system

Abstract

The system according to this embodiment aims to enable elderly people to easily access the support and services they need in their daily lives. [Solution] The system according to the embodiment comprises a health management unit, an anomaly detection unit, an emergency contact unit, a meal suggestion unit, and a food ingredient ordering unit. The health management unit manages the health data of elderly people. The anomaly detection unit detects anomalies based on the data managed by the health management unit. The emergency contact unit makes an emergency contact based on the anomaly detected by the anomaly detection unit. The meal suggestion unit proposes a meal plan based on the data managed by the health management unit. The food ingredient ordering unit orders food ingredients based on the meal plan proposed by the meal suggestion unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 prior art, there was a problem that it was difficult for the elderly to easily use the assistance and services necessary in daily life.

[0005] The system according to the embodiment aims to enable the elderly to easily use the assistance and services necessary in daily life.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a health management unit, an anomaly detection unit, an emergency contact unit, a meal suggestion unit, and a food ingredient ordering unit. The health management unit manages the health data of elderly individuals. The anomaly detection unit detects anomalies based on the data managed by the health management unit. The emergency contact unit makes emergency contacts based on the anomalies detected by the anomaly detection unit. The meal suggestion unit proposes meal plans based on the data managed by the health management unit. The food ingredient ordering unit orders food ingredients based on the meal plans proposed by the meal suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can make it possible for elderly people to easily access the support and services they need in their daily lives. [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 manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The elderly support system according to an embodiment of the present invention is a system that provides a mechanism for elderly people to easily access the support and services they need in their daily lives. This elderly support system includes the following five types of support. First, as nutritional management support, the generating AI proposes an optimal meal plan based on the health condition and preferences of each elderly person and automatically orders the necessary ingredients. Next, as health monitoring, the generating AI manages health data such as weight, blood pressure, and blood glucose levels in conjunction with healthcare devices and apps, and notifies a doctor if an abnormality is detected. Furthermore, as emergency support, if the generating AI detects abnormal movement of the elderly person, it automatically sends a notification to emergency contacts. As communication support, the generating AI suggests regular video calls with family members based on the elderly person's schedule. Finally, as support for daily life, the generating AI learns the elderly person's lifestyle patterns and automatically arranges the necessary support. This mechanism provides an environment in which elderly people living alone can live comfortably and with peace of mind, and also reduces the anxiety of their families. For example, the generating AI proposes an optimal meal plan based on the health condition and preferences of each elderly person and automatically orders the necessary ingredients. The generating AI manages health data such as weight, blood pressure, and blood glucose levels in conjunction with healthcare devices and apps, and notifies a doctor if an abnormality is detected. The generating AI automatically sends notifications to emergency contacts if it detects unusual movements in an elderly person. The generating AI suggests regular video calls with family members based on the elderly person's schedule. The generating AI learns the elderly person's lifestyle patterns and automatically arranges the necessary support. This ensures that the elderly support system makes it easy for elderly people to access the support and services they need in their daily lives. For example, the generating AI suggests optimal meal plans based on each elderly person's health condition and preferences and automatically orders necessary ingredients. The generating AI manages health data such as weight, blood pressure, and blood sugar levels through integration with healthcare devices and apps, and notifies doctors if abnormalities are detected. The generating AI automatically sends notifications to emergency contacts if it detects unusual movements in an elderly person. The generating AI suggests regular video calls with family members based on the elderly person's schedule. The generating AI learns the elderly person's lifestyle patterns and automatically arranges the necessary support.

[0029] The elderly support system according to this embodiment comprises a health management unit, an anomaly detection unit, an emergency contact unit, a meal suggestion unit, and a food ingredient ordering unit. The health management unit manages the health data of the elderly. The health management unit collects and manages health data such as weight, blood pressure, and blood glucose levels. The health management unit can automatically collect health data by linking with healthcare devices and apps. The health management unit acquires and manages data from healthcare devices such as smartwatches and blood pressure monitors. The health management unit can also collect and manage data by linking with health management apps and fitness apps. The anomaly detection unit detects anomalies based on the data managed by the health management unit. The anomaly detection unit detects anomalies by analyzing health data, for example. The anomaly detection unit can detect anomalies using statistical analysis and machine learning algorithms. The anomaly detection unit can detect abnormal fluctuations in weight and blood pressure, for example. The anomaly detection unit can also detect abnormal fluctuations in blood glucose levels. The anomaly detection unit can also detect anomalies by considering the interrelationships of health data. The Emergency Contact Department makes emergency contacts based on anomalies detected by the Anomaly Detection Department. For example, the Emergency Contact Department sends notifications to emergency contacts when an anomaly is detected. The Emergency Contact Department can send notifications by phone, email, SMS, etc. The Emergency Contact Department can also call nearby medical facilities or ambulances when an anomaly is detected. The Emergency Contact Department can also notify family members when an anomaly is detected. The Meal Suggestion Department proposes meal plans based on data managed by the Health Management Department. For example, the Meal Suggestion Department proposes optimal meal plans based on the health status and preferences of individual elderly individuals. The Meal Suggestion Department can propose meal plans based on nutritional balance and individual health status. The Meal Suggestion Department can also provide recipes based on meal plans. The Food Ordering Department orders food ingredients based on meal plans proposed by the Meal Suggestion Department. For example, the Food Ordering Department automatically orders the necessary ingredients based on meal plans. The Food Ordering Department can order ingredients through online ordering systems, subscriptions, etc. The Food Ordering Department can also arrange for food delivery.As a result, the elderly support system according to the embodiment allows elderly people to easily access the support and services they need in their daily lives. Some or all of the above-mentioned processes in the health management unit, anomaly detection unit, emergency contact unit, meal suggestion unit, and food ingredient ordering unit may be performed using AI, for example, or not using AI. For example, the health management unit can input data acquired from healthcare devices or apps into a generating AI and have the generating AI perform data management. The anomaly detection unit can input data managed by the health management unit into a generating AI and have the generating AI perform anomaly detection. The emergency contact unit can input anomalies detected by the anomaly detection unit into a generating AI and have the generating AI perform emergency contact. The meal suggestion unit can input data managed by the health management unit into a generating AI and have the generating AI perform meal plan suggestions. The food ingredient ordering unit can input meal plans suggested by the meal suggestion unit into a generating AI and have the generating AI perform food ingredient orders.

[0030] The Health Management Department manages the health data of elderly individuals. Specifically, it collects and manages health data such as weight, blood pressure, and blood sugar levels. This data is automatically collected through integration with healthcare devices and apps. For example, smartwatches record heart rate, steps, and sleep patterns, and blood pressure monitors measure blood pressure regularly. The data obtained from these devices is transmitted to the Health Management Department's system via Bluetooth® or Wi-Fi. The Health Management Department centrally manages this data and monitors the health status of individual elderly individuals in real time. Furthermore, the Health Management Department can also collect records of meals and exercise through integration with health management apps and fitness apps. For example, meal tracking apps record calories and nutrients consumed, and exercise apps record the type of exercise, duration, and calories burned. This allows the Health Management Department to collect comprehensive health data and gain a detailed understanding of the health status of individual elderly individuals. In addition, the Health Management Department can store the collected data on a cloud server and share it with medical institutions and families as needed. This allows medical institutions to remotely monitor the health status of elderly individuals and provide appropriate medical services. Furthermore, family members can monitor the health status of elderly individuals in real time and provide necessary support.

[0031] The anomaly detection unit detects abnormalities based on data managed by the health management unit. Specifically, it uses statistical analysis and machine learning algorithms to detect abnormal fluctuations in weight, blood pressure, and blood glucose levels. For example, if weight increases or decreases rapidly, or if blood pressure exceeds the normal range, the anomaly detection unit detects this as an anomaly. The machine learning algorithm learns from past data and has the ability to distinguish between normal and abnormal patterns. This allows the anomaly detection unit to detect unusual patterns early and respond quickly. Furthermore, the anomaly detection unit can also detect anomalies by considering the interrelationships of health data. For example, if fluctuations in blood pressure and heart rate are linked, this is detected as an anomaly. In addition, when an anomaly is detected, the anomaly detection unit can propose appropriate responses depending on the type and severity of the anomaly. For example, in the case of a mild anomaly, it may suggest lifestyle improvements, and in the case of a severe anomaly, it may recommend visiting a medical institution. This allows the anomaly detection unit to constantly monitor the health status of elderly individuals and respond quickly and appropriately when an anomaly occurs.

[0032] The Emergency Contact Unit makes emergency contacts based on abnormalities detected by the Anomaly Detection Unit. Specifically, it sends notifications to emergency contacts when an abnormality is detected. Notifications are sent via methods such as phone, email, and SMS. For example, if blood pressure rises sharply, the Emergency Contact Unit will call the registered emergency contact to inform them of the abnormality. It can also send detailed information about the abnormality via email or SMS. Furthermore, the Emergency Contact Unit can also call a nearby medical facility or ambulance when an abnormality is detected. For example, if the heart rate becomes abnormally high, the Emergency Contact Unit will contact the nearest medical facility and arrange for an ambulance. It can also notify family members when an abnormality is detected. This allows family members to quickly understand the abnormality of the elderly person and take appropriate action. Furthermore, the Emergency Contact Unit can also suggest appropriate actions depending on the type and severity of the abnormality. For example, in the case of a minor abnormality, it may suggest lifestyle improvements, and in the case of a severe abnormality, it may recommend visiting a medical facility. This allows the Emergency Contact Unit to constantly monitor the health status of the elderly person and respond quickly and appropriately when an abnormality occurs.

[0033] The Meal Planning Department proposes meal plans based on data managed by the Health Management Department. Specifically, it proposes the optimal meal plan based on the health condition and preferences of each elderly person. For example, it proposes a low-salt meal plan for elderly people with high blood pressure and a low-carbohydrate meal plan for elderly people with high blood sugar levels. The Meal Planning Department can also propose meal plans based on nutritional balance and individual health conditions. For example, if there is a deficiency in vitamins or minerals, it will propose a meal plan that includes ingredients to supplement them. The Meal Planning Department can also provide recipes based on the meal plan. For example, it will provide specific recipes based on the proposed meal plan, explaining the cooking method and necessary ingredients in detail. This makes it easy for elderly people to prepare meals that suit their health condition. Furthermore, the Meal Planning Department can monitor the implementation status of the meal plan and modify the plan as needed. For example, if the proposed meal plan is not being followed or the health condition does not improve, it will review the plan and make more appropriate suggestions. In this way, the Meal Planning Department can constantly monitor the health condition of elderly people and provide the optimal meal plan.

[0034] The grocery ordering department orders groceries based on meal plans proposed by the meal planning department. Specifically, it automatically orders the necessary groceries based on the meal plan. For example, it creates a list of necessary groceries based on the proposed meal plan and orders them through an online ordering system. The grocery ordering department can order groceries through methods such as regular subscriptions and bulk orders. For example, it can regularly order the necessary groceries based on a weekly meal plan and arrange for delivery. It can also place additional orders if certain groceries are in short supply. Furthermore, the grocery ordering department can also arrange for grocery delivery. For example, it can deliver ordered groceries to the elderly person's home through a partnered delivery company. This allows the elderly person to easily obtain the necessary groceries. In addition, the grocery ordering department can manage order history and optimize future orders based on past order content. For example, by analyzing past order history and understanding which groceries are frequently ordered and how often, it can efficiently plan future orders. This allows the grocery ordering department to efficiently order and deliver the necessary groceries based on the elderly person's meal plan.

[0035] The Health Management Department can manage health data such as weight, blood pressure, and blood sugar levels by integrating with healthcare devices and apps. For example, the Health Management Department can acquire and manage data from healthcare devices such as smartwatches and blood pressure monitors. It can also collect and manage data by integrating with health management apps and fitness apps. To streamline data collection and management, the Health Management Department can use generative AI. For instance, it can input data acquired from healthcare devices and apps into the generative AI and have the AI ​​manage the data. This streamlines the management of health data.

[0036] The anomaly detection unit can analyze data managed by the health management unit and detect anomalies. For example, the anomaly detection unit can analyze health data and detect anomalies. The anomaly detection unit can detect anomalies using statistical analysis and machine learning algorithms. For example, the anomaly detection unit can detect abnormal fluctuations in weight or blood pressure. The anomaly detection unit can also detect abnormal fluctuations in blood glucose levels. The anomaly detection unit can also detect anomalies by considering the interrelationships of health data. The anomaly detection unit can use generative AI to streamline data analysis and anomaly detection. For example, the anomaly detection unit can input data managed by the health management unit into the generative AI and have the generative AI perform anomaly detection. This enables early detection of anomalies.

[0037] The emergency contact unit can send notifications to emergency contacts when an anomaly is detected. For example, the emergency contact unit can send notifications to emergency contacts when an anomaly is detected. The emergency contact unit can send notifications via methods such as phone, email, and SMS. The emergency contact unit can also call nearby medical facilities or ambulances when an anomaly is detected. The emergency contact unit can also notify family members when an anomaly is detected. The emergency contact unit can use generative AI to streamline the execution of emergency communications. For example, the emergency contact unit can input anomalies detected by the anomaly detection unit into the generative AI, and have the generative AI execute the emergency communications. This enables a rapid response in emergencies.

[0038] The meal planning department can propose optimal meal plans based on the health condition and preferences of individual elderly individuals. For example, the department can propose meal plans based on nutritional balance and individual health conditions. The department can also provide recipes based on the meal plans. To streamline the meal planning process, the department can utilize generative AI. For instance, the department can input data managed by the health management department into the generative AI and have the AI ​​generate meal plans. This enables the provision of individually optimized meal plans.

[0039] The ingredient ordering department can automatically order ingredients based on meal plans proposed by the meal planning department. For example, the ingredient ordering department can automatically order the necessary ingredients based on the meal plan. The ingredient ordering department can order ingredients through methods such as online ordering systems or subscription services. The ingredient ordering department can also arrange for ingredient delivery. The ingredient ordering department can use generative AI to streamline ingredient ordering. For example, the ingredient ordering department can input meal plans proposed by the meal planning department into the generative AI and have the generative AI execute the ingredient order. This automates ingredient ordering.

[0040] The emergency contact unit can call nearby medical facilities or ambulances in the event of an emergency. For example, the emergency contact unit can call nearby medical facilities or ambulances in an emergency. The emergency contact unit can use generative AI to streamline rapid medical response in emergencies. For example, the emergency contact unit can input anomalies detected by the anomaly detection unit into the generative AI, and have the generative AI execute the emergency contact. This enables a rapid medical response in emergencies.

[0041] The meal planning department can provide recipes based on meal plans. For example, the meal planning department can provide recipes based on meal plans. To streamline recipe provision, the meal planning department can use generative AI. For example, the meal planning department can input meal plans into the generative AI and have the generative AI generate recipes. This enables the provision of recipes based on meal plans.

[0042] The health management department can provide medication reminders. For example, the health management department can provide medication reminders. To streamline medication reminders, the health management department can use generative AI. For instance, the health management department can input medication schedules into the generative AI and have the AI ​​generate reminders. This makes medication management easier by providing medication reminders.

[0043] The emergency contact unit can notify family members in the event of an emergency. For example, the emergency contact unit can notify family members in an emergency. To streamline the rapid notification of family members in an emergency, the emergency contact unit can use a generation AI. For example, the emergency contact unit can input anomalies detected by the anomaly detection unit into the generation AI, and have the generation AI execute a notification to the family. This enables rapid notification of family members in an emergency.

[0044] The food ordering department can arrange for the delivery of ingredients. For example, the food ordering department can arrange for the delivery of ingredients. The food ordering department can use generative AI to streamline ingredient delivery. For example, the food ordering department can input the ordered ingredients into the generative AI and have the generative AI execute the delivery arrangements. This makes it easier to obtain ingredients by arranging their delivery.

[0045] The Health Management Department can analyze the past health data of elderly individuals and select the optimal data collection method. For example, the Health Management Department can select a method for collecting data at specific time periods based on the elderly individuals' past health data. The Health Management Department can select the most effective device based on the elderly individuals' past health data. The Health Management Department can analyze the elderly individuals' past health data and optimize the frequency and timing of data collection. The Health Management Department can use generative AI to streamline data analysis and the selection of collection methods. For example, the Health Management Department can input past health data into the generative AI and have the generative AI select the optimal data collection method. This enables optimal data collection based on past data.

[0046] The Health Management Department can filter health data based on the lifestyle patterns of elderly individuals when collecting it. For example, the Health Management Department can analyze the lifestyle patterns of elderly individuals and collect data during their typical activity times. Based on these lifestyle patterns, the Health Management Department can filter out unnecessary data and collect only the important data. The Health Management Department can adjust the timing of data collection considering the lifestyle patterns of elderly individuals. The Health Management Department can use generative AI to streamline data filtering and collection. For example, the Health Management Department can input lifestyle pattern data into a generative AI and have the AI ​​perform the data filtering. This enables data filtering based on lifestyle patterns.

[0047] The Health Management Department can prioritize the collection of highly relevant data by considering the geographical location information of elderly individuals when collecting health data. For example, if an elderly person is out, the Health Management Department can prioritize the collection of data such as steps taken and distance traveled. If an elderly person is at home, the Health Management Department can prioritize the collection of indoor activity data. If an elderly person is in a specific location, the Health Management Department can prioritize the collection of health data related to that location. The Health Management Department can use generative AI to streamline data collection. For example, the Health Management Department can input geographical location information into the generative AI and have the AI ​​collect highly relevant data. This enables data collection based on geographical location information.

[0048] The Health Management Department can analyze the social media activities of older adults and collect relevant data when collecting health data. For example, if an older adult is very active on social media, the Health Management Department can collect stress levels associated with that activity. If an older adult is inactive on social media, the Health Management Department can collect data related to feelings of loneliness. The Health Management Department can analyze the social media activities of older adults and identify factors that affect their health status, and collect data on those factors. The Health Management Department can use generative AI to streamline data collection. For example, the Health Management Department can input social media activity data into a generative AI and have the AI ​​collect relevant data. This enables data collection based on social media activity.

[0049] The anomaly detection unit can improve the accuracy of detection by considering the interrelationships of health data when detecting anomalies. For example, the anomaly detection unit can detect anomalies by combining weight and blood pressure data. The anomaly detection unit can detect anomalies by correlating blood glucose and heart rate data. The anomaly detection unit can analyze the interrelationships of health data and optimize the anomaly detection algorithm. The anomaly detection unit can use generative AI to efficiently analyze the interrelationships of data. For example, the anomaly detection unit can input the interrelationships of health data into generative AI and have the generative AI perform anomaly detection. This enables anomaly detection that takes into account the interrelationships of health data.

[0050] The anomaly detection unit can perform detection while considering the attribute information of elderly individuals. For example, the anomaly detection unit can adjust the criteria for anomaly detection based on the elderly person's age and gender. The anomaly detection unit can improve the accuracy of anomaly detection by considering the elderly person's medical history. The anomaly detection unit can perform anomaly detection while considering the elderly person's lifestyle and activity level. The anomaly detection unit can use a generation AI to efficiently analyze attribute information. For example, the anomaly detection unit can input attribute information into a generation AI and have the generation AI perform anomaly detection. This enables anomaly detection based on attribute information.

[0051] The anomaly detection unit can perform detection while considering the geographical distribution of health data when an anomaly is detected. For example, if an elderly person is in a specific area, the anomaly detection unit can detect an anomaly based on the health data of that area. If an elderly person is on the move, the anomaly detection unit can detect an anomaly by considering the health data of their destination. The anomaly detection unit can analyze the health data of the elderly person's residential area and adjust the criteria for anomaly detection. The anomaly detection unit can use generative AI to efficiently analyze geographical distribution. For example, the anomaly detection unit can input geographical distribution data into the generative AI and have the generative AI perform anomaly detection. This enables anomaly detection based on geographical distribution.

[0052] The anomaly detection unit can improve the accuracy of its detection by referring to relevant literature when an anomaly is detected. For example, the anomaly detection unit can improve the accuracy of its detection by referring to the latest medical literature when an anomaly is detected. The anomaly detection unit can optimize its detection algorithm based on past research data. The anomaly detection unit can improve the accuracy of its detection by referring to relevant academic papers. The anomaly detection unit can use a generation AI to streamline the literature referencing process. For example, the anomaly detection unit can input data from relevant literature into the generation AI and have the generation AI perform the task of improving the accuracy of its detection. As a result, the accuracy of anomaly detection is improved by referring to relevant literature.

[0053] The emergency contact department can select the most suitable contact method by referring to past emergency contact history during an emergency. For example, the emergency contact department can select the most effective contact method based on past emergency contact history. The emergency contact department can analyze past emergency contact history and determine the priority of contacts. The emergency contact department can customize contact methods by referring to past emergency contact history. The emergency contact department can use generative AI to streamline the retrieval of history. For example, the emergency contact department can input past emergency contact history into generative AI and have the generative AI select the most suitable contact method. This will result in the selection of the most suitable contact method based on past emergency contact history.

[0054] The emergency contact unit can customize the means of contact based on the elderly person's current situation during an emergency. For example, if the elderly person is at home, the emergency contact unit will use their home landline phone to make an emergency call. If the elderly person is out, the emergency contact unit can use their mobile phone to make an emergency call. If the elderly person is at a medical facility, the emergency contact unit can use the medical facility's contact information to make an emergency call. The emergency contact unit can use generative AI to efficiently analyze the current situation. For example, the emergency contact unit can input data on the current situation into the generative AI and have the generative AI customize the means of contact. This makes it possible to customize the means of contact based on the current situation.

[0055] The emergency contact unit can select the most appropriate contact method when an emergency occurs, taking into account the geographical location of the elderly person. For example, if the elderly person is at home, the emergency contact unit can use their home landline phone to make an emergency call. If the elderly person is out, the emergency contact unit can use their mobile phone to make an emergency call. If the elderly person is at a medical institution, the emergency contact unit can use the medical institution's contact information to make an emergency call. The emergency contact unit can use generative AI to efficiently analyze geographical location information. For example, the emergency contact unit can input geographical location information into the generative AI and have the generative AI select the most appropriate contact method. This ensures that the most suitable contact method is selected based on geographical location information.

[0056] The emergency contact unit can analyze the social media activity of elderly individuals and suggest appropriate contact methods during emergencies. For example, if an elderly individual is actively using social media, the unit can use a messaging app for emergency contact. If an elderly individual is less active on social media, the unit can use telephone contact. The emergency contact unit can analyze the elderly individual's social media activity and suggest the most suitable contact method. The emergency contact unit can use generative AI to efficiently analyze social media activity. For example, the emergency contact unit can input social media activity data into generative AI and have the generative AI suggest contact methods. This enables the suggestion of contact methods based on social media activity.

[0057] The meal planning department can analyze an elderly person's past eating history to select the optimal plan when proposing meals. For example, the department can propose a plan that includes preferred ingredients based on the elderly person's past eating history. The department can select a nutritionally balanced plan based on the elderly person's past eating history. The department can analyze the elderly person's past eating history and propose a plan that accommodates allergies and dietary restrictions. The department can use generative AI to streamline data analysis and plan selection. For example, the department can input past eating history into the generative AI and have the generative AI select the optimal plan. This ensures that the optimal plan is selected based on past eating history.

[0058] The meal planning department can customize meal plans based on the elderly person's current health condition. For example, it can propose a low-sodium meal plan considering the elderly person's current health condition. It can propose a carbohydrate-restricted meal plan based on the elderly person's current health condition. It can analyze the elderly person's current health condition and customize a plan that includes the necessary nutrients. The meal planning department can use generative AI to streamline data analysis and plan customization. For example, it can input current health data into the generative AI and have the generative AI perform the plan customization. This makes it possible to customize plans based on the current health condition.

[0059] The meal planning department can select the optimal plan when proposing meals, taking into account the geographical location of the elderly person. For example, the department can propose a plan using ingredients from the area where the elderly person lives. If the elderly person is traveling, the department can propose a plan using local specialties from that area. Based on the elderly person's geographical location, the department can select a plan using ingredients that are easily available. The meal planning department can use generative AI to efficiently analyze geographical location information. For example, the department can input geographical location information into the generative AI and have the generative AI select the optimal plan. This ensures that the optimal plan is selected based on geographical location information.

[0060] The meal planning department can analyze the social media activity of elderly individuals to propose meal plans. For example, it can propose plans based on the food preferences that elderly individuals have shared on social media. It can propose plans that reflect the cooking trends that elderly individuals are following on social media. It can analyze the social media activity of elderly individuals and propose plans that are likely to interest them. The meal planning department can use generative AI to efficiently analyze social media activity. For example, it can input social media activity data into generative AI and have the generative AI generate plan proposals. This makes it possible to propose plans based on social media activity.

[0061] The grocery ordering system can select the optimal ordering method by referring to past order history when ordering groceries. For example, the grocery ordering system can prioritize displaying frequently ordered groceries based on the past order history of elderly customers. The grocery ordering system can analyze the past order history of elderly customers and suggest the optimal ordering method. The grocery ordering system can refer to the past order history of elderly customers and provide options to reduce the effort required for ordering. The grocery ordering system can use generative AI to streamline the process of referring to history and selecting ordering methods. For example, the grocery ordering system can input past order history into the generative AI and have the generative AI select the optimal ordering method. This will result in the selection of the optimal ordering method based on past order history.

[0062] The grocery ordering system can customize ordering methods based on the elderly person's current living situation when they place a grocery order. For example, if the elderly person is at home, the grocery ordering system will prioritize suggesting home delivery. If the elderly person is out, the grocery ordering system can suggest picking up the order at a nearby store. The grocery ordering system can customize the optimal ordering method considering the elderly person's living situation. The grocery ordering system can use generative AI to efficiently analyze living situations. For example, the grocery ordering system can input data on the current living situation into the generative AI and have the generative AI perform the customization of the ordering method. This makes it possible to customize ordering methods based on the current living situation.

[0063] The grocery ordering system can select the most suitable ordering method when an elderly person places an order, taking into account their geographical location. For example, the system can order groceries from stores in the elderly person's local area. If the elderly person is traveling, the system can order groceries from stores in their travel destination. The system can select the most convenient ordering method based on the elderly person's geographical location. The system can use generative AI to efficiently analyze geographical location information. For example, the system can input geographical location information into the generative AI and have the AI ​​select the most suitable ordering method. This ensures that the optimal ordering method is selected based on geographical location information.

[0064] The grocery ordering department can analyze the social media activity of elderly people when they place an order and suggest ordering methods. For example, the grocery ordering department can suggest ordering methods based on the groceries that elderly people have shared on social media. The grocery ordering department can order groceries from stores that elderly people follow on social media. The grocery ordering department can analyze the social media activity of elderly people and suggest the most suitable ordering method. The grocery ordering department can use generative AI to efficiently analyze social media activity. For example, the grocery ordering department can input social media activity data into the generative AI and have the generative AI generate ordering method suggestions. This makes it possible to suggest ordering methods based on social media activity.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The elderly support system can also be equipped with a reminder function. This reminder function can remind elderly individuals of medication, medical appointments, and daily tasks based on their schedules. For example, it can notify them of medication times to prevent them from forgetting. It can also notify them of medical appointment dates to prevent them from forgetting. Furthermore, it can notify them of daily tasks (such as taking out the trash or shopping) to support them in managing their daily lives. This helps elderly individuals remember and perform necessary daily tasks.

[0067] The elderly support system can also be equipped with an exercise suggestion unit. This unit can suggest an appropriate exercise plan based on the elderly person's health condition and lifestyle. For example, it can suggest light exercises appropriate to the elderly person's physical strength. It can also suggest exercises that are gentle on the joints based on the condition of the elderly person's joints and muscles. Furthermore, it can adjust the timing and frequency of exercise to suit the elderly person's lifestyle. This allows the elderly person to continue exercising without strain, contributing to the maintenance of their health.

[0068] The elderly support system can also include a community liaison department. This department can support elderly individuals in participating in local community activities. For example, it can provide information on local events and activities to make them more accessible. It can also introduce local volunteer activities and support groups to help elderly individuals maintain social connections. Furthermore, it can strengthen collaboration with local medical institutions and welfare services to make it easier for elderly individuals to receive necessary support. This allows elderly individuals to maintain connections with their communities and prevent isolation.

[0069] The elderly support system can also be equipped with a safety check unit. This unit can check the safety of the elderly person's living environment and daily life, and propose necessary countermeasures. For example, it can detect dangerous areas within the elderly person's home (e.g., steps or slippery floors) and propose solutions. It can also suggest safe routes for the elderly person when they go out, preventing accidents. Furthermore, it can conduct regular safety checks based on the elderly person's lifestyle patterns and notify them of any abnormalities. This ensures that the elderly person's living environment is safe and they can live with peace of mind.

[0070] The senior support system can also include a learning support section. This section can provide support for seniors to learn new knowledge and skills. For example, it could suggest online courses and workshops for seniors. The learning support section could customize learning content based on the seniors' interests and preferences. It could also provide progress tracking and reminder functions to help seniors continue learning. This allows seniors to improve their knowledge and skills and lead fulfilling lives by continuously learning new things.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The Health Management Department manages the health data of elderly individuals. For example, it collects and manages health data such as weight, blood pressure, and blood sugar levels. The Health Management Department can automatically collect health data by linking with healthcare devices and apps. For example, it can acquire and manage data from healthcare devices such as smartwatches and blood pressure monitors. It can also collect and manage data by linking with health management apps and fitness apps. Step 2: The anomaly detection unit detects anomalies based on data managed by the health management unit. For example, it analyzes health data to detect anomalies. Statistical analysis and machine learning algorithms can be used to detect anomalies. For example, it can detect abnormal fluctuations in weight or blood pressure. It can also detect abnormal fluctuations in blood glucose levels. It can also detect anomalies by considering the interrelationships between health data. Step 3: The emergency contact unit makes emergency contacts based on the anomaly detected by the anomaly detection unit. For example, if an anomaly is detected, it sends a notification to the emergency contact. Notifications can be sent via phone, email, SMS, etc. If an anomaly is detected, it can also call a nearby medical facility or an ambulance. If an anomaly is detected, it can also notify family members. Step 4: The Meal Planning Department proposes meal plans based on data managed by the Health Management Department. For example, it proposes the optimal meal plan based on the health condition and preferences of each elderly person. It can propose meal plans based on nutritional balance and individual health conditions. It can also provide recipes based on the meal plan. Step 5: The grocery ordering department orders ingredients based on the meal plan proposed by the meal planning department. For example, it automatically orders the necessary ingredients based on the meal plan. Ingredients can be ordered through online ordering systems or subscription services. Delivery of ingredients can also be arranged.

[0073] (Example of form 2) The elderly support system according to an embodiment of the present invention is a system that provides a mechanism for elderly people to easily access the support and services they need in their daily lives. This elderly support system includes the following five types of support. First, as nutritional management support, the generating AI proposes an optimal meal plan based on the health condition and preferences of each elderly person and automatically orders the necessary ingredients. Next, as health monitoring, the generating AI manages health data such as weight, blood pressure, and blood glucose levels in conjunction with healthcare devices and apps, and notifies a doctor if an abnormality is detected. Furthermore, as emergency support, if the generating AI detects abnormal movement of the elderly person, it automatically sends a notification to emergency contacts. As communication support, the generating AI suggests regular video calls with family members based on the elderly person's schedule. Finally, as support for daily life, the generating AI learns the elderly person's lifestyle patterns and automatically arranges the necessary support. This mechanism provides an environment in which elderly people living alone can live comfortably and with peace of mind, and also reduces the anxiety of their families. For example, the generating AI proposes an optimal meal plan based on the health condition and preferences of each elderly person and automatically orders the necessary ingredients. The generating AI manages health data such as weight, blood pressure, and blood glucose levels in conjunction with healthcare devices and apps, and notifies a doctor if an abnormality is detected. The generating AI automatically sends notifications to emergency contacts if it detects unusual movements in an elderly person. The generating AI suggests regular video calls with family members based on the elderly person's schedule. The generating AI learns the elderly person's lifestyle patterns and automatically arranges the necessary support. This ensures that the elderly support system makes it easy for elderly people to access the support and services they need in their daily lives. For example, the generating AI suggests optimal meal plans based on each elderly person's health condition and preferences and automatically orders necessary ingredients. The generating AI manages health data such as weight, blood pressure, and blood sugar levels through integration with healthcare devices and apps, and notifies doctors if abnormalities are detected. The generating AI automatically sends notifications to emergency contacts if it detects unusual movements in an elderly person. The generating AI suggests regular video calls with family members based on the elderly person's schedule. The generating AI learns the elderly person's lifestyle patterns and automatically arranges the necessary support.

[0074] The elderly support system according to this embodiment comprises a health management unit, an anomaly detection unit, an emergency contact unit, a meal suggestion unit, and a food ingredient ordering unit. The health management unit manages the health data of the elderly. The health management unit collects and manages health data such as weight, blood pressure, and blood glucose levels. The health management unit can automatically collect health data by linking with healthcare devices and apps. The health management unit acquires and manages data from healthcare devices such as smartwatches and blood pressure monitors. The health management unit can also collect and manage data by linking with health management apps and fitness apps. The anomaly detection unit detects anomalies based on the data managed by the health management unit. The anomaly detection unit detects anomalies by analyzing health data, for example. The anomaly detection unit can detect anomalies using statistical analysis and machine learning algorithms. The anomaly detection unit can detect abnormal fluctuations in weight and blood pressure, for example. The anomaly detection unit can also detect abnormal fluctuations in blood glucose levels. The anomaly detection unit can also detect anomalies by considering the interrelationships of health data. The Emergency Contact Department makes emergency contacts based on anomalies detected by the Anomaly Detection Department. For example, the Emergency Contact Department sends notifications to emergency contacts when an anomaly is detected. The Emergency Contact Department can send notifications by phone, email, SMS, etc. The Emergency Contact Department can also call nearby medical facilities or ambulances when an anomaly is detected. The Emergency Contact Department can also notify family members when an anomaly is detected. The Meal Suggestion Department proposes meal plans based on data managed by the Health Management Department. For example, the Meal Suggestion Department proposes optimal meal plans based on the health status and preferences of individual elderly individuals. The Meal Suggestion Department can propose meal plans based on nutritional balance and individual health status. The Meal Suggestion Department can also provide recipes based on meal plans. The Food Ordering Department orders food ingredients based on meal plans proposed by the Meal Suggestion Department. For example, the Food Ordering Department automatically orders the necessary ingredients based on meal plans. The Food Ordering Department can order ingredients through online ordering systems, subscriptions, etc. The Food Ordering Department can also arrange for food delivery.As a result, the elderly support system according to the embodiment allows elderly people to easily access the support and services they need in their daily lives. Some or all of the above-mentioned processes in the health management unit, anomaly detection unit, emergency contact unit, meal suggestion unit, and food ingredient ordering unit may be performed using AI, for example, or not using AI. For example, the health management unit can input data acquired from healthcare devices or apps into a generating AI and have the generating AI perform data management. The anomaly detection unit can input data managed by the health management unit into a generating AI and have the generating AI perform anomaly detection. The emergency contact unit can input anomalies detected by the anomaly detection unit into a generating AI and have the generating AI perform emergency contact. The meal suggestion unit can input data managed by the health management unit into a generating AI and have the generating AI perform meal plan suggestions. The food ingredient ordering unit can input meal plans suggested by the meal suggestion unit into a generating AI and have the generating AI perform food ingredient orders.

[0075] The Health Management Department manages the health data of elderly individuals. Specifically, it collects and manages health data such as weight, blood pressure, and blood sugar levels. This data is automatically collected through integration with healthcare devices and apps. For example, smartwatches record heart rate, steps, and sleep patterns, and blood pressure monitors measure blood pressure regularly. The data obtained from these devices is transmitted to the Health Management Department's system via Bluetooth or Wi-Fi. The Health Management Department centrally manages this data and monitors the health status of individual elderly individuals in real time. Furthermore, the Health Management Department can also collect records of meals and exercise through integration with health management apps and fitness apps. For example, meal tracking apps record calories and nutrients consumed, and exercise apps record the type of exercise, duration, and calories burned. This allows the Health Management Department to collect comprehensive health data and gain a detailed understanding of the health status of individual elderly individuals. In addition, the Health Management Department can store the collected data on a cloud server and share it with medical institutions and families as needed. This allows medical institutions to remotely monitor the health status of elderly individuals and provide appropriate medical services. Furthermore, family members can monitor the health status of elderly individuals in real time and provide necessary support.

[0076] The anomaly detection unit detects abnormalities based on data managed by the health management unit. Specifically, it uses statistical analysis and machine learning algorithms to detect abnormal fluctuations in weight, blood pressure, and blood glucose levels. For example, if weight increases or decreases rapidly, or if blood pressure exceeds the normal range, the anomaly detection unit detects this as an anomaly. The machine learning algorithm learns from past data and has the ability to distinguish between normal and abnormal patterns. This allows the anomaly detection unit to detect unusual patterns early and respond quickly. Furthermore, the anomaly detection unit can also detect anomalies by considering the interrelationships of health data. For example, if fluctuations in blood pressure and heart rate are linked, this is detected as an anomaly. In addition, when an anomaly is detected, the anomaly detection unit can propose appropriate responses depending on the type and severity of the anomaly. For example, in the case of a mild anomaly, it may suggest lifestyle improvements, and in the case of a severe anomaly, it may recommend visiting a medical institution. This allows the anomaly detection unit to constantly monitor the health status of elderly individuals and respond quickly and appropriately when an anomaly occurs.

[0077] The Emergency Contact Unit makes emergency contacts based on abnormalities detected by the Anomaly Detection Unit. Specifically, it sends notifications to emergency contacts when an abnormality is detected. Notifications are sent via methods such as phone, email, and SMS. For example, if blood pressure rises sharply, the Emergency Contact Unit will call the registered emergency contact to inform them of the abnormality. It can also send detailed information about the abnormality via email or SMS. Furthermore, the Emergency Contact Unit can also call a nearby medical facility or ambulance when an abnormality is detected. For example, if the heart rate becomes abnormally high, the Emergency Contact Unit will contact the nearest medical facility and arrange for an ambulance. It can also notify family members when an abnormality is detected. This allows family members to quickly understand the abnormality of the elderly person and take appropriate action. Furthermore, the Emergency Contact Unit can also suggest appropriate actions depending on the type and severity of the abnormality. For example, in the case of a minor abnormality, it may suggest lifestyle improvements, and in the case of a severe abnormality, it may recommend visiting a medical facility. This allows the Emergency Contact Unit to constantly monitor the health status of the elderly person and respond quickly and appropriately when an abnormality occurs.

[0078] The Meal Planning Department proposes meal plans based on data managed by the Health Management Department. Specifically, it proposes the optimal meal plan based on the health condition and preferences of each elderly person. For example, it proposes a low-salt meal plan for elderly people with high blood pressure and a low-carbohydrate meal plan for elderly people with high blood sugar levels. The Meal Planning Department can also propose meal plans based on nutritional balance and individual health conditions. For example, if there is a deficiency in vitamins or minerals, it will propose a meal plan that includes ingredients to supplement them. The Meal Planning Department can also provide recipes based on the meal plan. For example, it will provide specific recipes based on the proposed meal plan, explaining the cooking method and necessary ingredients in detail. This makes it easy for elderly people to prepare meals that suit their health condition. Furthermore, the Meal Planning Department can monitor the implementation status of the meal plan and modify the plan as needed. For example, if the proposed meal plan is not being followed or the health condition does not improve, it will review the plan and make more appropriate suggestions. In this way, the Meal Planning Department can constantly monitor the health condition of elderly people and provide the optimal meal plan.

[0079] The grocery ordering department orders groceries based on meal plans proposed by the meal planning department. Specifically, it automatically orders the necessary groceries based on the meal plan. For example, it creates a list of necessary groceries based on the proposed meal plan and orders them through an online ordering system. The grocery ordering department can order groceries through methods such as regular subscriptions and bulk orders. For example, it can regularly order the necessary groceries based on a weekly meal plan and arrange for delivery. It can also place additional orders if certain groceries are in short supply. Furthermore, the grocery ordering department can also arrange for grocery delivery. For example, it can deliver ordered groceries to the elderly person's home through a partnered delivery company. This allows the elderly person to easily obtain the necessary groceries. In addition, the grocery ordering department can manage order history and optimize future orders based on past order content. For example, by analyzing past order history and understanding which groceries are frequently ordered and how often, it can efficiently plan future orders. This allows the grocery ordering department to efficiently order and deliver the necessary groceries based on the elderly person's meal plan.

[0080] The Health Management Department can manage health data such as weight, blood pressure, and blood sugar levels by integrating with healthcare devices and apps. For example, the Health Management Department can acquire and manage data from healthcare devices such as smartwatches and blood pressure monitors. It can also collect and manage data by integrating with health management apps and fitness apps. To streamline data collection and management, the Health Management Department can use generative AI. For instance, it can input data acquired from healthcare devices and apps into the generative AI and have the AI ​​manage the data. This streamlines the management of health data.

[0081] The anomaly detection unit can analyze data managed by the health management unit and detect anomalies. For example, the anomaly detection unit can analyze health data and detect anomalies. The anomaly detection unit can detect anomalies using statistical analysis and machine learning algorithms. For example, the anomaly detection unit can detect abnormal fluctuations in weight or blood pressure. The anomaly detection unit can also detect abnormal fluctuations in blood glucose levels. The anomaly detection unit can also detect anomalies by considering the interrelationships of health data. The anomaly detection unit can use generative AI to streamline data analysis and anomaly detection. For example, the anomaly detection unit can input data managed by the health management unit into the generative AI and have the generative AI perform anomaly detection. This enables early detection of anomalies.

[0082] The emergency contact unit can send notifications to emergency contacts when an anomaly is detected. For example, the emergency contact unit can send notifications to emergency contacts when an anomaly is detected. The emergency contact unit can send notifications via methods such as phone, email, and SMS. The emergency contact unit can also call nearby medical facilities or ambulances when an anomaly is detected. The emergency contact unit can also notify family members when an anomaly is detected. The emergency contact unit can use generative AI to streamline the execution of emergency communications. For example, the emergency contact unit can input anomalies detected by the anomaly detection unit into the generative AI, and have the generative AI execute the emergency communications. This enables a rapid response in emergencies.

[0083] The meal planning department can propose optimal meal plans based on the health condition and preferences of individual elderly individuals. For example, the department can propose meal plans based on nutritional balance and individual health conditions. The department can also provide recipes based on the meal plans. To streamline the meal planning process, the department can utilize generative AI. For instance, the department can input data managed by the health management department into the generative AI and have the AI ​​generate meal plans. This enables the provision of individually optimized meal plans.

[0084] The ingredient ordering department can automatically order ingredients based on meal plans proposed by the meal planning department. For example, the ingredient ordering department can automatically order the necessary ingredients based on the meal plan. The ingredient ordering department can order ingredients through methods such as online ordering systems or subscription services. The ingredient ordering department can also arrange for ingredient delivery. The ingredient ordering department can use generative AI to streamline ingredient ordering. For example, the ingredient ordering department can input meal plans proposed by the meal planning department into the generative AI and have the generative AI execute the ingredient order. This automates ingredient ordering.

[0085] The emergency contact unit can call nearby medical facilities or ambulances in the event of an emergency. For example, the emergency contact unit can call nearby medical facilities or ambulances in an emergency. The emergency contact unit can use generative AI to streamline rapid medical response in emergencies. For example, the emergency contact unit can input anomalies detected by the anomaly detection unit into the generative AI, and have the generative AI execute the emergency contact. This enables a rapid medical response in emergencies.

[0086] The meal planning department can provide recipes based on meal plans. For example, the meal planning department can provide recipes based on meal plans. To streamline recipe provision, the meal planning department can use generative AI. For example, the meal planning department can input meal plans into the generative AI and have the generative AI generate recipes. This enables the provision of recipes based on meal plans.

[0087] The health management department can provide medication reminders. For example, the health management department can provide medication reminders. To streamline medication reminders, the health management department can use generative AI. For instance, the health management department can input medication schedules into the generative AI and have the AI ​​generate reminders. This makes medication management easier by providing medication reminders.

[0088] The emergency contact unit can notify family members in the event of an emergency. For example, the emergency contact unit can notify family members in an emergency. To streamline the rapid notification of family members in an emergency, the emergency contact unit can use a generation AI. For example, the emergency contact unit can input anomalies detected by the anomaly detection unit into the generation AI, and have the generation AI execute a notification to the family. This enables rapid notification of family members in an emergency.

[0089] The food ordering department can arrange for the delivery of ingredients. For example, the food ordering department can arrange for the delivery of ingredients. The food ordering department can use generative AI to streamline ingredient delivery. For example, the food ordering department can input the ordered ingredients into the generative AI and have the generative AI execute the delivery arrangements. This makes it easier to obtain ingredients by arranging their delivery.

[0090] The Health Management Department can estimate the emotions of elderly individuals and adjust the frequency of health data collection based on these estimates. For example, if an elderly individual is stressed, the Health Management Department can reduce the frequency of health data collection to alleviate their burden. If an elderly individual is relaxed, the Health Management Department can increase the frequency of health data collection to obtain more detailed data. If an elderly individual is anxious, the Health Management Department can appropriately adjust the collection frequency to provide a sense of security. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This enables the collection of health data tailored to the emotions of elderly individuals.

[0091] The Health Management Department can analyze the past health data of elderly individuals and select the optimal data collection method. For example, the Health Management Department can select a method for collecting data at specific time periods based on the elderly individuals' past health data. The Health Management Department can select the most effective device based on the elderly individuals' past health data. The Health Management Department can analyze the elderly individuals' past health data and optimize the frequency and timing of data collection. The Health Management Department can use generative AI to streamline data analysis and the selection of collection methods. For example, the Health Management Department can input past health data into the generative AI and have the generative AI select the optimal data collection method. This enables optimal data collection based on past data.

[0092] The Health Management Department can filter health data based on the lifestyle patterns of elderly individuals when collecting it. For example, the Health Management Department can analyze the lifestyle patterns of elderly individuals and collect data during their typical activity times. Based on these lifestyle patterns, the Health Management Department can filter out unnecessary data and collect only the important data. The Health Management Department can adjust the timing of data collection considering the lifestyle patterns of elderly individuals. The Health Management Department can use generative AI to streamline data filtering and collection. For example, the Health Management Department can input lifestyle pattern data into a generative AI and have the AI ​​perform the data filtering. This enables data filtering based on lifestyle patterns.

[0093] The Health Management Department can estimate the emotions of older adults and prioritize the health data to collect based on those estimated emotions. For example, if an older adult is stressed, the Health Management Department will prioritize the collection of stress-related data. If an older adult is relaxed, the Health Management Department can collect overall health data in a balanced manner. If an older adult is anxious, the Health Management Department can prioritize the collection of data that provides a sense of security. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the prioritization of data collection based on emotions.

[0094] The Health Management Department can prioritize the collection of highly relevant data by considering the geographical location information of elderly individuals when collecting health data. For example, if an elderly person is out, the Health Management Department can prioritize the collection of data such as steps taken and distance traveled. If an elderly person is at home, the Health Management Department can prioritize the collection of indoor activity data. If an elderly person is in a specific location, the Health Management Department can prioritize the collection of health data related to that location. The Health Management Department can use generative AI to streamline data collection. For example, the Health Management Department can input geographical location information into the generative AI and have the AI ​​collect highly relevant data. This enables data collection based on geographical location information.

[0095] The Health Management Department can analyze the social media activities of older adults and collect relevant data when collecting health data. For example, if an older adult is very active on social media, the Health Management Department can collect stress levels associated with that activity. If an older adult is inactive on social media, the Health Management Department can collect data related to feelings of loneliness. The Health Management Department can analyze the social media activities of older adults and identify factors that affect their health status, and collect data on those factors. The Health Management Department can use generative AI to streamline data collection. For example, the Health Management Department can input social media activity data into a generative AI and have the AI ​​collect relevant data. This enables data collection based on social media activity.

[0096] The anomaly detection unit can estimate the emotions of elderly individuals and adjust the anomaly detection criteria based on the estimated emotions. For example, if an elderly individual is experiencing stress, the anomaly detection unit will relax stress-related anomaly detection criteria. If an elderly individual is relaxed, the anomaly detection unit can apply the normal anomaly detection criteria. If an elderly individual is experiencing anxiety, the anomaly detection unit can set the anomaly detection criteria strictly, enabling early detection of anomalies. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for adjustment of the anomaly detection criteria based on emotions.

[0097] The anomaly detection unit can improve the accuracy of detection by considering the interrelationships of health data when detecting anomalies. For example, the anomaly detection unit can detect anomalies by combining weight and blood pressure data. The anomaly detection unit can detect anomalies by correlating blood glucose and heart rate data. The anomaly detection unit can analyze the interrelationships of health data and optimize the anomaly detection algorithm. The anomaly detection unit can use generative AI to efficiently analyze the interrelationships of data. For example, the anomaly detection unit can input the interrelationships of health data into generative AI and have the generative AI perform anomaly detection. This enables anomaly detection that takes into account the interrelationships of health data.

[0098] The anomaly detection unit can perform detection while considering the attribute information of elderly individuals. For example, the anomaly detection unit can adjust the criteria for anomaly detection based on the elderly person's age and gender. The anomaly detection unit can improve the accuracy of anomaly detection by considering the elderly person's medical history. The anomaly detection unit can perform anomaly detection while considering the elderly person's lifestyle and activity level. The anomaly detection unit can use a generation AI to efficiently analyze attribute information. For example, the anomaly detection unit can input attribute information into a generation AI and have the generation AI perform anomaly detection. This enables anomaly detection based on attribute information.

[0099] The anomaly detection unit can estimate the emotions of elderly individuals and adjust the order in which anomaly detection results are displayed based on the estimated emotions. For example, if an elderly individual is experiencing stress, the anomaly detection unit will prioritize displaying important anomaly detection results. If an elderly individual is relaxed, the anomaly detection unit can display all anomaly detection results in a balanced manner. If an elderly individual is feeling anxious, the anomaly detection unit can prioritize displaying anomaly detection results that provide a sense of security. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This enables the display of anomaly detection results based on emotions.

[0100] The anomaly detection unit can perform detection while considering the geographical distribution of health data when an anomaly is detected. For example, if an elderly person is in a specific area, the anomaly detection unit can detect an anomaly based on the health data of that area. If an elderly person is on the move, the anomaly detection unit can detect an anomaly by considering the health data of their destination. The anomaly detection unit can analyze the health data of the elderly person's residential area and adjust the criteria for anomaly detection. The anomaly detection unit can use generative AI to efficiently analyze geographical distribution. For example, the anomaly detection unit can input geographical distribution data into the generative AI and have the generative AI perform anomaly detection. This enables anomaly detection based on geographical distribution.

[0101] The anomaly detection unit can improve the accuracy of its detection by referring to relevant literature when an anomaly is detected. For example, the anomaly detection unit can improve the accuracy of its detection by referring to the latest medical literature when an anomaly is detected. The anomaly detection unit can optimize its detection algorithm based on past research data. The anomaly detection unit can improve the accuracy of its detection by referring to relevant academic papers. The anomaly detection unit can use a generation AI to streamline the literature referencing process. For example, the anomaly detection unit can input data from relevant literature into the generation AI and have the generation AI perform the task of improving the accuracy of its detection. As a result, the accuracy of anomaly detection is improved by referring to relevant literature.

[0102] The emergency contact unit can estimate the emotions of elderly individuals and adjust the method of emergency contact based on the estimated emotions. For example, if an elderly individual is feeling stressed, the emergency contact unit will make an emergency contact quickly. If an elderly individual is relaxed, the emergency contact unit can apply the normal emergency contact method. If an elderly individual is feeling anxious, the emergency contact unit can select an emergency contact method that provides reassurance. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the adjustment of emergency contact methods based on emotions.

[0103] The emergency contact department can select the most suitable contact method by referring to past emergency contact history during an emergency. For example, the emergency contact department can select the most effective contact method based on past emergency contact history. The emergency contact department can analyze past emergency contact history and determine the priority of contacts. The emergency contact department can customize contact methods by referring to past emergency contact history. The emergency contact department can use generative AI to streamline the retrieval of history. For example, the emergency contact department can input past emergency contact history into generative AI and have the generative AI select the most suitable contact method. This will result in the selection of the most suitable contact method based on past emergency contact history.

[0104] The emergency contact unit can customize the means of contact based on the elderly person's current situation during an emergency. For example, if the elderly person is at home, the emergency contact unit will use their home landline phone to make an emergency call. If the elderly person is out, the emergency contact unit can use their mobile phone to make an emergency call. If the elderly person is at a medical facility, the emergency contact unit can use the medical facility's contact information to make an emergency call. The emergency contact unit can use generative AI to efficiently analyze the current situation. For example, the emergency contact unit can input data on the current situation into the generative AI and have the generative AI customize the means of contact. This makes it possible to customize the means of contact based on the current situation.

[0105] The emergency contact unit can estimate the emotions of elderly individuals and determine the priority of emergency contacts based on those estimated emotions. For example, if an elderly individual is feeling stressed, the emergency contact unit will prioritize contacting the most important contacts. If the elderly individual is relaxed, the emergency contact unit can apply the normal contact order. If the elderly individual is feeling anxious, the emergency contact unit can prioritize contacting those who can provide reassurance. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the determination of priority for emergency contacts based on emotions.

[0106] The emergency contact unit can select the most appropriate contact method when an emergency occurs, taking into account the geographical location of the elderly person. For example, if the elderly person is at home, the emergency contact unit can use their home landline phone to make an emergency call. If the elderly person is out, the emergency contact unit can use their mobile phone to make an emergency call. If the elderly person is at a medical institution, the emergency contact unit can use the medical institution's contact information to make an emergency call. The emergency contact unit can use generative AI to efficiently analyze geographical location information. For example, the emergency contact unit can input geographical location information into the generative AI and have the generative AI select the most appropriate contact method. This ensures that the most suitable contact method is selected based on geographical location information.

[0107] The emergency contact unit can analyze the social media activity of elderly individuals and suggest appropriate contact methods during emergencies. For example, if an elderly individual is actively using social media, the unit can use a messaging app for emergency contact. If an elderly individual is less active on social media, the unit can use telephone contact. The emergency contact unit can analyze the elderly individual's social media activity and suggest the most suitable contact method. The emergency contact unit can use generative AI to efficiently analyze social media activity. For example, the emergency contact unit can input social media activity data into generative AI and have the generative AI suggest contact methods. This enables the suggestion of contact methods based on social media activity.

[0108] The meal planning function can estimate the emotions of elderly individuals and adjust its meal plan suggestions based on those estimated emotions. For example, if an elderly person is feeling stressed, the function can suggest a meal plan that is effective in reducing stress. If an elderly person is relaxed, the function can suggest a balanced meal plan. If an elderly person is feeling anxious, the function can suggest a meal plan that provides a sense of security. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for adjustment of the meal plan suggestion method based on emotions.

[0109] The meal planning department can analyze an elderly person's past eating history to select the optimal plan when proposing meals. For example, the department can propose a plan that includes preferred ingredients based on the elderly person's past eating history. The department can select a nutritionally balanced plan based on the elderly person's past eating history. The department can analyze the elderly person's past eating history and propose a plan that accommodates allergies and dietary restrictions. The department can use generative AI to streamline data analysis and plan selection. For example, the department can input past eating history into the generative AI and have the generative AI select the optimal plan. This ensures that the optimal plan is selected based on past eating history.

[0110] The meal planning department can customize meal plans based on the elderly person's current health condition. For example, it can propose a low-sodium meal plan considering the elderly person's current health condition. It can propose a carbohydrate-restricted meal plan based on the elderly person's current health condition. It can analyze the elderly person's current health condition and customize a plan that includes the necessary nutrients. The meal planning department can use generative AI to streamline data analysis and plan customization. For example, it can input current health data into the generative AI and have the generative AI perform the plan customization. This makes it possible to customize plans based on the current health condition.

[0111] The meal planning system can estimate the emotions of elderly individuals and prioritize meal plans based on those estimated emotions. For example, if an elderly individual is feeling stressed, the system will prioritize suggesting plans that are effective in reducing stress. If an elderly individual is relaxed, the system can prioritize suggesting balanced plans. If an elderly individual is feeling anxious, the system can prioritize suggesting plans that provide a sense of security. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the prioritization of meal plans based on emotions.

[0112] The meal planning department can select the optimal plan when proposing meals, taking into account the geographical location of the elderly person. For example, the department can propose a plan using ingredients from the area where the elderly person lives. If the elderly person is traveling, the department can propose a plan using local specialties from that area. Based on the elderly person's geographical location, the department can select a plan using ingredients that are easily available. The meal planning department can use generative AI to efficiently analyze geographical location information. For example, the department can input geographical location information into the generative AI and have the generative AI select the optimal plan. This ensures that the optimal plan is selected based on geographical location information.

[0113] The meal planning department can analyze the social media activity of elderly individuals to propose meal plans. For example, it can propose plans based on the food preferences that elderly individuals have shared on social media. It can propose plans that reflect the cooking trends that elderly individuals are following on social media. It can analyze the social media activity of elderly individuals and propose plans that are likely to interest them. The meal planning department can use generative AI to efficiently analyze social media activity. For example, it can input social media activity data into generative AI and have the generative AI generate plan proposals. This makes it possible to propose plans based on social media activity.

[0114] The grocery ordering system can estimate the emotions of elderly individuals and adjust the grocery ordering process based on those estimated emotions. For example, if an elderly person is feeling stressed, the grocery ordering system can provide an easy-to-use ordering interface. If an elderly person is relaxed, the grocery ordering system can offer detailed ordering options. If an elderly person is feeling anxious, the grocery ordering system can provide support to alleviate their anxiety. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the grocery ordering process to be adjusted based on the individual's emotions.

[0115] The grocery ordering system can select the optimal ordering method by referring to past order history when ordering groceries. For example, the grocery ordering system can prioritize displaying frequently ordered groceries based on the past order history of elderly customers. The grocery ordering system can analyze the past order history of elderly customers and suggest the optimal ordering method. The grocery ordering system can refer to the past order history of elderly customers and provide options to reduce the effort required for ordering. The grocery ordering system can use generative AI to streamline the process of referring to history and selecting ordering methods. For example, the grocery ordering system can input past order history into the generative AI and have the generative AI select the optimal ordering method. This will result in the selection of the optimal ordering method based on past order history.

[0116] The grocery ordering system can customize ordering methods based on the elderly person's current living situation when they place a grocery order. For example, if the elderly person is at home, the grocery ordering system will prioritize suggesting home delivery. If the elderly person is out, the grocery ordering system can suggest picking up the order at a nearby store. The grocery ordering system can customize the optimal ordering method considering the elderly person's living situation. The grocery ordering system can use generative AI to efficiently analyze living situations. For example, the grocery ordering system can input data on the current living situation into the generative AI and have the generative AI perform the customization of the ordering method. This makes it possible to customize ordering methods based on the current living situation.

[0117] The food ordering system can estimate the emotions of elderly individuals and determine the order priority of food items based on those estimated emotions. For example, if an elderly person is stressed, the system will prioritize ordering food items that are effective in reducing stress. If an elderly person is relaxed, the system can prioritize ordering balanced food items. If an elderly person is anxious, the system can prioritize ordering food items that provide a sense of security. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This determines the order priority of food items based on emotions.

[0118] The grocery ordering system can select the most suitable ordering method when an elderly person places an order, taking into account their geographical location. For example, the system can order groceries from stores in the elderly person's local area. If the elderly person is traveling, the system can order groceries from stores in their travel destination. The system can select the most convenient ordering method based on the elderly person's geographical location. The system can use generative AI to efficiently analyze geographical location information. For example, the system can input geographical location information into the generative AI and have the AI ​​select the most suitable ordering method. This ensures that the optimal ordering method is selected based on geographical location information.

[0119] The grocery ordering department can analyze the social media activity of elderly people when they place an order and suggest ordering methods. For example, the grocery ordering department can suggest ordering methods based on the groceries that elderly people have shared on social media. The grocery ordering department can order groceries from stores that elderly people follow on social media. The grocery ordering department can analyze the social media activity of elderly people and suggest the most suitable ordering method. The grocery ordering department can use generative AI to efficiently analyze social media activity. For example, the grocery ordering department can input social media activity data into the generative AI and have the generative AI generate ordering method suggestions. This makes it possible to suggest ordering methods based on social media activity.

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

[0121] The elderly support system can also be equipped with a reminder function. This reminder function can remind elderly individuals of medication, medical appointments, and daily tasks based on their schedules. For example, it can notify them of medication times to prevent them from forgetting. It can also notify them of medical appointment dates to prevent them from forgetting. Furthermore, it can notify them of daily tasks (such as taking out the trash or shopping) to support them in managing their daily lives. This helps elderly individuals remember and perform necessary daily tasks.

[0122] The elderly support system can also include an entertainment provision section. This section can provide appropriate entertainment content based on the elderly person's preferences and emotions. For example, if the elderly person is relaxed, the entertainment provision section can suggest relaxing music or movies. If the elderly person is stressed, the entertainment provision section can provide content that is effective in reducing stress. If the elderly person is feeling lonely, the entertainment provision section can suggest online interaction or community activities. This improves the quality of life for the elderly and provides entertainment that is tailored to their emotions.

[0123] The elderly support system can also be equipped with an exercise suggestion unit. This unit can suggest an appropriate exercise plan based on the elderly person's health condition and lifestyle. For example, it can suggest light exercises appropriate to the elderly person's physical strength. It can also suggest exercises that are gentle on the joints based on the condition of the elderly person's joints and muscles. Furthermore, it can adjust the timing and frequency of exercise to suit the elderly person's lifestyle. This allows the elderly person to continue exercising without strain, contributing to the maintenance of their health.

[0124] The elderly support system may also include an emotion analysis unit. This unit can estimate the emotions of the elderly person and adjust the overall system's operation based on the estimated emotions. For example, if the elderly person is stressed, the emotion analysis unit can provide effective support to reduce stress. If the elderly person is relaxed, the emotion analysis unit can provide support to maintain that relaxation. If the elderly person is anxious, the emotion analysis unit can provide support to provide a sense of security. This ensures that appropriate support is provided according to the elderly person's emotions.

[0125] The elderly support system can also include a community liaison department. This department can support elderly individuals in participating in local community activities. For example, it can provide information on local events and activities to make them more accessible. It can also introduce local volunteer activities and support groups to help elderly individuals maintain social connections. Furthermore, it can strengthen collaboration with local medical institutions and welfare services to make it easier for elderly individuals to receive necessary support. This allows elderly individuals to maintain connections with their communities and prevent isolation.

[0126] The elderly support system can also include a hobby suggestion section. This section can suggest new hobbies and activities based on the elderly person's interests and emotions. For example, if the elderly person is relaxed, the hobby suggestion section can suggest relaxing hobbies (e.g., gardening or reading). If the elderly person is stressed, the hobby suggestion section can suggest hobbies that are effective in reducing stress (e.g., yoga or meditation). If the elderly person is feeling lonely, the hobby suggestion section can suggest hobbies that allow them to interact with others (e.g., craft classes or online games). This improves the quality of life for the elderly and provides hobbies that are tailored to their emotions.

[0127] The elderly support system can also be equipped with a safety check unit. This unit can check the safety of the elderly person's living environment and daily life, and propose necessary countermeasures. For example, it can detect dangerous areas within the elderly person's home (e.g., steps or slippery floors) and propose solutions. It can also suggest safe routes for the elderly person when they go out, preventing accidents. Furthermore, it can conduct regular safety checks based on the elderly person's lifestyle patterns and notify them of any abnormalities. This ensures that the elderly person's living environment is safe and they can live with peace of mind.

[0128] The elderly support system can also include an emotion sharing section. This section can support elderly people in sharing their emotions with family and friends. For example, it can provide a function for elderly people to record their emotions and send them to family and friends. It can also provide tools for elderly people to express their emotions (e.g., emojis and stamps). By sharing their emotions, elderly people can deepen their communication with family and friends. This prevents elderly people from becoming isolated and allows them to gain a sense of security by sharing their emotions.

[0129] The senior support system can also include a learning support section. This section can provide support for seniors to learn new knowledge and skills. For example, it could suggest online courses and workshops for seniors. The learning support section could customize learning content based on the seniors' interests and preferences. It could also provide progress tracking and reminder functions to help seniors continue learning. This allows seniors to improve their knowledge and skills and lead fulfilling lives by continuously learning new things.

[0130] The elderly support system can also be equipped with an emotional feedback unit. This unit can provide real-time feedback on the elderly person's emotions and offer support tailored to their emotional changes. For example, if the elderly person is feeling stressed, the emotional feedback unit can provide advice to reduce stress. If the elderly person is relaxed, the emotional feedback unit can provide support to maintain that relaxation. If the elderly person is feeling anxious, the emotional feedback unit can provide support to reassure them. This ensures that appropriate feedback is provided according to the elderly person's emotions, promoting emotional stability.

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

[0132] Step 1: The Health Management Department manages the health data of elderly individuals. For example, it collects and manages health data such as weight, blood pressure, and blood sugar levels. The Health Management Department can automatically collect health data by linking with healthcare devices and apps. For example, it can acquire and manage data from healthcare devices such as smartwatches and blood pressure monitors. It can also collect and manage data by linking with health management apps and fitness apps. Step 2: The anomaly detection unit detects anomalies based on data managed by the health management unit. For example, it analyzes health data to detect anomalies. Statistical analysis and machine learning algorithms can be used to detect anomalies. For example, it can detect abnormal fluctuations in weight or blood pressure. It can also detect abnormal fluctuations in blood glucose levels. It can also detect anomalies by considering the interrelationships between health data. Step 3: The emergency contact unit makes emergency contacts based on the anomaly detected by the anomaly detection unit. For example, if an anomaly is detected, it sends a notification to the emergency contact. Notifications can be sent via phone, email, SMS, etc. If an anomaly is detected, it can also call a nearby medical facility or an ambulance. If an anomaly is detected, it can also notify family members. Step 4: The Meal Planning Department proposes meal plans based on data managed by the Health Management Department. For example, it proposes the optimal meal plan based on the health condition and preferences of each elderly person. It can propose meal plans based on nutritional balance and individual health conditions. It can also provide recipes based on the meal plan. Step 5: The grocery ordering department orders ingredients based on the meal plan proposed by the meal planning department. For example, it automatically orders the necessary ingredients based on the meal plan. Ingredients can be ordered through online ordering systems or subscription services. Delivery of ingredients can also be arranged.

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

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

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

[0136] Each of the multiple elements described above, including the health management unit, anomaly detection unit, emergency contact unit, meal suggestion unit, and ingredient ordering unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the health management unit acquires data from healthcare devices and apps on the smart device 14 and manages it with the specific processing unit 290 of the data processing unit 12. The anomaly detection unit analyzes health data with the specific processing unit 290 of the data processing unit 12 and detects anomalies. The emergency contact unit makes an emergency contact with the specific processing unit 290 of the data processing unit 12 based on the anomaly detected by the anomaly detection unit. The meal suggestion unit proposes a meal plan with the specific processing unit 290 of the data processing unit 12 based on the data managed by the health management unit. The ingredient ordering unit orders ingredients with the specific processing unit 290 of the data processing unit 12 based on the meal plan proposed by the meal suggestion unit. 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.

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

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

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

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

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

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

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

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

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

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

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

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

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

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

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

[0152] Each of the multiple elements described above, including the health management unit, anomaly detection unit, emergency contact unit, meal suggestion unit, and ingredient ordering unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the health management unit acquires data from the healthcare device or app of the smart glasses 214 and manages it by the specific processing unit 290 of the data processing unit 12. The anomaly detection unit analyzes health data by the specific processing unit 290 of the data processing unit 12 and detects anomalies. The emergency contact unit makes an emergency contact by the specific processing unit 290 of the data processing unit 12 based on the anomaly detected by the anomaly detection unit. The meal suggestion unit proposes a meal plan by the specific processing unit 290 of the data processing unit 12 based on the data managed by the health management unit. The ingredient ordering unit orders ingredients by the specific processing unit 290 of the data processing unit 12 based on the meal plan proposed by the meal suggestion unit. 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.

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

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

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

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

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

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

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

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

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

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

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

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

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

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

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

[0168] Each of the multiple elements described above, including the health management unit, anomaly detection unit, emergency contact unit, meal suggestion unit, and ingredient ordering unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the health management unit acquires data from the healthcare device or application of the headset terminal 314 and manages it with the specific processing unit 290 of the data processing unit 12. The anomaly detection unit analyzes health data with the specific processing unit 290 of the data processing unit 12 and detects anomalies. The emergency contact unit makes an emergency contact with the specific processing unit 290 of the data processing unit 12 based on the anomaly detected by the anomaly detection unit. The meal suggestion unit proposes a meal plan with the specific processing unit 290 of the data processing unit 12 based on the data managed by the health management unit. The ingredient ordering unit orders ingredients with the specific processing unit 290 of the data processing unit 12 based on the meal plan proposed by the meal suggestion unit. The correspondence between each unit and the devices or control units is not limited to the example described above and can be changed in various ways.

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

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

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

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

[0173] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

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

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

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

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

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

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

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

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

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

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

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

[0185] Each of the multiple elements described above, including the health management unit, anomaly detection unit, emergency contact unit, meal suggestion unit, and ingredient ordering unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the health management unit acquires data from the robot 414's healthcare device or application and manages it with the specific processing unit 290 of the data processing unit 12. The anomaly detection unit analyzes health data with the specific processing unit 290 of the data processing unit 12 and detects anomalies. The emergency contact unit makes an emergency contact with the specific processing unit 290 of the data processing unit 12 based on the anomaly detected by the anomaly detection unit. The meal suggestion unit proposes a meal plan with the specific processing unit 290 of the data processing unit 12 based on the data managed by the health management unit. The ingredient ordering unit orders ingredients with the specific processing unit 290 of the data processing unit 12 based on the meal plan proposed by the meal suggestion unit. The correspondence between each unit and the devices or control units is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0204] (Note 1) The Health Management Department manages the health data of the elderly, An anomaly detection unit that detects anomalies based on data managed by the aforementioned health management unit, An emergency contact unit that makes an emergency contact based on an anomaly detected by the anomaly detection unit, A meal plan proposal department proposes meal plans based on data managed by the aforementioned health management department, The system includes a food ordering unit that orders food ingredients based on the meal plan proposed by the aforementioned meal proposal unit. A system characterized by the following features. (Note 2) The aforementioned health management department, By integrating with healthcare devices and apps, you can manage health data such as weight, blood pressure, and blood sugar levels. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned abnormality detection unit, The data managed by the aforementioned health management department is analyzed to detect abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned emergency liaison department, If an anomaly is detected, a notification will be sent to the emergency contact. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned meal proposal department, We propose the optimal meal plan based on the health condition and preferences of each elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned food ordering department is, The system automatically orders ingredients based on the meal plan proposed by the aforementioned meal planning department. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned emergency liaison department, In an emergency, call a nearby medical facility or an ambulance. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned meal proposal department, Provide recipes based on your meal plan. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned health management department, Provides medication reminders. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned emergency liaison department, In case of emergency, notify family members. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned food ordering department is, Arrange for grocery delivery. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned health management department, The system estimates the emotions of older adults and adjusts the frequency of health data collection based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned health management department, Analyze past health data of elderly individuals to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned health management department, When collecting health data, the data is filtered based on the lifestyle patterns of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned health management department, This system estimates the emotions of older adults and prioritizes the health data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned health management department, When collecting health data, prioritize the collection of highly relevant data by considering the geographical location information of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned health management department, When collecting health data, analyze the social media activity of older adults and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned abnormality detection unit, The system estimates the emotions of elderly individuals and adjusts the criteria for detecting abnormalities based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned abnormality detection unit, When anomalies are detected, the accuracy of the detection is improved by considering the interrelationships of health data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned abnormality detection unit, When an anomaly is detected, the detection process takes into account the attribute information of the elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned abnormality detection unit, The system estimates the emotions of elderly individuals and adjusts the order in which anomaly detection results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned abnormality detection unit, When detecting an anomaly, the detection process takes into account the geographical distribution of health data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned abnormality detection unit, When detecting anomalies, we improve the accuracy of the detection by referring to relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned emergency liaison department, The system estimates the emotions of elderly individuals and adjusts emergency contact methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned emergency liaison department, In the event of an emergency, the system will refer to past emergency contact history to select the most appropriate contact method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned emergency liaison department, In emergency situations, customize the means of contact based on the elderly person's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned emergency liaison department, The system estimates the emotions of elderly individuals and prioritizes emergency contacts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned emergency liaison department, When making an emergency contact, the most appropriate contact method will be selected, taking into account the geographical location of the elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned emergency liaison department, In emergency situations, we analyze the social media activity of elderly individuals and suggest appropriate communication methods. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned meal proposal department, The system estimates the emotions of elderly individuals and adjusts the meal plan suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned meal proposal department, When proposing meal plans, we analyze the elderly person's past eating history to select the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned meal proposal department, When suggesting meals, customize the plan based on the elderly person's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned meal proposal department, The system estimates the emotions of elderly individuals and prioritizes meal plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned meal proposal department, When proposing meal plans, the optimal plan is selected by considering the geographical location of the elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned meal proposal department, When suggesting meals, we analyze the social media activity of elderly people and propose a plan based on that. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned food ordering department is, The system estimates the emotions of elderly people and adjusts how they order groceries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned food ordering department is, When ordering ingredients, refer to past order history to select the most suitable ordering method. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned food ordering department is, When ordering groceries, customize the ordering method based on the elderly person's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned food ordering department is, The system estimates the emotions of elderly people and determines the priority of grocery orders based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned food ordering department is, When ordering groceries, the system selects the most suitable ordering method by taking into account the geographical location of elderly customers. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned food ordering department is, When ordering groceries, we analyze the social media activity of elderly people and suggest ordering methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. The Health Management Department manages the health data of the elderly, An anomaly detection unit that detects anomalies based on data managed by the aforementioned health management unit, An emergency contact unit that makes an emergency contact based on an anomaly detected by the anomaly detection unit, A meal plan proposal department proposes meal plans based on data managed by the aforementioned health management department, The system includes a food ordering unit that orders food ingredients based on the meal plan proposed by the aforementioned meal proposal unit. A system characterized by the following features.

2. The aforementioned health management department, By integrating with healthcare devices and apps, you can manage health data such as weight, blood pressure, and blood sugar levels. The system according to feature 1.

3. The aforementioned abnormality detection unit, The data managed by the aforementioned health management department is analyzed to detect abnormalities. The system according to feature 1.

4. The aforementioned emergency liaison department, If an anomaly is detected, a notification will be sent to the emergency contact. The system according to feature 1.

5. The aforementioned meal proposal department, We propose the optimal meal plan based on the health condition and preferences of each elderly person. The system according to feature 1.

6. The aforementioned food ordering department is, The system automatically orders ingredients based on the meal plan proposed by the aforementioned meal planning department. The system according to feature 1.

7. The aforementioned emergency liaison department, In an emergency, call a nearby medical facility or an ambulance. The system according to feature 1.

8. The aforementioned meal proposal department, Provide recipes based on your meal plan. The system according to feature 1.

9. The aforementioned health management department, Provides medication reminders. The system according to feature 1.

10. The aforementioned emergency liaison department, In case of emergency, notify family members. The system according to feature 1.

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