system

Abstract

The system according to this embodiment aims to analyze the vital signs of elderly people and propose and implement an appropriate care plan. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a notification unit. The collection unit collects vital signs of elderly people. The analysis unit analyzes the vital signs collected by the collection unit. The proposal unit proposes a care plan based on the data analyzed by the analysis unit. The notification unit provides notification in case of an abnormality based on the care plan proposed by the proposal unit.

Description

Technical Field

[0006] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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, the vital signs of the elderly have not been sufficiently collected and analyzed efficiently, and an appropriate care plan has not been sufficiently proposed, leaving room for improvement.

[0005] The system according to the embodiment aims to analyze the vital signs of the elderly and propose and implement an appropriate care plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a notification unit. The data collection unit collects vital signs of elderly individuals. The analysis unit analyzes the vital signs collected by the data collection unit. The proposal unit proposes a care plan based on the data analyzed by the analysis unit. The notification unit provides notifications in case of abnormalities based on the care plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the vital signs of elderly people and propose and implement an appropriate care plan. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI elderly care assistant according to an embodiment of the present invention is an application that utilizes generative AI to support the lives of the elderly. This AI elderly care assistant collects and analyzes medical data and daily behavioral data in real time to provide health management and lifestyle support. This promotes independent living for the elderly and reduces the burden of care. For example, the AI ​​elderly care assistant works in conjunction with wearable devices and smart home devices to collect vital signs such as the elderly person's heart rate, blood pressure, body temperature, and activity level in real time. The generative AI analyzes this data and detects abnormal values ​​and changes in trends. If pre-set criteria are exceeded, an automatic alert is sent to the caregiver or family. Next, based on past health data, the generative AI automatically generates an individualized care plan for the elderly person. This includes daily meal suggestions, medication reminders, recommended exercise programs, and rest time management. Furthermore, the AI ​​assistant provides functions that can be operated by voice commands and accepts simple questions and tasks. In addition, in emergencies, the generative AI automatically contacts family members, caregivers, and emergency services registered in the contact list. Location information is used to support the rapid dispatch of emergency response teams. For example, if a fall detection sensor detects a fall, a notification is sent immediately, and necessary actions are taken quickly. In this way, the AI ​​elderly care assistant utilizes generative AI to collect and analyze health data of the elderly, proposes individualized care plans, and responds to emergencies, thereby supporting the independent living of the elderly and reducing the burden of care. As a result, the AI ​​elderly care assistant can support the independent living of the elderly and reduce the burden of care.

[0029] The AI ​​elderly care assistant according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, and a notification unit. The data collection unit collects vital signs of the elderly. The data collection unit can collect vital signs such as heart rate, blood pressure, body temperature, and activity level. The data collection unit can monitor heart rate in real time using a wearable device, for example. The data collection unit can also measure blood pressure using a smart home device. Furthermore, the data collection unit can measure body temperature using a body temperature sensor. The analysis unit analyzes the vital signs collected by the data collection unit. The analysis unit can perform statistical analysis of the collected data, for example. The analysis unit can also analyze the data using machine learning algorithms. Furthermore, the analysis unit can detect outliers and changes in trends. The suggestion unit proposes a care plan based on the data analyzed by the analysis unit. The suggestion unit can, for example, suggest a daily meal plan. The suggestion unit can also provide medication reminders. Furthermore, the suggestion unit can also suggest recommended exercise programs. The notification unit provides notifications in case of abnormalities based on the care plan proposed by the suggestion unit. The notification unit can, for example, send an automatic alert when an abnormal value is detected. It can also send an alert when a change in trend is detected. Furthermore, it can automatically contact someone in an emergency. As a result, the AI ​​elderly care assistant according to this embodiment can support the independent living of the elderly and reduce the burden of care by collecting and analyzing the vital signs of the elderly, proposing a care plan, and notifying them of abnormalities.

[0030] The data collection unit collects vital signs from elderly individuals. For example, it can collect vital signs such as heart rate, blood pressure, body temperature, and activity level. Specifically, the unit monitors heart rate in real time using a wearable device. The wearable device has a built-in heart rate sensor, allowing for continuous measurement of the elderly individual's heart rate. Furthermore, the unit can measure blood pressure using a smart home device. This smart home device is, for example, a blood pressure monitor worn on the arm, which periodically measures blood pressure and transmits the data to the data collection unit. Body temperature can also be measured using a body temperature sensor, which accurately measures body temperature through skin contact. These devices transmit data to the data collection unit via Bluetooth® or Wi-Fi, which centrally manages this data. Additionally, the data collection unit uses devices with built-in accelerometers and gyroscopes to monitor the elderly individual's activity level. This allows for accurate tracking of walking distance and exercise levels. The data collection unit integrates data from these diverse devices to comprehensively monitor the health status of the elderly individual. This allows the data collection unit to collect vital signs from elderly individuals in real time and respond quickly if any abnormalities occur.

[0031] The analysis unit analyzes vital signs collected by the data collection unit. For example, the analysis unit can perform statistical analysis of the collected data. Specifically, it statistically analyzes collected heart rate, blood pressure, body temperature, and activity level data to evaluate whether they fall within the normal range. The analysis unit can also analyze data using machine learning algorithms. These algorithms learn from past data and build models to detect outliers and changes in trends. For example, they can detect rapid fluctuations in heart rate or abnormal increases in blood pressure. Furthermore, the analysis unit can detect outliers and changes in trends. If an outlier is detected, the analysis unit identifies the cause and proposes appropriate countermeasures. For example, if the heart rate is abnormally high, the analysis unit can identify the cause based on past data and propose necessary measures. The analysis unit provides these analysis results in real time, enabling continuous monitoring of the health status of elderly individuals. Furthermore, the analysis unit can perform trend analysis based on long-term data to predict changes in the health status of elderly individuals. This allows the analysis unit to comprehensively analyze the health status of elderly individuals and respond quickly if an abnormality occurs.

[0032] The proposal department proposes care plans based on data analyzed by the analysis department. For example, the proposal department can suggest daily meal plans. Specifically, it proposes appropriate meal menus considering the health condition and nutritional balance of the elderly person. For example, if the heart rate or blood pressure is high, it can suggest a low-salt diet. The proposal department can also provide medication reminders. It reminds the elderly person of the time to take their regularly prescribed medications, encouraging them to take them without fail. Furthermore, the proposal department can suggest recommended exercise programs. It proposes exercise programs tailored to the elderly person's physical strength and health condition, promoting daily exercise. For example, it can suggest exercises within a reasonable range, such as light stretching or walking. The proposal department notifies the elderly person of these suggestions on their smartphone or tablet, supporting them in putting them into practice in their daily life. In addition, the proposal department can collect feedback from the elderly person and continuously improve the accuracy of its suggestions. As a result, the proposal department can propose appropriate care plans tailored to the health condition of the elderly person and support their independent living.

[0033] The notification unit sends notifications in the event of an abnormality based on the care plan proposed by the proposal unit. For example, the notification unit can send an automatic alert when an abnormal value is detected. Specifically, if heart rate or blood pressure shows abnormal values, the notification unit will send an alert to the elderly person's smartphone or tablet to draw their attention. The notification unit can also send an alert when a change in trend is detected. For example, if body temperature is gradually rising, the notification unit will detect this change and send an alert to encourage early action. Furthermore, the notification unit can automatically contact emergency contacts. For example, if heart rate drops sharply, the notification unit will automatically contact emergency contacts to encourage a quick response. The notification unit can also send and share these notifications with the elderly person's family and caregivers. This allows the notification unit to monitor the elderly person's health status in real time and respond quickly when an abnormality occurs. In addition, the notification unit saves a history of notification content, which can be reviewed later. This allows for a review of past abnormal values ​​and trend changes, which can then be reflected in future care plans. By constantly monitoring the elderly person's health status and responding quickly when an abnormality occurs, the notification unit can ensure the safety and peace of mind of the elderly person.

[0034] The voice command receiving unit can receive voice commands. For example, if a user says, "Tell me my schedule for today," the voice command receiving unit can receive that voice command. It can also receive if a user says, "Tell me when to take my medicine," and it can also receive if a user says, "Suggest some exercises." This allows the user to easily operate the system by receiving voice commands. Some or all of the above processing in the voice command receiving unit may be performed using AI, for example, or without AI. For example, the voice command receiving unit can input the user's voice data into a generating AI and have the generating AI perform the analysis of the voice command.

[0035] The emergency response unit can automatically make contact in an emergency. For example, the emergency response unit can immediately send a notification when a fall detection sensor detects a fall. The emergency response unit can also automatically make contact if the heart rate increases rapidly. Furthermore, the emergency response unit can automatically make contact if blood pressure shows an abnormal value. This enables a rapid response by automatically making contact in an emergency. Some or all of the above processing in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input data from the fall detection sensor into a generating AI and have the generating AI make a decision on whether to make an emergency contact.

[0036] The data collection unit can collect vital signs in conjunction with wearable devices and smart home devices. For example, the data collection unit can monitor heart rate in real time using a wearable device. It can also measure blood pressure using a smart home device. Furthermore, it can measure body temperature using a body temperature sensor. This makes the collection of vital signs more efficient by coordinating with wearable devices and smart home devices. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from a wearable device into a generating AI and have the generating AI perform vital sign analysis.

[0037] The proposal unit can automatically generate individualized care plans based on past health data. For example, the proposal unit can propose a meal plan based on health data from the past year. It can also propose an exercise plan based on past health data. Furthermore, it can propose a medical plan based on past health data. By automatically generating individualized care plans based on past health data, it is possible to provide more appropriate care plans. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input past health data into a generation AI and have the generation AI execute the generation of care plans.

[0038] The notification unit can automatically send alerts when it detects abnormal values ​​or changes in trends. For example, the notification unit can send an automatic alert when the heart rate exceeds 100. It can also send an alert when blood pressure shows an abnormal value. Furthermore, it can send an alert when body temperature is abnormally high. This enables a quick response by automatically sending alerts when abnormal values ​​or changes in trends are detected. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on detected abnormal values ​​into a generating AI and have the generating AI execute the sending of alerts.

[0039] The data collection unit can analyze past health data of elderly individuals and select the optimal collection timing. For example, the collection unit can detect from the elderly individual's past health data that abnormalities are more likely to occur during specific time periods and concentrate data collection during those times. The collection unit can also collect data during periods of high activity during the day. Furthermore, the collection unit can collect data during periods of rest at night. In this way, the optimal collection timing can be selected by analyzing past health data. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input past health data into a generating AI and have the generating AI select the optimal collection timing.

[0040] The data collection unit can filter vital signs based on the user's current activity level and environment. For example, if the user is exercising, the unit can filter out temporary fluctuations caused by exercise. If the user is resting, the unit can prioritize collecting resting data. Furthermore, if the user is outdoors, the unit can filter out environmental noise. This allows for more accurate data collection by filtering based on the user's current activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input user activity data into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location when collecting vital signs. For example, if the user is at high altitude, the data collection unit may prioritize the collection of oxygen saturation. It can also prioritize the collection of heart rate and blood pressure if the user is in an urban area. Furthermore, if the user is at home, the data collection unit may prioritize the collection of body temperature and activity level. This allows for more appropriate data collection by prioritizing the collection of highly relevant data based on geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI collect highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data when collecting vital signs. For example, if the user posts on social media indicating stress, the data collection unit may prioritize collecting heart rate and blood pressure. Conversely, if the user posts on social media indicating relaxation, the data collection unit may prioritize collecting body temperature and activity level. Furthermore, if the user posts on social media indicating anxiety, the data collection unit may collect multiple vital signs simultaneously. This allows for the collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of vital signs during the analysis. For example, the analysis unit will analyze important vital signs such as heart rate and blood pressure in detail. The analysis unit can also analyze relatively stable vital signs such as body temperature and activity level in a simplified manner. Furthermore, if an abnormal value is detected, the analysis unit can also analyze that vital sign in detail. In this way, by adjusting the level of detail of the analysis based on the importance of vital signs, more important data can be analyzed in detail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vital sign importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of vital signs during analysis. For example, the analysis unit can apply a circulatory system analysis algorithm to heart rate and blood pressure. It can also apply a thermoregulatory function analysis algorithm to body temperature. Furthermore, it can apply an exercise function analysis algorithm to activity level. By applying different analysis algorithms depending on the category of vital signs, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vital sign category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the timing of vital sign collection during the analysis. For example, the analysis unit may prioritize the analysis of recently collected vital signs. It can also prioritize the analysis of vital signs in which abnormal values ​​have been detected. Furthermore, the analysis unit may prioritize the analysis of vital signs collected periodically. By determining the priority of analysis based on the timing of vital sign collection, more important data can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vital sign collection timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0046] The analysis unit can adjust the order of analysis based on the relationships between vital signs during the analysis. For example, the analysis unit may prioritize the relationship between heart rate and blood pressure. It can also prioritize the relationship between body temperature and activity level. Furthermore, it can prioritize the relationship between vital signs in which abnormal values ​​have been detected. By adjusting the order of analysis based on the relationships between vital signs, a more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vital sign relationship data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The proposal unit can adjust the level of detail in its care plan proposals based on the importance of vital signs. For example, the proposal unit can propose a detailed care plan based on important vital signs such as heart rate and blood pressure. It can also propose a simplified care plan based on relatively stable vital signs such as body temperature and activity level. Furthermore, if an abnormal value is detected, the proposal unit can propose a detailed care plan based on that vital sign. This allows for the provision of care plans based on more important data by adjusting the level of detail in the proposals based on the importance of vital signs. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input vital sign importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposals.

[0048] The suggestion unit can apply different suggestion algorithms depending on the category of vital signs when proposing a care plan. For example, the suggestion unit can propose a cardiovascular care plan based on heart rate and blood pressure. It can also propose a care plan for thermoregulation based on body temperature. Furthermore, it can propose a care plan for exercise function based on activity level. By applying different suggestion algorithms depending on the category of vital signs, a more appropriate care plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input vital sign category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0049] The proposal unit can determine the priority of care plan proposals based on the timing of vital sign collection. For example, the proposal unit may prioritize care plans based on recently collected vital signs. It can also prioritize care plans based on vital signs in which abnormal values ​​have been detected. Furthermore, it can prioritize care plans based on vital signs collected regularly. By prioritizing proposals based on the timing of vital sign collection, it is possible to provide care plans based on more important data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input vital sign collection timing data into a generating AI and have the generating AI determine the priority of proposals.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of vital signs when proposing a care plan. For example, the suggestion unit can propose a care plan that emphasizes the relationship between heart rate and blood pressure. It can also propose a care plan that emphasizes the relationship between body temperature and activity level. Furthermore, it can propose a care plan that emphasizes the relationship between vital signs in which abnormal values ​​have been detected. By adjusting the order of suggestions based on the relevance of vital signs, a more appropriate care plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input vital sign relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0051] The notification unit can adjust the level of detail of notifications based on the importance of vital signs. For example, the notification unit can provide detailed notifications based on important vital signs such as heart rate and blood pressure. It can also provide simplified notifications based on relatively stable vital signs such as body temperature and activity level. Furthermore, if an abnormal value is detected, the notification unit can provide detailed notifications based on that vital sign. By adjusting the level of detail of notifications based on the importance of vital signs, it becomes possible to provide notifications based on more important data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input vital sign importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the notifications.

[0052] The notification unit can apply different notification algorithms depending on the category of vital signs when a notification is sent. For example, the notification unit can apply a circulatory system notification algorithm to heart rate and blood pressure. It can also apply a thermoregulatory function notification algorithm to body temperature. Furthermore, it can apply an exercise function notification algorithm to activity level. By applying different notification algorithms depending on the category of vital signs, more appropriate notifications can be made. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input vital sign category data into a generating AI and have the generating AI execute the application of the notification algorithm.

[0053] The notification unit can adjust the order of notifications based on when vital signs were collected. For example, the notification unit can prioritize notifications based on recently collected vital signs. It can also prioritize notifications based on vital signs in which abnormal values ​​have been detected. Furthermore, it can prioritize notifications based on vital signs collected periodically. By adjusting the order of notifications based on when vital signs were collected, it becomes possible to provide notifications based on more important data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input vital sign collection timing data into a generating AI and have the generating AI perform the adjustment of the notification order.

[0054] The notification unit can adjust the content of notifications based on the relevance of vital signs. For example, the notification unit may prioritize the relevance of heart rate and blood pressure when making notifications. It may also prioritize the relevance of body temperature and activity level when making notifications. Furthermore, the notification unit may prioritize the relevance of vital signs in which abnormal values ​​have been detected when making notifications. By adjusting the content of notifications based on the relevance of vital signs, more appropriate notifications can be made. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input vital sign relevance data into a generating AI and have the generating AI perform the adjustment of the notification content.

[0055] The voice command receiving unit can select the optimal receiving method by referring to the user's past voice command history when receiving a voice command. For example, the voice command receiving unit can prioritize receiving voice commands that the user has frequently used in the past. The voice command receiving unit can also predict and receive voice commands to be used during specific time periods based on the user's past voice command history. Furthermore, the voice command receiving unit can analyze the user's past voice command history and suggest the optimal voice command. This makes it possible to receive the optimal voice command by referring to the past voice command history. Some or all of the above processing in the voice command receiving unit may be performed using AI, for example, or without AI. For example, the voice command receiving unit can input the user's past voice command history data into a generating AI and have the generating AI select the optimal receiving method.

[0056] The voice command receiving unit can select the optimal receiving method based on the user's device information when it receives a voice command. For example, if the user is using a smartphone, the voice command receiving unit will receive a voice command optimized for the smartphone. It can also receive a voice command optimized for the tablet if the user is using a tablet. Furthermore, if the user is using a smartwatch, the voice command receiving unit can receive a voice command optimized for the smartwatch. This enables the receiving of the optimal voice command based on device information. Some or all of the above processing in the voice command receiving unit may be performed using AI, for example, or without AI. For example, the voice command receiving unit can input the user's device information into a generating AI and have the generating AI select the optimal receiving method.

[0057] The emergency response unit can select the optimal response method by referring to the user's past emergency response history during an emergency. For example, the emergency response unit may prioritize selecting emergency response methods that the user has used in the past. The emergency response unit can also predict and select an emergency response method to be used during a specific time period based on the user's past emergency response history. Furthermore, the emergency response unit can analyze the user's past emergency response history and propose the optimal emergency response method. This makes it possible to provide the best possible emergency response by referring to past emergency response history. Some or all of the above processing in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the user's past emergency response history data into a generating AI and have the generating AI select the optimal response method.

[0058] The emergency response unit can select the optimal response method based on the user's geographical location information during an emergency. For example, if the user is at home, the emergency response unit will perform an emergency response optimized for the user's home. If the user is out, the emergency response unit can also perform an emergency response optimized for the user's location. Furthermore, if the user is at a specific facility, the emergency response unit can perform an emergency response optimized for that facility. This enables the optimal emergency response based on geographical location information. Some or all of the above processing in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal response method.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The AI ​​elderly care assistant can predict future health risks and suggest preventive measures based on the user's past health data. For example, if past data indicates a high risk of heart disease, it can suggest a heart-healthy diet and exercise plan. Similarly, if past data indicates a high risk of diabetes, it can suggest a diet and exercise plan for blood sugar management. Furthermore, if past data indicates a high risk of osteoporosis, it can suggest a diet and exercise plan to increase bone density. This allows for more appropriate health management by predicting future health risks and suggesting preventive measures based on past health data. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input past health data into a generating AI and have the generating AI execute the suggestion of preventive measures.

[0061] The AI ​​elderly care assistant can propose care plans that take into account region-specific health risks based on the user's geographical location. For example, if the user lives in a high-altitude area, it can propose a care plan that emphasizes oxygen saturation management. If the user lives in an urban area, it can propose a care plan that includes measures against air pollution. Furthermore, if the user lives in a cold region, it can propose a care plan that includes measures against the cold. By proposing care plans that take into account region-specific health risks based on geographical location information, more appropriate care can be provided. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input geographical location information into a generating AI and have the generating AI execute the care plan proposal.

[0062] The AI ​​elderly care assistant can analyze a user's social media activity and propose a care plan based on the user's interests. For example, if a user shows interest in cooking on social media, it can suggest healthy recipes. If a user shows interest in exercise on social media, it can also suggest an exercise plan. Furthermore, if a user shows interest in travel on social media, it can suggest a health management plan for travel. In this way, by analyzing social media activity, it can propose a care plan based on the user's interests. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input social media activity data into a generating AI and have the generating AI execute the care plan proposal.

[0063] The AI ​​elderly care assistant can propose the most suitable care plan based on the user's device usage history. For example, if the user frequently uses a smartphone, it can propose a care plan that can be performed on a smartphone. Similarly, if the user frequently uses a tablet, it can propose a care plan that can be performed on a tablet. Furthermore, if the user frequently uses a smartwatch, it can propose a care plan that can be performed on a smartwatch. This allows for more appropriate care to be provided by proposing the most suitable care plan based on device usage history. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input device usage history data into a generating AI and have the generating AI execute the care plan proposal.

[0064] The AI ​​elderly care assistant can suggest the most appropriate emergency response method based on the user's past emergency response history. For example, for a user with a history of frequent falls, it can suggest an emergency response method that includes fall prevention measures. Similarly, for a user with a history of frequent heart attacks, it can suggest an emergency response method that includes measures for dealing with heart attacks. Furthermore, for a user with a history of frequent sudden increases in blood pressure, it can suggest an emergency response method for blood pressure management. This allows for more appropriate emergency responses by suggesting the most suitable method based on past emergency response history. Some or all of the above processing in the emergency response unit may be performed using AI, or without AI. For example, the emergency response unit can input past emergency response history data into a generating AI and have the generating AI propose emergency response methods.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The data collection unit collects vital signs from elderly individuals. The data collection unit can collect vital signs such as heart rate, blood pressure, body temperature, and activity level. For example, it can monitor heart rate in real time using a wearable device, measure blood pressure using a smart home device, and measure body temperature using a body temperature sensor. Step 2: The analysis unit analyzes the vital signs collected by the collection unit. The analysis unit can perform statistical analysis on the collected data, use machine learning algorithms to analyze the data, and detect outliers and changes in trends. Step 3: The proposal unit proposes a care plan based on the data analyzed by the analysis unit. The proposal unit can suggest daily meal plans, medication reminders, and recommended exercise programs. Step 4: The notification unit will send notifications in case of abnormalities based on the care plan proposed by the proposal unit. The notification unit can automatically send alerts when abnormal values ​​are detected or when changes in trends are detected, and can automatically contact people in case of emergencies.

[0067] (Example of form 2) An AI elderly care assistant according to an embodiment of the present invention is an application that utilizes generative AI to support the lives of the elderly. This AI elderly care assistant collects and analyzes medical data and daily behavioral data in real time to provide health management and lifestyle support. This promotes independent living for the elderly and reduces the burden of care. For example, the AI ​​elderly care assistant works in conjunction with wearable devices and smart home devices to collect vital signs such as the elderly person's heart rate, blood pressure, body temperature, and activity level in real time. The generative AI analyzes this data and detects abnormal values ​​and changes in trends. If pre-set criteria are exceeded, an automatic alert is sent to the caregiver or family. Next, based on past health data, the generative AI automatically generates an individualized care plan for the elderly person. This includes daily meal suggestions, medication reminders, recommended exercise programs, and rest time management. Furthermore, the AI ​​assistant provides functions that can be operated by voice commands and accepts simple questions and tasks. In addition, in emergencies, the generative AI automatically contacts family members, caregivers, and emergency services registered in the contact list. Location information is used to support the rapid dispatch of emergency response teams. For example, if a fall detection sensor detects a fall, a notification is sent immediately, and necessary actions are taken quickly. In this way, the AI ​​elderly care assistant utilizes generative AI to collect and analyze health data of the elderly, proposes individualized care plans, and responds to emergencies, thereby supporting the independent living of the elderly and reducing the burden of care. As a result, the AI ​​elderly care assistant can support the independent living of the elderly and reduce the burden of care.

[0068] The AI ​​elderly care assistant according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, and a notification unit. The data collection unit collects vital signs of the elderly. The data collection unit can collect vital signs such as heart rate, blood pressure, body temperature, and activity level. The data collection unit can monitor heart rate in real time using a wearable device, for example. The data collection unit can also measure blood pressure using a smart home device. Furthermore, the data collection unit can measure body temperature using a body temperature sensor. The analysis unit analyzes the vital signs collected by the data collection unit. The analysis unit can perform statistical analysis of the collected data, for example. The analysis unit can also analyze the data using machine learning algorithms. Furthermore, the analysis unit can detect outliers and changes in trends. The suggestion unit proposes a care plan based on the data analyzed by the analysis unit. The suggestion unit can, for example, suggest a daily meal plan. The suggestion unit can also provide medication reminders. Furthermore, the suggestion unit can also suggest recommended exercise programs. The notification unit provides notifications in case of abnormalities based on the care plan proposed by the suggestion unit. The notification unit can, for example, send an automatic alert when an abnormal value is detected. It can also send an alert when a change in trend is detected. Furthermore, it can automatically contact someone in an emergency. As a result, the AI ​​elderly care assistant according to this embodiment can support the independent living of the elderly and reduce the burden of care by collecting and analyzing the vital signs of the elderly, proposing a care plan, and notifying them of abnormalities.

[0069] The data collection unit collects vital signs from elderly individuals. For example, it can collect vital signs such as heart rate, blood pressure, body temperature, and activity level. Specifically, the unit monitors heart rate in real time using a wearable device. The wearable device has a built-in heart rate sensor, allowing for continuous measurement of the elderly individual's heart rate. Furthermore, the unit can measure blood pressure using a smart home device. This smart home device is, for example, an arm-worn blood pressure monitor that periodically measures blood pressure and transmits the data to the data collection unit. Body temperature can also be measured using a body temperature sensor, which accurately measures temperature through skin contact. These devices transmit data to the data collection unit via Bluetooth or Wi-Fi, which centrally manages this data. Additionally, the data collection unit uses devices with built-in accelerometers and gyroscopes to monitor the elderly individual's activity level. This allows for accurate tracking of walking distance and exercise levels. The data collection unit integrates data from these diverse devices to comprehensively monitor the health status of the elderly individual. This allows the data collection unit to collect vital signs from elderly individuals in real time and respond quickly if any abnormalities occur.

[0070] The analysis unit analyzes vital signs collected by the data collection unit. For example, the analysis unit can perform statistical analysis of the collected data. Specifically, it statistically analyzes collected heart rate, blood pressure, body temperature, and activity level data to evaluate whether they fall within the normal range. The analysis unit can also analyze data using machine learning algorithms. These algorithms learn from past data and build models to detect outliers and changes in trends. For example, they can detect rapid fluctuations in heart rate or abnormal increases in blood pressure. Furthermore, the analysis unit can detect outliers and changes in trends. If an outlier is detected, the analysis unit identifies the cause and proposes appropriate countermeasures. For example, if the heart rate is abnormally high, the analysis unit can identify the cause based on past data and propose necessary measures. The analysis unit provides these analysis results in real time, enabling continuous monitoring of the health status of elderly individuals. Furthermore, the analysis unit can perform trend analysis based on long-term data to predict changes in the health status of elderly individuals. This allows the analysis unit to comprehensively analyze the health status of elderly individuals and respond quickly if an abnormality occurs.

[0071] The proposal department proposes care plans based on data analyzed by the analysis department. For example, the proposal department can suggest daily meal plans. Specifically, it proposes appropriate meal menus considering the health condition and nutritional balance of the elderly person. For example, if the heart rate or blood pressure is high, it can suggest a low-salt diet. The proposal department can also provide medication reminders. It reminds the elderly person of the time to take their regularly prescribed medications, encouraging them to take them without fail. Furthermore, the proposal department can suggest recommended exercise programs. It proposes exercise programs tailored to the elderly person's physical strength and health condition, promoting daily exercise. For example, it can suggest exercises within a reasonable range, such as light stretching or walking. The proposal department notifies the elderly person of these suggestions on their smartphone or tablet, supporting them in putting them into practice in their daily life. In addition, the proposal department can collect feedback from the elderly person and continuously improve the accuracy of its suggestions. As a result, the proposal department can propose appropriate care plans tailored to the health condition of the elderly person and support their independent living.

[0072] The notification unit sends notifications in the event of an abnormality based on the care plan proposed by the proposal unit. For example, the notification unit can send an automatic alert when an abnormal value is detected. Specifically, if heart rate or blood pressure shows abnormal values, the notification unit will send an alert to the elderly person's smartphone or tablet to draw their attention. The notification unit can also send an alert when a change in trend is detected. For example, if body temperature is gradually rising, the notification unit will detect this change and send an alert to encourage early action. Furthermore, the notification unit can automatically contact emergency contacts. For example, if heart rate drops sharply, the notification unit will automatically contact emergency contacts to encourage a quick response. The notification unit can also send and share these notifications with the elderly person's family and caregivers. This allows the notification unit to monitor the elderly person's health status in real time and respond quickly when an abnormality occurs. In addition, the notification unit saves a history of notification content, which can be reviewed later. This allows for a review of past abnormal values ​​and trend changes, which can then be reflected in future care plans. By constantly monitoring the elderly person's health status and responding quickly when an abnormality occurs, the notification unit can ensure the safety and peace of mind of the elderly person.

[0073] The voice command receiving unit can receive voice commands. For example, if a user says, "Tell me my schedule for today," the voice command receiving unit can receive that voice command. It can also receive if a user says, "Tell me when to take my medicine," and it can also receive if a user says, "Suggest some exercises." This allows the user to easily operate the system by receiving voice commands. Some or all of the above processing in the voice command receiving unit may be performed using AI, for example, or without AI. For example, the voice command receiving unit can input the user's voice data into a generating AI and have the generating AI perform the analysis of the voice command.

[0074] The emergency response unit can automatically make contact in an emergency. For example, the emergency response unit can immediately send a notification when a fall detection sensor detects a fall. The emergency response unit can also automatically make contact if the heart rate increases rapidly. Furthermore, the emergency response unit can automatically make contact if blood pressure shows an abnormal value. This enables a rapid response by automatically making contact in an emergency. Some or all of the above processing in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input data from the fall detection sensor into a generating AI and have the generating AI make a decision on whether to make an emergency contact.

[0075] The data collection unit can collect vital signs in conjunction with wearable devices and smart home devices. For example, the data collection unit can monitor heart rate in real time using a wearable device. It can also measure blood pressure using a smart home device. Furthermore, it can measure body temperature using a body temperature sensor. This makes the collection of vital signs more efficient by coordinating with wearable devices and smart home devices. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from a wearable device into a generating AI and have the generating AI perform vital sign analysis.

[0076] The proposal unit can automatically generate individualized care plans based on past health data. For example, the proposal unit can propose a meal plan based on health data from the past year. It can also propose an exercise plan based on past health data. Furthermore, it can propose a medical plan based on past health data. By automatically generating individualized care plans based on past health data, it is possible to provide more appropriate care plans. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input past health data into a generation AI and have the generation AI execute the generation of care plans.

[0077] The notification unit can automatically send alerts when it detects abnormal values ​​or changes in trends. For example, the notification unit can send an automatic alert when the heart rate exceeds 100. It can also send an alert when blood pressure shows an abnormal value. Furthermore, it can send an alert when body temperature is abnormally high. This enables a quick response by automatically sending alerts when abnormal values ​​or changes in trends are detected. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on detected abnormal values ​​into a generating AI and have the generating AI execute the sending of alerts.

[0078] The data collection unit can estimate the user's emotions and adjust the frequency of vital sign data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the collection frequency to collect more detailed data. Conversely, if the user is relaxed, the data collection unit can decrease the collection frequency to reduce the burden. Furthermore, if the user is anxious, the data collection unit can appropriately adjust the collection frequency to provide a sense of security. This allows for more appropriate data collection by adjusting the collection frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the collection frequency.

[0079] The data collection unit can analyze past health data of elderly individuals and select the optimal collection timing. For example, the collection unit can detect from the elderly individual's past health data that abnormalities are more likely to occur during specific time periods and concentrate data collection during those times. The collection unit can also collect data during periods of high activity during the day. Furthermore, the collection unit can collect data during periods of rest at night. In this way, the optimal collection timing can be selected by analyzing past health data. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input past health data into a generating AI and have the generating AI select the optimal collection timing.

[0080] The data collection unit can filter vital signs based on the user's current activity level and environment. For example, if the user is exercising, the unit can filter out temporary fluctuations caused by exercise. If the user is resting, the unit can prioritize collecting resting data. Furthermore, if the user is outdoors, the unit can filter out environmental noise. This allows for more accurate data collection by filtering based on the user's current activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input user activity data into a generating AI and have the generating AI perform the filtering.

[0081] The data collection unit can estimate the user's emotions and determine the priority of vital signs to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting heart rate and blood pressure. If the user is relaxed, the data collection unit may also prioritize collecting body temperature and activity level. Furthermore, if the user is anxious, the data collection unit may collect multiple vital signs simultaneously. This allows for the collection of more important data by prioritizing the vital signs to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of vital signs to collect.

[0082] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location when collecting vital signs. For example, if the user is at high altitude, the data collection unit may prioritize the collection of oxygen saturation. It can also prioritize the collection of heart rate and blood pressure if the user is in an urban area. Furthermore, if the user is at home, the data collection unit may prioritize the collection of body temperature and activity level. This allows for more appropriate data collection by prioritizing the collection of highly relevant data based on geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI collect highly relevant data.

[0083] The data collection unit can analyze the user's social media activity and collect relevant data when collecting vital signs. For example, if the user posts on social media indicating stress, the data collection unit may prioritize collecting heart rate and blood pressure. Conversely, if the user posts on social media indicating relaxation, the data collection unit may prioritize collecting body temperature and activity level. Furthermore, if the user posts on social media indicating anxiety, the data collection unit may collect multiple vital signs simultaneously. This allows for the collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform an analysis that emphasizes stress-related vital signs. If the user is relaxed, the analysis unit can also perform an analysis that emphasizes overall health status. Furthermore, if the user is anxious, the analysis unit can also perform an analysis that emphasizes early detection of anomalies. By adjusting the analysis algorithm based on the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis algorithm.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of vital signs during the analysis. For example, the analysis unit will analyze important vital signs such as heart rate and blood pressure in detail. The analysis unit can also analyze relatively stable vital signs such as body temperature and activity level in a simplified manner. Furthermore, if an abnormal value is detected, the analysis unit can also analyze that vital sign in detail. In this way, by adjusting the level of detail of the analysis based on the importance of vital signs, more important data can be analyzed in detail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vital sign importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the category of vital signs during analysis. For example, the analysis unit can apply a circulatory system analysis algorithm to heart rate and blood pressure. It can also apply a thermoregulatory function analysis algorithm to body temperature. Furthermore, it can apply an exercise function analysis algorithm to activity level. By applying different analysis algorithms depending on the category of vital signs, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vital sign category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is feeling anxious, the analysis unit can provide a display method that provides reassurance. This allows for a more appropriate display by adjusting the display method of the analysis results based on the user's emotions. 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 analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0088] The analysis unit can determine the priority of analysis based on the timing of vital sign collection during the analysis. For example, the analysis unit may prioritize the analysis of recently collected vital signs. It can also prioritize the analysis of vital signs in which abnormal values ​​have been detected. Furthermore, the analysis unit may prioritize the analysis of vital signs collected periodically. By determining the priority of analysis based on the timing of vital sign collection, more important data can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vital sign collection timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0089] The analysis unit can adjust the order of analysis based on the relationships between vital signs during the analysis. For example, the analysis unit may prioritize the relationship between heart rate and blood pressure. It can also prioritize the relationship between body temperature and activity level. Furthermore, it can prioritize the relationship between vital signs in which abnormal values ​​have been detected. By adjusting the order of analysis based on the relationships between vital signs, a more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vital sign relationship data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0090] The suggestion unit can estimate the user's emotions and adjust the presentation of the care plan based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide a simple and easy-to-understand care plan. If the user is relaxed, the suggestion unit can also provide a care plan with more detailed information. Furthermore, if the user is anxious, the suggestion unit can provide a reassuring care plan. By adjusting the presentation of the care plan based on the user's emotions, a more appropriate care plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the care plan.

[0091] The proposal unit can adjust the level of detail in its care plan proposals based on the importance of vital signs. For example, the proposal unit can propose a detailed care plan based on important vital signs such as heart rate and blood pressure. It can also propose a simplified care plan based on relatively stable vital signs such as body temperature and activity level. Furthermore, if an abnormal value is detected, the proposal unit can propose a detailed care plan based on that vital sign. This allows for the provision of care plans based on more important data by adjusting the level of detail in the proposals based on the importance of vital signs. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input vital sign importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposals.

[0092] The suggestion unit can apply different suggestion algorithms depending on the category of vital signs when proposing a care plan. For example, the suggestion unit can propose a cardiovascular care plan based on heart rate and blood pressure. It can also propose a care plan for thermoregulation based on body temperature. Furthermore, it can propose a care plan for exercise function based on activity level. By applying different suggestion algorithms depending on the category of vital signs, a more appropriate care plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input vital sign category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0093] The suggestion unit can estimate the user's emotions and adjust the length of the care plan based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide a short, concise care plan. If the user is relaxed, the suggestion unit can also provide a longer care plan with more detailed explanations. Furthermore, if the user is anxious, the suggestion unit can provide a reassuring care plan. By adjusting the length of the care plan based on the user's emotions, a more appropriate care plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the care plan.

[0094] The proposal unit can determine the priority of care plan proposals based on the timing of vital sign collection. For example, the proposal unit may prioritize care plans based on recently collected vital signs. It can also prioritize care plans based on vital signs in which abnormal values ​​have been detected. Furthermore, it can prioritize care plans based on vital signs collected regularly. By prioritizing proposals based on the timing of vital sign collection, it is possible to provide care plans based on more important data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input vital sign collection timing data into a generating AI and have the generating AI determine the priority of proposals.

[0095] The suggestion unit can adjust the order of suggestions based on the relevance of vital signs when proposing a care plan. For example, the suggestion unit can propose a care plan that emphasizes the relationship between heart rate and blood pressure. It can also propose a care plan that emphasizes the relationship between body temperature and activity level. Furthermore, it can propose a care plan that emphasizes the relationship between vital signs in which abnormal values ​​have been detected. By adjusting the order of suggestions based on the relevance of vital signs, a more appropriate care plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input vital sign relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0096] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a simple and highly visible notification method. If the user is relaxed, the notification unit can also provide a notification method that includes detailed information. Furthermore, if the user is anxious, the notification unit can provide a reassuring notification method. By adjusting the notification method based on the user's emotions, more appropriate notifications become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the notification method.

[0097] The notification unit can adjust the level of detail of notifications based on the importance of vital signs. For example, the notification unit can provide detailed notifications based on important vital signs such as heart rate and blood pressure. It can also provide simplified notifications based on relatively stable vital signs such as body temperature and activity level. Furthermore, if an abnormal value is detected, the notification unit can provide detailed notifications based on that vital sign. By adjusting the level of detail of notifications based on the importance of vital signs, it becomes possible to provide notifications based on more important data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input vital sign importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the notifications.

[0098] The notification unit can apply different notification algorithms depending on the category of vital signs when a notification is sent. For example, the notification unit can apply a circulatory system notification algorithm to heart rate and blood pressure. It can also apply a thermoregulatory function notification algorithm to body temperature. Furthermore, it can apply an exercise function notification algorithm to activity level. By applying different notification algorithms depending on the category of vital signs, more appropriate notifications can be made. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input vital sign category data into a generating AI and have the generating AI execute the application of the notification algorithm.

[0099] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is feeling stressed, the notification unit can prioritize important notifications. It can also prioritize detailed notifications if the user is relaxed. Furthermore, if the user is feeling anxious, the notification unit can prioritize reassuring notifications. This allows for prioritizing more important notifications based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI determine the priority of notifications.

[0100] The notification unit can adjust the order of notifications based on when vital signs were collected. For example, the notification unit can prioritize notifications based on recently collected vital signs. It can also prioritize notifications based on vital signs in which abnormal values ​​have been detected. Furthermore, it can prioritize notifications based on vital signs collected periodically. By adjusting the order of notifications based on when vital signs were collected, it becomes possible to provide notifications based on more important data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input vital sign collection timing data into a generating AI and have the generating AI perform the adjustment of the notification order.

[0101] The notification unit can adjust the content of notifications based on the relevance of vital signs. For example, the notification unit may prioritize the relevance of heart rate and blood pressure when making notifications. It may also prioritize the relevance of body temperature and activity level when making notifications. Furthermore, the notification unit may prioritize the relevance of vital signs in which abnormal values ​​have been detected when making notifications. By adjusting the content of notifications based on the relevance of vital signs, more appropriate notifications can be made. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input vital sign relevance data into a generating AI and have the generating AI perform the adjustment of the notification content.

[0102] The voice command receiver can estimate the user's emotions and adjust how it receives voice commands based on those emotions. For example, if the user is stressed, the voice command receiver will prioritize simple voice commands. If the user is relaxed, it can also prioritize detailed voice commands. Furthermore, if the user is anxious, it can prioritize reassuring voice commands. By adjusting how it receives voice commands based on the user's emotions, it becomes possible to receive more appropriate voice commands. Emotion estimation is achieved using an emotion estimation function, for example, 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 voice command receiver may be performed using AI, or not using AI. For example, the voice command receiver can input user emotion data into the generative AI and have the generative AI adjust how it receives voice commands.

[0103] The voice command receiving unit can select the optimal receiving method by referring to the user's past voice command history when receiving a voice command. For example, the voice command receiving unit can prioritize receiving voice commands that the user has frequently used in the past. The voice command receiving unit can also predict and receive voice commands to be used during specific time periods based on the user's past voice command history. Furthermore, the voice command receiving unit can analyze the user's past voice command history and suggest the optimal voice command. This makes it possible to receive the optimal voice command by referring to the past voice command history. Some or all of the above processing in the voice command receiving unit may be performed using AI, for example, or without AI. For example, the voice command receiving unit can input the user's past voice command history data into a generating AI and have the generating AI select the optimal receiving method.

[0104] The voice command receiver can estimate the user's emotions and determine the priority of voice commands based on the estimated emotions. For example, if the user is stressed, the voice command receiver will prioritize important voice commands. It can also prioritize detailed voice commands if the user is relaxed. Furthermore, if the user is anxious, it can prioritize reassuring voice commands. This allows for prioritizing more important voice commands based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the voice command receiver may be performed using AI or not. For example, the voice command receiver can input user emotion data into a generative AI and have the generative AI determine the priority of voice commands.

[0105] The voice command receiving unit can select the optimal receiving method based on the user's device information when it receives a voice command. For example, if the user is using a smartphone, the voice command receiving unit will receive a voice command optimized for the smartphone. It can also receive a voice command optimized for the tablet if the user is using a tablet. Furthermore, if the user is using a smartwatch, the voice command receiving unit can receive a voice command optimized for the smartwatch. This enables the receiving of the optimal voice command based on device information. Some or all of the above processing in the voice command receiving unit may be performed using AI, for example, or without AI. For example, the voice command receiving unit can input the user's device information into a generating AI and have the generating AI select the optimal receiving method.

[0106] The emergency response unit can estimate the user's emotions and adjust its emergency response method based on the estimated emotions. For example, if the user is stressed, the emergency response unit will provide a quick and concise emergency response. If the user is relaxed, the emergency response unit can also provide an emergency response that includes detailed information. Furthermore, if the user is anxious, the emergency response unit can provide an emergency response that provides reassurance. By adjusting the emergency response method based on the user's emotions, a more appropriate emergency response becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emergency response unit may be performed using AI, for example, or not using AI. For example, the emergency response unit can input user emotion data into a generative AI and have the generative AI adjust the emergency response method.

[0107] The emergency response unit can select the optimal response method by referring to the user's past emergency response history during an emergency. For example, the emergency response unit may prioritize selecting emergency response methods that the user has used in the past. The emergency response unit can also predict and select an emergency response method to be used during a specific time period based on the user's past emergency response history. Furthermore, the emergency response unit can analyze the user's past emergency response history and propose the optimal emergency response method. This makes it possible to provide the best possible emergency response by referring to past emergency response history. Some or all of the above processing in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the user's past emergency response history data into a generating AI and have the generating AI select the optimal response method.

[0108] The emergency response unit can estimate the user's emotions and determine the priority of emergency responses based on the estimated emotions. For example, if the user is feeling stressed, the emergency response unit will prioritize important emergency responses. It can also prioritize detailed emergency responses if the user is relaxed. Furthermore, if the user is feeling anxious, the emergency response unit can prioritize emergency responses that provide reassurance. This allows for prioritizing more important emergency responses based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emergency response unit may be performed using AI or not. For example, the emergency response unit can input user emotion data into a generative AI and have the generative AI determine the priority of emergency responses.

[0109] The emergency response unit can select the optimal response method based on the user's geographical location information during an emergency. For example, if the user is at home, the emergency response unit will perform an emergency response optimized for the user's home. If the user is out, the emergency response unit can also perform an emergency response optimized for the user's location. Furthermore, if the user is at a specific facility, the emergency response unit can perform an emergency response optimized for that facility. This enables the optimal emergency response based on geographical location information. Some or all of the above processing in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal response method.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] An AI elderly care assistant can estimate the user's emotions and adjust the care plan suggestions based on those emotions. For example, if the user is stressed, it can suggest relaxing meals and exercise plans. If the user is relaxed, it can suggest active exercise plans or new hobbies. Furthermore, if the user is anxious, it can suggest care plans that provide a sense of security. By adjusting the care plan based on the user's emotions, more appropriate care can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's emotion data into the generative AI and have the generative AI adjust the care plan.

[0112] The AI ​​elderly care assistant can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is stressed, notifications can be kept to a minimum, with only important notifications being sent. If the user is relaxed, notifications can be sent as usual. Furthermore, if the user is anxious, reassuring notifications can be prioritized. By adjusting the timing of notifications based on the user's emotions, more appropriate notifications can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into the generative AI and have the generative AI adjust the timing of notifications.

[0113] An AI elderly care assistant can estimate the user's emotions and adjust the content of voice command responses based on those emotions. For example, if the user is stressed, it can provide a simple and quick response. If the user is relaxed, it can provide a response that includes more detailed information. Furthermore, if the user is anxious, it can provide a reassuring response. By adjusting the content of voice command responses based on the user's emotions, more appropriate responses become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the voice command reception unit may be performed using AI, or not using AI. For example, the voice command reception unit can input user emotion data into the generative AI and have the generative AI adjust the content of the response.

[0114] The AI ​​elderly care assistant can estimate the user's emotions and determine the priority of emergency responses based on those emotions. For example, if the user is stressed, it can prioritize quick and concise emergency responses. If the user is relaxed, it can prioritize emergency responses that include detailed information. Furthermore, if the user is anxious, it can prioritize emergency responses that provide reassurance. This allows for more appropriate emergency responses by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emergency response unit may be performed using AI or not. For example, the emergency response unit can input user emotion data into a generative AI and have the generative AI determine the priority of emergency responses.

[0115] An AI elderly care assistant can estimate a user's emotions and adjust the types of data collected based on those emotions. For example, if a user is stressed, it can prioritize collecting stress-related data such as heart rate and blood pressure. If a user is relaxed, it can prioritize collecting data such as body temperature and activity level. Furthermore, if a user is anxious, it can collect multiple vital signs simultaneously. This allows for more appropriate data collection by adjusting the types of data collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's emotion data into the generative AI and have the generative AI adjust the types of data to be collected.

[0116] The AI ​​elderly care assistant can predict future health risks and suggest preventive measures based on the user's past health data. For example, if past data indicates a high risk of heart disease, it can suggest a heart-healthy diet and exercise plan. Similarly, if past data indicates a high risk of diabetes, it can suggest a diet and exercise plan for blood sugar management. Furthermore, if past data indicates a high risk of osteoporosis, it can suggest a diet and exercise plan to increase bone density. This allows for more appropriate health management by predicting future health risks and suggesting preventive measures based on past health data. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input past health data into a generating AI and have the generating AI execute the suggestion of preventive measures.

[0117] The AI ​​elderly care assistant can propose care plans that take into account region-specific health risks based on the user's geographical location. For example, if the user lives in a high-altitude area, it can propose a care plan that emphasizes oxygen saturation management. If the user lives in an urban area, it can propose a care plan that includes measures against air pollution. Furthermore, if the user lives in a cold region, it can propose a care plan that includes measures against the cold. By proposing care plans that take into account region-specific health risks based on geographical location information, more appropriate care can be provided. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input geographical location information into a generating AI and have the generating AI execute the care plan proposal.

[0118] The AI ​​elderly care assistant can analyze a user's social media activity and propose a care plan based on the user's interests. For example, if a user shows interest in cooking on social media, it can suggest healthy recipes. If a user shows interest in exercise on social media, it can also suggest an exercise plan. Furthermore, if a user shows interest in travel on social media, it can suggest a health management plan for travel. In this way, by analyzing social media activity, it can propose a care plan based on the user's interests. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input social media activity data into a generating AI and have the generating AI execute the care plan proposal.

[0119] The AI ​​elderly care assistant can propose the most suitable care plan based on the user's device usage history. For example, if the user frequently uses a smartphone, it can propose a care plan that can be performed on a smartphone. Similarly, if the user frequently uses a tablet, it can propose a care plan that can be performed on a tablet. Furthermore, if the user frequently uses a smartwatch, it can propose a care plan that can be performed on a smartwatch. This allows for more appropriate care to be provided by proposing the most suitable care plan based on device usage history. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input device usage history data into a generating AI and have the generating AI execute the care plan proposal.

[0120] The AI ​​elderly care assistant can suggest the most appropriate emergency response method based on the user's past emergency response history. For example, for a user with a history of frequent falls, it can suggest an emergency response method that includes fall prevention measures. Similarly, for a user with a history of frequent heart attacks, it can suggest an emergency response method that includes measures for dealing with heart attacks. Furthermore, for a user with a history of frequent sudden increases in blood pressure, it can suggest an emergency response method for blood pressure management. This allows for more appropriate emergency responses by suggesting the most suitable method based on past emergency response history. Some or all of the above processing in the emergency response unit may be performed using AI, or without AI. For example, the emergency response unit can input past emergency response history data into a generating AI and have the generating AI propose emergency response methods.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects vital signs from elderly individuals. The data collection unit can collect vital signs such as heart rate, blood pressure, body temperature, and activity level. For example, it can monitor heart rate in real time using a wearable device, measure blood pressure using a smart home device, and measure body temperature using a body temperature sensor. Step 2: The analysis unit analyzes the vital signs collected by the collection unit. The analysis unit can perform statistical analysis on the collected data, use machine learning algorithms to analyze the data, and detect outliers and changes in trends. Step 3: The proposal unit proposes a care plan based on the data analyzed by the analysis unit. The proposal unit can suggest daily meal plans, medication reminders, and recommended exercise programs. Step 4: The notification unit will send notifications in case of abnormalities based on the care plan proposed by the proposal unit. The notification unit can automatically send alerts when abnormal values ​​are detected or when changes in trends are detected, and can automatically contact people in case of emergencies.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, notification unit, voice command reception unit, and emergency response unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects the vital signs of elderly people using the sensors of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes a care plan using the specific processing unit 290 of the data processing unit 12. The notification unit notifies in case of an abnormality using the control unit 46A of the smart device 14. The voice command reception unit receives voice commands using the microphone 38B of the smart device 14. The emergency response unit automatically makes contact in an emergency using the sensors of the smart device 14 and 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 can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, notification unit, voice command reception unit, and emergency response unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects the vital signs of elderly people using the sensors of the smart glasses 214. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12. The proposal unit proposes a care plan using the identification processing unit 290 of the data processing unit 12. The notification unit notifies in case of an abnormality using the control unit 46A of the smart glasses 214. The voice command reception unit receives voice commands using the microphone 238 of the smart glasses 214. The emergency response unit automatically makes contact in an emergency using the sensors of the smart glasses 214 and the identification 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 can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The 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.

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

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 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.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the 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.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 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.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, notification unit, voice command reception unit, and emergency response unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects the vital signs of elderly people using the sensors of the headset terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes a care plan using the specific processing unit 290 of the data processing unit 12. The notification unit notifies in case of an abnormality using the control unit 46A of the headset terminal 314. The voice command reception unit receives voice commands using the microphone 238 of the headset terminal 314. The emergency response unit automatically makes contact in an emergency using the sensors of the headset terminal 314 and 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 can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The 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.

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

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, notification unit, voice command reception unit, and emergency response unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit collects the vital signs of elderly people using the sensors of the robot 414. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes a care plan using the specific processing unit 290 of the data processing unit 12. The notification unit notifies in case of an abnormality using the control unit 46A of the robot 414. The voice command reception unit receives voice commands using the microphone 238 of the robot 414. The emergency response unit automatically makes contact in an emergency using the sensors of the robot 414 and the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A collection department that collects vital signs from elderly people, An analysis unit analyzes the vital signs collected by the aforementioned collection unit, A proposal unit proposes a care plan based on the data analyzed by the aforementioned analysis unit, The system includes a notification unit that notifies in case of an abnormality based on the care plan proposed by the proposal unit. A system characterized by the following features. (Note 2) It is equipped with a voice command reception unit that accepts voice commands. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with an emergency response unit that automatically contacts others in emergencies. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is It collects vital signs in conjunction with wearable devices and smart home devices. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, It automatically generates individualized care plans based on past health data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, Automatically sends alerts when abnormal values ​​or changes in trends are detected. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of vital sign collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past health data of elderly individuals to select the optimal timing for data collection. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting vital signs, filtering is performed based on the user's current activity level and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of vital signs to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting vital signs, the system prioritizes the collection of highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting vital signs, analyze the user's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when vital signs were collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the way the care plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When proposing a care plan, adjust the level of detail based on the importance of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When proposing a care plan, different proposal algorithms are applied depending on the category of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the length of the care plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When proposing a care plan, prioritize the proposals based on when vital signs are collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When proposing a care plan, adjust the order of suggestions based on the relevance of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, When a notification is sent, adjust the level of detail based on the importance of the vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When a notification is sent, a different notification algorithm is applied depending on the category of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When sending notifications, adjust the order of notifications based on when vital signs were collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending a notification, adjust the content of the notification based on the relevance of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned voice command receiving unit is It estimates the user's emotions and adjusts how voice commands are received based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned voice command receiving unit is When a voice command is received, the system selects the optimal reception method by referring to the user's past voice command history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned voice command receiving unit is It estimates the user's emotions and prioritizes voice commands based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned voice command receiving unit is When a voice command is received, the system selects the optimal response method based on the user's device information. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned emergency response unit, It estimates the user's emotions and adjusts emergency response methods based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned emergency response unit, During an emergency, the system will refer to the user's past emergency response history to select the most appropriate response method. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned emergency response unit, It estimates the user's emotions and determines the priority of emergency responses based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned emergency response unit, In emergency situations, the system selects the optimal response method based on the user's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0195] 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 department that collects vital signs from elderly people, An analysis unit analyzes the vital signs collected by the aforementioned collection unit, A proposal unit proposes a care plan based on the data analyzed by the aforementioned analysis unit, The system includes a notification unit that notifies in case of an abnormality based on the care plan proposed by the proposal unit. A system characterized by the following features.

2. It is equipped with a voice command reception unit that accepts voice commands. The system according to feature 1.

3. It is equipped with an emergency response unit that automatically contacts others in emergencies. The system according to feature 1.

4. The aforementioned collection unit is It collects vital signs in conjunction with wearable devices and smart home devices. The system according to feature 1.

5. The aforementioned proposal section is, It automatically generates individualized care plans based on past health data. The system according to feature 1.

6. The aforementioned notification unit, Automatically sends alerts when abnormal values ​​or changes in trends are detected. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of vital sign collection based on the estimated emotions. The system according to feature 1.

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

9. The aforementioned collection unit is When collecting vital signs, filtering is performed based on the user's current activity level and environment. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of vital signs to collect based on the estimated user emotions. The system according to feature 1.

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