System

Abstract

To provide a system according to an embodiment that analyzes data on a user's health and provides an individual treatment plan and precautionary measures.SOLUTION: A system comprises a collection unit, an analysis unit, a provision unit, and a detection unit. The collection unit collects data on a user's health. The analysis unit analyzes the data collected by the collection unit. The provision unit provides a treatment plan or precautionary measures on the basis of an analysis result obtained by the analysis unit. The detection unit detects an abnormality on the basis of advice provided by the provision unit.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] Existing technologies have not done enough to effectively utilize a user's health data to provide personalized treatment plans and preventative measures, and there is room for improvement.

[0005] The system of the embodiment aims to analyze a user's health data and provide individualized treatment plans and preventive measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a detection unit. The collection unit collects health data of a user. The analysis unit analyzes the data collected by the collection unit. The provision unit provides a treatment plan or preventive measures based on the analysis results obtained by the analysis unit. The detection unit detects an abnormality based on the advice provided by the provision unit. Effect of the Invention

[0007] The system of the embodiment can analyze a user's health data and provide personalized treatment plans and preventative measures. [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 that analyzes a user's health data and medical history and proposes individualized treatment plans and preventive measures. In this system, a user inputs symptoms by text or voice, and a generation AI analyzes the input to provide support for medical professionals in diagnosis and treatment. In addition, the system measures pulse and heart rate through the user's mobile device to detect abnormalities. This makes it possible to provide personalized care tailored to the individual needs of patients. For example, when a user inputs symptoms such as "I have a headache" or "My chest hurts," the information is input to the generation AI. The generation AI proposes optimal treatment plans and preventive measures based on past medical data and medical history. In addition, a sensor installed in the mobile device is used to measure pulse and heart rate in real time to detect abnormalities. For example, if the heart rate is abnormally high, the generation AI analyzes the information and proposes an appropriate response. This mechanism improves the efficiency and accuracy of medical care. This allows the health management system to analyze the user's health data and medical history and propose individual treatment plans and preventive measures.

[0029] The health management system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a detection unit. The collection unit collects health data of a user. The collection unit includes, for example, a reception unit that inputs the user's symptoms by text or voice. The user's symptoms include, but are not limited to, headache, fever, cough, and the like. The reception unit can efficiently input the user's symptoms using voice recognition technology or a text input interface. The analysis unit analyzes the data collected by the collection unit. The analysis unit performs analysis based on, for example, past medical data and medical history. For the analysis, statistical analysis, machine learning algorithms, and the like are used. The provision unit provides a treatment plan and preventive measures based on the analysis results obtained by the analysis unit. For example, the provision unit proposes treatment plans and preventive measures such as drug therapy, exercise therapy, and diet therapy based on the analysis results. The detection unit detects an abnormality based on the advice provided by the provision unit. For example, the detection unit measures pulse and heart rate through a mobile terminal to detect an abnormality. The detection of an abnormality takes into account the type of sensor and the frequency of data acquisition. This enables the health management system according to the embodiment to efficiently collect, analyze, provide, and detect health data of users.

[0030] The collection unit collects the health data of the user. The collection unit includes, for example, a reception unit that inputs the user's symptoms by text or voice. The user's symptoms include, but are not limited to, headache, fever, cough, and the like. The reception unit can efficiently input the user's symptoms using voice recognition technology and a text input interface. Specifically, the voice recognition technology converts the words spoken by the user into text in real time and accurately records the details of the symptoms. For example, if the user says "I have a headache," the voice recognition system analyzes the voice and records it as "headache" as text. In addition, the text input interface allows the user to input the symptoms using a keyboard or touch screen. This allows the user to describe their symptoms in detail, and the collection unit accurately acquires the information. In addition, the collection unit can also automatically collect the user's vital signs (e.g., heart rate, blood pressure, body temperature, etc.) using sensors in a wearable device or smartphone. This makes it possible to monitor the user's health condition in real time and respond immediately if an abnormality occurs. The collected data is securely stored in a cloud server and made accessible to the analysis unit. This enables the collection unit to collect the user's health data efficiently and accurately, improving the performance of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit performs the analysis based on, for example, past medical data and medical history. Statistical analysis, machine learning algorithms, and the like are used for the analysis. Specifically, statistical analysis clarifies trends and patterns of the user's health state based on the collected data. For example, past data can be used to analyze the frequency of occurrence of a particular symptom and seasonal fluctuations. The machine learning algorithm builds a predictive model based on the collected data and predicts changes in the user's health state. For example, a model can be created that predicts future health risks using the user's vital signs and symptom data as input. Furthermore, the analysis unit can use natural language processing technology to analyze text data of symptoms entered by the user and extract related medical information. This makes it possible to propose appropriate treatment plans and preventive measures based on the user's symptoms. The analysis unit combines these technologies to comprehensively evaluate the user's health state and provide optimal advice. The analysis results are transmitted to the provision unit, and specific treatment plans and preventive measures are proposed to the user. This allows the analysis unit to quickly and accurately analyze the collected data and support the user's health management.

[0032] The providing unit provides a treatment plan and preventive measures based on the analysis results obtained by the analyzing unit. The providing unit proposes treatment plans and preventive measures such as drug therapy, exercise therapy, and diet therapy based on the analysis results. Specifically, the providing unit proposes an optimal drug therapy to the user based on the analysis results. For example, it proposes the type and dosage of medicine effective for a specific symptom, so that the user can receive appropriate treatment. In addition, as exercise therapy, it provides an exercise plan according to the user's health condition and lifestyle. For example, when mild exercise is recommended, it proposes specific methods of walking and stretching so that the user can practice it without difficulty. In addition, as diet therapy, it provides a meal plan based on the user's nutritional condition and health goals. For example, when a specific nutrient is lacking, it recommends foods containing that nutrient and suggests a balanced meal. The providing unit presents these treatment plans and preventive measures to the user in an easy-to-understand manner and supports their implementation. For example, it encourages the user to record daily exercise and meals through a smartphone app and monitors the progress. In addition, it regularly sends reminders to help the user continue the treatment plan and preventive measures. This enables the providing unit to provide specific advice based on the analysis results and effectively support the user's health management.

[0033] The detection unit detects an abnormality based on the advice provided by the provision unit. For example, the detection unit measures the pulse and heart rate through a mobile terminal to detect an abnormality. The type of sensor and the frequency of data acquisition are taken into consideration when detecting an abnormality. Specifically, the user's pulse and heart rate are measured in real time using an optical sensor mounted on the mobile terminal. This allows the user's heart rate to be immediately detected as abnormally high or low and an alert to be issued. The detection unit can also monitor the user's activity level and sleep pattern using a wearable device. For example, if the user is not getting enough sleep or has an extremely low amount of activity, the detection unit detects this as an abnormality and alerts the user. Furthermore, the detection unit can analyze data on the user's vital signs and detect abnormal patterns and trends. For example, if the fluctuations in heart rate or blood pressure are different from normal, the detection unit detects this as an abnormality and recommends that the user visit a medical institution. Through these abnormality detection functions, the detection unit can constantly monitor the user's health condition and discover abnormalities early on. In this way, the detection unit reduces health risks to the user and enables rapid response.

[0034] The collection unit includes a reception unit that inputs the user's symptoms by text or voice. The reception unit inputs the user's symptoms by text or voice. The symptoms include, but are not limited to, for example, headache, fever, cough, etc. The reception unit can efficiently input the user's symptoms using voice recognition technology or a text input interface. For example, when a user inputs "I have a headache" by voice, the voice recognition technology converts the content into text and inputs it into the system. Also, when a user inputs "I have chest pain" by text, the information is directly input into the system. This allows the user's symptoms to be input by text or voice.

[0035] The providing unit includes a notification unit that notifies a medical professional when an abnormality is detected. The notification unit notifies the medical professional when an abnormality is detected. Abnormalities include, but are not limited to, for example, an abnormal increase or decrease in heart rate, an abnormal change in blood pressure, and the like. The notification unit can quickly notify the medical professional of the abnormality using a method such as email notification or app notification. For example, if the heart rate is abnormally high, the notification unit notifies the medical professional of the information by email. In addition, the medical professional can be notified of the abnormality in real time using app notification. This makes it possible to notify the medical professional when an abnormality is detected.

[0036] The system further includes a monitoring unit that continuously monitors the health data of the user. The monitoring unit continuously monitors the health data of the user. The monitoring includes, but is not limited to, real-time monitoring and periodic data collection. The monitoring unit uses a sensor to measure the pulse and heart rate of the user in real time and transmits the data to the system. The system can also periodically collect the health data of the user and store it in the system. For example, the monitoring unit measures the user's heart rate every minute and transmits the data to the system. The system can also periodically measure the user's blood pressure and store the data in the system. This allows the user's health data to be continuously monitored.

[0037] The collection unit can analyze the user's past health data and select the optimal collection method. The collection unit analyzes the user's past health data and selects the optimal collection method. The collection method includes, but is not limited to, the use of a wearable device and manual input. For example, the collection unit selects the most effective collection method from the user's past data. The collection unit can also adjust the collection frequency based on the user's past data. Furthermore, the collection unit can analyze the user's past data and select a method for collecting in a specific time period. This makes it possible to analyze the user's past health data and select the optimal collection method. 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 past health data to the generation AI and cause the generation AI to select the optimal collection method.

[0038] The collection unit can perform filtering based on the user's current living situation and field of interest when collecting health data. The collection unit performs filtering based on the user's current living situation and field of interest when collecting health data. Living situations include, but are not limited to, daily activity levels and eating habits. Fields of interest include, but are not limited to, hobbies and occupations. For example, when the user is exercising, the collection unit preferentially collects data related to exercise. In addition, when the user is resting, the collection unit can also preferentially collect data related to relaxation. Furthermore, when the user has a specific field of interest, the collection unit can also preferentially collect data related to that field. This makes it possible to filter health data based on the user's current living situation and field of interest. 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 living situation data to the generation AI and cause the generation AI to perform filtering.

[0039] The collection unit can preferentially collect highly relevant data based on the geographical location information of the user when collecting health data. The collection unit preferentially collects highly relevant data taking into account the geographical location information of the user when collecting health data. The geographical location information includes, but is not limited to, GPS data and address information. For example, when the user is in a specific area, the collection unit preferentially collects health data related to the area. In addition, when the user is traveling, the collection unit can also preferentially collect data related to health risks at the travel destination. Furthermore, when the user is at home, the collection unit can also preferentially collect data related to daily life. This makes it possible to preferentially collect highly relevant data taking into account the geographical location information of the user. 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 collection of highly relevant data.

[0040] The collection unit can analyze the user's social media activity and collect related data when collecting health data. The collection unit analyzes the user's social media activity and collects related data when collecting health data. Social media activity includes, but is not limited to, the content of posts and the number of likes. For example, when a user posts about health on social media, the collection unit collects data related to the content. In addition, when a user shows interest in a specific health topic, the collection unit can also collect data related to the topic. Furthermore, the collection unit can predict health risks from the user's social media activity and collect related data. This makes it possible to analyze the user's social media activity and collect related data. 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 related data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the health data during the analysis. The level of importance includes, but is not limited to, the evaluation of a medical professional and statistical importance. For example, the analysis unit performs a detailed analysis on important health data. Also, a simplified analysis can be performed on general health data. Furthermore, data with high urgency can be quickly analyzed. This makes it possible to adjust the level of detail of the analysis based on the importance of the health data. A part 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 importance of the health data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. The analysis unit applies different analysis algorithms depending on the category of health data during analysis. The categories include, but are not limited to, cardiovascular system data and respiratory system data. For example, the analysis unit applies a heart rate analysis algorithm to heart rate data. Also, a pulse analysis algorithm can be applied to pulse data. Furthermore, a symptom analysis algorithm can be applied to symptom data. This makes it possible to apply different analysis algorithms depending on the category of health data. A part 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 category of health data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0043] The analysis unit can determine the priority of the analysis based on the time of submission of the health data during the analysis. The analysis unit determines the priority of the analysis based on the time of submission of the health data during the analysis. The submission time includes, but is not limited to, a timestamp of the data and a frequency of submission. For example, the analysis unit preferentially analyzes recently submitted data. In addition, the analysis unit can preferentially analyze data with high urgency. Furthermore, the analysis unit can preferentially analyze data that is submitted periodically. This makes it possible to determine the priority of the analysis based on the time of submission of the health data. 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 time of submission of the health data to the generation AI and cause the generation AI to execute the determination of the priority of the analysis.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. The analysis unit adjusts the order of analysis based on the relevance of the health data during analysis. The relevance includes, but is not limited to, correlation analysis and evaluation of causal relationships. For example, the analysis unit preferentially analyzes highly relevant data. Also, data with low relevance can be postponed. Furthermore, the analysis order can be dynamically adjusted based on the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the health data. 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 relevance of the health data to the generation AI and cause the generation AI to adjust the order of analysis.

[0045] The providing unit can adjust the level of detail of the provision based on the importance of the treatment plan and preventive measures at the time of provision. The providing unit adjusts the level of detail of the provision based on the importance of the treatment plan and preventive measures at the time of provision. The level of importance includes, but is not limited to, the evaluation of a medical professional and statistical importance. For example, the providing unit provides detailed information for an important treatment plan. Also, simplified information can be provided for general preventive measures. Furthermore, information can be provided quickly for a treatment plan with high urgency. This makes it possible to adjust the level of detail of the provision based on the importance of the treatment plan and preventive measures. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the importance of the treatment plan and preventive measures to the generating AI and cause the generating AI to adjust the level of detail of the provision.

[0046] The providing unit can apply different providing algorithms according to categories of treatment plans and preventive measures at the time of providing. The providing unit applies different providing algorithms according to categories of treatment plans and preventive measures at the time of providing. The categories include, but are not limited to, cardiovascular system data and respiratory system data. For example, the providing unit applies a drug providing algorithm to drug treatment. Also, a lifestyle providing algorithm can be applied to lifestyle improvement. Furthermore, a checkup providing algorithm can be applied to regular checkups. This makes it possible to apply different providing algorithms according to categories of treatment plans and preventive measures. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input categories of treatment plans and preventive measures to the generating AI and cause the generating AI to apply the providing algorithm.

[0047] The providing unit can determine the priority of provision based on the submission timing of the treatment plan or preventive measures at the time of provision. The providing unit determines the priority of provision based on the submission timing of the treatment plan or preventive measures at the time of provision. The submission timing includes, but is not limited to, a timestamp of data and a frequency of submission. For example, the providing unit provides a treatment plan that has been submitted recently with priority. It is also possible to provide a treatment plan with high urgency with priority. Furthermore, it is also possible to provide a treatment plan that is submitted regularly with priority. This makes it possible to determine the priority of provision based on the submission timing of the treatment plan or preventive measures. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the submission timing of the treatment plan or preventive measures to the generating AI and cause the generating AI to execute the determination of the priority of provision.

[0048] The providing unit can adjust the order of provision based on the relevance of the treatment plans and preventive measures at the time of provision. The providing unit adjusts the order of provision based on the relevance of the treatment plans and preventive measures at the time of provision. The relevance includes, but is not limited to, correlation analysis and evaluation of causal relationships. For example, the providing unit provides highly relevant treatment plans preferentially. In addition, treatment plans with low relevance can be postponed. Furthermore, the order of provision can be dynamically adjusted based on the relevance of the treatment plans. This makes it possible to adjust the order of provision based on the relevance of the treatment plans and preventive measures. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance of the treatment plans and preventive measures to the generating AI and cause the generating AI to adjust the order of provision.

[0049] The detection unit can select an optimal anomaly detection method by analyzing the user's past health data at the time of detection. The detection unit can select an optimal anomaly detection method by analyzing the user's past health data at the time of detection. The anomaly detection method includes, but is not limited to, an algorithm selection and a threshold setting. For example, the detection unit selects the most effective anomaly detection method from the user's past data. The detection unit can also adjust the frequency of anomaly detection based on the user's past data. Furthermore, the detection unit can also analyze the user's past data and select a method for detecting anomalies in a specific time period. This makes it possible to select an optimal anomaly detection method by analyzing the user's past health data. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input the user's past health data to the generation AI and cause the generation AI to select an optimal anomaly detection method.

[0050] The detection unit can customize the means of anomaly detection based on the user's current living situation at the time of detection. The detection unit customizes the means of anomaly detection based on the user's current living situation at the time of detection. Living situations include, but are not limited to, daily activity levels and eating habits. For example, when the user is exercising, the detection unit preferentially detects anomalies related to exercise. Also, when the user is resting, the detection unit can preferentially detect anomalies related to relaxation. Furthermore, when the user is in a specific living situation, the detection unit can preferentially detect anomalies related to that situation. This makes it possible to customize the means of anomaly detection based on the user's current living situation. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input the user's living situation data to the generation AI and cause the generation AI to execute customization of the means of anomaly detection.

[0051] The detection unit can select the optimal anomaly detection method in consideration of the geographical location information of the user at the time of detection. The detection unit selects the optimal anomaly detection method in consideration of the geographical location information of the user at the time of detection. The geographical location information includes, but is not limited to, GPS data and address information. For example, when the user is in a specific area, the detection unit preferentially detects anomalies related to the area. In addition, when the user is traveling, the detection unit can also preferentially detect anomalies related to health risks at the travel destination. Furthermore, when the user is at home, the detection unit can also preferentially detect anomalies related to daily life. This makes it possible to select the optimal anomaly detection method in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input the geographical location information of the user to the generation AI and cause the generation AI to select the optimal anomaly detection method.

[0052] The detection unit can analyze the user's social media activity at the time of detection and propose a means of anomaly detection. The detection unit can analyze the user's social media activity at the time of detection and propose a means of anomaly detection. The social media activity includes, but is not limited to, the content of the post and the number of likes. For example, when the user posts about health on social media, the detection unit detects anomalies related to the content. In addition, when the user shows interest in a specific health topic, the detection unit can also detect anomalies related to the topic. Furthermore, it is also possible to predict health risks from the user's social media activity and detect related anomalies. This makes it possible to analyze the user's social media activity and propose a means of anomaly detection. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input the user's social media data to the generation AI and cause the generation AI to execute the proposal of a means of anomaly detection.

[0053] The reception unit can provide an optimal input method by referring to the user's past symptom input history at the time of reception. The reception unit can provide an optimal input method by referring to the user's past symptom input history at the time of reception. The input method includes, but is not limited to, voice input and manual input. For example, the reception unit automatically displays symptoms frequently input by the user in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest symptoms to be used in a specific time period from the user's past input history. This makes it possible to provide an optimal input method by referring to the user's past symptom input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past symptom input history to the generation AI and cause the generation AI to provide the optimal input method.

[0054] The reception unit can provide the optimal input method in consideration of the device information of the user at the time of reception. The reception unit can provide the optimal input method in consideration of the device information of the user at the time of reception. The device information includes, but is not limited to, a smartphone, a wearable device, and the like. For example, when the user is using a smartphone, the reception unit provides an input method that matches the screen size. In addition, when the user is using a tablet, the reception unit can also provide an input method optimized for a large screen. Furthermore, when the user is using a smart watch, the reception unit can also provide an input method that is simple and has high visibility. This makes it possible to provide the optimal input method in consideration of the device information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the device information of the user to the generation AI and cause the generation AI to execute the provision of the optimal input method.

[0055] The notification unit can adjust the level of detail of the notification based on the importance of the abnormality at the time of notification. The notification unit adjusts the level of detail of the notification based on the importance of the abnormality at the time of notification. The importance includes, but is not limited to, the evaluation of a medical professional and statistical importance. For example, the notification unit provides a detailed notification for an important abnormality. Also, a simplified notification can be provided for a general abnormality. Furthermore, a notification can be provided quickly for an abnormality with high urgency. This makes it possible to adjust the level of detail of the notification based on the importance of the abnormality. 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 importance of the abnormality to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0056] The notification unit can provide an optimal notification method in consideration of the device information of the user at the time of notification. The notification unit can provide an optimal notification method in consideration of the device information of the user at the time of notification. The device information includes, but is not limited to, a smartphone or a wearable device. For example, when the user is using a smartphone, the notification unit provides a notification method that matches the screen size. In addition, when the user is using a tablet, the notification unit can also provide a notification method that is optimized for a large screen. Furthermore, when the user is using a smart watch, the notification unit can also provide a simple and highly visible notification method. This makes it possible to provide an optimal notification method in consideration of the device information of the user. 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 device information of the user to the generation AI and cause the generation AI to provide the optimal notification method.

[0057] The monitoring unit can provide an optimal monitoring method by referring to the user's past health data during monitoring. The monitoring unit can provide an optimal monitoring method by referring to the user's past health data during monitoring. The monitoring method includes, but is not limited to, real-time monitoring and regular data collection. For example, the monitoring unit selects the most effective monitoring method from the user's past data. The monitoring unit can also adjust the frequency of monitoring based on the user's past data. Furthermore, the monitoring unit can analyze the user's past data and select a method of monitoring in a specific time period. This makes it possible to provide an optimal monitoring method by referring to the user's past health data. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input the user's past health data to the generation AI and cause the generation AI to execute the provision of the optimal monitoring method.

[0058] The monitoring unit can provide an optimal monitoring method in consideration of the device information of the user during monitoring. The monitoring unit can provide an optimal monitoring method in consideration of the device information of the user during monitoring. The device information includes, but is not limited to, a smartphone or a wearable device. For example, when the user is using a smartphone, the monitoring unit provides a monitoring method that matches the screen size. In addition, when the user is using a tablet, the monitoring unit can also provide a monitoring method that is optimized for a large screen. Furthermore, when the user is using a smart watch, the monitoring unit can also provide a simple and highly visible monitoring method. This makes it possible to provide an optimal monitoring method in consideration of the device information of the user. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the device information of the user to the generation AI and cause the generation AI to execute the provision of the optimal monitoring method.

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

[0060] When analyzing the user's health data, the analysis unit can adjust the analysis results by taking into account the user's lifestyle and environmental factors. For example, if the user works in a high-stress environment, the analysis unit can highlight stress-related health risks. In addition, if the user has an exercise habit, the analysis unit can provide analysis results that take into account the effects of exercise. Furthermore, if the user lives in a specific area, the analysis results can be adjusted by taking into account health risks specific to that area. This makes it possible to provide more personalized analysis results that take into account the user's lifestyle and environmental factors.

[0061] The providing unit can provide health advice tailored to the user's lifestyle based on the user's health data. For example, if the user lives a desk-work-centered lifestyle, the providing unit can suggest regular stretching and exercise. Also, if the user works night shifts, the providing unit can provide advice on how to improve the quality of sleep. Furthermore, if the user has specific dietary restrictions, the providing unit can suggest a meal plan that matches those restrictions. This makes it possible to provide specific health advice tailored to the user's lifestyle.

[0062] When collecting the user's health data, the collection unit can adjust the frequency of data collection based on the user's activity level. For example, if the user has a high activity level, the collection unit can collect data frequently to monitor a detailed health condition. Also, if the user has a low activity level, the collection unit can reduce the frequency of data collection and collect only necessary data. Furthermore, if the user's activity level fluctuates, the frequency of data collection can be dynamically adjusted according to the fluctuation. This enables efficient data collection according to the user's activity level.

[0063] The detection unit can detect anomalies according to seasonal and weather changes based on the user's health data. For example, since the risk of influenza and colds increases in winter, the detection unit prioritizes monitoring data related to these symptoms. Also, since the risk of heatstroke increases in summer, the detection unit can focus on detecting data related to body temperature and water intake. Furthermore, in the event of a sudden change in the weather, the detection unit can also detect anomalies according to changes in air pressure and humidity. This makes it possible to appropriately detect anomalies according to seasonal and weather changes.

[0064] The providing unit can provide health advice related to the user's hobbies and interests based on the user's health data. For example, if the user's hobby is running, the providing unit can provide training plans and nutritional advice related to running. If the user's hobby is cooking, the providing unit can also suggest healthy recipes and how to select ingredients. Furthermore, if the user's hobby is gardening, the providing unit can also provide advice on proper posture and how to take breaks while gardening. This makes it possible to provide specific health advice tailored to the user's hobbies and interests.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The collection unit collects health data of a user. The collection unit includes, for example, a reception unit that inputs the user's symptoms by text or voice. The user's symptoms include, but are not limited to, headache, fever, cough, and the like. The reception unit can efficiently input the user's symptoms using voice recognition technology or a text input interface. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit performs analysis based on, for example, past medical data and medical history. For the analysis, statistical analysis, machine learning algorithms, etc. are used. Step 3: The providing unit provides a treatment plan and preventive measures based on the analysis results obtained by the analyzing unit. For example, the providing unit proposes a treatment plan and preventive measures such as drug therapy, exercise therapy, and diet therapy based on the analysis results. Step 4: The detection unit detects an abnormality based on the advice provided by the provision unit. For example, the detection unit measures pulse and heart rate via a mobile terminal to detect an abnormality. The type of sensor and the frequency of data acquisition are taken into consideration when detecting an abnormality.

[0067] (Example 2) A health management system according to an embodiment of the present invention is a system that analyzes a user's health data and medical history and proposes individualized treatment plans and preventive measures. In this system, a user inputs symptoms by text or voice, and a generation AI analyzes the input to provide support for medical professionals in diagnosis and treatment. In addition, the system measures pulse and heart rate through the user's mobile device to detect abnormalities. This makes it possible to provide personalized care tailored to the individual needs of patients. For example, when a user inputs symptoms such as "I have a headache" or "My chest hurts," the information is input to the generation AI. The generation AI proposes optimal treatment plans and preventive measures based on past medical data and medical history. In addition, a sensor installed in the mobile device is used to measure pulse and heart rate in real time to detect abnormalities. For example, if the heart rate is abnormally high, the generation AI analyzes the information and proposes an appropriate response. This mechanism improves the efficiency and accuracy of medical care. This allows the health management system to analyze the user's health data and medical history and propose individual treatment plans and preventive measures.

[0068] The health management system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a detection unit. The collection unit collects health data of a user. The collection unit includes, for example, a reception unit that inputs the user's symptoms by text or voice. The user's symptoms include, but are not limited to, headache, fever, cough, and the like. The reception unit can efficiently input the user's symptoms using voice recognition technology or a text input interface. The analysis unit analyzes the data collected by the collection unit. The analysis unit performs analysis based on, for example, past medical data and medical history. For the analysis, statistical analysis, machine learning algorithms, and the like are used. The provision unit provides a treatment plan and preventive measures based on the analysis results obtained by the analysis unit. For example, the provision unit proposes treatment plans and preventive measures such as drug therapy, exercise therapy, and diet therapy based on the analysis results. The detection unit detects an abnormality based on the advice provided by the provision unit. For example, the detection unit measures pulse and heart rate through a mobile terminal to detect an abnormality. The detection of an abnormality takes into account the type of sensor and the frequency of data acquisition. This enables the health management system according to the embodiment to efficiently collect, analyze, provide, and detect health data of users.

[0069] The collection unit collects the health data of the user. The collection unit includes, for example, a reception unit that inputs the user's symptoms by text or voice. The user's symptoms include, but are not limited to, headache, fever, cough, and the like. The reception unit can efficiently input the user's symptoms using voice recognition technology and a text input interface. Specifically, the voice recognition technology converts the words spoken by the user into text in real time and accurately records the details of the symptoms. For example, if the user says "I have a headache," the voice recognition system analyzes the voice and records it as "headache" as text. In addition, the text input interface allows the user to input the symptoms using a keyboard or touch screen. This allows the user to describe their symptoms in detail, and the collection unit accurately acquires the information. In addition, the collection unit can also automatically collect the user's vital signs (e.g., heart rate, blood pressure, body temperature, etc.) using sensors in a wearable device or smartphone. This makes it possible to monitor the user's health condition in real time and respond immediately if an abnormality occurs. The collected data is securely stored in a cloud server and made accessible to the analysis unit. This enables the collection unit to collect the user's health data efficiently and accurately, improving the performance of the entire system.

[0070] The analysis unit analyzes the data collected by the collection unit. The analysis unit performs the analysis based on, for example, past medical data and medical history. Statistical analysis, machine learning algorithms, and the like are used for the analysis. Specifically, statistical analysis clarifies trends and patterns of the user's health state based on the collected data. For example, past data can be used to analyze the frequency of occurrence of a particular symptom and seasonal fluctuations. The machine learning algorithm builds a predictive model based on the collected data and predicts changes in the user's health state. For example, a model can be created that predicts future health risks using the user's vital signs and symptom data as input. Furthermore, the analysis unit can use natural language processing technology to analyze text data of symptoms entered by the user and extract related medical information. This makes it possible to propose appropriate treatment plans and preventive measures based on the user's symptoms. The analysis unit combines these technologies to comprehensively evaluate the user's health state and provide optimal advice. The analysis results are transmitted to the provision unit, and specific treatment plans and preventive measures are proposed to the user. This allows the analysis unit to quickly and accurately analyze the collected data and support the user's health management.

[0071] The providing unit provides a treatment plan and preventive measures based on the analysis results obtained by the analyzing unit. The providing unit proposes treatment plans and preventive measures such as drug therapy, exercise therapy, and diet therapy based on the analysis results. Specifically, the providing unit proposes an optimal drug therapy to the user based on the analysis results. For example, it proposes the type and dosage of medicine effective for a specific symptom, so that the user can receive appropriate treatment. In addition, as exercise therapy, it provides an exercise plan according to the user's health condition and lifestyle. For example, when mild exercise is recommended, it proposes specific methods of walking and stretching so that the user can practice it without difficulty. In addition, as diet therapy, it provides a meal plan based on the user's nutritional condition and health goals. For example, when a specific nutrient is lacking, it recommends foods containing that nutrient and suggests a balanced meal. The providing unit presents these treatment plans and preventive measures to the user in an easy-to-understand manner and supports their implementation. For example, it encourages the user to record daily exercise and meals through a smartphone app and monitors the progress. In addition, it regularly sends reminders to help the user continue the treatment plan and preventive measures. This enables the providing unit to provide specific advice based on the analysis results and effectively support the user's health management.

[0072] The detection unit detects an abnormality based on the advice provided by the provision unit. For example, the detection unit measures the pulse and heart rate through a mobile terminal to detect an abnormality. The type of sensor and the frequency of data acquisition are taken into consideration when detecting an abnormality. Specifically, the user's pulse and heart rate are measured in real time using an optical sensor mounted on the mobile terminal. This allows the user's heart rate to be immediately detected as abnormally high or low and an alert to be issued. The detection unit can also monitor the user's activity level and sleep pattern using a wearable device. For example, if the user is not getting enough sleep or has an extremely low amount of activity, the detection unit detects this as an abnormality and alerts the user. Furthermore, the detection unit can analyze data on the user's vital signs and detect abnormal patterns and trends. For example, if the fluctuations in heart rate or blood pressure are different from normal, the detection unit detects this as an abnormality and recommends that the user visit a medical institution. Through these abnormality detection functions, the detection unit can constantly monitor the user's health condition and discover abnormalities early on. In this way, the detection unit reduces health risks to the user and enables rapid response.

[0073] The collection unit includes a reception unit that inputs the user's symptoms by text or voice. The reception unit inputs the user's symptoms by text or voice. The symptoms include, but are not limited to, for example, headache, fever, cough, etc. The reception unit can efficiently input the user's symptoms using voice recognition technology or a text input interface. For example, when a user inputs "I have a headache" by voice, the voice recognition technology converts the content into text and inputs it into the system. Also, when a user inputs "I have chest pain" by text, the information is directly input into the system. This allows the user's symptoms to be input by text or voice.

[0074] The providing unit includes a notification unit that notifies a medical professional when an abnormality is detected. The notification unit notifies the medical professional when an abnormality is detected. Abnormalities include, but are not limited to, for example, an abnormal increase or decrease in heart rate, an abnormal change in blood pressure, and the like. The notification unit can quickly notify the medical professional of the abnormality using a method such as email notification or app notification. For example, if the heart rate is abnormally high, the notification unit notifies the medical professional of the information by email. In addition, the medical professional can be notified of the abnormality in real time using app notification. This makes it possible to notify the medical professional when an abnormality is detected.

[0075] The system further includes a monitoring unit that continuously monitors the health data of the user. The monitoring unit continuously monitors the health data of the user. The monitoring includes, but is not limited to, real-time monitoring and periodic data collection. The monitoring unit uses a sensor to measure the pulse and heart rate of the user in real time and transmits the data to the system. The system can also periodically collect the health data of the user and store it in the system. For example, the monitoring unit measures the user's heart rate every minute and transmits the data to the system. The system can also periodically measure the user's blood pressure and store the data in the system. This allows the user's health data to be continuously monitored.

[0076] The collection unit can estimate the user's emotion and adjust the timing of collecting health data based on the estimated user's emotion. The collection unit estimates the user's emotion and adjusts the timing of collecting health data based on the estimated user's emotion. Facial expression recognition, voice analysis, and the like are used to estimate the emotion. For example, when the user is stressed, the collection unit collects health data during a relaxed time period. Also, when the user is relaxed, the collection unit can collect health data during a regular time period. Furthermore, when the user is in a hurry, the collection unit can also prioritize and collect data that can be collected in a short time. This makes it possible to adjust the timing of collecting health data based on the user's emotion. 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 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 facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0077] The collection unit can analyze the user's past health data and select the optimal collection method. The collection unit analyzes the user's past health data and selects the optimal collection method. The collection method includes, but is not limited to, the use of a wearable device and manual input. For example, the collection unit selects the most effective collection method from the user's past data. The collection unit can also adjust the collection frequency based on the user's past data. Furthermore, the collection unit can analyze the user's past data and select a method for collecting in a specific time period. This makes it possible to analyze the user's past health data and select the optimal collection method. 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 past health data to the generation AI and cause the generation AI to select the optimal collection method.

[0078] The collection unit can perform filtering based on the user's current living situation and field of interest when collecting health data. The collection unit performs filtering based on the user's current living situation and field of interest when collecting health data. Living situations include, but are not limited to, daily activity levels and eating habits. Fields of interest include, but are not limited to, hobbies and occupations. For example, when the user is exercising, the collection unit preferentially collects data related to exercise. In addition, when the user is resting, the collection unit can also preferentially collect data related to relaxation. Furthermore, when the user has a specific field of interest, the collection unit can also preferentially collect data related to that field. This makes it possible to filter health data based on the user's current living situation and field of interest. 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 living situation data to the generation AI and cause the generation AI to perform filtering.

[0079] The collection unit can estimate the user's emotion and determine the priority of the health data to be collected based on the estimated user's emotion. The collection unit estimates the user's emotion and determines the priority of the health data to be collected based on the estimated user's emotion. Facial expression recognition, voice analysis, and the like are used to estimate the emotion. For example, when the user is stressed, the collection unit prioritizes and collects stress-related data. In addition, when the user is relaxed, the collection unit can also prioritize and collect general health data. Furthermore, when the user is in a hurry, the collection unit can also prioritize and collect important data. This makes it possible to determine the priority of the health data to be collected based on the user's emotion. 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 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 priority of the data to be collected.

[0080] The collection unit can preferentially collect highly relevant data based on the geographical location information of the user when collecting health data. The collection unit preferentially collects highly relevant data taking into account the geographical location information of the user when collecting health data. The geographical location information includes, but is not limited to, GPS data and address information. For example, when the user is in a specific area, the collection unit preferentially collects health data related to the area. In addition, when the user is traveling, the collection unit can also preferentially collect data related to health risks at the travel destination. Furthermore, when the user is at home, the collection unit can also preferentially collect data related to daily life. This makes it possible to preferentially collect highly relevant data taking into account the geographical location information of the user. 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 collection of highly relevant data.

[0081] The collection unit can analyze the user's social media activity and collect related data when collecting health data. The collection unit analyzes the user's social media activity and collects related data when collecting health data. Social media activity includes, but is not limited to, the content of posts and the number of likes. For example, when a user posts about health on social media, the collection unit collects data related to the content. In addition, when a user shows interest in a specific health topic, the collection unit can also collect data related to the topic. Furthermore, the collection unit can predict health risks from the user's social media activity and collect related data. This makes it possible to analyze the user's social media activity and collect related data. 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 related data.

[0082] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the expression method of the analysis based on the estimated user's emotion. Facial expression recognition, voice analysis, etc. are used for estimating the emotion. For example, if the user is stressed, the analysis unit provides a simple and easy-to-understand analysis result. In addition, if the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can also provide an analysis result that covers the main points. This makes it possible to adjust the expression method of the analysis based on the user's 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 expression method of the analysis.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the health data during the analysis. The level of importance includes, but is not limited to, the evaluation of a medical professional and statistical importance. For example, the analysis unit performs a detailed analysis on important health data. Also, a simplified analysis can be performed on general health data. Furthermore, data with high urgency can be quickly analyzed. This makes it possible to adjust the level of detail of the analysis based on the importance of the health data. A part 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 importance of the health data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. The analysis unit applies different analysis algorithms depending on the category of health data during analysis. The categories include, but are not limited to, cardiovascular system data and respiratory system data. For example, the analysis unit applies a heart rate analysis algorithm to heart rate data. Also, a pulse analysis algorithm can be applied to pulse data. Furthermore, a symptom analysis algorithm can be applied to symptom data. This makes it possible to apply different analysis algorithms depending on the category of health data. A part 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 category of health data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0085] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. Facial expression recognition, voice analysis, etc. are used to estimate the emotion. For example, if the user is in a hurry, the analysis unit performs a short and to the point analysis. Also, if the user is relaxed, a detailed analysis can be performed. Furthermore, if the user is excited, the analysis unit can perform a visually stimulating analysis. This allows the length of the analysis to be adjusted based on the user's emotion. 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 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 length of the analysis.

[0086] The analysis unit can determine the priority of the analysis based on the time of submission of the health data during the analysis. The analysis unit determines the priority of the analysis based on the time of submission of the health data during the analysis. The submission time includes, but is not limited to, a timestamp of the data and a frequency of submission. For example, the analysis unit preferentially analyzes recently submitted data. In addition, the analysis unit can preferentially analyze data with high urgency. Furthermore, the analysis unit can preferentially analyze data that is submitted periodically. This makes it possible to determine the priority of the analysis based on the time of submission of the health data. 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 time of submission of the health data to the generation AI and cause the generation AI to execute the determination of the priority of the analysis.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. The analysis unit adjusts the order of analysis based on the relevance of the health data during analysis. The relevance includes, but is not limited to, correlation analysis and evaluation of causal relationships. For example, the analysis unit preferentially analyzes highly relevant data. Also, data with low relevance can be postponed. Furthermore, the analysis order can be dynamically adjusted based on the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the health data. 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 relevance of the health data to the generation AI and cause the generation AI to adjust the order of analysis.

[0088] The providing unit can estimate the user's emotion and adjust the expression method of the treatment plan and preventive measures to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion and adjusts the expression method of the treatment plan and preventive measures to be provided based on the estimated user's emotion. Facial expression recognition, voice analysis, and the like are used for estimating the emotion. For example, when the user is stressed, the providing unit provides a simple and easy-to-understand expression method. In addition, when the user is relaxed, a detailed expression method can be provided. Furthermore, when the user is in a hurry, the providing unit can provide an expression method that covers the main points. This makes it possible to adjust the expression method of the treatment plan and preventive measures based on the user's emotion. 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input a user's emotional data into the generating AI and have the generating AI adjust the way in which treatment plans and preventive measures are expressed.

[0089] The providing unit can adjust the level of detail of the provision based on the importance of the treatment plan and preventive measures at the time of provision. The providing unit adjusts the level of detail of the provision based on the importance of the treatment plan and preventive measures at the time of provision. The level of importance includes, but is not limited to, the evaluation of a medical professional and statistical importance. For example, the providing unit provides detailed information for an important treatment plan. Also, simplified information can be provided for general preventive measures. Furthermore, information can be provided quickly for a treatment plan with high urgency. This makes it possible to adjust the level of detail of the provision based on the importance of the treatment plan and preventive measures. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the importance of the treatment plan and preventive measures to the generating AI and cause the generating AI to adjust the level of detail of the provision.

[0090] The providing unit can apply different providing algorithms according to categories of treatment plans and preventive measures at the time of providing. The providing unit applies different providing algorithms according to categories of treatment plans and preventive measures at the time of providing. The categories include, but are not limited to, cardiovascular system data and respiratory system data. For example, the providing unit applies a drug providing algorithm to drug treatment. Also, a lifestyle providing algorithm can be applied to lifestyle improvement. Furthermore, a checkup providing algorithm can be applied to regular checkups. This makes it possible to apply different providing algorithms according to categories of treatment plans and preventive measures. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input categories of treatment plans and preventive measures to the generating AI and cause the generating AI to apply the providing algorithm.

[0091] The providing unit can estimate the user's emotion and adjust the length of the treatment plan and preventive measures to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion and adjusts the length of the treatment plan and preventive measures to be provided based on the estimated user's emotion. Facial expression recognition, voice analysis, and the like are used to estimate the emotion. For example, if the user is in a hurry, the providing unit provides a short and to the point treatment plan. Also, if the user is relaxed, a detailed treatment plan can be provided. Furthermore, if the user is excited, the providing unit can provide a visually stimulating treatment plan. This makes it possible to adjust the length of the treatment plan and preventive measures based on the user's emotion. 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the treatment plan and preventive measures.

[0092] The providing unit can determine the priority of provision based on the submission timing of the treatment plan or preventive measures at the time of provision. The providing unit determines the priority of provision based on the submission timing of the treatment plan or preventive measures at the time of provision. The submission timing includes, but is not limited to, a timestamp of data and a frequency of submission. For example, the providing unit provides a treatment plan that has been submitted recently with priority. It is also possible to provide a treatment plan with high urgency with priority. Furthermore, it is also possible to provide a treatment plan that is submitted regularly with priority. This makes it possible to determine the priority of provision based on the submission timing of the treatment plan or preventive measures. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the submission timing of the treatment plan or preventive measures to the generating AI and cause the generating AI to execute the determination of the priority of provision.

[0093] The providing unit can adjust the order of provision based on the relevance of the treatment plans and preventive measures at the time of provision. The providing unit adjusts the order of provision based on the relevance of the treatment plans and preventive measures at the time of provision. The relevance includes, but is not limited to, correlation analysis and evaluation of causal relationships. For example, the providing unit provides highly relevant treatment plans preferentially. In addition, treatment plans with low relevance can be postponed. Furthermore, the order of provision can be dynamically adjusted based on the relevance of the treatment plans. This makes it possible to adjust the order of provision based on the relevance of the treatment plans and preventive measures. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance of the treatment plans and preventive measures to the generating AI and cause the generating AI to adjust the order of provision.

[0094] The detection unit can estimate the user's emotion and adjust the anomaly detection method based on the estimated user's emotion. The detection unit estimates the user's emotion and adjusts the anomaly detection method based on the estimated user's emotion. Facial expression recognition, voice analysis, and the like are used to estimate the emotion. For example, if the user is stressed, the detection unit preferentially detects stress-related anomalies. Also, if the user is relaxed, the detection unit can also detect general anomalies. Furthermore, if the user is in a hurry, the detection unit can preferentially detect important anomalies. This allows the anomaly detection method to be adjusted based on the user's emotion. 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 detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the anomaly detection method.

[0095] The detection unit can select an optimal anomaly detection method by analyzing the user's past health data at the time of detection. The detection unit can select an optimal anomaly detection method by analyzing the user's past health data at the time of detection. The anomaly detection method includes, but is not limited to, an algorithm selection and a threshold setting. For example, the detection unit selects the most effective anomaly detection method from the user's past data. The detection unit can also adjust the frequency of anomaly detection based on the user's past data. Furthermore, the detection unit can also analyze the user's past data and select a method for detecting anomalies in a specific time period. This makes it possible to select an optimal anomaly detection method by analyzing the user's past health data. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input the user's past health data to the generation AI and cause the generation AI to select an optimal anomaly detection method.

[0096] The detection unit can customize the means of anomaly detection based on the user's current living situation at the time of detection. The detection unit customizes the means of anomaly detection based on the user's current living situation at the time of detection. Living situations include, but are not limited to, daily activity levels and eating habits. For example, when the user is exercising, the detection unit preferentially detects anomalies related to exercise. Also, when the user is resting, the detection unit can preferentially detect anomalies related to relaxation. Furthermore, when the user is in a specific living situation, the detection unit can preferentially detect anomalies related to that situation. This makes it possible to customize the means of anomaly detection based on the user's current living situation. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input the user's living situation data to the generation AI and cause the generation AI to execute customization of the means of anomaly detection.

[0097] The detection unit can estimate the user's emotion and determine the priority of anomaly detection based on the estimated user's emotion. The detection unit estimates the user's emotion and determines the priority of anomaly detection based on the estimated user's emotion. Facial expression recognition, voice analysis, and the like are used for estimating the emotion. For example, if the user is feeling stressed, the detection unit can preferentially detect stress-related anomalies. In addition, if the user is relaxed, the detection unit can also preferentially detect general anomalies. Furthermore, if the user is in a hurry, the detection unit can also preferentially detect important anomalies. This makes it possible to determine the priority of anomaly detection based on the user's emotion. 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 detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection 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 anomaly detection.

[0098] The detection unit can select the optimal anomaly detection method in consideration of the geographical location information of the user at the time of detection. The detection unit selects the optimal anomaly detection method in consideration of the geographical location information of the user at the time of detection. The geographical location information includes, but is not limited to, GPS data and address information. For example, when the user is in a specific area, the detection unit preferentially detects anomalies related to the area. In addition, when the user is traveling, the detection unit can also preferentially detect anomalies related to health risks at the travel destination. Furthermore, when the user is at home, the detection unit can also preferentially detect anomalies related to daily life. This makes it possible to select the optimal anomaly detection method in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input the geographical location information of the user to the generation AI and cause the generation AI to select the optimal anomaly detection method.

[0099] The detection unit can analyze the user's social media activity at the time of detection and propose a means of anomaly detection. The detection unit can analyze the user's social media activity at the time of detection and propose a means of anomaly detection. The social media activity includes, but is not limited to, the content of the post and the number of likes. For example, when the user posts about health on social media, the detection unit detects anomalies related to the content. In addition, when the user shows interest in a specific health topic, the detection unit can also detect anomalies related to the topic. Furthermore, it is also possible to predict health risks from the user's social media activity and detect related anomalies. This makes it possible to analyze the user's social media activity and propose a means of anomaly detection. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input the user's social media data to the generation AI and cause the generation AI to execute the proposal of a means of anomaly detection.

[0100] The reception unit can estimate the user's emotion and adjust the symptom input method based on the estimated user's emotion. The reception unit estimates the user's emotion and adjusts the symptom input method based on the estimated user's emotion. Facial expression recognition, voice analysis, and the like are used to estimate the emotion. For example, when the user is stressed, the reception unit provides a simple interface and minimizes the input procedure. Also, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to input symptoms quickly. This makes it possible to adjust the symptom input method based on the user's emotion. 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the symptom input method.

[0101] The reception unit can provide an optimal input method by referring to the user's past symptom input history at the time of reception. The reception unit can provide an optimal input method by referring to the user's past symptom input history at the time of reception. The input method includes, but is not limited to, voice input and manual input. For example, the reception unit automatically displays symptoms frequently input by the user in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest symptoms to be used in a specific time period from the user's past input history. This makes it possible to provide an optimal input method by referring to the user's past symptom input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past symptom input history to the generation AI and cause the generation AI to provide the optimal input method.

[0102] The reception unit can estimate the user's emotion and determine the input priority of symptoms based on the estimated user's emotion. The reception unit estimates the user's emotion and determines the input priority of symptoms based on the estimated user's emotion. Facial expression recognition, voice analysis, and the like are used for estimating the emotion. For example, when the user is feeling stressed, the reception unit preferentially inputs stress-related symptoms. In addition, when the user is relaxed, the reception unit can also preferentially input general symptoms. Furthermore, when the user is in a hurry, the reception unit can also preferentially input important symptoms. This makes it possible to determine the input priority of symptoms based on the user's emotion. 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the input priority of symptoms.

[0103] The reception unit can provide the optimal input method in consideration of the device information of the user at the time of reception. The reception unit can provide the optimal input method in consideration of the device information of the user at the time of reception. The device information includes, but is not limited to, a smartphone, a wearable device, and the like. For example, when the user is using a smartphone, the reception unit provides an input method that matches the screen size. In addition, when the user is using a tablet, the reception unit can also provide an input method optimized for a large screen. Furthermore, when the user is using a smart watch, the reception unit can also provide an input method that is simple and has high visibility. This makes it possible to provide the optimal input method in consideration of the device information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the device information of the user to the generation AI and cause the generation AI to execute the provision of the optimal input method.

[0104] The notification unit can estimate the user's emotion and adjust the notification expression method based on the estimated user's emotion. The notification unit estimates the user's emotion and adjusts the notification expression method based on the estimated user's emotion. Facial expression recognition, voice analysis, etc. are used for estimating the emotion. For example, if the user is stressed, the notification unit provides a simple and easy-to-understand notification. Also, if the user is relaxed, a detailed notification can be provided. Furthermore, if the user is in a hurry, the notification unit can provide a notification that covers the main points. This makes it possible to adjust the notification expression method based on the user's emotion. 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 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 notification expression method.

[0105] The notification unit can adjust the level of detail of the notification based on the importance of the abnormality at the time of notification. The notification unit adjusts the level of detail of the notification based on the importance of the abnormality at the time of notification. The importance includes, but is not limited to, the evaluation of a medical professional and statistical importance. For example, the notification unit provides a detailed notification for an important abnormality. Also, a simplified notification can be provided for a general abnormality. Furthermore, a notification can be provided quickly for an abnormality with high urgency. This makes it possible to adjust the level of detail of the notification based on the importance of the abnormality. 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 importance of the abnormality to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0106] 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 estimates the user's emotion and determines the priority of notifications based on the estimated user's emotion. Facial expression recognition, voice analysis, and the like are used to estimate the emotion. For example, if the user is feeling stressed, the notification unit can provide stress-related notifications with priority. In addition, if the user is relaxed, the notification unit can also provide general notifications with priority. Furthermore, if the user is in a hurry, the notification unit can also provide important notifications with priority. This makes it possible to determine the priority of notifications based on the user's emotion. 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 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.

[0107] The notification unit can provide an optimal notification method in consideration of the device information of the user at the time of notification. The notification unit can provide an optimal notification method in consideration of the device information of the user at the time of notification. The device information includes, but is not limited to, a smartphone or a wearable device. For example, when the user is using a smartphone, the notification unit provides a notification method that matches the screen size. In addition, when the user is using a tablet, the notification unit can also provide a notification method that is optimized for a large screen. Furthermore, when the user is using a smart watch, the notification unit can also provide a simple and highly visible notification method. This makes it possible to provide an optimal notification method in consideration of the device information of the user. 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 device information of the user to the generation AI and cause the generation AI to provide the optimal notification method.

[0108] The monitoring unit can estimate the user's emotion and adjust the monitoring method based on the estimated user's emotion. The monitoring unit estimates the user's emotion and adjusts the monitoring method based on the estimated user's emotion. Facial expression recognition, voice analysis, and the like are used to estimate the emotion. For example, if the user is feeling stressed, the monitoring unit prioritizes monitoring of stress-related data. Also, if the user is relaxed, general health data can be monitored. Furthermore, if the user is in a hurry, the monitoring unit can prioritize monitoring of important data. This allows the monitoring method to be adjusted based on the user's emotion. 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 monitoring unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the monitoring method.

[0109] The monitoring unit can provide an optimal monitoring method by referring to the user's past health data during monitoring. The monitoring unit can provide an optimal monitoring method by referring to the user's past health data during monitoring. The monitoring method includes, but is not limited to, real-time monitoring and regular data collection. For example, the monitoring unit selects the most effective monitoring method from the user's past data. The monitoring unit can also adjust the frequency of monitoring based on the user's past data. Furthermore, the monitoring unit can analyze the user's past data and select a method of monitoring in a specific time period. This makes it possible to provide an optimal monitoring method by referring to the user's past health data. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input the user's past health data to the generation AI and cause the generation AI to execute the provision of the optimal monitoring method.

[0110] The monitoring unit can estimate the user's emotion and determine the monitoring priority based on the estimated user's emotion. The monitoring unit estimates the user's emotion and determines the monitoring priority based on the estimated user's emotion. Facial expression recognition, voice analysis, and the like are used to estimate the emotion. For example, if the user is feeling stressed, the monitoring unit prioritizes monitoring of stress-related data. Also, if the user is relaxed, general health data can be prioritized for monitoring. Furthermore, if the user is in a hurry, the monitoring unit can also prioritize monitoring of important data. This allows the monitoring priority to be determined based on the user's emotion. 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 monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the monitoring priority.

[0111] The monitoring unit can provide an optimal monitoring method in consideration of the device information of the user during monitoring. The monitoring unit can provide an optimal monitoring method in consideration of the device information of the user during monitoring. The device information includes, but is not limited to, a smartphone or a wearable device. For example, when the user is using a smartphone, the monitoring unit provides a monitoring method that matches the screen size. In addition, when the user is using a tablet, the monitoring unit can also provide a monitoring method that is optimized for a large screen. Furthermore, when the user is using a smart watch, the monitoring unit can also provide a simple and highly visible monitoring method. This makes it possible to provide an optimal monitoring method in consideration of the device information of the user. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the device information of the user to the generation AI and cause the generation AI to execute the provision of the optimal monitoring method.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] When analyzing the user's health data, the analysis unit can adjust the analysis results by taking into account the user's lifestyle and environmental factors. For example, if the user works in a high-stress environment, the analysis unit can highlight stress-related health risks. In addition, if the user has an exercise habit, the analysis unit can provide analysis results that take into account the effects of exercise. Furthermore, if the user lives in a specific area, the analysis results can be adjusted by taking into account health risks specific to that area. This makes it possible to provide more personalized analysis results that take into account the user's lifestyle and environmental factors.

[0114] The providing unit can provide health advice tailored to the user's lifestyle based on the user's health data. For example, if the user lives a desk-work-centered lifestyle, the providing unit can suggest regular stretching and exercise. Also, if the user works night shifts, the providing unit can provide advice on how to improve the quality of sleep. Furthermore, if the user has specific dietary restrictions, the providing unit can suggest a meal plan that matches those restrictions. This makes it possible to provide specific health advice tailored to the user's lifestyle.

[0115] When collecting the user's health data, the collection unit can adjust the frequency of data collection based on the user's activity level. For example, if the user has a high activity level, the collection unit can collect data frequently to monitor a detailed health condition. Also, if the user has a low activity level, the collection unit can reduce the frequency of data collection and collect only necessary data. Furthermore, if the user's activity level fluctuates, the frequency of data collection can be dynamically adjusted according to the fluctuation. This enables efficient data collection according to the user's activity level.

[0116] The detection unit can detect anomalies according to seasonal and weather changes based on the user's health data. For example, since the risk of influenza and colds increases in winter, the detection unit prioritizes monitoring data related to these symptoms. Also, since the risk of heatstroke increases in summer, the detection unit can focus on detecting data related to body temperature and water intake. Furthermore, in the event of a sudden change in the weather, the detection unit can also detect anomalies according to changes in air pressure and humidity. This makes it possible to appropriately detect anomalies according to seasonal and weather changes.

[0117] The providing unit can provide health advice related to the user's hobbies and interests based on the user's health data. For example, if the user's hobby is running, the providing unit can provide training plans and nutritional advice related to running. If the user's hobby is cooking, the providing unit can also suggest healthy recipes and how to select ingredients. Furthermore, if the user's hobby is gardening, the providing unit can also provide advice on proper posture and how to take breaks while gardening. This makes it possible to provide specific health advice tailored to the user's hobbies and interests.

[0118] The analysis unit can estimate the user's emotions and adjust the timing of notifying the analysis results based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit notifies the user of the analysis results during a time period when the user is relaxed. Also, if the user is relaxed, the analysis results can be notified immediately. Furthermore, if the user is in a hurry, only important analysis results can be notified with priority. This makes it possible to notify the user of the analysis results at the optimal timing based on the user's emotions.

[0119] The providing unit can estimate the user's emotions and adjust the tone of the health advice to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide advice in a gentle tone. If the user is relaxed, detailed and specific advice can be provided. Furthermore, if the user is in a hurry, concise advice that gets to the point can be provided. In this way, health advice can be provided in an optimal tone based on the user's emotions.

[0120] The collection unit can estimate the user's emotion and adjust the data collection interface based on the estimated user's emotion. For example, when the user is stressed, the collection unit can provide a simple and intuitive interface. When the user is relaxed, the collection unit can provide detailed input options. Furthermore, when the user is in a hurry, the collection unit can prioritize voice input and collect data quickly. In this way, it is possible to provide an optimal data collection interface based on the user's emotion.

[0121] The detection unit can estimate the user's emotion and adjust the anomaly detection alert method based on the estimated user's emotion. For example, if the user is stressed, the detection unit can use a gentle alert sound. If the user is relaxed, a detailed alert message can be provided. Furthermore, if the user is in a hurry, a short and to the point alert can be provided. This makes it possible to provide an optimal alert method based on the user's emotion.

[0122] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring data based on the estimated user's emotions. For example, if the user is feeling stressed, the monitoring unit can provide a simple and easy-to-understand display method. If the user is relaxed, the monitoring unit can provide a detailed data display. Furthermore, if the user is in a hurry, the monitoring unit can provide a concise data display that focuses on the main points. In this way, it is possible to provide an optimal display method of the monitoring data based on the user's emotions.

[0123] The process flow of the second embodiment will be briefly described below.

[0124] Step 1: The collection unit collects health data of a user. The collection unit includes, for example, a reception unit that inputs the user's symptoms by text or voice. The user's symptoms include, but are not limited to, headache, fever, cough, and the like. The reception unit can efficiently input the user's symptoms using voice recognition technology or a text input interface. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit performs analysis based on, for example, past medical data and medical history. For the analysis, statistical analysis, machine learning algorithms, etc. are used. Step 3: The providing unit provides a treatment plan and preventive measures based on the analysis results obtained by the analyzing unit. For example, the providing unit proposes a treatment plan and preventive measures such as drug therapy, exercise therapy, and diet therapy based on the analysis results. Step 4: The detection unit detects an abnormality based on the advice provided by the provision unit. For example, the detection unit measures pulse and heart rate via a mobile terminal to detect an abnormality. The type of sensor and the frequency of data acquisition are taken into consideration when detecting an abnormality.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements including the collection unit, analysis unit, provision unit, and detection unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can input the user's symptoms by text or voice using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs analysis based on past medical data and medical history. The provision unit proposes a treatment plan and preventive measures by the specific processing unit 290 of the data processing device 12. The detection unit measures pulse and heart rate using the sensor of the smart device 14 and detects abnormalities. The correspondence between each unit and the device and control unit is not limited to the above example, and various changes are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, and detection 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 can input the user's symptoms by voice using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs analysis based on past medical data and medical history. The provision unit proposes a treatment plan and preventive measures by the specific processing unit 290 of the data processing device 12. The detection unit measures pulse and heart rate using the sensor of the smart glasses 214 and detects abnormalities. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, and detection 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 can input the user's symptoms by voice using the microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and performs analysis based on past medical data and medical history. The provision unit proposes treatment plans and preventive measures by the specific processing unit 290 of the data processing device 12. The detection unit measures pulse and heart rate using the sensor of the headset type terminal 314 and detects abnormalities. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, and detection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can input the user's symptoms by voice using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and performs analysis based on past medical data and medical history. The provision unit proposes a treatment plan and preventive measures by the specific processing unit 290 of the data processing device 12. The detection unit measures pulse and heart rate using the sensor of the robot 414 and detects abnormalities. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0178] 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.

[0179] 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.

[0180] These emotions are distributed in the 3 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.

[0181] 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).

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] As the hardware resource for executing the specific process, various processors as shown below 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.

[0190] 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.

[0191] 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 using a processor 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Appendix 1) A collection unit that collects health data of a user; an analysis unit that analyzes the data collected by the collection unit; a providing unit that provides a treatment plan or a preventive measure based on the analysis result obtained by the analyzing unit; A detection unit that detects an abnormality based on the advice provided by the provision unit. A system characterized by: (Appendix 2) The collecting unit includes: Equipped with a reception section for inputting the user's symptoms by text or voice 2. The system of claim 1, (Appendix 3) The providing unit is Equipped with a notification unit that notifies medical professionals if an abnormality is detected 2. The system of claim 1, (Appendix 4) The system comprises: The device further includes a monitoring unit that continuously monitors the user's health data. 2. The system of claim 1, (Appendix 5) The collecting unit includes: To estimate a user's emotion and adjust the timing of collection of health data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 6) The collecting unit includes: Analyze the user's past health data and select the appropriate collection method 2. The system of claim 1, (Appendix 7) The collecting unit includes: When collecting health data, it will be filtered based on the user's current life situation and areas of interest. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Estimating a user's emotions and prioritizing the health data to be collected based on the estimated user's emotions 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting health data, prioritize collection of relevant data based on the user's geographic location 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting health data, analyze users' social media activity to collect relevant data 2. The system of claim 1, (Appendix 11) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, adjust the level of detail based on the importance of the health data 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of health data. 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, prioritize analysis based on when health data was submitted 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, adjust the order of analysis based on the relevance of health data 2. The system of claim 1, (Appendix 17) The providing unit is Inferring the user's emotions and adjusting the presentation of treatment plans and preventive measures based on the inferred user emotions 2. The system of claim 1, (Appendix 18) The providing unit is At the time of delivery, adjust the level of detail provided based on the importance of the treatment plan or preventative measures 2. The system of claim 1, (Appendix 19) The providing unit is At the time of delivery, apply different delivery algorithms depending on the category of treatment plan or preventive measures. 2. The system of claim 1, (Appendix 20) The providing unit is Inferring a user's emotions and adjusting the length of the treatment plan or preventive measures provided based on the inferred user emotions 2. The system of claim 1, (Appendix 21) The providing unit is At the time of donation, prioritize donations based on the timing of submission of treatment plans and preventive measures. 2. The system of claim 1, (Appendix 22) The providing unit is At the time of delivery, adjust the order of delivery based on the relevance of treatment plans and preventive measures 2. The system of claim 1, (Appendix 23) The detection unit is Infer user emotions and adjust anomaly detection methods based on the inferred user emotions 2. The system of claim 1, (Appendix 24) The detection unit is Upon detection, the system analyzes the user's past health data to select the optimal anomaly detection method. 2. The system of claim 1, (Appendix 25) The detection unit is At the time of detection, customize anomaly detection measures based on the user's current living situation 2. The system of claim 1, (Appendix 26) The detection unit is Estimate user emotions and prioritize anomaly detection based on the estimated user emotions 2. The system of claim 1, (Appendix 27) The detection unit is At the time of detection, the optimal anomaly detection method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 28) The detection unit is Upon detection, it analyzes the user's social media activity and suggests ways to detect anomalies. 2. The system of claim 1, (Appendix 29) The reception unit is Inferring a user's emotions and adjusting the symptom input method based on the estimated user's emotions 3. The system of claim 2, (Appendix 30) The reception unit is When a patient checks in, the system refers to the user's symptom input history and provides the optimal input method. 3. The system of claim 2, (Appendix 31) The reception unit is Estimating a user's emotion and determining the input priority of symptoms based on the estimated user's emotion 3. The system of claim 2, (Appendix 32) The reception unit is When accepting a call, provide the optimal input method by taking into account the user's device information. 3. The system of claim 2, (Appendix 33) The notification unit is Infer user emotions and adjust notification presentation based on the inferred user emotions 4. The system of claim 3. (Appendix 34) The notification unit is When notifying, adjust notification detail based on the severity of the anomaly 4. The system of claim 3. (Appendix 35) The notification unit is Infer user emotions and prioritize notifications based on the inferred user emotions 4. The system of claim 3. (Appendix 36) The notification unit is When notifying users, provide the optimal notification method taking into account the user's device information 4. The system of claim 3. (Appendix 37) The monitoring unit is Estimate the user's emotions and adjust the monitoring method based on the estimated user's emotions. 5. The system of claim 4. (Appendix 38) The monitoring unit is When monitoring, the system provides optimal monitoring methods by referencing the user's past health data. 5. The system of claim 4. (Appendix 39) The monitoring unit is Estimate user emotions and determine monitoring priorities based on the estimated user emotions 5. The system of claim 4. (Appendix 40) The monitoring unit is When monitoring, provide the optimal monitoring method by considering the user's device information 5. The system of claim 4. [Explanation of symbols]

[0197] 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 data of a user; an analysis unit that analyzes the data collected by the collection unit; a providing unit that provides a treatment plan or a preventive measure based on the analysis result obtained by the analyzing unit; A detection unit that detects an abnormality based on the advice provided by the provision unit. A system characterized by:

2. The collecting unit includes: Equipped with a reception section for inputting the user's symptoms by text or voice 2. The system of claim 1 .

3. The providing unit is Equipped with a notification unit that notifies medical professionals if an abnormality is detected 2. The system of claim 1 .

4. The system comprises: The device further includes a monitoring unit that continuously monitors the user's health data.

2. The system of claim 1 .

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

2. The system of claim 1 .

6. The collecting unit includes: Analyze the user's past health data and select the appropriate collection method 2. The system of claim 1 .

7. The collecting unit includes: When collecting health data, it will be filtered based on the user's current life situation and areas of interest.

2. The system of claim 1 .

8. The collecting unit includes: Estimating a user's emotions and prioritizing the health data to be collected based on the estimated user's emotions 2. The system of claim 1 .

9. The collecting unit includes: When collecting health data, prioritize collection of relevant data based on the user's geographic location 2. The system of claim 1 .

10. The collecting unit includes: When collecting health data, analyze users' social media activity to collect relevant data 2. The system of claim 1 .

11. The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1 .

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