System

Abstract

To provide a system which effectively utilizes health management data of elderly people and care givers in order to detect and report abnormalities.SOLUTION: A system includes a collection part, an analysis part, a notification part, and a question part. The collection part collects health management data. The analysis part analyzes the data collected by the collection part. The notification part detects and reports abnormalities based on an analysis result acquired by the analysis part. The question part generates questions for caregivers, and analyzes their answers.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that it is not sufficient to effectively utilize health management data of elderly people and their caregivers to detect abnormalities and provide appropriate notifications.

[0005] The system according to the embodiment aims to effectively utilize health management data of elderly people and their caregivers to detect and notify abnormalities. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a notification unit, and a questioning unit. The collection unit collects health management data. The analysis unit analyzes the data collected by the collection unit. The notification unit detects and notifies an abnormality based on an analysis result obtained by the analysis unit. The questioning unit generates questions for a caregiver and analyzes the answers. Effect of the Invention

[0007] The system according to the embodiment can effectively utilize health management data of elderly people and their caregivers to detect and notify abnormalities. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A health management system according to an embodiment of the present invention is a system in which elderly people and caregivers register health management data, and an AI analyzes the data to provide regular health advice. In this health management system, first, elderly people collect health management data using a wearable device. This data includes blood pressure, pulse, body temperature, number of steps, sleep patterns, etc. Next, the collected data is sent to an AI, which analyzes it. The AI ​​predicts abnormalities in blood pressure and pulse, mood fluctuations, etc., and notifies the caregiver as necessary. For example, if blood pressure rises suddenly, the AI ​​sends an alert to the caregiver and prompts appropriate action. In addition, for caregivers, a function is provided to promote mental health management through daily questions. The AI ​​analyzes the caregiver's answers and evaluates the stress level and mental health state. For example, it asks questions such as "How tired are you today?" and "Is there anything you're worried about recently?" to the caregiver, and provides advice based on the answers. This eliminates things that caregivers cannot express in words or things that they push themselves too hard, and reduces daily stress. This solution allows both elderly people and caregivers to efficiently manage their health, improving their quality of life. For example, simply by wearing a wearable device, the elderly can go about their daily lives with peace of mind, as AI automatically collects data and notifies caregivers if there is an abnormality. Caregivers can also receive advice from AI to manage their own mental health and reduce stress. This allows the health management system to efficiently manage the health of elderly people and caregivers, improving their quality of life.

[0029] The health management system according to the embodiment includes a collection unit, an analysis unit, a notification unit, and a question unit. The collection unit collects health management data. The health management data includes, but is not limited to, blood pressure, pulse, body temperature, number of steps, and sleep patterns. The collection unit collects these data using, for example, a wearable device. The wearable device is, for example, a smart watch or a fitness tracker, and these devices can monitor the user's physical information in real time and collect data. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI to detect abnormalities. The AI ​​identifies abnormal patterns from the collected data using, for example, a machine learning algorithm. For example, the AI ​​can detect a sudden increase in blood pressure or an abnormal fluctuation in pulse rate. The notification unit detects and notifies the abnormality based on the analysis result obtained by the analysis unit. For example, when an abnormality is detected, the notification unit sends an alert to a caregiver. The alert is sent, for example, through a smartphone notification, email, or voice assistant. The questioning unit generates questions for the caregiver and analyzes the answers. The questioning unit, for example, uses AI to evaluate the mental health state of the caregiver. The AI, for example, analyzes the answers of the caregiver and evaluates the stress level and mental health state. For example, the questioning unit generates questions such as "How tired are you today?" and "Have you had any worries recently?" and asks them to the caregiver. As a result, the health management system according to the embodiment allows the elderly and the caregiver to efficiently manage their health and improve their quality of life. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit inputs the data collected by the collection unit to the generation AI and allows the generation AI to analyze the data. As a result, the health management system allows the elderly and the caregiver to efficiently manage their health and improve their quality of life.

[0030] The collection unit collects health management data. The health management data includes, but is not limited to, blood pressure, pulse, body temperature, number of steps, sleep patterns, and the like. The collection unit collects these data using, for example, a wearable device. The wearable device is, for example, a smart watch or a fitness tracker, and these devices can monitor the user's physical information in real time and collect data. Specifically, a smart watch is worn on the user's wrist and measures the heart rate and blood pressure using a built-in sensor. A fitness tracker measures the number of steps and the amount of exercise, and records the user's activity level. These devices transmit data to a smartphone or a cloud server via Bluetooth (registered trademark) or Wi-Fi, and the data is stored in a central database. In addition, the collection unit can also collect data from medical institutions and fitness centers with the user's consent. For example, the results of regular health checkups and training records can be imported to perform comprehensive health management. This allows the collection unit to collect a wide range of health management data from various data sources and comprehensively grasp the user's health condition.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses AI to analyze the data and detect abnormalities. The AI, for example, uses a machine learning algorithm to identify abnormal patterns from the collected data. Specifically, the AI ​​can learn past data and distinguish between normal and abnormal patterns. For example, the AI ​​can detect a sudden increase in blood pressure or an abnormal fluctuation in pulse rate. This allows a quick response when an abnormality occurs in the user's health condition. Furthermore, the analysis unit can also analyze trends in the user's health condition based on the collected data. For example, by analyzing data over a long period of time and tracking changes in the user's health condition, future risks can be predicted. This enables preventive health management and improves the quality of life of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the data collected by the collection unit into the generation AI and have the generation AI perform analysis of the data. The generation AI can use natural language processing technology to understand the meaning of the data and identify abnormal patterns. This allows the analysis unit to perform more advanced analysis and accurately grasp the user's health condition.

[0032] The notification unit detects and notifies the user of an abnormality based on the analysis result obtained by the analysis unit. For example, when an abnormality is detected, the notification unit sends an alert to the caregiver. The alert is sent, for example, through a smartphone notification, email, or voice assistant. Specifically, the occurrence of an abnormality is notified in real time through a smartphone application, so that the caregiver can respond quickly. In addition, the email notification can include detailed analysis results and recommended countermeasures. Notification using a voice assistant is highly convenient because the caregiver can receive information without using his or her hands. Furthermore, the notification unit can also send a notification to the user when an abnormality is detected. This allows the user to understand his or her own health condition and take necessary measures. For example, if blood pressure rises suddenly, a notification can be sent to encourage the user to take a rest. The notification unit can customize the content and method of the notification, allowing for flexible response according to the needs of the user and the caregiver. This allows the notification unit to support early detection of abnormalities and rapid response, and effectively support the user's health management.

[0033] The questioning unit generates questions for the caregiver and analyzes the answers. The questioning unit evaluates the mental health state of the caregiver using, for example, AI. The AI ​​analyzes the caregiver's answers and evaluates the stress level and mental health state. Specifically, the AI ​​can use natural language processing technology to understand the content of the caregiver's answers and identify emotions and signs of stress. For example, the questioning unit generates questions such as "How tired are you today?" and "Have you been worried about anything recently?" and poses them to the caregiver. The caregiver's answers are input in text format and analyzed by AI. The AI ​​extracts keywords and emotional expressions in the answers and evaluates the caregiver's mental health state. Furthermore, the questioning unit can provide appropriate advice and resources based on the caregiver's answers. For example, if stress is evaluated as high, information on relaxation methods and counseling services can be provided. This allows the questioning unit to continuously monitor the caregiver's mental health state and provide necessary support. The questioning unit can continuously improve the content of the questions and the evaluation method based on the caregiver's feedback. This enables the questioning unit to accurately assess the mental health state of the caregiver and provide appropriate support.

[0034] The collection unit can collect data on blood pressure, pulse, body temperature, number of steps, and sleep patterns. For example, the collection unit uses a blood pressure monitor built into the wearable device to measure blood pressure. For example, the systolic blood pressure and the diastolic blood pressure are measured and data are collected. The collection unit also uses a heart rate sensor built into the wearable device to measure pulse. For example, the resting pulse and the exercise pulse are measured and data are collected. The collection unit also uses a body temperature sensor built into the wearable device to measure body temperature. For example, the body temperature is measured based on the standard of normal body temperature and fever, and data is collected. The collection unit uses an acceleration sensor built into the wearable device to measure the number of steps. For example, the number of steps is measured based on the evaluation standard of the target number of steps per day and the amount of exercise, and data is collected. The collection unit uses a sleep tracker built into the wearable device to measure sleep patterns. For example, the sleep time, sleep quality, and the ratio of REM sleep to non-REM sleep are measured and data is collected. This allows for more detailed health management by collecting a variety of health data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data acquired from a wearable device to the generation AI and cause the generation AI to collect data.

[0035] The analysis unit may include an algorithm for detecting abnormalities in blood pressure or pulse based on the collected data. The analysis unit may include, for example, an algorithm for detecting abnormalities based on collected blood pressure data. For example, if the systolic blood pressure is 140 mmHg or more and the diastolic blood pressure is 90 mmHg or more, it is determined to be abnormal. The analysis unit may also include an algorithm for detecting abnormalities based on collected pulse data. For example, if the resting pulse is 100 bpm or more and the exercise pulse is 180 bpm or more, it is determined to be abnormal. This allows for early detection of abnormalities and allows for rapid response. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the collected data to a generation AI and cause the generation AI to detect abnormalities.

[0036] The questioning unit can generate specific questions for the caregiver. The questioning unit generates specific questions, for example, to evaluate the mental health state of the caregiver. For example, questions such as "How tired are you today?" and "Have you had any worries recently?" are generated and posed to the caregiver. This can promote mental health management of the caregiver. Some or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI or may be performed without using AI. For example, the questioning unit can input the caregiver's answers to the generation AI and cause the generation AI to generate questions.

[0037] The questioning unit may include an algorithm that analyzes the answer to the question and evaluates the stress level. The questioning unit includes, for example, an algorithm that analyzes the answer of the caregiver and evaluates the stress level. For example, the stress level is evaluated based on the result of a psychological test or an analysis of heart rate variability. This makes it possible to evaluate the stress level of the caregiver and provide appropriate advice. A part or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI or may be performed without using AI. For example, the questioning unit may input the answer of the caregiver to the generation AI and cause the generation AI to perform the evaluation of the stress level.

[0038] The collection unit can determine an appropriate collection timing by referring to the user's past health data at the time of collection. The collection unit, for example, determines the optimal collection timing by referring to the user's past health data. For example, data is collected from the user's past data during the most stable time period. Also, from the user's past data, collection can be concentrated on time periods when abnormalities are likely to occur. Furthermore, from the user's past data, the collection timing can be adjusted to match the user's daily routine. In this way, data can be collected at the optimal timing by referring to the past data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past health data to the generation AI and cause the generation AI to execute the determination of the collection timing.

[0039] The collection unit can dynamically change the target of data collection based on the user's lifestyle pattern at the time of collection. The collection unit dynamically changes the target of data collection based on the user's lifestyle pattern, for example. For example, when the user is exercising, the collection unit focuses on collecting heart rate and steps. Also, when the user is sleeping, the collection unit can focus on collecting sleep patterns and body temperature. Furthermore, when the user is resting, the collection unit can focus on collecting blood pressure and pulse. This allows more appropriate data to be collected by changing the target of data collection according to the lifestyle pattern. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's lifestyle pattern data to the generation AI and cause the generation AI to change the target of data collection.

[0040] The collection unit can collect highly relevant data preferentially based on the geographical location information of the user at the time of collection. The collection unit, for example, preferentially collects highly relevant data taking into account the geographical location information of the user. For example, when the user is at high altitude, the oxygen concentration and heart rate are collected preferentially. Also, when the user is in an urban area, the number of steps and the amount of exercise can be collected preferentially. Furthermore, when the user is at home, the sleep pattern and body temperature can be collected preferentially. In this way, highly relevant data can be collected by taking into account the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the determination of the priority order of data.

[0041] The collection unit can analyze the user's social media activity at the time of collection and collect related health data. The collection unit, for example, analyzes the user's social media activity and collects related health data. For example, if the user posts that he or she is feeling stressed, the collection unit can prioritize collecting heart rate and blood pressure. In addition, if the user posts that he or she is relaxing, the collection unit can prioritize collecting sleep patterns and body temperature. Furthermore, if the user posts about exercise, the collection unit can prioritize collecting step counts and exercise amount. In this way, related health data can be collected by analyzing social media activities. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media data to the generation AI and cause the generation AI to collect the data.

[0042] The analysis unit can improve the accuracy of abnormality detection by referring to past health data during analysis. The analysis unit, for example, improves the accuracy of abnormality detection by referring to past health data. For example, a normal range is set from the user's past data to detect abnormalities. In addition, a pattern in which abnormalities are likely to occur can be identified from the user's past data to improve the detection accuracy. Furthermore, individual criteria can be set based on the user's past data to detect abnormalities. As a result, the accuracy of abnormality detection is improved by referring to past data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input past health data to the generation AI and cause the generation AI to improve the accuracy of abnormality detection.

[0043] The analysis unit can dynamically change the abnormality detection criteria based on the user's lifestyle during analysis. The analysis unit dynamically changes the abnormality detection criteria based on the user's lifestyle, for example. For example, when the user is exercising, the heart rate criteria can be set high. Also, when the user is sleeping, the body temperature criteria can be set low. Furthermore, when the user is eating, the blood pressure criteria can be dynamically changed. This allows for more appropriate abnormality detection by changing the criteria based on the lifestyle. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's lifestyle data to the generation AI and cause the generation AI to change the abnormality detection criteria.

[0044] The analysis unit can improve the accuracy of anomaly detection by considering the geographical location information of the user during analysis. The analysis unit improves the accuracy of anomaly detection by considering, for example, the geographical location information of the user. For example, when the user is at high altitude, the analysis unit detects anomalies by considering the oxygen concentration. Also, when the user is in an urban area, the analysis unit can detect anomalies by considering the amount of exercise. Furthermore, when the user is at home, the analysis unit can detect anomalies by considering the sleep pattern. As a result, the accuracy of anomaly detection is improved by considering the geographical location information. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the user to the generation AI and cause the generation AI to perform the improvement of the accuracy of anomaly detection.

[0045] The analysis unit can improve the accuracy of anomaly detection by referring to the user's social media activity during analysis. The analysis unit improves the accuracy of anomaly detection, for example, by referring to the user's social media activity. For example, if the user posts that he or she is feeling stressed, anomalies in heart rate and blood pressure can be detected. In addition, if the user posts that he or she is relaxing, anomalies in sleep patterns can also be detected. Furthermore, if the user posts about exercise, anomalies in the amount of exercise can also be detected. This improves the accuracy of anomaly detection by referring to social media activity. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's social media data to the generation AI and cause the generation AI to perform an improvement in the accuracy of anomaly detection.

[0046] The notification unit can select the optimal notification method by referring to the past notification history at the time of notification. The notification unit, for example, selects the optimal notification method by referring to the past notification history. For example, the notification method that the user has preferred in the past is preferentially selected. It is also possible to avoid notification methods that the user has ignored in the past. Furthermore, it is also possible to perform notification at the optimal timing based on the user's past notification history. As a result, it is possible to perform notification in the optimal method by referring to the past notification history. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the past notification history to the generation AI and cause the generation AI to select the notification method.

[0047] The notification unit can customize the notification contents based on the user's lifestyle pattern at the time of notification. The notification unit customizes the notification contents based on the user's lifestyle pattern, for example. For example, when the user is exercising, a notification related to exercise can be given priority. Also, when the user is sleeping, a notification related to sleep can be given priority. Furthermore, when the user is taking a break, a notification related to relaxation can be given priority. This allows for more appropriate notification by customizing the notification contents based on the lifestyle pattern. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's lifestyle pattern data to the generation AI and cause the generation AI to customize the notification contents.

[0048] The notification unit can select the optimal notification method in consideration of the geographical location information of the user at the time of notification. The notification unit selects the optimal notification method in consideration of, for example, the geographical location information of the user. For example, when the user is at high altitude, notification regarding oxygen concentration can be given priority. In addition, when the user is in an urban area, notification regarding the amount of exercise can also be given priority. Furthermore, when the user is at home, notification regarding sleep patterns can also be given priority. In this way, notification can be given in the optimal method by taking the geographical location information into consideration. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the geographical location information of the user to the generation AI and cause the generation AI to select the notification method.

[0049] The notification unit can customize the notification contents by analyzing the social media activity of the user at the time of notification. The notification unit, for example, analyzes the social media activity of the user to customize the notification contents. For example, if the user posts that he / she is feeling stressed, notifications regarding heart rate and blood pressure can be given priority. In addition, if the user posts that he / she is relaxing, notifications regarding sleep patterns can also be given priority. Furthermore, if the user posts about exercise, notifications regarding the amount of exercise can also be given priority. In this way, by analyzing social media activities, more appropriate notification contents can be provided. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the social media data of the user to the generation AI and cause the generation AI to customize the notification contents.

[0050] The questioning unit can generate an optimal question by referring to the past answer history when asking a question. The questioning unit generates an optimal question by referring to the past answer history, for example. For example, a related question is generated based on the pattern of questions answered by the user in the past. In addition, the stress level can be evaluated from the user's past answer history, and a question based on the evaluation can be generated. Furthermore, a new question can be generated by taking into account the frequency of questions answered by the user in the past. In this way, a more relevant question can be generated by referring to the past answer history. A part or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can input the past answer history to the generation AI and cause the generation AI to generate a question.

[0051] The questioning unit can dynamically change the timing of the question based on the user's life pattern when asking a question. The questioning unit dynamically changes the timing of the question based on the user's life pattern, for example. For example, when the user is exercising, the question is asked after the user exercises. Also, when the user is sleeping, the question can be asked after waking up. Furthermore, when the user is resting, the question can be asked at a timing when the user is relaxing. In this way, by changing the timing of the question based on the life pattern, the question can be asked at a more appropriate timing. A part or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can input the user's life pattern data to the generation AI and cause the generation AI to execute the change in the timing of the question.

[0052] The questioning unit can generate an optimal question by considering the geographical location information of the user when asking a question. The questioning unit generates an optimal question by considering, for example, the geographical location information of the user. For example, when the user is at high altitude, a question about oxygen concentration can be given priority. Also, when the user is in an urban area, a question about the amount of exercise can be given priority. Furthermore, when the user is at home, a question about sleep patterns can be given priority. In this way, by considering the geographical location information, a more relevant question can be generated. A part or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can input the geographical location information of the user to the generation AI and cause the generation AI to generate a question.

[0053] The questioning unit can customize the question content by analyzing the social media activity of the user at the time of the question. The questioning unit, for example, analyzes the social media activity of the user to customize the question content. For example, if the user posts that he / she is feeling stressed, questions about the heart rate and blood pressure can be prioritized. In addition, if the user posts that he / she is relaxed, questions about sleep patterns can be prioritized. Furthermore, if the user posts about exercise, questions about the amount of exercise can be prioritized. In this way, by analyzing the social media activity, more appropriate question content can be provided. Part or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can input the social media data of the user to the generation AI and cause the generation AI to customize the question content.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The health management system may further include a diet management unit that collects and analyzes the user's dietary data. The diet management unit records the contents, calories, and nutrients of the meals consumed by the user, and evaluates the overall health condition by combining the health data. For example, if the user consumes a high-calorie meal, it may advise the user to increase the amount of exercise. Also, if a specific nutrient is lacking, it may suggest complementary ingredients or supplements. Furthermore, the diet management unit may analyze the user's dietary patterns and provide specific advice for maintaining a healthy diet. This allows the user to manage their health more effectively while maintaining a balance between diet and health.

[0056] The health management system may further include an exercise management unit that collects and analyzes the user's exercise data. The exercise management unit records the type and time of exercise performed by the user, and calories burned, and evaluates the overall health condition by combining the health data. For example, if the user is not getting enough exercise, an appropriate exercise plan can be suggested. The exercise management unit can also analyze the effects of specific exercises on health and advise the user on the most suitable exercise method. Furthermore, the exercise management unit can analyze the user's exercise patterns and provide specific advice to maintain motivation. This allows the user to manage their health more effectively while maintaining a balance between exercise and health.

[0057] The health management system may further include a sleep management unit that collects and analyzes the user's sleep data. The sleep management unit records the user's sleep time and quality, and the ratio of REM sleep to non-REM sleep, and evaluates the overall health condition in combination with the health data. For example, if the user is sleep deprived, it can suggest an appropriate sleeping environment. It can also analyze the impact of a specific sleep pattern on health and advise the user on the optimal sleeping method. Furthermore, the sleep management unit can analyze the user's sleep pattern and provide specific advice on maintaining comfortable sleep. This allows the user to manage their health more effectively while maintaining a balance between sleep and health.

[0058] The health management system may further include a stress management unit that collects and analyzes the user's stress data. The stress management unit records the user's heart rate variability and self-reported stress level, and combines it with the health data to evaluate the overall health condition. For example, if the user is in a high stress state, it may suggest a relaxation method. It may also analyze the impact of specific stress factors on health and advise the user on the most suitable stress management method. Furthermore, the stress management unit may analyze the user's stress pattern and provide specific advice for reducing stress. This allows the user to manage their health more effectively while maintaining a balance between stress and health.

[0059] The health management system may further include an environment management unit that collects and analyzes the user's environmental data. The environment management unit records data on the user's living and working environments, and combines the data with the health data to evaluate the overall health condition. For example, if the user is in a noisy environment, it may suggest relaxation in a quiet place. It may also analyze the effects of specific environmental factors on health and advise the user on the optimal method of improving the environment. Furthermore, the environment management unit may analyze the user's environmental patterns and provide specific advice on maintaining a comfortable environment. This allows the user to perform more effective health management while maintaining a balance between the environment and health.

[0060] The health management system may further include an economic management unit that collects and analyzes the user's economic data. The economic management unit records the user's income, expenditure, and savings status, and combines the health data to evaluate the overall health condition. For example, if the user feels financial stress, it may suggest budget management and saving methods. It may also analyze the impact of a particular economic situation on health and advise the user on the optimal economic management method. Furthermore, the economic management unit may analyze the user's economic patterns and provide specific advice for maintaining economic stability. This allows the user to manage his or her health more effectively while maintaining a balance between finances and health.

[0061] The health management system may further include a learning management unit that collects and analyzes the user's learning data. The learning management unit records the content and progress of the user's learning, and combines it with the health data to evaluate the overall health condition. For example, if the user feels stressed by studying, it may suggest ways to improve the study method or how to take a break. It may also analyze the impact of specific study activities on health and advise the user on the optimal study method. Furthermore, the learning management unit may analyze the user's study patterns and provide specific advice for maintaining a balance between study and health. This allows the user to manage their health more effectively while maintaining a balance between study and health.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The collection unit collects health management data. The health management data includes, for example, blood pressure, pulse, body temperature, number of steps, sleep patterns, and the like. The collection unit collects this data using, for example, a wearable device. The wearable device is, for example, a smart watch or a fitness tracker, and can monitor the user's physical information in real time and collect data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and detect anomalies. The AI ​​uses machine learning algorithms to identify abnormal patterns from the collected data. For example, it can detect a sudden increase in blood pressure or an abnormal fluctuation in pulse rate. Step 3: The notification unit detects and notifies an abnormality based on the analysis results obtained by the analysis unit. If an abnormality is detected, the notification unit sends an alert to the caregiver. The alert is sent via smartphone notification, email, or voice assistant. Step 4: The questioning unit generates questions for the caregiver and analyzes the responses. The questioning unit uses AI to assess the caregiver's mental health. The AI ​​analyzes the caregiver's responses and evaluates the stress level and mental health of the caregiver. For example, it generates questions such as "How tired are you today?" and "Are you worried about anything recently?" and poses them to the caregiver.

[0064] (Example 2) A health management system according to an embodiment of the present invention is a system in which elderly people and caregivers register health management data, and an AI analyzes the data to provide regular health advice. In this health management system, first, elderly people collect health management data using a wearable device. This data includes blood pressure, pulse, body temperature, number of steps, sleep patterns, etc. Next, the collected data is sent to an AI, which analyzes it. The AI ​​predicts abnormalities in blood pressure and pulse, mood fluctuations, etc., and notifies the caregiver as necessary. For example, if blood pressure rises suddenly, the AI ​​sends an alert to the caregiver and prompts appropriate action. In addition, for caregivers, a function is provided to promote mental health management through daily questions. The AI ​​analyzes the caregiver's answers and evaluates the stress level and mental health state. For example, it asks questions such as "How tired are you today?" and "Is there anything you're worried about recently?" to the caregiver, and provides advice based on the answers. This eliminates things that caregivers cannot express in words or things that they push themselves too hard, and reduces daily stress. This solution allows both elderly people and caregivers to efficiently manage their health, improving their quality of life. For example, simply by wearing a wearable device, the elderly can go about their daily lives with peace of mind, as AI automatically collects data and notifies caregivers if there is an abnormality. Caregivers can also receive advice from AI to manage their own mental health and reduce stress. This allows the health management system to efficiently manage the health of elderly people and caregivers, improving their quality of life.

[0065] The health management system according to the embodiment includes a collection unit, an analysis unit, a notification unit, and a question unit. The collection unit collects health management data. The health management data includes, but is not limited to, blood pressure, pulse, body temperature, number of steps, and sleep patterns. The collection unit collects these data using, for example, a wearable device. The wearable device is, for example, a smart watch or a fitness tracker, and these devices can monitor the user's physical information in real time and collect data. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI to detect abnormalities. The AI ​​identifies abnormal patterns from the collected data using, for example, a machine learning algorithm. For example, the AI ​​can detect a sudden increase in blood pressure or an abnormal fluctuation in pulse rate. The notification unit detects and notifies the abnormality based on the analysis result obtained by the analysis unit. For example, when an abnormality is detected, the notification unit sends an alert to a caregiver. The alert is sent, for example, through a smartphone notification, email, or voice assistant. The questioning unit generates questions for the caregiver and analyzes the answers. The questioning unit, for example, uses AI to evaluate the mental health state of the caregiver. The AI, for example, analyzes the answers of the caregiver and evaluates the stress level and mental health state. For example, the questioning unit generates questions such as "How tired are you today?" and "Have you had any worries recently?" and asks them to the caregiver. As a result, the health management system according to the embodiment allows the elderly and the caregiver to efficiently manage their health and improve their quality of life. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit inputs the data collected by the collection unit to the generation AI and allows the generation AI to analyze the data. As a result, the health management system allows the elderly and the caregiver to efficiently manage their health and improve their quality of life.

[0066] The collection unit collects health management data. The health management data includes, but is not limited to, blood pressure, pulse, body temperature, number of steps, sleep patterns, and the like. The collection unit collects these data using, for example, a wearable device. The wearable device is, for example, a smart watch or a fitness tracker, and these devices can monitor the user's physical information in real time and collect data. Specifically, a smart watch is worn on the user's wrist and measures the heart rate and blood pressure using a built-in sensor. A fitness tracker measures the number of steps and the amount of exercise, and records the user's activity level. These devices transmit data to a smartphone or a cloud server via Bluetooth or Wi-Fi, and the data is stored in a central database. In addition, the collection unit can also collect data from medical institutions and fitness centers with the user's consent. For example, the results of regular health checkups and training records can be imported to perform comprehensive health management. This allows the collection unit to collect a wide range of health management data from various data sources and comprehensively grasp the user's health condition.

[0067] The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses AI to analyze the data and detect abnormalities. The AI, for example, uses a machine learning algorithm to identify abnormal patterns from the collected data. Specifically, the AI ​​can learn past data and distinguish between normal and abnormal patterns. For example, the AI ​​can detect a sudden increase in blood pressure or an abnormal fluctuation in pulse rate. This allows a quick response when an abnormality occurs in the user's health condition. Furthermore, the analysis unit can also analyze trends in the user's health condition based on the collected data. For example, by analyzing data over a long period of time and tracking changes in the user's health condition, future risks can be predicted. This enables preventive health management and improves the quality of life of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the data collected by the collection unit into the generation AI and have the generation AI perform analysis of the data. The generation AI can use natural language processing technology to understand the meaning of the data and identify abnormal patterns. This allows the analysis unit to perform more advanced analysis and accurately grasp the user's health condition.

[0068] The notification unit detects and notifies the user of an abnormality based on the analysis result obtained by the analysis unit. For example, when an abnormality is detected, the notification unit sends an alert to the caregiver. The alert is sent, for example, through a smartphone notification, email, or voice assistant. Specifically, the occurrence of an abnormality is notified in real time through a smartphone application, so that the caregiver can respond quickly. In addition, the email notification can include detailed analysis results and recommended countermeasures. Notification using a voice assistant is highly convenient because the caregiver can receive information without using his or her hands. Furthermore, the notification unit can also send a notification to the user when an abnormality is detected. This allows the user to understand his or her own health condition and take necessary measures. For example, if blood pressure rises suddenly, a notification can be sent to encourage the user to take a rest. The notification unit can customize the content and method of the notification, allowing for flexible response according to the needs of the user and the caregiver. This allows the notification unit to support early detection of abnormalities and rapid response, and effectively support the user's health management.

[0069] The questioning unit generates questions for the caregiver and analyzes the answers. The questioning unit evaluates the mental health state of the caregiver using, for example, AI. The AI ​​analyzes the caregiver's answers and evaluates the stress level and mental health state. Specifically, the AI ​​can use natural language processing technology to understand the content of the caregiver's answers and identify emotions and signs of stress. For example, the questioning unit generates questions such as "How tired are you today?" and "Have you been worried about anything recently?" and poses them to the caregiver. The caregiver's answers are input in text format and analyzed by AI. The AI ​​extracts keywords and emotional expressions in the answers and evaluates the caregiver's mental health state. Furthermore, the questioning unit can provide appropriate advice and resources based on the caregiver's answers. For example, if stress is evaluated as high, information on relaxation methods and counseling services can be provided. This allows the questioning unit to continuously monitor the caregiver's mental health state and provide necessary support. The questioning unit can continuously improve the content of the questions and the evaluation method based on the caregiver's feedback. This enables the questioning unit to accurately assess the mental health state of the caregiver and provide appropriate support.

[0070] The collection unit can collect data on blood pressure, pulse, body temperature, number of steps, and sleep patterns. For example, the collection unit uses a blood pressure monitor built into the wearable device to measure blood pressure. For example, the systolic blood pressure and the diastolic blood pressure are measured and data are collected. The collection unit also uses a heart rate sensor built into the wearable device to measure pulse. For example, the resting pulse and the exercise pulse are measured and data are collected. The collection unit also uses a body temperature sensor built into the wearable device to measure body temperature. For example, the body temperature is measured based on the standard of normal body temperature and fever, and data is collected. The collection unit uses an acceleration sensor built into the wearable device to measure the number of steps. For example, the number of steps is measured based on the evaluation standard of the target number of steps per day and the amount of exercise, and data is collected. The collection unit uses a sleep tracker built into the wearable device to measure sleep patterns. For example, the sleep time, sleep quality, and the ratio of REM sleep to non-REM sleep are measured and data is collected. This allows for more detailed health management by collecting a variety of health data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data acquired from a wearable device to the generation AI and cause the generation AI to collect data.

[0071] The analysis unit may include an algorithm for detecting abnormalities in blood pressure or pulse based on the collected data. The analysis unit may include, for example, an algorithm for detecting abnormalities based on collected blood pressure data. For example, if the systolic blood pressure is 140 mmHg or more and the diastolic blood pressure is 90 mmHg or more, it is determined to be abnormal. The analysis unit may also include an algorithm for detecting abnormalities based on collected pulse data. For example, if the resting pulse is 100 bpm or more and the exercise pulse is 180 bpm or more, it is determined to be abnormal. This allows for early detection of abnormalities and allows for rapid response. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the collected data to a generation AI and cause the generation AI to detect abnormalities.

[0072] The questioning unit can generate specific questions for the caregiver. The questioning unit generates specific questions, for example, to evaluate the mental health state of the caregiver. For example, questions such as "How tired are you today?" and "Have you had any worries recently?" are generated and posed to the caregiver. This can promote mental health management of the caregiver. Some or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI or may be performed without using AI. For example, the questioning unit can input the caregiver's answers to the generation AI and cause the generation AI to generate questions.

[0073] The questioning unit may include an algorithm that analyzes the answer to the question and evaluates the stress level. The questioning unit includes, for example, an algorithm that analyzes the answer of the caregiver and evaluates the stress level. For example, the stress level is evaluated based on the result of a psychological test or an analysis of heart rate variability. This makes it possible to evaluate the stress level of the caregiver and provide appropriate advice. A part or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI or may be performed without using AI. For example, the questioning unit may input the answer of the caregiver to the generation AI and cause the generation AI to perform the evaluation of the stress level.

[0074] The collection unit can estimate the user's emotion and adjust the frequency of collection of health data based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the frequency of collection of health data based on the estimated user's emotion. For example, when the user is stressed, the collection frequency is reduced to reduce the burden. Also, when the user is relaxed, the collection frequency can be increased to obtain detailed data. Furthermore, when the user is in a hurry, the collection frequency can be temporarily reduced and data can be collected later in a lump. In this way, the burden can be reduced by adjusting the collection frequency according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the collection frequency.

[0075] The collection unit can determine an appropriate collection timing by referring to the user's past health data at the time of collection. The collection unit, for example, determines the optimal collection timing by referring to the user's past health data. For example, data is collected from the user's past data during the most stable time period. Also, from the user's past data, collection can be concentrated on time periods when abnormalities are likely to occur. Furthermore, from the user's past data, the collection timing can be adjusted to match the user's daily routine. In this way, data can be collected at the optimal timing by referring to the past data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past health data to the generation AI and cause the generation AI to execute the determination of the collection timing.

[0076] The collection unit can dynamically change the target of data collection based on the user's lifestyle pattern at the time of collection. The collection unit dynamically changes the target of data collection based on the user's lifestyle pattern, for example. For example, when the user is exercising, the collection unit focuses on collecting heart rate and steps. Also, when the user is sleeping, the collection unit can focus on collecting sleep patterns and body temperature. Furthermore, when the user is resting, the collection unit can focus on collecting blood pressure and pulse. This allows more appropriate data to be collected by changing the target of data collection according to the lifestyle pattern. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's lifestyle pattern data to the generation AI and cause the generation AI to change the target of data collection.

[0077] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of data to be collected based on the estimated user's emotion. For example, when the user is stressed, the heart rate and blood pressure are collected with priority. Also, when the user is relaxed, the sleep pattern and body temperature can be collected with priority. Furthermore, when the user is in a hurry, the number of steps and the amount of exercise can be collected with priority. In this way, important data can be collected with priority by determining the priority of data based on emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the data.

[0078] The collection unit can collect highly relevant data preferentially based on the geographical location information of the user at the time of collection. The collection unit, for example, preferentially collects highly relevant data taking into account the geographical location information of the user. For example, when the user is at high altitude, the oxygen concentration and heart rate are collected preferentially. Also, when the user is in an urban area, the number of steps and the amount of exercise can be collected preferentially. Furthermore, when the user is at home, the sleep pattern and body temperature can be collected preferentially. In this way, highly relevant data can be collected by taking into account the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the determination of the priority order of data.

[0079] The collection unit can analyze the user's social media activity at the time of collection and collect related health data. The collection unit, for example, analyzes the user's social media activity and collects related health data. For example, if the user posts that he or she is feeling stressed, the collection unit can prioritize collecting heart rate and blood pressure. In addition, if the user posts that he or she is relaxing, the collection unit can prioritize collecting sleep patterns and body temperature. Furthermore, if the user posts about exercise, the collection unit can prioritize collecting step counts and exercise amount. In this way, related health data can be collected by analyzing social media activities. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media data to the generation AI and cause the generation AI to collect the data.

[0080] The analysis unit can estimate the user's emotion and adjust the parameters of the analysis algorithm based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the parameters of the analysis algorithm based on the estimated user's emotion. For example, if the user is stressed, the threshold for anomaly detection can be set low. Also, if the user is relaxed, the threshold for anomaly detection can be set high. Furthermore, if the user is in a hurry, the speed of analysis can be prioritized and detailed analysis can be postponed. This allows for more accurate analysis by adjusting the parameters of the analysis algorithm based on the emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the parameters of the analysis algorithm.

[0081] The analysis unit can improve the accuracy of abnormality detection by referring to past health data during analysis. The analysis unit, for example, improves the accuracy of abnormality detection by referring to past health data. For example, a normal range is set from the user's past data to detect abnormalities. In addition, a pattern in which abnormalities are likely to occur can be identified from the user's past data to improve the detection accuracy. Furthermore, individual criteria can be set based on the user's past data to detect abnormalities. As a result, the accuracy of abnormality detection is improved by referring to past data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input past health data to the generation AI and cause the generation AI to improve the accuracy of abnormality detection.

[0082] The analysis unit can dynamically change the abnormality detection criteria based on the user's lifestyle during analysis. The analysis unit dynamically changes the abnormality detection criteria based on the user's lifestyle, for example. For example, when the user is exercising, the heart rate criteria can be set high. Also, when the user is sleeping, the body temperature criteria can be set low. Furthermore, when the user is eating, the blood pressure criteria can be dynamically changed. This allows for more appropriate abnormality detection by changing the criteria based on the lifestyle. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's lifestyle data to the generation AI and cause the generation AI to change the abnormality detection criteria.

[0083] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, a simple and highly visible display method is provided. Also, when the user is relaxed, a display method including detailed information can be provided. Furthermore, when the user is in a hurry, a display method that focuses on the main points can be provided. This allows for a display that is easy for the user to see by adjusting the display method based on the emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0084] The analysis unit can improve the accuracy of anomaly detection by considering the geographical location information of the user during analysis. The analysis unit improves the accuracy of anomaly detection by considering, for example, the geographical location information of the user. For example, when the user is at high altitude, the analysis unit detects anomalies by considering the oxygen concentration. Also, when the user is in an urban area, the analysis unit can detect anomalies by considering the amount of exercise. Furthermore, when the user is at home, the analysis unit can detect anomalies by considering the sleep pattern. As a result, the accuracy of anomaly detection is improved by considering the geographical location information. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the user to the generation AI and cause the generation AI to perform the improvement of the accuracy of anomaly detection.

[0085] The analysis unit can improve the accuracy of anomaly detection by referring to the user's social media activity during analysis. The analysis unit improves the accuracy of anomaly detection, for example, by referring to the user's social media activity. For example, if the user posts that he or she is feeling stressed, anomalies in heart rate and blood pressure can be detected. In addition, if the user posts that he or she is relaxing, anomalies in sleep patterns can also be detected. Furthermore, if the user posts about exercise, anomalies in the amount of exercise can also be detected. This improves the accuracy of anomaly detection by referring to social media activity. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's social media data to the generation AI and cause the generation AI to perform an improvement in the accuracy of anomaly detection.

[0086] The notification unit can estimate the user's emotion and adjust the timing of the notification based on the estimated user's emotion. The notification unit, for example, estimates the user's emotion and adjusts the timing of the notification based on the estimated user's emotion. For example, when the user is stressed, the frequency of notifications is reduced to reduce the burden. Also, when the user is relaxed, the frequency of notifications can be increased to provide detailed information. Furthermore, when the user is in a hurry, the timing of notifications can be temporarily delayed. As a result, the burden on the user can be reduced by adjusting the timing of notifications based on the emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of the notification.

[0087] The notification unit can select the optimal notification method by referring to the past notification history at the time of notification. The notification unit, for example, selects the optimal notification method by referring to the past notification history. For example, the notification method that the user has preferred in the past is preferentially selected. It is also possible to avoid notification methods that the user has ignored in the past. Furthermore, it is also possible to perform notification at the optimal timing based on the user's past notification history. As a result, it is possible to perform notification in the optimal method by referring to the past notification history. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the past notification history to the generation AI and cause the generation AI to select the notification method.

[0088] The notification unit can customize the notification contents based on the user's lifestyle pattern at the time of notification. The notification unit customizes the notification contents based on the user's lifestyle pattern, for example. For example, when the user is exercising, a notification related to exercise can be given priority. Also, when the user is sleeping, a notification related to sleep can be given priority. Furthermore, when the user is taking a break, a notification related to relaxation can be given priority. This allows for more appropriate notification by customizing the notification contents based on the lifestyle pattern. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's lifestyle pattern data to the generation AI and cause the generation AI to customize the notification contents.

[0089] The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. The notification unit, for example, estimates the user's emotion and determines the priority of notifications based on the estimated user's emotion. For example, when the user is stressed, important notifications are given priority. Also, when the user is relaxed, detailed notifications can be given priority. Furthermore, when the user is in a hurry, notifications with high urgency can also be given priority. As a result, important notifications can be given priority by determining the priority of notifications based on emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of notifications.

[0090] The notification unit can select the optimal notification method in consideration of the geographical location information of the user at the time of notification. The notification unit selects the optimal notification method in consideration of, for example, the geographical location information of the user. For example, when the user is at high altitude, notification regarding oxygen concentration can be given priority. In addition, when the user is in an urban area, notification regarding the amount of exercise can also be given priority. Furthermore, when the user is at home, notification regarding sleep patterns can also be given priority. In this way, notification can be given in the optimal method by taking the geographical location information into consideration. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the geographical location information of the user to the generation AI and cause the generation AI to select the notification method.

[0091] The notification unit can customize the notification contents by analyzing the social media activity of the user at the time of notification. The notification unit, for example, analyzes the social media activity of the user to customize the notification contents. For example, if the user posts that he / she is feeling stressed, notifications regarding heart rate and blood pressure can be given priority. In addition, if the user posts that he / she is relaxing, notifications regarding sleep patterns can also be given priority. Furthermore, if the user posts about exercise, notifications regarding the amount of exercise can also be given priority. In this way, by analyzing social media activities, more appropriate notification contents can be provided. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the social media data of the user to the generation AI and cause the generation AI to customize the notification contents.

[0092] The questioning unit can estimate the user's emotion and adjust the content of the question based on the estimated user's emotion. The questioning unit, for example, estimates the user's emotion and adjusts the content of the question based on the estimated user's emotion. For example, when the user is stressed, a simple and easy-to-answer question is provided. Also, when the user is relaxed, a detailed question can be provided. Furthermore, when the user is in a hurry, a short question that focuses on the main points can be provided. In this way, by adjusting the content of the question based on the emotion, a question that is easy for the user to answer can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the questioning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the questioning unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the content of the question.

[0093] The questioning unit can generate an optimal question by referring to the past answer history when asking a question. The questioning unit generates an optimal question by referring to the past answer history, for example. For example, a related question is generated based on the pattern of questions answered by the user in the past. In addition, the stress level can be evaluated from the user's past answer history, and a question based on the evaluation can be generated. Furthermore, a new question can be generated by taking into account the frequency of questions answered by the user in the past. In this way, a more relevant question can be generated by referring to the past answer history. A part or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can input the past answer history to the generation AI and cause the generation AI to generate a question.

[0094] The questioning unit can dynamically change the timing of the question based on the user's life pattern when asking a question. The questioning unit dynamically changes the timing of the question based on the user's life pattern, for example. For example, when the user is exercising, the question is asked after the user exercises. Also, when the user is sleeping, the question can be asked after waking up. Furthermore, when the user is resting, the question can be asked at a timing when the user is relaxing. In this way, by changing the timing of the question based on the life pattern, the question can be asked at a more appropriate timing. A part or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can input the user's life pattern data to the generation AI and cause the generation AI to execute the change in the timing of the question.

[0095] The questioning unit can estimate the user's emotion and determine the priority of questions based on the estimated user's emotion. The questioning unit, for example, estimates the user's emotion and determines the priority of questions based on the estimated user's emotion. For example, when the user is stressed, important questions are given priority. Also, when the user is relaxed, detailed questions can be given priority. Furthermore, when the user is in a hurry, questions with high urgency can also be given priority. As a result, important questions can be given priority by determining the priority of questions based on emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the questioning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the questioning unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of questions.

[0096] The questioning unit can generate an optimal question by considering the geographical location information of the user when asking a question. The questioning unit generates an optimal question by considering, for example, the geographical location information of the user. For example, when the user is at high altitude, a question about oxygen concentration can be given priority. Also, when the user is in an urban area, a question about the amount of exercise can be given priority. Furthermore, when the user is at home, a question about sleep patterns can be given priority. In this way, by considering the geographical location information, a more relevant question can be generated. A part or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can input the geographical location information of the user to the generation AI and cause the generation AI to generate a question.

[0097] The questioning unit can customize the question content by analyzing the social media activity of the user at the time of the question. The questioning unit, for example, analyzes the social media activity of the user to customize the question content. For example, if the user posts that he / she is feeling stressed, questions about the heart rate and blood pressure can be prioritized. In addition, if the user posts that he / she is relaxed, questions about sleep patterns can be prioritized. Furthermore, if the user posts about exercise, questions about the amount of exercise can be prioritized. In this way, by analyzing the social media activity, more appropriate question content can be provided. Part or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can input the social media data of the user to the generation AI and cause the generation AI to customize the question content.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The health management system may further include a diet management unit that collects and analyzes the user's dietary data. The diet management unit records the contents, calories, and nutrients of the meals consumed by the user, and evaluates the overall health condition by combining the health data. For example, if the user consumes a high-calorie meal, it may advise the user to increase the amount of exercise. Also, if a specific nutrient is lacking, it may suggest complementary ingredients or supplements. Furthermore, the diet management unit may analyze the user's dietary patterns and provide specific advice for maintaining a healthy diet. This allows the user to manage their health more effectively while maintaining a balance between diet and health.

[0100] The health management system may further include an exercise management unit that collects and analyzes the user's exercise data. The exercise management unit records the type and time of exercise performed by the user, and calories burned, and evaluates the overall health condition by combining the health data. For example, if the user is not getting enough exercise, an appropriate exercise plan can be suggested. The exercise management unit can also analyze the effects of specific exercises on health and advise the user on the most suitable exercise method. Furthermore, the exercise management unit can analyze the user's exercise patterns and provide specific advice to maintain motivation. This allows the user to manage their health more effectively while maintaining a balance between exercise and health.

[0101] The health management system may further include a sleep management unit that collects and analyzes the user's sleep data. The sleep management unit records the user's sleep time and quality, and the ratio of REM sleep to non-REM sleep, and evaluates the overall health condition in combination with the health data. For example, if the user is sleep deprived, it can suggest an appropriate sleeping environment. It can also analyze the impact of a specific sleep pattern on health and advise the user on the optimal sleeping method. Furthermore, the sleep management unit can analyze the user's sleep pattern and provide specific advice on maintaining comfortable sleep. This allows the user to manage their health more effectively while maintaining a balance between sleep and health.

[0102] The health management system may further include a stress management unit that collects and analyzes the user's stress data. The stress management unit records the user's heart rate variability and self-reported stress level, and combines it with the health data to evaluate the overall health condition. For example, if the user is in a high stress state, it may suggest a relaxation method. It may also analyze the impact of specific stress factors on health and advise the user on the most suitable stress management method. Furthermore, the stress management unit may analyze the user's stress pattern and provide specific advice for reducing stress. This allows the user to manage their health more effectively while maintaining a balance between stress and health.

[0103] The health management system may further include an emotion management unit that collects and analyzes the user's emotion data. The emotion management unit records the user's facial expressions, voice, and self-reported emotional state, and combines the health data to evaluate the overall health condition. For example, if the user is feeling negative emotions, it may suggest positive activities. It may also analyze the impact of specific emotions on health and advise the user on the optimal emotion management method. Furthermore, the emotion management unit may analyze the user's emotion patterns and provide specific advice for stabilizing emotions. This allows the user to manage their health more effectively while maintaining a balance between emotions and health.

[0104] The health management system may further include a social activity management unit that collects and analyzes the user's social activity data. The social activity management unit records the user's social media posts and interaction history, and combines them with the health data to evaluate the overall health condition. For example, if the user is isolated, it may suggest events and activities to promote social interaction. It may also analyze the impact of specific social activities on health and advise the user on the most suitable social activities. Furthermore, the social activity management unit may analyze the user's social activity patterns and provide specific advice for strengthening social connections. This allows the user to manage their health more effectively while maintaining a balance between social activities and health.

[0105] The health management system may further include a hobby management unit that collects and analyzes hobby and interest data of the user. The hobby management unit records the activities and areas of interest that the user enjoys and combines them with the health data to evaluate the overall health condition. For example, if the user has not spent time on a hobby, it may advise the user to resume the hobby activity. It may also analyze the effect of a specific hobby on health and suggest the most suitable hobby activity to the user. Furthermore, the hobby management unit may analyze the hobby pattern of the user and provide specific advice on reducing stress through a hobby. This allows the user to manage his / her health more effectively while maintaining a balance between his / her hobby and health.

[0106] The health management system may further include an environment management unit that collects and analyzes the user's environmental data. The environment management unit records data on the user's living and working environments, and combines the data with the health data to evaluate the overall health condition. For example, if the user is in a noisy environment, it may suggest relaxation in a quiet place. It may also analyze the effects of specific environmental factors on health and advise the user on the optimal method of improving the environment. Furthermore, the environment management unit may analyze the user's environmental patterns and provide specific advice on maintaining a comfortable environment. This allows the user to perform more effective health management while maintaining a balance between the environment and health.

[0107] The health management system may further include an economic management unit that collects and analyzes the user's economic data. The economic management unit records the user's income, expenditure, and savings status, and combines the health data to evaluate the overall health condition. For example, if the user feels financial stress, it may suggest budget management and saving methods. It may also analyze the impact of a particular economic situation on health and advise the user on the optimal economic management method. Furthermore, the economic management unit may analyze the user's economic patterns and provide specific advice for maintaining economic stability. This allows the user to manage his or her health more effectively while maintaining a balance between finances and health.

[0108] The health management system may further include a learning management unit that collects and analyzes the user's learning data. The learning management unit records the content and progress of the user's learning, and combines it with the health data to evaluate the overall health condition. For example, if the user feels stressed by studying, it may suggest ways to improve the study method or how to take a break. It may also analyze the impact of specific study activities on health and advise the user on the optimal study method. Furthermore, the learning management unit may analyze the user's study patterns and provide specific advice for maintaining a balance between study and health. This allows the user to manage their health more effectively while maintaining a balance between study and health.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The collection unit collects health management data. The health management data includes, for example, blood pressure, pulse, body temperature, number of steps, sleep patterns, and the like. The collection unit collects this data using, for example, a wearable device. The wearable device is, for example, a smart watch or a fitness tracker, and can monitor the user's physical information in real time and collect data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and detect anomalies. The AI ​​uses machine learning algorithms to identify abnormal patterns from the collected data. For example, it can detect a sudden increase in blood pressure or an abnormal fluctuation in pulse rate. Step 3: The notification unit detects and notifies an abnormality based on the analysis results obtained by the analysis unit. If an abnormality is detected, the notification unit sends an alert to the caregiver. The alert is sent via smartphone notification, email, or voice assistant. Step 4: The questioning unit generates questions for the caregiver and analyzes the responses. The questioning unit uses AI to assess the caregiver's mental health. The AI ​​analyzes the caregiver's responses and evaluates the stress level and mental health of the caregiver. For example, it generates questions such as "How tired are you today?" and "Are you worried about anything recently?" and poses them to the caregiver.

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned collection unit, analysis unit, notification unit, and question unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart device 14 and collects health management data from the wearable device. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The notification unit is realized, for example, by the control unit 46A of the smart device 14 and notifies the caregiver when an abnormality is detected. The question unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates questions for the caregiver and analyzes the answers. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0122] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned collection unit, analysis unit, notification unit, and question unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart glasses 214 and collects health management data from the wearable device. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214 and notifies the caregiver when an abnormality is detected. The question unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates questions for the caregiver and analyzes the answers. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned collection unit, analysis unit, notification unit, and question unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the headset type terminal 314, and collects health management data from the wearable device. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314, and notifies the caregiver when an abnormality is detected. The question unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates questions for the caregiver and analyzes the answers. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a 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.

[0149] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] Each of the multiple elements including the above-mentioned collection unit, analysis unit, notification unit, and question unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the robot 414 and collects health management data from the wearable device. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The notification unit is realized, for example, by the control unit 46A of the robot 414 and notifies the caregiver when an abnormality is detected. The question unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates questions for the caregiver and analyzes the answers. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] As the hardware resource for executing the specific process, the following various processors can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application specific integrated circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration in which a processor is used that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) A collection unit that collects health management data; an analysis unit that analyzes the data collected by the collection unit; a notification unit that detects and notifies an abnormality based on the analysis result obtained by the analysis unit; A questioning unit that generates questions for the caregiver and analyzes the answers. A system characterized by: (Appendix 2) The collecting unit includes: Collect data on blood pressure, pulse, temperature, steps taken and sleep patterns 2. The system of claim 1, (Appendix 3) The analysis unit includes: Includes algorithms that detect abnormalities in blood pressure or pulse rate based on collected data 2. The system of claim 1, (Appendix 4) The interrogation unit is Generate specific questions for caregivers 2. The system of claim 1, (Appendix 5) The interrogation unit is Includes an algorithm that analyzes answers to questions and assesses stress levels 2. The system of claim 1, (Appendix 6) The collecting unit includes: To estimate a user's emotion and adjust a frequency of collection of health data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The collecting unit includes: When collecting data, the user's past health data is referenced to determine the appropriate collection timing. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Dynamically change the target of data collection based on the user's lifestyle patterns at the time of collection 2. The system of claim 1, (Appendix 9) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 10) The collecting unit includes: At the time of collection, prioritize collection of relevant data based on the user's geographic location 2. The system of claim 1, (Appendix 11) The collecting unit includes: At the time of collection, your social media activity will be analysed to gather relevant health data 2. The system of claim 1, (Appendix 12) The analysis unit includes: Inferring user sentiment and adjusting parameters of an analysis algorithm based on the estimated user sentiment 2. The system of claim 1, (Appendix 13) The analysis unit includes: During analysis, historical health data is referenced to improve anomaly detection accuracy. 2. The system of claim 1, (Appendix 14) The analysis unit includes: During analysis, dynamically change the criteria for anomaly detection based on the user's lifestyle 2. The system of claim 1, (Appendix 15) The analysis unit includes: Infer user sentiment and adjust how analysis results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 16) The analysis unit includes: During analysis, the geographic location of the user is taken into account to improve anomaly detection. 2. The system of claim 1, (Appendix 17) The analysis unit includes: During analysis, look at users' social media activity to improve anomaly detection 2. The system of claim 1, (Appendix 18) The notification unit is Infer user emotions and adjust notification timing based on the estimated user emotions 2. The system of claim 1, (Appendix 19) The notification unit is When notifying, select the optimal notification method by referring to past notification history 2. The system of claim 1, (Appendix 20) The notification unit is Customize notification content based on the user's lifestyle patterns 2. The system of claim 1, (Appendix 21) The notification unit is Infer user emotions and prioritize notifications based on the inferred user emotions 2. The system of claim 1, (Appendix 22) The notification unit is When notifying users, the optimal notification method is selected based on the user's geographic location. 2. The system of claim 1, (Appendix 23) The notification unit is When sending notifications, analyze the user's social media activity to customize the notification content 2. The system of claim 1, (Appendix 24) The interrogation unit is Infer user emotions and adjust questions based on the estimated user emotions 2. The system of claim 1, (Appendix 25) The interrogation unit is When asking a question, the most appropriate question is generated by referring to past answer history 2. The system of claim 1, (Appendix 26) The interrogation unit is When asking a question, the timing of the question is dynamically changed based on the user's life pattern. 2. The system of claim 1, (Appendix 27) The interrogation unit is Estimate user sentiment and prioritize questions based on the estimated user sentiment 2. The system of claim 1, (Appendix 28) The interrogation unit is When asking a question, the system generates the most appropriate question based on the user's geographic location. 2. The system of claim 1, (Appendix 29) The interrogation unit is When asking a question, analyze the user's social media activity to customize the question 2. The system of claim 1, [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects health management data; an analysis unit that analyzes the data collected by the collection unit; a notification unit that detects and notifies an abnormality based on the analysis result obtained by the analysis unit; A questioning unit that generates questions for the caregiver and analyzes the answers. A system characterized by:

2. The collecting unit includes: Collect data on blood pressure, pulse, temperature, steps taken and sleep patterns 2. The system of claim 1 .

3. The analysis unit includes: An algorithm for detecting abnormalities in blood pressure or pulse based on the data collected by the collecting unit is included.

2. The system of claim 1 .

4. The interrogation unit is Includes an algorithm that analyzes answers to questions and assesses stress levels 2. The system of claim 1 .

5. The collecting unit includes: To estimate a user's emotion and adjust a frequency of collection of health data based on the estimated user's emotion.

2. The system of claim 1 .

6. The collecting unit includes: When collecting data, the user's past health data is referenced to determine the appropriate collection timing.

2. The system of claim 1 .

7. The collecting unit includes: Dynamically change the target of data collection based on the user's lifestyle patterns at the time of collection 2. The system of claim 1 .

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