system

Abstract

The system according to this embodiment aims to provide comprehensive health management and mental support for the elderly. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a dialogue unit, a management unit, a nutrition unit, and a collaboration unit. The data collection unit collects vital signs and sleep patterns. The analysis unit analyzes the data collected by the data collection unit and detects abnormalities early. The dialogue unit provides mental support through everyday conversation and cognitive function training. The management unit provides appropriate management of prescription drugs and reminders for taking them. The nutrition unit analyzes dietary content and provides individualized nutritional advice. The collaboration unit arranges online consultations with medical professionals.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to comprehensively perform health management and mental support for the elderly, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively perform health management and mental support for the elderly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a dialogue unit, a management unit, a nutrition unit, and a collaboration unit. The data collection unit collects vital signs and sleep patterns. The analysis unit analyzes the data collected by the data collection unit and detects abnormalities early. The dialogue unit provides mental support through everyday conversation and cognitive function training. The management unit provides appropriate management of prescription medications and reminders for taking them. The nutrition unit analyzes dietary content and provides individualized nutritional advice. The collaboration unit arranges online consultations with medical professionals. [Effects of the Invention]

[0007] The system according to this embodiment can comprehensively manage the health and provide mental support to the elderly. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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 HealthMate AI System, according to an embodiment of the present invention, is an AI-equipped smart device and application service that supports the health management and mental well-being of elderly people living alone. The HealthMate AI System utilizes the latest generative AI technology to support the daily lives of the elderly, continuously monitoring their health status, reducing feelings of loneliness, and maintaining cognitive function. For example, the HealthMate AI System includes a health monitoring AI, a conversational AI companion, a medication management AI, a nutrition management AI, and telemedicine collaboration. These functions are interconnected to form a system that comprehensively supports the health of the elderly. For instance, the conversational AI companion can use data collected by the health monitoring AI to provide appropriate advice to the user. Similarly, the medication management AI and nutrition management AI can use data collected by the health monitoring AI to provide appropriate reminders and advice to the user. Furthermore, telemedicine collaboration facilitates smoother communication with medical professionals, making health management for the elderly more effective. This is expected to improve the quality of life (QOL) of the elderly. Thus, the HealthMate AI System can comprehensively manage the health of the elderly and achieve an improved QOL.

[0029] The Healthmate AI system according to this embodiment comprises a data collection unit, an analysis unit, a dialogue unit, a management unit, a nutrition unit, and a collaboration unit. The data collection unit collects vital signs and sleep patterns. The data collection unit collects vital signs such as heart rate, blood pressure, and body temperature. The data collection unit can also collect sleep patterns such as sleep duration, sleep quality, and the ratio of REM sleep to non-REM sleep. The data collection unit monitors vital signs in real time using a wearable device, for example. The data collection unit can also record sleep patterns using a smartphone app. Furthermore, the data collection unit can perform regular health checks and collect data. The analysis unit analyzes the data collected by the data collection unit and detects abnormalities early. The analysis unit can detect abnormal fluctuations in heart rate or sudden increases in blood pressure, for example. The analysis unit can also detect a decline in sleep quality or abnormalities in the ratio of REM sleep to non-REM sleep. The analysis unit analyzes the data using an AI algorithm, for example, and detects abnormalities. The analysis unit can also detect abnormalities by comparing them with past data. Furthermore, the analysis unit can issue alerts if an anomaly is detected. The dialogue unit provides mental support through everyday conversation and cognitive function training. The dialogue unit can offer topics such as weather and news. It can also provide memory training and problem-solving skills training. The dialogue unit interacts with users using, for example, an AI chatbot. It can also understand user statements using speech recognition technology and provide appropriate responses. Furthermore, the dialogue unit can estimate the user's emotions and engage in emotionally appropriate conversations. The management unit provides proper management of prescription medications and reminders for taking them. The management unit can, for example, manage medication schedules and remind users. It can also manage how medications are stored. The management unit can, for example, provide medication reminders using a smartphone app. It can also optimize medication schedules using AI algorithms. Furthermore, it can estimate the user's emotions and provide emotionally appropriate reminders. The nutrition unit analyzes dietary content and provides personalized nutritional advice. The nutrition department, for example, analyzes the balance of calories and nutrients.Furthermore, the nutrition department can offer suggestions for dietary improvements and recommend supplements. For example, the nutrition department can record and analyze meal content using a smartphone app. The nutrition department can also provide nutritional advice using AI algorithms. In addition, the nutrition department can estimate the user's emotions and provide advice tailored to those emotions. The liaison department arranges online consultations with medical professionals. For example, the liaison department arranges consultations via video call or chat. The liaison department can also facilitate smooth collaboration with medical professionals. For example, the liaison department arranges online consultations using a smartphone app. The liaison department can also select the optimal consultation timing using AI algorithms. In addition, the liaison department can estimate the user's emotions and arrange consultations tailored to those emotions. As a result, the HealthMate AI system according to this embodiment can comprehensively manage the health of the elderly and improve their quality of life (QOL).

[0030] The data collection unit collects vital signs and sleep patterns. For example, it collects vital signs such as heart rate, blood pressure, and body temperature. Specifically, it uses wearable devices to monitor heart rate, blood pressure, and body temperature in real time. These devices are worn on the user's body and can collect data 24 hours a day. Examples include smartwatches and fitness trackers. These devices transmit data to smartphones or cloud servers via Bluetooth® or Wi-Fi for centralized management. The data collection unit can also collect sleep patterns, such as sleep duration, sleep quality, and the ratio of REM to non-REM sleep. This is done using smartphone apps or dedicated sleep trackers. Smartphone apps, placed by the user at bedtime, use accelerometers and microphones to record sleep quality. Dedicated sleep trackers are placed under the mattress and provide detailed sleep data by monitoring the user's movements and breathing. Furthermore, the data collection unit can also perform regular health checks and collect data. For example, users can undergo regular health checkups at medical institutions and input the results into the system to track long-term changes in their health. This allows the data collection unit to comprehensively monitor the user's health status and collect data in real time using a variety of devices and methods.

[0031] The analysis unit analyzes data collected by the data collection unit to detect anomalies early. For example, the analysis unit can detect abnormal fluctuations in heart rate or sudden increases in blood pressure. Specifically, it uses AI algorithms to analyze collected data in real time and detect abnormal patterns. For instance, if heart rate suddenly rises beyond the normal range, or if blood pressure fluctuates significantly in a short period, the system immediately issues an alert. The analysis unit can also detect declines in sleep quality or abnormalities in the ratio of REM to non-REM sleep. This involves analyzing sleep data and comparing it to normal patterns to identify anomalies. For example, if the percentage of REM sleep is extremely low, or if sleep quality consistently declines, the system alerts the user. Furthermore, the analysis unit can detect anomalies by comparing current data with past data. This allows for tracking long-term changes in health status and early detection of abnormalities. For example, it can analyze heart rate and blood pressure trends based on data from the past few months and issue an alert if abnormal fluctuations are observed. The analysis unit can also issue alerts when an anomaly is detected. Alerts are communicated to the user via smartphone notifications, email, or voice assistants. This allows users to recognize abnormalities early and take appropriate measures. The analysis department utilizes AI technology to quickly and accurately analyze the collected data and monitor users' health status in real time.

[0032] The dialogue unit provides mental support through everyday conversation and cognitive function training. For example, it offers topics such as weather and news. Specifically, it uses an AI chatbot to interact with users and support their mental health through everyday conversation. The chatbot uses natural language processing technology to understand user statements and provide appropriate responses. For example, if a user says, "The weather is nice today," the chatbot will respond, "Yes, it's sunny and pleasant today." The dialogue unit can also provide memory training and problem-solving skills training. This includes quizzes, puzzles, and memory games. For example, the chatbot might ask, "What did you have for dinner yesterday?" and the user can train their memory by answering. Furthermore, the dialogue unit can use speech recognition technology to understand user statements and provide appropriate responses, allowing users to enjoy natural conversations. In addition, the dialogue unit can estimate user emotions and engage in emotionally appropriate conversations. For example, if a user speaks in a sad voice, the chatbot will gently ask, "Is there something bothering you?" This allows the dialogue unit to support the user's mental health and reduce feelings of loneliness. The dialogue unit utilizes AI technology to enable natural conversations with the user and provide emotional support.

[0033] The management department ensures the proper management of prescription medications and provides reminders for taking them. For example, the management department manages medication schedules and sends reminders to users. Specifically, it uses a smartphone app to set the user's medication schedule and send reminder notifications. For example, if medication needs to be taken after breakfast, the app will display a reminder at breakfast time, prompting the user to take their medication. The management department can also manage how medications are stored. This includes the storage location and temperature control of the medications. For example, if a particular medication requires refrigeration, the app will notify the user and instruct them on the appropriate storage method. Furthermore, the management department can optimize medication schedules using AI algorithms. This makes it possible to suggest the optimal timing for taking medication based on the user's lifestyle and health condition. For example, if taking medication at night reduces side effects, the app will set a reminder for that time. The management department can also estimate the user's emotions and provide reminders accordingly. For example, if the user is feeling stressed, the app will send reminders in gentle language to reduce the user's burden. This allows the management department to support users' medication management and promote appropriate medication adherence. The management department can utilize AI technology to efficiently and effectively manage users' health.

[0034] The nutrition department analyzes dietary content and provides personalized nutritional advice. For example, it analyzes calorie and nutrient balance. Specifically, a smartphone app allows users to record their meals, and based on this data, it calculates calorie and nutrient intake. For instance, if a user enters the ingredients they ate for breakfast into the app, the app automatically calculates the calories and nutrients of those ingredients and displays the total intake. The nutrition department can also offer suggestions for dietary improvements and recommend supplements. This includes specific advice tailored to the user's health condition and goals. For example, if a user is trying to lose weight, the app suggests low-calorie, nutritionally balanced meal menus. Furthermore, the nutrition department can use AI algorithms to provide nutritional advice. This allows for optimal nutritional advice based on the user's dietary history and health data. For example, if a user is deficient in vitamin D, the app recommends foods and supplements rich in vitamin D. In addition, the nutrition department can estimate the user's emotions and provide emotionally appropriate advice. For example, if a user is stressed, the app suggests foods and recipes effective for stress reduction. This allows the nutrition department to support users in managing their diets and promote healthy eating habits. The nutrition department can utilize AI technology to provide nutritional advice tailored to the individual needs of each user.

[0035] The Collaboration Department arranges online consultations with medical professionals. For example, it arranges consultations via video call or chat. Specifically, it enables users to easily contact medical professionals using a smartphone app. For instance, users can book appointments through the app and consult with a doctor via video call at a designated time. The Collaboration Department also facilitates smooth collaboration with medical professionals. This includes a function to share the user's health data with medical professionals. For example, the user's vital signs and sleep data can be provided to doctors for use in treatment. Furthermore, the Collaboration Department can use AI algorithms to select the optimal timing for consultations. This allows for the suggestion of an optimal consultation schedule tailored to the user's lifestyle and health condition. For example, if a user's symptoms worsen at night, the app recommends booking a nighttime consultation. The Collaboration Department can also estimate the user's emotions and arrange consultations accordingly. For example, if a user is feeling anxious, the app suggests a consultation in a relaxing environment. This allows the Collaboration Department to support users' health management and provide appropriate medical services. The collaboration department can utilize AI technology to efficiently and effectively facilitate collaboration between users and medical professionals.

[0036] The data collection unit can analyze the user's past health data and select the optimal collection timing. For example, the data collection unit can analyze the user's past vital sign data and concentrate collection during times when abnormalities are likely to occur. It can also analyze the user's sleep patterns and collect data during times when sleep quality is poor. Furthermore, the data collection unit can analyze the user's activity level and focus on collecting vital signs after exercise. By selecting the optimal collection timing based on past data, the accuracy of the data is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI and have the generating AI select the optimal collection timing.

[0037] The data collection unit can filter data based on the user's current activity level during collection. For example, if the user is exercising, the data collection unit will prioritize collecting vital sign data related to exercise. It can also collect resting vital sign data if the user is resting. Furthermore, if the user is eating, the data collection unit can collect data related to eating and filter other data. This allows for the collection of more relevant data by filtering data based on the user's current activity level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current activity data into a generating AI and have the generating AI perform data filtering.

[0038] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is at high altitude, the data collection unit will prioritize the collection of oxygen saturation data. It can also prioritize the collection of heart rate and stress level data if the user is in an urban area. Furthermore, if the user is at home, the data collection unit can prioritize the collection of sleep patterns and resting vital signs data. This allows for more appropriate data collection by basing data collection on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI collect highly relevant data.

[0039] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, if a user posts on social media indicating they are stressed, the unit can collect data on heart rate and blood pressure. It can also collect data on sleep patterns if the user posts indicating relaxation. Furthermore, if the user posts indicating anxiety, the unit can collect data on respiratory rate and oxygen saturation. This allows for data collection tailored to the user's situation by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input social media activity data into a generating AI and have the generating AI collect the relevant data.

[0040] The analysis unit can improve the accuracy of anomaly detection by referring to past data during analysis. For example, the analysis unit can improve the accuracy of anomaly detection by referring to the user's past vital sign data. It can also improve the accuracy of anomaly detection by referring to the user's past sleep pattern data. Furthermore, the analysis unit can improve the accuracy of anomaly detection by referring to the user's past activity data. In this way, the accuracy of anomaly detection is improved by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the anomaly detection accuracy improvement.

[0041] The analysis unit can apply different analysis algorithms to each data category during analysis. For example, the analysis unit can apply anomaly detection algorithms for heart rate and blood pressure to vital sign data. It can also apply anomaly detection algorithms for sleep quality and sleep duration to sleep pattern data. Furthermore, it can apply anomaly detection algorithms for exercise volume and activity level to activity data. By applying different analysis algorithms to each data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analysis algorithms for each data category into a generating AI and have the generating AI perform the analysis.

[0042] The analysis unit can perform anomaly detection while considering the geographical distribution of the data during analysis. For example, if the user is at high altitude, the analysis unit can tighten the detection of anomalies in oxygen saturation. Similarly, if the user is in an urban area, the analysis unit can tighten the detection of anomalies in heart rate and stress levels. Furthermore, if the user is at home, the analysis unit can relax the detection of anomalies in sleep patterns and resting vital signs. This improves the accuracy of anomaly detection by considering geographical distribution. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical distribution data into a generating AI and have the generating AI perform anomaly detection.

[0043] The analysis unit can improve the accuracy of anomaly detection by referring to relevant literature during analysis. For example, the analysis unit can improve the anomaly detection algorithm by referring to the latest medical research. The analysis unit can also adjust the criteria for anomaly detection by referring to past case data. Furthermore, the analysis unit can improve the accuracy of anomaly detection by referring to the opinions of medical professionals. Thus, the accuracy of anomaly detection is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the anomaly detection accuracy improvement.

[0044] The dialogue unit can select the most appropriate dialogue content during a conversation by referring to the user's past conversation history. For example, the dialogue unit can select dialogue content based on topics the user has shown interest in in the past. It can also select dialogue content while avoiding topics the user has shown discomfort with in the past. Furthermore, the dialogue unit can select dialogue content appropriate to the current situation from the user's past conversation history. In this way, by referring to past conversation history, it can provide more appropriate dialogue content. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input past conversation history data into a generating AI and have the generating AI perform the selection of the most appropriate dialogue content.

[0045] The dialogue unit can customize the conversation content based on the user's current health status during the conversation. For example, if the user is tired, the dialogue unit can offer relaxing topics. If the user is energetic, the dialogue unit can also offer active topics. Furthermore, if the user is unwell, the dialogue unit can offer health advice. By customizing the conversation content based on the current health status, a more appropriate conversation can be provided. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input current health status data into a generating AI and have the generating AI perform the customization of the conversation content.

[0046] The dialogue unit can select the most appropriate dialogue content during a conversation, taking into account the user's geographical location. For example, if the user is in a park, the dialogue unit can offer topics related to nature or walking. If the user is at home, the dialogue unit can offer topics related to activities that can be done at home. Furthermore, if the user is in a hospital, the dialogue unit can offer topics related to health. By selecting dialogue content based on geographical location information, more appropriate conversations can be provided. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input geographical location data into a generating AI and have the generating AI select the most appropriate dialogue content.

[0047] The dialogue unit can analyze the user's social media activity during a conversation and suggest dialogue content. For example, the dialogue unit can suggest dialogue content based on topics the user has shown interest in on social media. It can also suggest dialogue content while avoiding topics the user has expressed discomfort with on social media. Furthermore, the dialogue unit can suggest dialogue content appropriate to the current situation based on the user's social media activity. In this way, by analyzing social media activity, it is possible to provide dialogue content that is tailored to the user's situation. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input social media activity data into a generating AI and have the generating AI suggest dialogue content.

[0048] The management department can select the optimal management method by referring to the user's past medication history during management. For example, the management department can refer to the user's past medication history and strengthen reminders during times when medication is frequently forgotten. The management department can also optimize the timing of medication based on the user's past medication history. Furthermore, the management department can analyze the user's past medication history and select a management method to maximize the effectiveness of the medication. This makes more appropriate medication management possible by referring to past medication history. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input past medication history data into a generating AI and have the generating AI select the optimal management method.

[0049] The management unit can customize the content of medication reminders based on the user's current health status during management. For example, if the user is feeling unwell, the management unit can issue a reminder emphasizing the importance of taking medication. If the user is feeling well, the management unit can also issue a concise reminder. Furthermore, if the user is tired, the management unit can issue a relaxing reminder. By customizing the reminder content based on the user's current health status, more appropriate medication management becomes possible. Some or all of the above processes in the management unit may be performed using AI, for example, or not. For example, the management unit can input current health status data into a generating AI and have the generating AI customize the reminder content.

[0050] The management department can select the optimal medication reminder method during management, taking into account the user's geographical location. For example, if the user is out, the management department may prioritize voice reminders. It may also prioritize visual reminders if the user is at home. Furthermore, if the user is in a hospital, the management department can provide reminders that take into account coordination with medical staff. This allows for more appropriate medication management by selecting reminder methods based on geographical location. Some or all of the above processing in the management department may be performed using AI, or not. For example, the management department can input geographical location data into a generating AI and have the generating AI select the optimal reminder method.

[0051] The management department can analyze users' social media activity during management and propose medication reminder methods. For example, if a user posts on social media indicating they are feeling stressed, the management department can propose a relaxing reminder. If a user posts on social media indicating they are relaxed, the management department can also propose a standard reminder. Furthermore, if a user posts on social media indicating anxiety, the management department can propose a reassuring reminder. In this way, by analyzing social media activity, the management department can propose reminder methods tailored to the user's situation. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input social media activity data into a generating AI and have the generating AI propose reminder methods.

[0052] The nutrition department can provide optimal advice by referring to the user's past eating history when giving nutritional advice. For example, the nutrition department can refer to the user's past eating history and suggest a nutritionally balanced meal. It can also suggest ingredients to supplement specific nutrients if they are deficient based on the user's past eating history. Furthermore, the nutrition department can analyze the user's past eating history and suggest a meal tailored to their health condition. This allows for more appropriate nutritional advice to be provided by referring to past eating history. Some or all of the above processes in the nutrition department may be performed using AI, for example, or not. For example, the nutrition department can input past eating history data into a generating AI and have the generating AI provide optimal advice.

[0053] The nutrition department can customize nutritional advice based on the user's current health condition. For example, if the user is feeling unwell, the nutrition department can suggest easily digestible foods. If the user is healthy, the nutrition department can also suggest a balanced meal. Furthermore, if the user is tired, the nutrition department can suggest foods that help replenish energy. By customizing the advice based on the user's current health condition, more appropriate nutritional advice can be provided. Some or all of the above processes in the nutrition department may be performed using AI, for example, or not. For example, the nutrition department can input current health condition data into a generating AI and have the generating AI customize the advice.

[0054] The nutrition department can provide optimal nutritional advice by taking into account the user's geographical location. For example, if the user is at a high altitude, the nutrition department can suggest ingredients that are easily available at high altitudes. Similarly, if the user is in an urban area, the nutrition department can suggest ingredients that are easily available in urban areas. Furthermore, if the user is at home, the nutrition department can suggest ingredients that are easy to cook at home. This allows for more appropriate nutritional advice to be provided based on geographical location information. Some or all of the above processing in the nutrition department may be performed using AI, for example, or without AI. For example, the nutrition department can input geographical location data into a generating AI and have the generating AI provide optimal advice.

[0055] The nutrition department can analyze a user's social media activity when providing nutritional advice and propose advice accordingly. For example, the nutrition department can propose advice based on foods the user has shown interest in on social media. It can also propose advice that avoids foods the user has expressed discomfort with on social media. Furthermore, the nutrition department can propose advice that is appropriate to the user's current situation based on their social media activity. In this way, by analyzing social media activity, it is possible to provide advice tailored to the user's situation. Some or all of the above processes in the nutrition department may be performed using AI, for example, or not. For example, the nutrition department can input social media activity data into a generating AI and have the generating AI generate advice suggestions.

[0056] The collaboration unit can select the most suitable medical professional by referring to the user's past medical history during the collaboration process. For example, the collaboration unit can refer to the user's past medical history and select a medical professional suitable for their specialty. The collaboration unit can also select a highly reliable medical professional from the user's past medical history. Furthermore, the collaboration unit can analyze the user's past medical history and select the most suitable medical professional. This allows for the selection of a more appropriate medical professional by referring to past medical history. Some or all of the above-described processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input past medical history data into a generating AI and have the generating AI perform the selection of the most suitable medical professional.

[0057] The integration unit can customize the content of online medical consultations based on the user's current health status during integration. For example, if the user is unwell, the integration unit can arrange a consultation for a high-priority issue. If the user is healthy, the integration unit can also arrange a regular health check. Furthermore, if the user is tired, the integration unit can arrange a consultation that promotes relaxation. By customizing the consultation content based on the user's current health status, a more appropriate online medical consultation can be provided. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input current health status data into a generating AI and have the generating AI perform the customization of the consultation content.

[0058] The collaboration unit can select the most suitable medical professional by considering the user's geographical location information during the collaboration process. For example, if the user is in an urban area, the collaboration unit will select a medical professional in an urban area. Furthermore, if the user is in a rural area, the collaboration unit can select a medical professional in a rural area. In addition, if the user is overseas, the collaboration unit can select a medical professional overseas. This allows for the provision of more appropriate online medical consultations by selecting medical professionals based on geographical location information. Some or all of the above-described processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input geographical location data into a generating AI and have the generating AI select the most suitable medical professional.

[0059] The collaboration unit can analyze the user's social media activity during collaboration and propose online medical consultation methods. For example, if the user posts on social media expressing stress, the collaboration unit can propose a relaxing medical consultation method. It can also propose a standard medical consultation method if the user posts on social media expressing relaxation. Furthermore, if the user posts on social media expressing anxiety, the collaboration unit can propose a reassuring medical consultation method. This allows for the provision of medical consultation methods tailored to the user's situation by analyzing social media activity. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input social media activity data into a generating AI and have the generating AI propose medical consultation methods.

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

[0061] The data collection unit can analyze the user's past health data and select the optimal collection timing. For example, the data collection unit can analyze the user's past vital sign data and concentrate collection during times when abnormalities are likely to occur. It can also analyze the user's sleep patterns and collect data during times when sleep quality is poor. Furthermore, the data collection unit can analyze the user's activity level and focus on collecting vital signs after exercise. By selecting the optimal collection timing based on past data, the accuracy of the data is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI and have the generating AI select the optimal collection timing.

[0062] The data collection unit can filter data based on the user's current activity level during collection. For example, if the user is exercising, the data collection unit will prioritize collecting vital sign data related to exercise. It can also collect resting vital sign data if the user is resting. Furthermore, if the user is eating, the data collection unit can collect data related to eating and filter other data. This allows for the collection of more relevant data by filtering data based on the user's current activity level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current activity data into a generating AI and have the generating AI perform data filtering.

[0063] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is at high altitude, the data collection unit will prioritize the collection of oxygen saturation data. It can also prioritize the collection of heart rate and stress level data if the user is in an urban area. Furthermore, if the user is at home, the data collection unit can prioritize the collection of sleep patterns and resting vital signs data. This allows for more appropriate data collection by basing data collection on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI collect highly relevant data.

[0064] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, if a user posts on social media indicating they are stressed, the unit can collect data on heart rate and blood pressure. It can also collect data on sleep patterns if the user posts indicating relaxation. Furthermore, if the user posts indicating anxiety, the unit can collect data on respiratory rate and oxygen saturation. This allows for data collection tailored to the user's situation by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input social media activity data into a generating AI and have the generating AI collect the relevant data.

[0065] The analysis unit can improve the accuracy of anomaly detection by referring to past data during analysis. For example, the analysis unit can improve the accuracy of anomaly detection by referring to the user's past vital sign data. It can also improve the accuracy of anomaly detection by referring to the user's past sleep pattern data. Furthermore, the analysis unit can improve the accuracy of anomaly detection by referring to the user's past activity data. In this way, the accuracy of anomaly detection is improved by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the anomaly detection accuracy improvement.

[0066] The analysis unit can apply different analysis algorithms to each data category during analysis. For example, the analysis unit can apply anomaly detection algorithms for heart rate and blood pressure to vital sign data. It can also apply anomaly detection algorithms for sleep quality and sleep duration to sleep pattern data. Furthermore, it can apply anomaly detection algorithms for exercise volume and activity level to activity data. By applying different analysis algorithms to each data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analysis algorithms for each data category into a generating AI and have the generating AI perform the analysis.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects vital signs and sleep patterns. The data collection unit collects vital signs such as heart rate, blood pressure, and body temperature. The data collection unit can also collect sleep patterns such as sleep duration, sleep quality, and the ratio of REM to non-REM sleep. The data collection unit can monitor vital signs in real time using, for example, a wearable device. The data collection unit can also record sleep patterns using a smartphone app. Furthermore, the data collection unit can perform regular health checks and collect data. Step 2: The analysis unit analyzes the data collected by the data collection unit to detect anomalies early. For example, the analysis unit can detect abnormal fluctuations in heart rate or sudden increases in blood pressure. It can also detect a decline in sleep quality or abnormalities in the ratio of REM sleep to non-REM sleep. For example, the analysis unit can analyze the data using AI algorithms to detect anomalies. It can also detect anomalies by comparing the current data with past data. Furthermore, the analysis unit can issue an alert when an anomaly is detected. Step 3: The dialogue unit provides emotional support through everyday conversation and cognitive function training. The dialogue unit can offer topics such as weather and news. It can also provide memory training and problem-solving skills training. The dialogue unit interacts with the user using, for example, an AI chatbot. It can also use speech recognition technology to understand the user's statements and provide appropriate responses. Furthermore, the dialogue unit can estimate the user's emotions and engage in emotionally appropriate conversations. Step 4: The management department manages prescription medications appropriately and provides reminders for taking them. For example, the management department manages medication schedules and sends reminders to users. The management department can also manage how medications are stored. For example, the management department uses a smartphone app to send medication reminders. The management department can also optimize medication schedules using AI algorithms. Furthermore, the management department can estimate the user's emotions and provide reminders tailored to those emotions. Step 5: The nutrition department analyzes the user's diet and provides personalized nutritional advice. For example, the nutrition department analyzes the balance of calories and nutrients. The nutrition department can also suggest dietary improvements and recommend supplements. For example, the nutrition department uses a smartphone app to record and analyze the user's diet. The nutrition department can also provide nutritional advice using AI algorithms. Furthermore, the nutrition department can estimate the user's emotions and provide advice tailored to those emotions. Step 6: The liaison department arranges online consultations with medical professionals. For example, the liaison department arranges consultations via video call or chat. The liaison department can also facilitate smooth collaboration with medical professionals. For example, the liaison department can arrange online consultations using smartphone apps. Furthermore, the liaison department can use AI algorithms to select the optimal timing for consultations. In addition, the liaison department can estimate the user's emotions and arrange consultations that are tailored to those emotions.

[0069] (Example of form 2) The HealthMate AI System, according to an embodiment of the present invention, is an AI-equipped smart device and application service that supports the health management and mental well-being of elderly people living alone. The HealthMate AI System utilizes the latest generative AI technology to support the daily lives of the elderly, continuously monitoring their health status, reducing feelings of loneliness, and maintaining cognitive function. For example, the HealthMate AI System includes a health monitoring AI, a conversational AI companion, a medication management AI, a nutrition management AI, and telemedicine collaboration. These functions are interconnected to form a system that comprehensively supports the health of the elderly. For instance, the conversational AI companion can use data collected by the health monitoring AI to provide appropriate advice to the user. Similarly, the medication management AI and nutrition management AI can use data collected by the health monitoring AI to provide appropriate reminders and advice to the user. Furthermore, telemedicine collaboration facilitates smoother communication with medical professionals, making health management for the elderly more effective. This is expected to improve the quality of life (QOL) of the elderly. Thus, the HealthMate AI System can comprehensively manage the health of the elderly and achieve an improved QOL.

[0070] The Healthmate AI system according to this embodiment comprises a data collection unit, an analysis unit, a dialogue unit, a management unit, a nutrition unit, and a collaboration unit. The data collection unit collects vital signs and sleep patterns. The data collection unit collects vital signs such as heart rate, blood pressure, and body temperature. The data collection unit can also collect sleep patterns such as sleep duration, sleep quality, and the ratio of REM sleep to non-REM sleep. The data collection unit monitors vital signs in real time using a wearable device, for example. The data collection unit can also record sleep patterns using a smartphone app. Furthermore, the data collection unit can perform regular health checks and collect data. The analysis unit analyzes the data collected by the data collection unit and detects abnormalities early. The analysis unit can detect abnormal fluctuations in heart rate or sudden increases in blood pressure, for example. The analysis unit can also detect a decline in sleep quality or abnormalities in the ratio of REM sleep to non-REM sleep. The analysis unit analyzes the data using an AI algorithm, for example, and detects abnormalities. The analysis unit can also detect abnormalities by comparing them with past data. Furthermore, the analysis unit can issue alerts if an anomaly is detected. The dialogue unit provides mental support through everyday conversation and cognitive function training. The dialogue unit can offer topics such as weather and news. It can also provide memory training and problem-solving skills training. The dialogue unit interacts with users using, for example, an AI chatbot. It can also understand user statements using speech recognition technology and provide appropriate responses. Furthermore, the dialogue unit can estimate the user's emotions and engage in emotionally appropriate conversations. The management unit provides proper management of prescription medications and reminders for taking them. The management unit can, for example, manage medication schedules and remind users. It can also manage how medications are stored. The management unit can, for example, provide medication reminders using a smartphone app. It can also optimize medication schedules using AI algorithms. Furthermore, it can estimate the user's emotions and provide emotionally appropriate reminders. The nutrition unit analyzes dietary content and provides personalized nutritional advice. The nutrition department, for example, analyzes the balance of calories and nutrients.Furthermore, the nutrition department can offer suggestions for dietary improvements and recommend supplements. For example, the nutrition department can record and analyze meal content using a smartphone app. The nutrition department can also provide nutritional advice using AI algorithms. In addition, the nutrition department can estimate the user's emotions and provide advice tailored to those emotions. The liaison department arranges online consultations with medical professionals. For example, the liaison department arranges consultations via video call or chat. The liaison department can also facilitate smooth collaboration with medical professionals. For example, the liaison department arranges online consultations using a smartphone app. The liaison department can also select the optimal consultation timing using AI algorithms. In addition, the liaison department can estimate the user's emotions and arrange consultations tailored to those emotions. As a result, the HealthMate AI system according to this embodiment can comprehensively manage the health of the elderly and improve their quality of life (QOL).

[0071] The data collection unit collects vital signs and sleep patterns. For example, it collects vital signs such as heart rate, blood pressure, and body temperature. Specifically, it uses wearable devices to monitor heart rate, blood pressure, and body temperature in real time. These devices are worn on the user's body and can collect data 24 hours a day. Examples include smartwatches and fitness trackers. These devices transmit data to smartphones or cloud servers via Bluetooth or Wi-Fi for centralized management. The data collection unit can also collect sleep patterns, such as sleep duration, sleep quality, and the ratio of REM to non-REM sleep. This is done using smartphone apps or dedicated sleep trackers. Smartphone apps, placed by the user's pillow at bedtime, use accelerometers and microphones to record sleep quality. Dedicated sleep trackers are placed under the mattress and provide detailed sleep data by monitoring the user's movements and breathing. Furthermore, the data collection unit can also perform regular health checks and collect data. For example, users can undergo regular health checkups at medical institutions and input the results into the system to track long-term changes in their health. This allows the data collection unit to comprehensively monitor the user's health status and collect data in real time using a variety of devices and methods.

[0072] The analysis unit analyzes data collected by the data collection unit to detect anomalies early. For example, the analysis unit can detect abnormal fluctuations in heart rate or sudden increases in blood pressure. Specifically, it uses AI algorithms to analyze collected data in real time and detect abnormal patterns. For instance, if heart rate suddenly rises beyond the normal range, or if blood pressure fluctuates significantly in a short period, the system immediately issues an alert. The analysis unit can also detect declines in sleep quality or abnormalities in the ratio of REM to non-REM sleep. This involves analyzing sleep data and comparing it to normal patterns to identify anomalies. For example, if the percentage of REM sleep is extremely low, or if sleep quality consistently declines, the system alerts the user. Furthermore, the analysis unit can detect anomalies by comparing current data with past data. This allows for tracking long-term changes in health status and early detection of abnormalities. For example, it can analyze heart rate and blood pressure trends based on data from the past few months and issue an alert if abnormal fluctuations are observed. The analysis unit can also issue alerts when an anomaly is detected. Alerts are communicated to the user via smartphone notifications, email, or voice assistants. This allows users to recognize abnormalities early and take appropriate measures. The analysis department utilizes AI technology to quickly and accurately analyze the collected data and monitor users' health status in real time.

[0073] The dialogue unit provides mental support through everyday conversation and cognitive function training. For example, it offers topics such as weather and news. Specifically, it uses an AI chatbot to interact with users and support their mental health through everyday conversation. The chatbot uses natural language processing technology to understand user statements and provide appropriate responses. For example, if a user says, "The weather is nice today," the chatbot will respond, "Yes, it's sunny and pleasant today." The dialogue unit can also provide memory training and problem-solving skills training. This includes quizzes, puzzles, and memory games. For example, the chatbot might ask, "What did you have for dinner yesterday?" and the user can train their memory by answering. Furthermore, the dialogue unit can use speech recognition technology to understand user statements and provide appropriate responses, allowing users to enjoy natural conversations. In addition, the dialogue unit can estimate user emotions and engage in emotionally appropriate conversations. For example, if a user speaks in a sad voice, the chatbot will gently ask, "Is there something bothering you?" This allows the dialogue unit to support the user's mental health and reduce feelings of loneliness. The dialogue unit utilizes AI technology to enable natural conversations with the user and provide emotional support.

[0074] The management department ensures the proper management of prescription medications and provides reminders for taking them. For example, the management department manages medication schedules and sends reminders to users. Specifically, it uses a smartphone app to set the user's medication schedule and send reminder notifications. For example, if medication needs to be taken after breakfast, the app will display a reminder at breakfast time, prompting the user to take their medication. The management department can also manage how medications are stored. This includes the storage location and temperature control of the medications. For example, if a particular medication requires refrigeration, the app will notify the user and instruct them on the appropriate storage method. Furthermore, the management department can optimize medication schedules using AI algorithms. This makes it possible to suggest the optimal timing for taking medication based on the user's lifestyle and health condition. For example, if taking medication at night reduces side effects, the app will set a reminder for that time. The management department can also estimate the user's emotions and provide reminders accordingly. For example, if the user is feeling stressed, the app will send reminders in gentle language to reduce the user's burden. This allows the management department to support users' medication management and promote appropriate medication adherence. The management department can utilize AI technology to efficiently and effectively manage users' health.

[0075] The nutrition department analyzes dietary content and provides personalized nutritional advice. For example, it analyzes calorie and nutrient balance. Specifically, a smartphone app allows users to record their meals, and based on this data, it calculates calorie and nutrient intake. For instance, if a user enters the ingredients they ate for breakfast into the app, the app automatically calculates the calories and nutrients of those ingredients and displays the total intake. The nutrition department can also offer suggestions for dietary improvements and recommend supplements. This includes specific advice tailored to the user's health condition and goals. For example, if a user is trying to lose weight, the app suggests low-calorie, nutritionally balanced meal menus. Furthermore, the nutrition department can use AI algorithms to provide nutritional advice. This allows for optimal nutritional advice based on the user's dietary history and health data. For example, if a user is deficient in vitamin D, the app recommends foods and supplements rich in vitamin D. In addition, the nutrition department can estimate the user's emotions and provide emotionally appropriate advice. For example, if a user is stressed, the app suggests foods and recipes effective for stress reduction. This allows the nutrition department to support users in managing their diets and promote healthy eating habits. The nutrition department can utilize AI technology to provide nutritional advice tailored to the individual needs of each user.

[0076] The Collaboration Department arranges online consultations with medical professionals. For example, it arranges consultations via video call or chat. Specifically, it enables users to easily contact medical professionals using a smartphone app. For instance, users can book appointments through the app and consult with a doctor via video call at a designated time. The Collaboration Department also facilitates smooth collaboration with medical professionals. This includes a function to share the user's health data with medical professionals. For example, the user's vital signs and sleep data can be provided to doctors for use in treatment. Furthermore, the Collaboration Department can use AI algorithms to select the optimal timing for consultations. This allows for the suggestion of an optimal consultation schedule tailored to the user's lifestyle and health condition. For example, if a user's symptoms worsen at night, the app recommends booking a nighttime consultation. The Collaboration Department can also estimate the user's emotions and arrange consultations accordingly. For example, if a user is feeling anxious, the app suggests a consultation in a relaxing environment. This allows the Collaboration Department to support users' health management and provide appropriate medical services. The collaboration department can utilize AI technology to efficiently and effectively facilitate collaboration between users and medical professionals.

[0077] The data collection unit can estimate the user's emotions and adjust the frequency of collecting vital signs and sleep patterns based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the collection frequency to obtain more detailed data. Conversely, if the user is relaxed, the data collection unit can reduce the collection frequency to alleviate the burden. Furthermore, if the user is anxious, the data collection unit can appropriately adjust the collection frequency to provide a sense of security. By adjusting the collection frequency according to the user's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The data collection unit can analyze the user's past health data and select the optimal collection timing. For example, the data collection unit can analyze the user's past vital sign data and concentrate collection during times when abnormalities are likely to occur. It can also analyze the user's sleep patterns and collect data during times when sleep quality is poor. Furthermore, the data collection unit can analyze the user's activity level and focus on collecting vital signs after exercise. By selecting the optimal collection timing based on past data, the accuracy of the data is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI and have the generating AI select the optimal collection timing.

[0079] The data collection unit can filter data based on the user's current activity level during collection. For example, if the user is exercising, the data collection unit will prioritize collecting vital sign data related to exercise. It can also collect resting vital sign data if the user is resting. Furthermore, if the user is eating, the data collection unit can collect data related to eating and filter other data. This allows for the collection of more relevant data by filtering data based on the user's current activity level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current activity data into a generating AI and have the generating AI perform data filtering.

[0080] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting heart rate and blood pressure data. It may also prioritize collecting sleep pattern data if the user is relaxed. Furthermore, if the user is anxious, the data collection unit may prioritize collecting respiratory rate and oxygen saturation data. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is at high altitude, the data collection unit will prioritize the collection of oxygen saturation data. It can also prioritize the collection of heart rate and stress level data if the user is in an urban area. Furthermore, if the user is at home, the data collection unit can prioritize the collection of sleep patterns and resting vital signs data. This allows for more appropriate data collection by basing data collection on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI collect highly relevant data.

[0082] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, if a user posts on social media indicating they are stressed, the unit can collect data on heart rate and blood pressure. It can also collect data on sleep patterns if the user posts indicating relaxation. Furthermore, if the user posts indicating anxiety, the unit can collect data on respiratory rate and oxygen saturation. This allows for data collection tailored to the user's situation by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input social media activity data into a generating AI and have the generating AI collect the relevant data.

[0083] The analysis unit can estimate the user's emotions and adjust the anomaly detection criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit can tighten the anomaly detection criteria for heart rate and blood pressure. It can also relax the anomaly detection criteria for sleep patterns if the user is relaxed. Furthermore, if the user is anxious, the analysis unit can adjust the anomaly detection criteria for respiratory rate and oxygen saturation. This allows for more accurate anomaly detection by adjusting the criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The analysis unit can improve the accuracy of anomaly detection by referring to past data during analysis. For example, the analysis unit can improve the accuracy of anomaly detection by referring to the user's past vital sign data. It can also improve the accuracy of anomaly detection by referring to the user's past sleep pattern data. Furthermore, the analysis unit can improve the accuracy of anomaly detection by referring to the user's past activity data. In this way, the accuracy of anomaly detection is improved by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the anomaly detection accuracy improvement.

[0085] The analysis unit can apply different analysis algorithms to each data category during analysis. For example, the analysis unit can apply anomaly detection algorithms for heart rate and blood pressure to vital sign data. It can also apply anomaly detection algorithms for sleep quality and sleep duration to sleep pattern data. Furthermore, it can apply anomaly detection algorithms for exercise volume and activity level to activity data. By applying different analysis algorithms to each data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analysis algorithms for each data category into a generating AI and have the generating AI perform the analysis.

[0086] The analysis unit can estimate the user's emotions and adjust the order in which anomaly detection results are displayed based on the estimated emotions. For example, if the user is stressed, the analysis unit may prioritize displaying anomaly detection results for heart rate and blood pressure. It can also prioritize displaying anomaly detection results for sleep patterns if the user is relaxed. Furthermore, if the user is anxious, the analysis unit may prioritize displaying anomaly detection results for respiratory rate and oxygen saturation. This allows for prioritizing the display of important information by adjusting the order in which anomaly detection results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The analysis unit can perform anomaly detection while considering the geographical distribution of the data during analysis. For example, if the user is at high altitude, the analysis unit can tighten the detection of anomalies in oxygen saturation. Similarly, if the user is in an urban area, the analysis unit can tighten the detection of anomalies in heart rate and stress levels. Furthermore, if the user is at home, the analysis unit can relax the detection of anomalies in sleep patterns and resting vital signs. This improves the accuracy of anomaly detection by considering geographical distribution. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical distribution data into a generating AI and have the generating AI perform anomaly detection.

[0088] The analysis unit can improve the accuracy of anomaly detection by referring to relevant literature during analysis. For example, the analysis unit can improve the anomaly detection algorithm by referring to the latest medical research. The analysis unit can also adjust the criteria for anomaly detection by referring to past case data. Furthermore, the analysis unit can improve the accuracy of anomaly detection by referring to the opinions of medical professionals. Thus, the accuracy of anomaly detection is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the anomaly detection accuracy improvement.

[0089] The dialogue unit can estimate the user's emotions and adjust the content and tone of the dialogue based on the estimated emotions. For example, if the user is stressed, the dialogue unit will use a calm tone. If the user is relaxed, the dialogue unit can use a bright tone. Furthermore, if the user is anxious, the dialogue unit can use a reassuring tone. By adjusting the content and tone of the dialogue according to the user's emotions, more appropriate emotional support can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0090] The dialogue unit can select the most appropriate dialogue content during a conversation by referring to the user's past conversation history. For example, the dialogue unit can select dialogue content based on topics the user has shown interest in in the past. It can also select dialogue content while avoiding topics the user has shown discomfort with in the past. Furthermore, the dialogue unit can select dialogue content appropriate to the current situation from the user's past conversation history. In this way, by referring to past conversation history, it can provide more appropriate dialogue content. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input past conversation history data into a generating AI and have the generating AI perform the selection of the most appropriate dialogue content.

[0091] The dialogue unit can customize the conversation content based on the user's current health status during the conversation. For example, if the user is tired, the dialogue unit can offer relaxing topics. If the user is energetic, the dialogue unit can also offer active topics. Furthermore, if the user is unwell, the dialogue unit can offer health advice. By customizing the conversation content based on the current health status, a more appropriate conversation can be provided. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input current health status data into a generating AI and have the generating AI perform the customization of the conversation content.

[0092] The dialogue unit can estimate the user's emotions and determine the priority of the conversation based on the estimated emotions. For example, if the user is stressed, the dialogue unit will prioritize conversations related to stress reduction. It can also prioritize pleasant topics if the user is relaxed. Furthermore, if the user is anxious, the dialogue unit can prioritize conversations that provide reassurance. This allows important conversations to be prioritized by determining the priority of the conversation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, or not. For example, the dialogue unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The dialogue unit can select the most appropriate dialogue content during a conversation, taking into account the user's geographical location. For example, if the user is in a park, the dialogue unit can offer topics related to nature or walking. If the user is at home, the dialogue unit can offer topics related to activities that can be done at home. Furthermore, if the user is in a hospital, the dialogue unit can offer topics related to health. By selecting dialogue content based on geographical location information, more appropriate conversations can be provided. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input geographical location data into a generating AI and have the generating AI select the most appropriate dialogue content.

[0094] The dialogue unit can analyze the user's social media activity during a conversation and suggest dialogue content. For example, the dialogue unit can suggest dialogue content based on topics the user has shown interest in on social media. It can also suggest dialogue content while avoiding topics the user has expressed discomfort with on social media. Furthermore, the dialogue unit can suggest dialogue content appropriate to the current situation based on the user's social media activity. In this way, by analyzing social media activity, it is possible to provide dialogue content that is tailored to the user's situation. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input social media activity data into a generating AI and have the generating AI suggest dialogue content.

[0095] The management unit can estimate the user's emotions and adjust the timing of medication reminders based on the estimated emotions. For example, if the user is feeling stressed, the management unit can expedite the reminder to encourage medication. Conversely, if the user is relaxed, the management unit can delay the reminder to reduce the burden. Furthermore, if the user is feeling anxious, the management unit can appropriately adjust the reminder timing to provide a sense of security. This allows for more appropriate medication management by adjusting the reminder timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, or not using AI. For example, the management unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The management department can select the optimal management method by referring to the user's past medication history during management. For example, the management department can refer to the user's past medication history and strengthen reminders during times when medication is frequently forgotten. The management department can also optimize the timing of medication based on the user's past medication history. Furthermore, the management department can analyze the user's past medication history and select a management method to maximize the effectiveness of the medication. This makes more appropriate medication management possible by referring to past medication history. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input past medication history data into a generating AI and have the generating AI select the optimal management method.

[0097] The management unit can customize the content of medication reminders based on the user's current health status during management. For example, if the user is feeling unwell, the management unit can issue a reminder emphasizing the importance of taking medication. If the user is feeling well, the management unit can also issue a concise reminder. Furthermore, if the user is tired, the management unit can issue a relaxing reminder. By customizing the reminder content based on the user's current health status, more appropriate medication management becomes possible. Some or all of the above processes in the management unit may be performed using AI, for example, or not. For example, the management unit can input current health status data into a generating AI and have the generating AI customize the reminder content.

[0098] The management unit can estimate the user's emotions and determine the priority of medication reminders based on the estimated emotions. For example, if the user is feeling stressed, the management unit will prioritize important medication reminders. It can also prioritize regular medication reminders if the user is relaxed. Furthermore, if the user is feeling anxious, the management unit can prioritize reassuring medication reminders. This allows for priority reminders of important medications by determining the priority of reminders according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, or not. For example, the management unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The management department can select the optimal medication reminder method during management, taking into account the user's geographical location. For example, if the user is out, the management department may prioritize voice reminders. It may also prioritize visual reminders if the user is at home. Furthermore, if the user is in a hospital, the management department can provide reminders that take into account coordination with medical staff. This allows for more appropriate medication management by selecting reminder methods based on geographical location. Some or all of the above processing in the management department may be performed using AI, or not. For example, the management department can input geographical location data into a generating AI and have the generating AI select the optimal reminder method.

[0100] The management department can analyze users' social media activity during management and propose medication reminder methods. For example, if a user posts on social media indicating they are feeling stressed, the management department can propose a relaxing reminder. If a user posts on social media indicating they are relaxed, the management department can also propose a standard reminder. Furthermore, if a user posts on social media indicating anxiety, the management department can propose a reassuring reminder. In this way, by analyzing social media activity, the management department can propose reminder methods tailored to the user's situation. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input social media activity data into a generating AI and have the generating AI propose reminder methods.

[0101] The nutrition department can estimate the user's emotions and adjust the nutritional advice based on those emotions. For example, if the user is stressed, the nutrition department can suggest foods that help reduce stress. If the user is relaxed, the nutrition department can also suggest a balanced meal. Furthermore, if the user is anxious, the nutrition department can suggest foods that provide a sense of security. By adjusting the advice according to the user's emotions, more appropriate nutritional advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the nutrition department may be performed using AI, or not using AI. For example, the nutrition department can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0102] The nutrition department can provide optimal advice by referring to the user's past eating history when giving nutritional advice. For example, the nutrition department can refer to the user's past eating history and suggest a nutritionally balanced meal. It can also suggest ingredients to supplement specific nutrients if they are deficient based on the user's past eating history. Furthermore, the nutrition department can analyze the user's past eating history and suggest a meal tailored to their health condition. This allows for more appropriate nutritional advice to be provided by referring to past eating history. Some or all of the above processes in the nutrition department may be performed using AI, for example, or not. For example, the nutrition department can input past eating history data into a generating AI and have the generating AI provide optimal advice.

[0103] The nutrition department can customize nutritional advice based on the user's current health condition. For example, if the user is feeling unwell, the nutrition department can suggest easily digestible foods. If the user is healthy, the nutrition department can also suggest a balanced meal. Furthermore, if the user is tired, the nutrition department can suggest foods that help replenish energy. By customizing the advice based on the user's current health condition, more appropriate nutritional advice can be provided. Some or all of the above processes in the nutrition department may be performed using AI, for example, or not. For example, the nutrition department can input current health condition data into a generating AI and have the generating AI customize the advice.

[0104] The nutrition department can estimate the user's emotions and prioritize nutritional advice based on those emotions. For example, if the user is stressed, the nutrition department will prioritize suggesting foods that help reduce stress. If the user is relaxed, the nutrition department may prioritize suggesting balanced meals. Furthermore, if the user is anxious, the nutrition department may prioritize suggesting foods that provide a sense of security. This allows for the prioritization of important advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the nutrition department may be performed using AI or not. For example, the nutrition department can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0105] The nutrition department can provide optimal nutritional advice by taking into account the user's geographical location. For example, if the user is at a high altitude, the nutrition department can suggest ingredients that are easily available at high altitudes. Similarly, if the user is in an urban area, the nutrition department can suggest ingredients that are easily available in urban areas. Furthermore, if the user is at home, the nutrition department can suggest ingredients that are easy to cook at home. This allows for more appropriate nutritional advice to be provided based on geographical location information. Some or all of the above processing in the nutrition department may be performed using AI, for example, or without AI. For example, the nutrition department can input geographical location data into a generating AI and have the generating AI provide optimal advice.

[0106] The nutrition department can analyze a user's social media activity when providing nutritional advice and propose advice accordingly. For example, the nutrition department can propose advice based on foods the user has shown interest in on social media. It can also propose advice that avoids foods the user has expressed discomfort with on social media. Furthermore, the nutrition department can propose advice that is appropriate to the user's current situation based on their social media activity. In this way, by analyzing social media activity, it is possible to provide advice tailored to the user's situation. Some or all of the above processes in the nutrition department may be performed using AI, for example, or not. For example, the nutrition department can input social media activity data into a generating AI and have the generating AI generate advice suggestions.

[0107] The collaboration unit can estimate the user's emotions and adjust the timing of online consultations based on the estimated emotions. For example, if the user is feeling stressed, the collaboration unit can arrange an online consultation earlier. It can also arrange an online consultation at a normal time if the user is relaxed. Furthermore, if the user is feeling anxious, the collaboration unit can arrange an online consultation at an appropriate time. This allows for more appropriate online consultations by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI, or not. For example, the collaboration unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0108] The collaboration unit can select the most suitable medical professional by referring to the user's past medical history during the collaboration process. For example, the collaboration unit can refer to the user's past medical history and select a medical professional suitable for their specialty. The collaboration unit can also select a highly reliable medical professional from the user's past medical history. Furthermore, the collaboration unit can analyze the user's past medical history and select the most suitable medical professional. This allows for the selection of a more appropriate medical professional by referring to past medical history. Some or all of the above-described processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input past medical history data into a generating AI and have the generating AI perform the selection of the most suitable medical professional.

[0109] The integration unit can customize the content of online medical consultations based on the user's current health status during integration. For example, if the user is unwell, the integration unit can arrange a consultation for a high-priority issue. If the user is healthy, the integration unit can also arrange a regular health check. Furthermore, if the user is tired, the integration unit can arrange a consultation that promotes relaxation. By customizing the consultation content based on the user's current health status, a more appropriate online medical consultation can be provided. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input current health status data into a generating AI and have the generating AI perform the customization of the consultation content.

[0110] The collaboration unit can estimate the user's emotions and determine the priority of online consultations based on the estimated emotions. For example, if the user is feeling stressed, the collaboration unit will prioritize urgent consultations. It can also prioritize routine consultations if the user is relaxed. Furthermore, if the user is feeling anxious, the collaboration unit can prioritize consultations that provide reassurance. This allows for the priority of important consultations to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI, or not. For example, the collaboration unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0111] The collaboration unit can select the most suitable medical professional by considering the user's geographical location information during the collaboration process. For example, if the user is in an urban area, the collaboration unit will select a medical professional in an urban area. Furthermore, if the user is in a rural area, the collaboration unit can select a medical professional in a rural area. In addition, if the user is overseas, the collaboration unit can select a medical professional overseas. This allows for the provision of more appropriate online medical consultations by selecting medical professionals based on geographical location information. Some or all of the above-described processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input geographical location data into a generating AI and have the generating AI select the most suitable medical professional.

[0112] The collaboration unit can analyze the user's social media activity during collaboration and propose online medical consultation methods. For example, if the user posts on social media expressing stress, the collaboration unit can propose a relaxing medical consultation method. It can also propose a standard medical consultation method if the user posts on social media expressing relaxation. Furthermore, if the user posts on social media expressing anxiety, the collaboration unit can propose a reassuring medical consultation method. This allows for the provision of medical consultation methods tailored to the user's situation by analyzing social media activity. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input social media activity data into a generating AI and have the generating AI propose medical consultation methods.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The data collection unit can estimate the user's emotions and adjust the frequency of collecting vital signs and sleep patterns based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the collection frequency to obtain more detailed data. Conversely, if the user is relaxed, the data collection unit can reduce the collection frequency to alleviate the burden. Furthermore, if the user is anxious, the data collection unit can appropriately adjust the collection frequency to provide a sense of security. By adjusting the collection frequency according to the user's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0115] The data collection unit can analyze the user's past health data and select the optimal collection timing. For example, the data collection unit can analyze the user's past vital sign data and concentrate collection during times when abnormalities are likely to occur. It can also analyze the user's sleep patterns and collect data during times when sleep quality is poor. Furthermore, the data collection unit can analyze the user's activity level and focus on collecting vital signs after exercise. By selecting the optimal collection timing based on past data, the accuracy of the data is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI and have the generating AI select the optimal collection timing.

[0116] The data collection unit can filter data based on the user's current activity level during collection. For example, if the user is exercising, the data collection unit will prioritize collecting vital sign data related to exercise. It can also collect resting vital sign data if the user is resting. Furthermore, if the user is eating, the data collection unit can collect data related to eating and filter other data. This allows for the collection of more relevant data by filtering data based on the user's current activity level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current activity data into a generating AI and have the generating AI perform data filtering.

[0117] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting heart rate and blood pressure data. It may also prioritize collecting sleep pattern data if the user is relaxed. Furthermore, if the user is anxious, the data collection unit may prioritize collecting respiratory rate and oxygen saturation data. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0118] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is at high altitude, the data collection unit will prioritize the collection of oxygen saturation data. It can also prioritize the collection of heart rate and stress level data if the user is in an urban area. Furthermore, if the user is at home, the data collection unit can prioritize the collection of sleep patterns and resting vital signs data. This allows for more appropriate data collection by basing data collection on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI collect highly relevant data.

[0119] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, if a user posts on social media indicating they are stressed, the unit can collect data on heart rate and blood pressure. It can also collect data on sleep patterns if the user posts indicating relaxation. Furthermore, if the user posts indicating anxiety, the unit can collect data on respiratory rate and oxygen saturation. This allows for data collection tailored to the user's situation by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input social media activity data into a generating AI and have the generating AI collect the relevant data.

[0120] The analysis unit can estimate the user's emotions and adjust the anomaly detection criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit can tighten the anomaly detection criteria for heart rate and blood pressure. It can also relax the anomaly detection criteria for sleep patterns if the user is relaxed. Furthermore, if the user is anxious, the analysis unit can adjust the anomaly detection criteria for respiratory rate and oxygen saturation. This allows for more accurate anomaly detection by adjusting the criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0121] The analysis unit can improve the accuracy of anomaly detection by referring to past data during analysis. For example, the analysis unit can improve the accuracy of anomaly detection by referring to the user's past vital sign data. It can also improve the accuracy of anomaly detection by referring to the user's past sleep pattern data. Furthermore, the analysis unit can improve the accuracy of anomaly detection by referring to the user's past activity data. In this way, the accuracy of anomaly detection is improved by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the anomaly detection accuracy improvement.

[0122] The analysis unit can apply different analysis algorithms to each data category during analysis. For example, the analysis unit can apply anomaly detection algorithms for heart rate and blood pressure to vital sign data. It can also apply anomaly detection algorithms for sleep quality and sleep duration to sleep pattern data. Furthermore, it can apply anomaly detection algorithms for exercise volume and activity level to activity data. By applying different analysis algorithms to each data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analysis algorithms for each data category into a generating AI and have the generating AI perform the analysis.

[0123] The analysis unit can estimate the user's emotions and adjust the order in which anomaly detection results are displayed based on the estimated emotions. For example, if the user is stressed, the analysis unit may prioritize displaying anomaly detection results for heart rate and blood pressure. It can also prioritize displaying anomaly detection results for sleep patterns if the user is relaxed. Furthermore, if the user is anxious, the analysis unit may prioritize displaying anomaly detection results for respiratory rate and oxygen saturation. This allows for prioritizing the display of important information by adjusting the order in which anomaly detection results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The data collection unit collects vital signs and sleep patterns. The data collection unit collects vital signs such as heart rate, blood pressure, and body temperature. The data collection unit can also collect sleep patterns such as sleep duration, sleep quality, and the ratio of REM to non-REM sleep. The data collection unit can monitor vital signs in real time using, for example, a wearable device. The data collection unit can also record sleep patterns using a smartphone app. Furthermore, the data collection unit can perform regular health checks and collect data. Step 2: The analysis unit analyzes the data collected by the data collection unit to detect anomalies early. For example, the analysis unit can detect abnormal fluctuations in heart rate or sudden increases in blood pressure. It can also detect a decline in sleep quality or abnormalities in the ratio of REM sleep to non-REM sleep. For example, the analysis unit can analyze the data using AI algorithms to detect anomalies. It can also detect anomalies by comparing the current data with past data. Furthermore, the analysis unit can issue an alert when an anomaly is detected. Step 3: The dialogue unit provides emotional support through everyday conversation and cognitive function training. The dialogue unit can offer topics such as weather and news. It can also provide memory training and problem-solving skills training. The dialogue unit interacts with the user using, for example, an AI chatbot. It can also use speech recognition technology to understand the user's statements and provide appropriate responses. Furthermore, the dialogue unit can estimate the user's emotions and engage in emotionally appropriate conversations. Step 4: The management department manages prescription medications appropriately and provides reminders for taking them. For example, the management department manages medication schedules and sends reminders to users. The management department can also manage how medications are stored. For example, the management department uses a smartphone app to send medication reminders. The management department can also optimize medication schedules using AI algorithms. Furthermore, the management department can estimate the user's emotions and provide reminders tailored to those emotions. Step 5: The nutrition department analyzes the user's diet and provides personalized nutritional advice. For example, the nutrition department analyzes the balance of calories and nutrients. The nutrition department can also suggest dietary improvements and recommend supplements. For example, the nutrition department uses a smartphone app to record and analyze the user's diet. The nutrition department can also provide nutritional advice using AI algorithms. Furthermore, the nutrition department can estimate the user's emotions and provide advice tailored to those emotions. Step 6: The liaison department arranges online consultations with medical professionals. For example, the liaison department arranges consultations via video call or chat. The liaison department can also facilitate smooth collaboration with medical professionals. For example, the liaison department can arrange online consultations using smartphone apps. Furthermore, the liaison department can use AI algorithms to select the optimal timing for consultations. In addition, the liaison department can estimate the user's emotions and arrange consultations that are tailored to those emotions.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the data collection unit, analysis unit, dialogue unit, management unit, nutrition unit, and collaboration unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects vital signs and sleep patterns using the camera 42 and sensors of the smart device 14. The analysis unit analyzes the collected data by the identification processing unit 290 of the data processing unit 12 and detects abnormalities. The dialogue unit interacts with the user by the control unit 46A of the smart device 14 and provides emotional support. The management unit provides medication reminders by the control unit 46A of the smart device 14. The nutrition unit records meal contents and provides nutritional advice by the control unit 46A of the smart device 14. The collaboration unit arranges online consultations with medical professionals by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the data collection unit, analysis unit, dialogue unit, management unit, nutrition unit, and collaboration unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects vital signs and sleep patterns using the camera 42 and sensors of the smart glasses 214. The analysis unit analyzes the collected data using, for example, the identification processing unit 290 of the data processing unit 12 and detects abnormalities. The dialogue unit interacts with the user using, for example, the control unit 46A of the smart glasses 214 and provides emotional support. The management unit uses, for example, the control unit 46A of the smart glasses 214 to provide medication reminders. The nutrition unit records meal contents and provides nutritional advice using, for example, the control unit 46A of the smart glasses 214. The collaboration unit uses, for example, the identification processing unit 290 of the data processing unit 12 to arrange online consultations with medical professionals. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the data collection unit, analysis unit, dialogue unit, management unit, nutrition unit, and collaboration unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects vital signs and sleep patterns using the camera 42 and sensors of the headset terminal 314. The analysis unit analyzes the collected data by, for example, the identification processing unit 290 of the data processing unit 12 and detects abnormalities. The dialogue unit interacts with the user by, for example, the control unit 46A of the headset terminal 314 and provides emotional support. The management unit provides medication reminders by, for example, the control unit 46A of the headset terminal 314. The nutrition unit records meal contents and provides nutritional advice by, for example, the control unit 46A of the headset terminal 314. The collaboration unit arranges online consultations with medical professionals by, for example, the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Each of the multiple elements described above, including the data collection unit, analysis unit, dialogue unit, management unit, nutrition unit, and collaboration unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects vital signs and sleep patterns using the camera 42 and sensors of the robot 414. The analysis unit analyzes the collected data by, for example, the identification processing unit 290 of the data processing unit 12 and detects abnormalities. The dialogue unit interacts with the user and provides emotional support by, for example, the control unit 46A of the robot 414. The management unit provides medication reminders by, for example, the control unit 46A of the robot 414. The nutrition unit records meal contents and provides nutritional advice by, for example, the control unit 46A of the robot 414. The collaboration unit arranges online consultations with medical professionals by, for example, the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] (Note 1) A system characterized by comprising: a data collection unit for collecting vital signs and sleep patterns; an analysis unit for analyzing the data collected by the data collection unit and detecting abnormalities early; a dialogue unit for providing mental support through everyday conversation and cognitive function training; a management unit for appropriate management of prescription drugs and reminders for taking them; a nutrition unit for analyzing dietary content and providing individualized nutritional advice; and a collaboration unit for arranging online consultations with medical professionals. (Note 2) The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of collecting vital signs and sleep patterns based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past health data to select the optimal timing for data collection. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is During data collection, the data is filtered based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The system described in Appendix 1 is characterized in that the collection unit prioritizes collecting highly relevant data based on the user's geographical location information during collection. (Note 7) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is The system estimates the user's emotions and adjusts the anomaly detection criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is During analysis, historical data is referenced to improve the accuracy of anomaly detection. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is During analysis, different analytical algorithms are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is It estimates the user's emotions and adjusts the order in which anomaly detection results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The system according to Appendix 1, characterized in that the analysis unit performs anomaly detection based on the geographical distribution of the data during analysis. (Note 13) The aforementioned analysis unit is During analysis, we refer to relevant literature to improve the accuracy of anomaly detection. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the content and tone of the conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned dialogue unit, During a conversation, the system selects the most appropriate dialogue content by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned dialogue unit, During conversations, the dialogue content is customized based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned dialogue unit, It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The system described in Appendix 1 is characterized in that the dialogue unit selects the optimal dialogue content based on the user's geographical location information during the dialogue. (Note 19) The aforementioned dialogue unit, During conversations, the system analyzes the user's social media activity and suggests conversation topics. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, The system estimates the user's emotions and adjusts the timing of medication reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, During management, the system selects the optimal management method by referring to the user's past medication history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, During administration, the content of medication reminders can be customized based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, The system estimates the user's emotions and prioritizes medication reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The system described in Appendix 1 is characterized in that the management unit selects the optimal medication reminder method based on the user's geographical location information during management. (Note 25) The aforementioned management department, During management, we analyze users' social media activity and suggest methods for medication reminders. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned nutritional section is, The system estimates the user's emotions and adjusts the nutritional advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned nutritional section is, When providing nutritional advice, the system refers to the user's past eating history to provide the most suitable advice. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned nutritional section is, When providing nutritional advice, customize the advice based on the user's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned nutritional section is, It estimates the user's emotions and prioritizes nutritional advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The system described in Appendix 1 is characterized in that the nutrition unit provides optimal advice based on the user's geographical location information when giving nutritional advice. (Note 31) The aforementioned nutritional section is, When providing nutritional advice, we analyze the user's social media activity to suggest appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, The system estimates the user's emotions and adjusts the timing of online consultations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned linkage unit is, During integration, the system selects the most suitable medical professional by referring to the user's past medical history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned linkage unit is, During integration, the content of the online consultation will be customized based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned linkage unit is, The system estimates the user's emotions and prioritizes online consultations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned collaboration unit is characterized by selecting the most suitable medical professional based on the user's geographical location information during collaboration, as described in Appendix 1. (Note 37) The aforementioned linkage unit is, During the integration process, we analyze the user's social media activity and propose methods for online medical consultations. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A system characterized by comprising: a data collection unit for collecting vital signs and sleep patterns; an analysis unit for analyzing the data collected by the data collection unit and detecting abnormalities early; a dialogue unit for providing mental support through everyday conversation and cognitive function training; a management unit for appropriate management of prescription drugs and reminders for taking them; a nutrition unit for analyzing dietary content and providing individualized nutritional advice; and a collaboration unit for arranging online consultations with medical professionals.

2. The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of collecting vital signs and sleep patterns based on the estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze the user's past health data to select the optimal timing for data collection. The system according to feature 1.

4. The aforementioned collection unit is During data collection, the data is filtered based on the user's current activity status. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

6. The system according to claim 1, characterized in that the collection unit prioritizes collecting highly relevant data based on the user's geographical location information during collection.

7. The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system according to feature 1.

8. The aforementioned analysis unit is The system estimates the user's emotions and adjusts the anomaly detection criteria based on the estimated emotions. The system according to feature 1.

9. The aforementioned analysis unit is During analysis, historical data is referenced to improve the accuracy of anomaly detection. The system according to feature 1.

10. The aforementioned analysis unit is During analysis, different analytical algorithms are applied to each data category. The system according to feature 1.

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