjusting the notification method according to the emotions of the elderly individual, the effectiveness of the notification is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input the emotional data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0103] The notification unit can adjust the level of detail of the notification based on the severity of the anomaly. For example, the notification unit can provide detailed notifications for highly severe anomalies, and simplified notifications for less severe anomalies. It can also provide notifications with an appropriate level of detail for moderately severe anomalies. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input the severity of the anomaly into the AI ​​and have the AI ​​adjust the level of detail of the notification. This allows for more efficient notifications by adjusting the level of detail of the notification based on the severity of the anomaly.

[0104] The notification unit can apply different notification methods depending on the category of the anomaly when issuing a notification. For example, the notification unit can provide voice notifications for health-related anomalies, text notifications for behavior-related anomalies, and visual notifications for environmental anomalies. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the category of the anomaly into the AI ​​and have the AI ​​apply the notification method. This allows for appropriate notifications by applying the appropriate notification method according to the category of the anomaly.

[0105] The notification unit can estimate the emotions of elderly individuals and adjust the timing of notifications based on the estimated emotions. For example, if an elderly individual is feeling stressed, the notification unit can delay the notification to reduce stress. For example, if an elderly individual is relaxed, the notification unit can send a notification at the normal time. The notification unit can also send a notification quickly if an elderly individual is feeling anxious. By adjusting the timing of notifications according to the emotions of elderly individuals, the effectiveness of notifications is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the emotional data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0106] The notification unit can determine the priority of notifications based on when the anomaly occurred. For example, the notification unit may prioritize notifying of the most recent anomaly. For example, it may notify of past anomalies as needed. The notification unit can also focus on notifying of anomalies within a specific period. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the timing of the anomaly occurrence into the AI ​​and have the AI ​​determine the notification priority. This enables efficient notification by determining the notification priority based on when the anomaly occurred.

[0107] The notification unit can adjust the order of notifications based on the relevance of the anomalies. For example, the notification unit may prioritize notifying of anomalies with high relevance. For example, it may then notify of anomalies with moderate relevance. It may also notify of anomalies with low relevance last. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the relevance of the anomalies into the AI ​​and have the AI ​​perform the adjustment of the notification order. This allows for efficient notification by adjusting the order of notifications based on the relevance of the anomalies.

[0108] The prediction unit can estimate the emotions of elderly individuals and adjust the behavioral prediction algorithm based on the estimated emotions. For example, if an elderly individual is experiencing stress, the prediction unit can apply a stress-related behavioral prediction algorithm. For example, if an elderly individual is relaxed, the prediction unit can apply a relaxation-related behavioral prediction algorithm. Furthermore, if an elderly individual is experiencing anxiety, the prediction unit can apply an anxiety-related behavioral prediction algorithm. By adjusting the behavioral prediction algorithm according to the emotions of the elderly individual, the prediction accuracy is improved. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the prediction unit may be performed using AI or not. For example, the prediction unit can input the emotional data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0109] The prediction unit can improve the accuracy of its predictions by referring to past behavioral data during the prediction process. For example, the prediction unit can analyze the past behavioral patterns of elderly people to improve prediction accuracy. For example, the prediction unit can refer to the past health data of elderly people to improve prediction accuracy. The prediction unit can also refer to the past environmental data of elderly people to improve prediction accuracy. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input past behavioral data into the AI ​​and have the AI ​​perform the prediction accuracy improvement. This improves the accuracy of the prediction by referring to past behavioral data.

[0110] The prediction unit can apply different prediction methods to each category of health information and behavioral data during prediction. For example, the prediction unit can apply a health prediction method to health information data. For example, the prediction unit can apply a behavioral prediction method to behavioral data. The prediction unit can also apply an environmental prediction method to environmental data. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input the data categories into the AI ​​and have the AI ​​apply the prediction method. This improves prediction accuracy by applying different prediction methods to each category of health information and behavioral data.

[0111] The prediction unit can estimate the emotions of elderly individuals and adjust the display method of the prediction results based on the estimated emotions. For example, if an elderly individual is feeling stressed, the prediction unit can provide a simple and highly visible display method. For example, if an elderly individual is relaxed, the prediction unit can provide a display method that includes detailed information. Furthermore, if an elderly individual is feeling anxious, the prediction unit can provide a display method that provides a sense of security. By adjusting the display method of the prediction results according to the emotions of the elderly individual, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input the emotional data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0112] The prediction unit can determine the priority of predictions based on the timing of behavioral data collection. For example, the prediction unit may prioritize predictions based on the most recent behavioral data. For example, it may also predict predictions based on past behavioral data as needed. Furthermore, the prediction unit can focus on predicting behavioral data for a specific period. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input the timing of behavioral data collection into the AI ​​and have the AI ​​determine the priority of predictions. This enables efficient prediction by determining the priority of predictions based on the timing of behavioral data collection.

[0113] The prediction unit can adjust the order of predictions based on the relevance of behavioral data during the prediction process. For example, the prediction unit may prioritize predicting behavioral data with high relevance. For example, it may then predict behavioral data with moderate relevance. It may also predict behavioral data with low relevance last. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input the relevance of behavioral data into the AI ​​and have the AI ​​adjust the order of predictions. This allows for more efficient predictions by adjusting the order of predictions based on the relevance of behavioral data.

[0114] The optimization unit can estimate the emotions of elderly individuals and adjust the optimization algorithms for health management and care plans based on the estimated emotions. For example, if an elderly individual is experiencing stress, the optimization unit can apply an optimization algorithm that prioritizes stress reduction. For example, if an elderly individual is relaxed, the optimization unit can apply an optimization algorithm that maintains relaxation. Furthermore, if an elderly individual is experiencing anxiety, the optimization unit can apply an optimization algorithm that prioritizes anxiety reduction. By adjusting the optimization algorithm according to the emotions of elderly individuals, the accuracy of health management and care plans is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input the emotional data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0115] The optimization unit can improve the accuracy of optimization by referring to past health management data during the optimization process. For example, the optimization unit can analyze past health management data of elderly individuals to improve the accuracy of optimization. For example, the optimization unit can refer to past care plan data of elderly individuals to improve the accuracy of optimization. The optimization unit can also refer to past environmental data of elderly individuals to improve the accuracy of optimization. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input past health management data into the AI ​​and have the AI ​​perform the optimization accuracy improvement. This improves the accuracy of optimization by referring to past health management data.

[0116] The optimization unit can apply different optimization methods to each category of health information and care plan during optimization. For example, the optimization unit can apply a health management optimization method to health information data. For example, the optimization unit can apply a care plan optimization method to care plan data. The optimization unit can also apply an environment optimization method to environment data. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input the data categories into the AI ​​and have the AI ​​execute the application of the optimization method. This improves the accuracy of optimization by applying different optimization methods to each category of health information and care plan.

[0117] The optimization unit can estimate the emotions of elderly individuals and adjust the display method of the optimization results based on the estimated emotions. For example, if an elderly individual is feeling stressed, the optimization unit can provide a simple and highly visible display method. For example, if an elderly individual is relaxed, the optimization unit can provide a display method that includes detailed information. Furthermore, if an elderly individual is feeling anxious, the optimization unit can provide a display method that provides a sense of security. By adjusting the display method of the optimization results according to the emotions of the elderly individual, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not using AI. For example, the optimization unit can input the emotional data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0118] The optimization unit can determine optimization priorities based on the timing of health management data collection during the optimization process. For example, the optimization unit may prioritize optimizing the most recent health management data. For example, it may optimize past health management data as needed. The optimization unit can also focus on optimizing health management data for a specific period. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input the timing of health management data collection into the AI ​​and have the AI ​​determine the optimization priorities. This enables efficient optimization by determining optimization priorities based on the timing of health management data collection.

[0119] The optimization unit can adjust the optimization order based on the relevance of the health management data during the optimization process. For example, the optimization unit may prioritize optimizing health management data with high relevance. For example, it may then optimize health management data with moderate relevance. It may also optimize health management data with low relevance last. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input the relevance of the health management data into the AI ​​and have the AI ​​adjust the optimization order. This allows for efficient optimization by adjusting the optimization order based on the relevance of the health management data.

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

[0121] The data collection unit can adjust the frequency of data collection based on the season and weather when collecting health information and behavioral data. For example, in winter, the frequency of collecting heart rate and body temperature data can be increased because the health risks due to the cold are higher. Conversely, in summer, the frequency of collecting body temperature and fluid intake data can be increased because of the risk of heatstroke. Also, in rainy weather, people tend to go outside less, so the frequency of collecting indoor activity data can be increased. This makes it possible to optimize data collection according to the season and weather.

[0122] The analysis unit can evaluate the reliability of the collected data and exclude unreliable data when analyzing it. For example, it can detect abnormal values ​​caused by sensor malfunctions and exclude them from the analysis. It can also verify data consistency and exclude data with abnormal fluctuations. Furthermore, it can evaluate reliability based on the data collection environment and exclude data collected in noisy environments. This enables analysis based on highly reliable data.

[0123] The detection unit can set an anomaly priority based on the frequency of occurrence when detecting an anomaly. For example, frequently occurring anomalies can be given a high priority for a quick response. Conversely, rarely occurring anomalies can be given a low priority for a response as needed. It can also classify the type of anomaly based on its frequency of occurrence and optimize the response method. This enables efficient anomaly detection and response according to the frequency of occurrence.

[0124] The notification unit can customize the content of notifications based on the recipient's role when an abnormality is reported. For example, it can notify medical institutions of detailed health information to encourage prompt medical response. It can notify caregivers of an overview of the abnormality and how to respond, supporting appropriate care. It can also notify family members of a brief explanation of the abnormality and a reassuring message. This enables the provision of appropriate information tailored to the recipient of the notification.

[0125] The prediction unit can improve the accuracy of behavioral predictions based on the lifestyle habits of elderly individuals. For example, it can consider the behavioral patterns of elderly individuals with regular exercise habits and predict health risks during exercise. It can also predict post-meal health conditions based on meal times and content. Furthermore, it can predict health risks due to sleep deprivation based on sleep patterns. This enables highly accurate behavioral predictions tailored to the lifestyle habits of elderly individuals.

[0126] The data collection unit can estimate the emotions of elderly individuals and select the types of data to collect based on those estimated emotions. For example, if an elderly person is stressed, it can prioritize the collection of heart rate and blood pressure data. If an elderly person is relaxed, the data collection unit can prioritize the collection of sleep data and activity level data. Furthermore, if an elderly person is anxious, the data collection unit can prioritize the collection of respiratory data and body temperature data. This allows for the efficient collection of necessary data by selecting the types of data to collect according to the emotions of the elderly person.

[0127] The analysis unit can estimate the emotions of elderly individuals and adjust the data analysis algorithm based on the estimated emotions. For example, if an elderly person is experiencing stress, the analysis unit can apply an algorithm that prioritizes the analysis of stress-related data. If an elderly person is relaxed, the analysis unit can apply an algorithm that prioritizes the analysis of relaxation-related data. Furthermore, if an elderly person is experiencing anxiety, the analysis unit can apply an algorithm that prioritizes the analysis of anxiety-related data. By adjusting the data analysis algorithm according to the emotions of elderly individuals, the accuracy of the analysis is improved.

[0128] The detection unit can estimate the emotions of elderly individuals and adjust the anomaly detection criteria based on the estimated emotions. For example, if an elderly person is stressed, the stress-related anomaly detection criteria can be tightened. The detection unit can also loosen the relaxation-related anomaly detection criteria if an elderly person is relaxed. Furthermore, if an elderly person is anxious, the detection unit can tighten the anxiety-related anomaly detection criteria. By adjusting the anomaly detection criteria according to the emotions of elderly individuals, the accuracy of anomaly detection is improved.

[0129] The notification unit can estimate the emotions of elderly individuals and adjust the notification method based on those estimates. For example, if an elderly person is feeling stressed, a calm notification sound will be used. If an elderly person is relaxed, the notification unit can use a normal notification sound. Furthermore, if an elderly person is feeling anxious, the notification unit can use a reassuring notification sound. This improves the effectiveness of notifications by adjusting the notification method according to the elderly person's emotions.

[0130] The prediction unit can estimate the emotions of elderly individuals and adjust the behavioral prediction algorithm based on the estimated emotions. For example, if an elderly person is feeling stressed, a stress-related behavioral prediction algorithm can be applied. If an elderly person is relaxed, a relaxation-related behavioral prediction algorithm can be applied. Furthermore, if an elderly person is feeling anxious, an anxiety-related behavioral prediction algorithm can be applied. By adjusting the behavioral prediction algorithm according to the emotions of elderly individuals, the prediction accuracy can be improved.

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

[0132] Step 1: The data collection unit collects health information and behavioral data. The data collection unit can collect health information and behavioral data of elderly individuals in real time, for example, using chip-type sensors. The data collection unit can collect vital signs such as heart rate, blood pressure, body temperature, and respiratory rate, as well as behavioral data such as daily activity patterns, movement history, and meal records. Processing in the data collection unit may or may not be performed using AI. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using AI, analyze the data using machine learning models, and set criteria for detecting anomalies. The analysis unit can also analyze trends in health status based on the data analysis results. Processing in the analysis unit may be performed using AI, or it may be performed without AI. Step 3: The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit can detect anomalies based on data analyzed using AI, and can detect abnormal values ​​in vital signs or abnormal behavioral patterns. The detection unit can also set an anomaly priority according to the type and severity of the anomaly. Processing in the detection unit may be performed using AI, or it may be performed without AI. Step 4: The notification unit notifies the detection unit of the abnormality detected. The notification unit can notify caregivers and medical institutions of the abnormality and can use means such as email, SMS, or telephone to notify them. The notification unit can also adjust the method and timing of notification depending on the type and severity of the abnormality. Processing in the notification unit may be performed using AI, or it may be performed without AI.

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

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

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

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, notification unit, prediction unit, and optimization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects health information and behavioral data of the elderly in real time using the chip-type sensor of the smart device 14. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that detects abnormalities based on the analyzed data. The notification unit is implemented by the control unit 46A of the smart device 14 as a processing unit that notifies caregivers and medical institutions of the detected abnormalities. The prediction unit performs behavioral prediction and health analysis by the specific processing unit 290 of the data processing unit 12. The optimization unit optimizes health management and care plans by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

[0141] The microphone 238 receives voice 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.

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

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

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

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

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

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

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

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

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

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

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, notification unit, prediction unit, and optimization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects health information and behavioral data of the elderly in real time using the chip-type sensor of the smart glasses 214. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that detects abnormalities based on the analyzed data. The notification unit is implemented by the control unit 46A of the smart glasses 214 as a processing unit that notifies caregivers and medical institutions of the detected abnormalities. The prediction unit performs behavioral prediction and health analysis by the specific processing unit 290 of the data processing unit 12. The optimization unit optimizes health management and care plans by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

[0157] The microphone 238 receives voice 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.

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

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

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

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

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

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

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

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

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

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

[0168] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, notification unit, prediction unit, and optimization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects health information and behavioral data of the elderly in real time using the chip-type sensor of the headset terminal 314. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that detects abnormalities based on the analyzed data. The notification unit is implemented by the control unit 46A of the headset terminal 314 as a processing unit that notifies caregivers and medical institutions of the detected abnormalities. The prediction unit performs behavioral prediction and health analysis by the specific processing unit 290 of the data processing unit 12. The optimization unit optimizes health management and care plans by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

[0173] The microphone 238 receives voice 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.

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

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

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

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

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

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

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

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

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

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

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

[0185] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, notification unit, prediction unit, and optimization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects health information and behavioral data of the elderly in real time using the chip-type sensor of the robot 414. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that detects abnormalities based on the analyzed data. The notification unit is implemented by the control unit 46A of the robot 414 as a processing unit that notifies caregivers and medical institutions of the detected abnormalities. The prediction unit performs behavioral prediction and health analysis by the specific processing unit 290 of the data processing unit 12. The optimization unit optimizes health management and care plans by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

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

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

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

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

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

[0195] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0204] (Note 1) A collection unit that collects health information and behavioral data, 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 notification unit that notifies of an abnormality detected by the detection unit, A system characterized by the following features. (Note 2) It also features a prediction unit that performs AI-based behavioral prediction and health analysis. The system described in Appendix 1, characterized by the features described herein. (Note 3) The facility will also include an efficiency department that streamlines data-driven health management and care planning. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We collect health information and behavioral data in real time using chip-type sensors. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, The collected data is analyzed using AI. The system described in Appendix 1, characterized by the features described herein. (Note 6) The detection unit is Detect anomalies based on the analyzed data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned notification unit, The system notifies caregivers and medical institutions of any detected abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is This system estimates the emotions of older adults and adjusts the frequency of collecting health information and behavioral data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze past health data of elderly individuals to select the optimal sensor placement. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting health information and behavioral data, adjust the data based on environmental factors. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The system estimates the emotions of elderly individuals and selects the types of data to collect based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting health information and behavioral data, the geographical location of elderly individuals should be taken into consideration to prioritize the collection of highly relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting health information and behavioral 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 14) The aforementioned analysis unit, We estimate the emotions of elderly people and adjust the data analysis algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of health information and behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system estimates the emotions of elderly individuals and adjusts the display method of the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit is 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 21) The detection unit is When anomaly detection occurs, the accuracy of anomaly detection is improved based on the interrelationship between health information and behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit is When detection occurs, abnormalities are detected based on the attribute information of the elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 23) The detection unit is The system estimates the emotions of elderly individuals and adjusts the order in which anomaly detection results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The detection unit is When detection occurs, anomalies are detected based on the geographical distribution of health information and behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The detection unit is When detecting anomalies, improve the accuracy of anomaly detection based on relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, The system estimates the emotions of older adults and adjusts notification methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When a notification is sent, adjust the level of detail in the notification based on the severity of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, When a notification is sent, different notification methods will be applied depending on the category of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, The system estimates the emotions of elderly individuals and adjusts the timing of notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When a notification is sent, the notification priority is determined based on when the anomaly occurred. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending notifications, adjust the order of notifications based on the relevance of the anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 32) The prediction unit, The system estimates the emotions of older adults and adjusts behavioral prediction algorithms based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The prediction unit, When making predictions, historical behavioral data is referenced to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 34) The prediction unit, When making predictions, different prediction methods are applied to each category of health information and behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The prediction unit, The system estimates the emotions of elderly people and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The prediction unit, When making predictions, prioritize predictions based on when behavioral data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 37) The prediction unit, During prediction, the order of predictions is adjusted based on the relevance of behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 38) The optimization unit, This system estimates the emotions of elderly individuals and adjusts algorithms to optimize health management and care plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The optimization unit, During optimization, past health management data is referenced to improve the accuracy of the optimization. The system described in Appendix 1, characterized by the features described herein. (Note 40) The optimization unit, During optimization, different optimization methods are applied to each category of health information and care plan. The system described in Appendix 1, characterized by the features described herein. (Note 41) The optimization unit, It estimates the emotions of elderly people and adjusts the display method of the optimization results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The optimization unit, During optimization, the optimization priority is determined based on when health management data is collected. The system described in Appendix 1, characterized by the features described herein. (Note 43) The optimization unit, During optimization, the optimization order is adjusted based on the relevance of health management data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A collection unit that collects health information and behavioral data, 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 notification unit that notifies of an abnormality detected by the detection unit, A system characterized by the following features.

2. It also includes a prediction unit that performs AI-based behavioral prediction and health analysis. The system according to feature 1.

3. The facility will also include an efficiency department that streamlines data-driven health management and care planning. The system according to feature 1.

4. The aforementioned collection unit is We collect health information and behavioral data in real time using chip-type sensors. The system according to feature 1.

5. The aforementioned analysis unit, The collected data is analyzed using AI. The system according to feature 1.

6. The detection unit is Detect anomalies based on the analyzed data. The system according to feature 1.

7. The aforementioned notification unit, The system notifies caregivers and medical institutions of any detected abnormalities. The system according to feature 1.

8. The aforementioned collection unit is This system estimates the emotions of older adults and adjusts the frequency of collecting health information and behavioral data based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze past health data of elderly individuals to select the optimal sensor placement. The system according to feature 1.

10. The aforementioned collection unit is When collecting health information and behavioral data, adjust the data based on environmental factors. The system according to feature 1.

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