system

Abstract

The system according to this embodiment aims to analyze patient data and propose and implement an appropriate care plan. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, a detection unit, and a generation unit. The analysis unit analyzes patient data. The proposal unit proposes a care plan based on the data analyzed by the analysis unit. The detection unit detects abnormalities in real time based on the care plan proposed by the proposal unit. The generation unit automatically generates a report based on the care plan proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, proposal of a care plan and detection of abnormalities based on patient data have not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze patient data and propose and implement an appropriate care plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a proposal unit, a detection unit, and a generation unit. The analysis unit analyzes patient data. The proposal unit proposes a care plan based on the data analyzed by the analysis unit. The detection unit detects abnormalities in real time based on the care plan proposed by the proposal unit. The generation unit automatically generates a report based on the care plan proposed by the proposal unit. [Effects of the Invention]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that reduces the burden on doctors and nurses and improves the quality of life for patients by providing emergency response and home care support for patients. This AI agent system analyzes the patient's past medical data and real-time data and proposes an optimal care plan. Next, it analyzes data from medical records and sensors and proposes specific countermeasures according to the symptoms. Furthermore, the AI ​​automatically generates reports that can be reviewed by doctors and nurses. Through this mechanism, the burden on doctors and nurses can be reduced and the quality of patient care can be improved by centrally managing and automatically analyzing patient data. For example, the AI ​​agent system can support home medical care by linking with devices that can be used at home (such as smart speakers and wearable devices) to assist in planning the patient's home care. This can strengthen early discharge and post-discharge care, and improve the quality of life for patients. As a specific example, the AI ​​agent analyzes the patient's past medical data, detects abnormalities in real time, and proposes countermeasures. In addition, the AI ​​agent automatically generates reports and shares them with doctors and nurses, which can reduce the time spent on recording medical records and creating reports. Furthermore, the AI ​​agent proposes home care plans and supports patients' home care in conjunction with smart speakers and wearable devices. This system improves efficiency in medical settings and patient satisfaction, and reduces the burden on doctors and nurses. In this way, the AI ​​agent system can reduce the burden on doctors and nurses and improve patients' quality of life by providing emergency response and home care support.

[0029] The AI ​​agent system according to this embodiment comprises an analysis unit, a proposal unit, a detection unit, and a generation unit. The analysis unit analyzes patient data. Patient data includes, but is not limited to, medical records, vital signs, and test results. The analysis unit analyzes patient data using, for example, data mining techniques. The analysis unit can also analyze data using statistical analysis techniques. The analysis unit can also analyze data using machine learning algorithms. For example, the analysis unit extracts important patterns from the patient's medical records using data mining techniques. Statistical analysis techniques are used to analyze the distribution and correlation of data. Machine learning algorithms are used to learn from large amounts of data and build predictive models. The proposal unit proposes a care plan based on the data analyzed by the analysis unit. The care plan includes, but is not limited to, treatment plans, rehabilitation plans, and lifestyle guidance. The proposal unit proposes, for example, a treatment plan. The proposal unit can also propose a rehabilitation plan. The proposal unit can also propose lifestyle guidance. For example, the proposal unit proposes the optimal treatment plan for the patient based on the analysis results. A rehabilitation plan is a specific plan to promote the patient's recovery. Lifestyle guidance is advice to support the patient's health management in their daily life. The detection unit detects abnormalities in real time based on the care plan proposed by the proposal unit. To detect abnormalities in real time, monitoring on a second-by-second basis is necessary, for example. The detection unit can detect abnormalities in vital signs, for example. The detection unit can also detect changes in behavioral patterns. The detection unit can also detect abnormalities in environmental data. For example, the detection unit can detect abnormalities in heart rate and blood pressure in real time. Changes in behavioral patterns are detected by monitoring changes in the patient's movements and activity levels. Abnormalities in environmental data are detected by monitoring changes in temperature and humidity. The generation unit automatically generates reports based on the care plan proposed by the proposal unit. Reports include, but are not limited to, summaries of diagnostic results and records of treatment history. The generation unit automatically generates summaries of diagnostic results, for example.Furthermore, the generation unit can automatically generate records of treatment history. It can also automatically generate reports on the patient's condition. For example, the generation unit generates a report that concisely summarizes the diagnostic results. Records of treatment history document the details of the treatments the patient received. Reports on the patient's condition provide information for doctors and nurses to understand the patient's condition. Thus, the AI ​​agent system according to this embodiment can reduce the burden on doctors and nurses and improve the patient's quality of life by analyzing patient data, proposing care plans, detecting abnormalities, and generating reports.

[0030] The analysis unit analyzes patient data. Patient data includes, but is not limited to, medical records, vital signs, and test results. The analysis unit analyzes patient data using, for example, data mining techniques. Data mining techniques are methods for extracting useful information from large amounts of data, and can identify important patterns from patients' medical records. For example, by finding common patterns before the appearance of specific symptoms, early diagnosis and preventive measures can be taken. The analysis unit can also analyze data using statistical analysis techniques. Statistical analysis techniques are used to analyze the distribution and correlation of data, and can identify trends in health status and risk factors from patient data. For example, it can analyze the incidence of diseases in specific age groups or genders and propose preventive measures for high-risk patients. Furthermore, the analysis unit can also analyze data using machine learning algorithms. Machine learning algorithms are used to learn from large amounts of data and build predictive models. For example, a model can be built to predict the risk of developing a specific disease based on past patient data, and future risks can be predicted by inputting new patient data. This allows the analysis unit to combine data mining techniques, statistical analysis techniques, and machine learning algorithms to analyze patient data from multiple perspectives, supporting more accurate diagnoses and treatment planning.

[0031] The proposal department proposes care plans based on data analyzed by the analysis department. These care plans may include, but are not limited to, treatment plans, rehabilitation plans, and lifestyle guidance. For example, the proposal department may propose a treatment plan. A treatment plan involves selecting the optimal treatment method and medication based on the patient's condition and treatment goals. For example, if the analysis results indicate that a particular medication is effective, the proposal department can propose a treatment plan using that medication. The proposal department can also propose a rehabilitation plan. A rehabilitation plan is a specific plan to promote the patient's recovery and may include exercise therapy and occupational therapy. For example, if the analysis results indicate that a particular exercise is effective, the proposal department can propose a rehabilitation plan that includes that exercise. The proposal department can also propose lifestyle guidance. Lifestyle guidance is advice to support the patient's health management in their daily life and includes guidance on diet, exercise, and sleep. For example, if the analysis results indicate that a particular diet is healthy, the proposal department can propose lifestyle guidance that includes that diet. In this way, the proposal department can propose the optimal treatment plan, rehabilitation plan, and lifestyle guidance to the patient based on the analysis results, thereby supporting the patient's health management.

[0032] The detection unit detects abnormalities in real time based on the care plan proposed by the proposal unit. Real-time detection of abnormalities requires, for example, monitoring on a second-by-second basis. The detection unit can detect abnormalities in vital signs, such as heart rate, blood pressure, body temperature, and respiratory rate. By monitoring this data in real time, abnormalities can be detected early. For example, a sudden increase in heart rate may indicate a cardiac abnormality, allowing for immediate notification to a doctor. The detection unit can also detect changes in behavioral patterns. These changes are detected by monitoring changes in the patient's movements and activity level. For example, if a normally active patient suddenly becomes inactive, it may indicate a deterioration in their health, requiring attention. Furthermore, the detection unit can detect abnormalities in environmental data. Environmental data includes temperature, humidity, and illuminance. By monitoring this data, changes in the patient's living environment can be detected. For example, a sudden increase in room temperature increases the risk of heatstroke, allowing for appropriate measures to be taken. This allows the detection unit to monitor vital signs, behavioral patterns, and environmental data in real time, enabling early detection of abnormalities, thereby ensuring patient safety and supporting a rapid response.

[0033] The generation unit automatically generates reports based on the care plan proposed by the proposal unit. These reports may include, but are not limited to, summaries of diagnostic results and records of treatment history. For example, the generation unit automatically generates summaries of diagnostic results. These summaries provide a concise overview of the diagnosis made by the physician, presented in a format easily understood by patients and other medical staff. For example, they may include the diagnosed illness, a summary of symptoms, and a treatment plan. The generation unit can also automatically generate records of treatment history. These records detail the treatments the patient received and are important for understanding the progress and effectiveness of the treatment. For example, they may include the type and dosage of medications used, the date of treatment, and the effectiveness of the treatment. The generation unit can also automatically generate reports on the patient's condition. These reports provide information for physicians and nurses to understand the patient's condition and may include vital signs, test results, and changes in behavioral patterns. For example, reports on a patient's condition include information such as fluctuations in heart rate and blood pressure, abnormal test results, and changes in behavioral patterns. This allows the generation unit to automatically generate summaries of diagnostic results, records of treatment history, and reports on the patient's condition, thereby reducing the burden on doctors and nurses and providing information to accurately understand the patient's state.

[0034] The system includes a Home Care Proposal Department that proposes home care plans. These plans may include, but are not limited to, home-based rehabilitation, dietary guidance, and medication management. For example, the Home Care Proposal Department may propose home-based rehabilitation. It may also propose dietary guidance. It may also propose medication management. For example, the Home Care Proposal Department may propose a home-based rehabilitation plan according to the patient's condition. Dietary guidance is advice to support the patient's nutritional management. Medication management is guidance to ensure the patient takes their medication appropriately. By proposing home care plans, the system can support home-based medical care and improve the patient's quality of life. Some or all of the above-described processes in the Home Care Proposal Department may be performed using, for example, AI, or not. For example, the Home Care Proposal Department may input patient data into a generating AI and have the generating AI generate a home care plan proposal.

[0035] The system includes a connectivity unit that interacts with smart speakers and wearable devices. The connectivity unit interacts with smart speakers and wearable devices. Smart speakers and wearable devices include, but are not limited to, devices with voice assistant functionality and devices that collect health data. The connectivity unit interacts with, for example, smart speakers with voice assistant functionality. The connectivity unit can also interact with wearable devices that collect health data. The connectivity unit can also interact with smartphones. For example, the connectivity unit provides care plans to patients through smart speakers with voice assistant functionality. Wearable devices that collect health data monitor patients' vital signs and activity levels. Smartphones are used to manage patient data and share information with doctors and nurses. This allows for support of patients' home care through interaction with smart speakers and wearable devices. Some or all of the above-described processes in the connectivity unit may be performed using, for example, AI, or not using AI. For example, the connectivity unit can input data acquired from smart speakers and wearable devices into a generating AI, and have the generating AI perform data analysis and propose care plans.

[0036] The detection unit, in cooperation with the home care proposal unit, can detect abnormalities in the home in real time and propose countermeasures. For example, the detection unit monitors vital signs to detect abnormalities in the home in real time. The detection unit can also detect changes in behavioral patterns. Furthermore, the detection unit can detect abnormalities in environmental data. For example, the detection unit can detect abnormalities in heart rate and blood pressure in real time and propose countermeasures. Changes in behavioral patterns are detected by monitoring changes in the patient's movements and activity levels. Abnormalities in environmental data are detected by monitoring changes in temperature and humidity. This allows for the detection of abnormalities in the home in real time and the proposal of countermeasures, thereby ensuring patient safety. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the care plan provided by the home care proposal unit into a generating AI, and have the generating AI perform abnormality detection and propose countermeasures.

[0037] The generation unit can generate reports based on the results of the proposal unit. For example, the generation unit can generate a report summarizing the contents of the care plan proposed by the proposal unit. The generation unit can also generate reports recording the patient's diagnosis and treatment history. Furthermore, the generation unit can generate detailed reports on the patient's condition. For example, the generation unit generates a report including a summary of the diagnosis based on the results of the proposal unit. Recording the treatment history records the details of the treatment the patient received. Detailed reports on the patient's condition provide information for doctors and nurses to understand the patient's condition. This reduces the burden on doctors and nurses by generating reports based on the results of the proposal unit. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the care plan data provided by the proposal unit into a generation AI and have the generation AI perform report generation.

[0038] The analysis unit can generate a more accurate care plan by combining and analyzing the patient's past medical data and real-time data. For example, the analysis unit can refer to the patient's past medical data and analyze it in comparison to the current symptoms. The analysis unit can also collect real-time data and integrate it with past data for analysis. Furthermore, the analysis unit can combine past medical data and real-time data to generate an optimal care plan. For example, the analysis unit can propose an optimal treatment plan for the patient's current symptoms based on the patient's past medical data. Real-time data is used to monitor the patient's vital signs and activity level in real time. By combining past medical data and real-time data, a more accurate care plan can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past medical data and real-time data into a generation AI and have the generation AI perform the generation of the care plan.

[0039] The analysis unit can adjust the analysis results by considering the patient's lifestyle and environmental data during the analysis. For example, the analysis unit can collect the patient's lifestyle data and reflect it in the analysis results. The analysis unit can also adjust the analysis results by considering the patient's environmental data. Furthermore, the analysis unit can provide more accurate analysis results based on lifestyle and environmental data. For example, the analysis unit evaluates the patient's health status based on their dietary records and exercise habits. Environmental data includes information about the patient's living environment and lifestyle. By considering lifestyle and environmental data, more accurate analysis results can be provided. This allows for the provision of more accurate analysis results by considering lifestyle and environmental data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input lifestyle and environmental data into a generating AI and have the generating AI perform the adjustment of the analysis results.

[0040] The analysis unit can perform analysis while considering the patient's family history and genetic information. For example, the analysis unit can refer to the patient's family history and perform analysis while considering genetic risk. The analysis unit can also analyze the risk of specific diseases based on the patient's genetic information. Furthermore, the analysis unit can integrate family history and genetic information to provide more accurate analysis results. For example, the analysis unit can assess genetic risk based on the patient's family history. Genetic information includes the patient's genetic test results. By considering family history and genetic information, more accurate analysis results can be provided. This allows for the provision of more accurate analysis results by considering family history and genetic information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input family history and genetic information into a generating AI and have the generating AI perform the analysis.

[0041] The analysis unit can analyze a patient's social media activity and obtain relevant health information during the analysis. For example, the analysis unit can analyze a patient's social media activity and extract posts related to health. The analysis unit can also incorporate health information obtained from social media into the analysis. Furthermore, the analysis unit can estimate a patient's lifestyle and health status based on their social media activity. For example, the analysis unit can analyze a patient's social media posts and extract information related to health. Social media activity provides information about a patient's lifestyle and health status. By incorporating information obtained from social media into the analysis, more accurate analysis results can be provided. Thus, by analyzing social media activity, relevant health information can be obtained and reflected in the analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input social media activity data into a generating AI and have the generating AI perform the acquisition of health information.

[0042] The suggestion unit can adjust the level of detail in its suggestions based on the severity of the patient's symptoms. For example, if the patient's symptoms are mild, the suggestion unit will provide a concise suggestion. If the patient's symptoms are moderate, the suggestion unit can provide a more detailed suggestion. If the patient's symptoms are severe, the suggestion unit can also provide suggestions that include emergency response. For example, the suggestion unit will assess the severity of the patient's symptoms and adjust the level of detail in its suggestions based on that assessment. The severity of symptoms will be assessed based on a disease score or a doctor's diagnosis. By adjusting the level of detail in suggestions based on the severity of symptoms, a more appropriate care plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input patient symptom data into a generating AI and have the generating AI perform the adjustment of the level of detail in its suggestions.

[0043] The proposal unit can customize the proposed plan by considering the patient's lifestyle and environmental data. For example, the proposal unit can propose an appropriate care plan based on the patient's lifestyle data. The proposal unit can also make optimal proposals by considering the patient's environmental data. Furthermore, the proposal unit can integrate lifestyle and environmental data to provide customized proposals. For example, the proposal unit can provide health management advice based on the patient's dietary records and exercise habits. Environmental data includes information about the patient's living environment. By considering lifestyle and environmental data, a more appropriate care plan can be provided. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input lifestyle and environmental data into a generating AI and have the generating AI customize the proposed plan.

[0044] The proposal unit can propose an optimal care plan by considering the patient's geographical location information. For example, the proposal unit considers medical facilities in the area where the patient lives when making a proposal. The proposal unit can also propose an optimal care plan based on the patient's geographical location information. Furthermore, the proposal unit can consider geographical location information to make proposals that address region-specific health risks. For example, the proposal unit proposes an appropriate care plan based on information about medical facilities in the area where the patient lives. Geographical location information includes information about the area where the patient lives and their travel history. By considering geographical location information, a more appropriate care plan can be provided. This allows for the provision of a more appropriate care plan by considering geographical location information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input geographical location information into a generating AI and have the generating AI execute the care plan proposal.

[0045] The proposal unit can analyze the patient's social media activity and propose a relevant care plan when making a proposal. For example, the proposal unit can analyze the patient's social media activity and make a proposal based on health-related posts. The proposal unit can also propose an appropriate care plan based on information obtained from social media. Furthermore, the proposal unit can make proposals tailored to the patient's lifestyle and health condition based on their social media activity. For example, the proposal unit can analyze the patient's social media posts and extract health-related information. Social media activity provides information about the patient's lifestyle and health condition. By proposing an appropriate care plan based on information obtained from social media, a more accurate care plan can be provided. Thus, by analyzing social media activity, a relevant care plan can be provided. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input social media activity data into a generating AI and have the generating AI execute the care plan proposal.

[0046] The detection unit can optimize its detection algorithm by referring to the patient's past abnormal data when an abnormality is detected. For example, the detection unit adjusts the detection algorithm based on the patient's past abnormal data. The detection unit can also improve the accuracy of abnormality detection by referring to past abnormal data. Furthermore, the detection unit can build an optimal detection algorithm by integrating the patient's abnormal data. For example, the detection unit sets an abnormality detection threshold based on the patient's past abnormal data. Past abnormal data includes the patient's diagnosis results and abnormality detection history. By referring to past abnormal data, the accuracy of abnormality detection can be improved. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input past abnormal data into a generating AI and have the generating AI perform the optimization of the detection algorithm.

[0047] The detection unit can improve the accuracy of anomaly detection by considering the patient's lifestyle and environmental data when detecting an anomaly. For example, the detection unit can improve the accuracy of anomaly detection based on the patient's lifestyle data. The detection unit can also improve the accuracy of anomaly detection by considering the patient's environmental data. Furthermore, the detection unit can perform more accurate anomaly detection by integrating lifestyle and environmental data. For example, the detection unit sets an anomaly detection threshold based on the patient's dietary records and exercise habits. Environmental data includes information about the patient's living environment. By considering lifestyle and environmental data, the accuracy of anomaly detection can be improved. This allows for improved accuracy of anomaly detection by considering lifestyle and environmental data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input lifestyle and environmental data into a generating AI and have the generating AI perform the anomaly detection accuracy improvement.

[0048] The detection unit can perform anomaly detection while considering the patient's geographical location information. For example, the detection unit can perform anomaly detection while considering medical facilities in the area where the patient lives. The detection unit can also perform anomaly detection based on the patient's geographical location information. Furthermore, the detection unit can perform anomaly detection that addresses region-specific health risks while considering geographical location information. For example, the detection unit sets an anomaly detection threshold based on information about medical facilities in the area where the patient lives. Geographical location information includes information about the area where the patient lives and their travel history. By considering geographical location information, more appropriate anomaly detection can be performed. This makes it possible to perform more appropriate anomaly detection by considering geographical location information. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input geographical location information into a generating AI and have the generating AI perform anomaly detection.

[0049] The detection unit can improve the accuracy of anomaly detection by referring to relevant literature on the patient when detection occurs. For example, the detection unit can improve the accuracy of anomaly detection by referring to relevant literature on the patient. The detection unit can also optimize the anomaly detection algorithm based on the relevant literature. Furthermore, the detection unit can perform more accurate anomaly detection by integrating relevant literature on the patient. For example, the detection unit sets an anomaly detection threshold based on relevant literature on the patient. Relevant literature includes medical papers, guidelines, and professional books. By referring to relevant literature, the accuracy of anomaly detection can be improved. This means that by referring to relevant literature, the accuracy of anomaly detection can be improved. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data from relevant literature into a generating AI and have the generating AI perform the anomaly detection accuracy improvement.

[0050] The generation unit can adjust the level of detail in a report based on the severity of the patient's symptoms. For example, if the patient's symptoms are mild, the generation unit can generate a concise report. If the patient's symptoms are moderate, the generation unit can also generate a detailed report. Furthermore, if the patient's symptoms are severe, the generation unit can generate a detailed report including emergency response information. For example, the generation unit can assess the severity of the patient's symptoms and adjust the level of detail in the report accordingly. The severity of symptoms is assessed based on disease scoring or the doctor's diagnosis. By adjusting the level of detail in the report based on the severity of symptoms, it becomes possible to provide more appropriate information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input patient symptom data into a generation AI and have the generation AI perform the adjustment of the level of detail in the report.

[0051] The generation unit can customize report content by considering the patient's lifestyle and environmental data when generating reports. For example, the generation unit can generate an appropriate report based on the patient's lifestyle data. It can also generate an optimal report by considering the patient's environmental data. Furthermore, the generation unit can provide a customized report by integrating lifestyle and environmental data. For example, the generation unit can generate a report including health management advice based on the patient's dietary records and exercise habits. Environmental data includes information about the patient's living environment. By considering lifestyle and environmental data, a more appropriate report can be provided. This makes it possible to provide more appropriate information by considering lifestyle and environmental data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input lifestyle and environmental data into a generation AI and have the generation AI perform the customization of the report content.

[0052] The generation unit can generate reports while considering the patient's geographical location information. For example, the generation unit can generate reports while considering medical facilities in the area where the patient lives. The generation unit can also generate optimal reports based on the patient's geographical location information. Furthermore, the generation unit can generate reports that address region-specific health risks while considering geographical location information. For example, the generation unit generates appropriate reports based on information about medical facilities in the area where the patient lives. Geographical location information includes information about the area where the patient lives and their travel history. By considering geographical location information, more appropriate reports can be provided. This makes it possible to provide more appropriate information by considering geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input geographical location information into a generation AI and have the generation AI perform report generation.

[0053] The generation unit can analyze the patient's social media activity and include relevant information in the report during report generation. For example, the generation unit analyzes the patient's social media activity and generates a report based on health-related posts. The generation unit can also generate an appropriate report based on information obtained from social media. Furthermore, the generation unit can generate a report tailored to the patient's lifestyle and health status based on their social media activity. For example, the generation unit analyzes the patient's social media posts and extracts health-related information. Social media activity provides information about the patient's lifestyle and health status. By generating an appropriate report based on information obtained from social media, it becomes possible to provide more accurate information. This allows relevant information to be included in the report by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input social media activity data into a generation AI and have the generation AI perform report generation.

[0054] The Home Care Proposal Department can customize the proposed home care plan by considering the patient's lifestyle and environmental data. For example, the Home Care Proposal Department can propose an appropriate home care plan based on the patient's lifestyle data. It can also propose an optimal home care plan by considering the patient's environmental data. Furthermore, the Home Care Proposal Department can provide a customized home care plan by integrating lifestyle and environmental data. For example, the Home Care Proposal Department can propose a home care plan that includes health management advice based on the patient's dietary records and exercise habits. Environmental data includes information about the patient's living environment. By considering lifestyle and environmental data, a more appropriate home care plan can be provided. This allows for the provision of a more appropriate care plan by considering lifestyle and environmental data. Some or all of the above processing in the Home Care Proposal Department may be performed using AI, for example, or without AI. For example, the Home Care Proposal Department can input lifestyle and environmental data into a generating AI and have the generating AI perform the customization of the proposed content.

[0055] The Home Care Proposal Department can propose an optimal home care plan by considering the patient's geographical location information. For example, the Home Care Proposal Department proposes a home care plan by considering medical facilities in the area where the patient lives. The Home Care Proposal Department can also propose an optimal home care plan based on the patient's geographical location information. Furthermore, the Home Care Proposal Department can propose a home care plan that addresses region-specific health risks by considering geographical location information. For example, the Home Care Proposal Department proposes an appropriate home care plan based on information about medical facilities in the area where the patient lives. Geographical location information includes information about the area where the patient lives and their travel history. By considering geographical location information, a more appropriate home care plan can be provided. This allows for the provision of a more appropriate care plan by considering geographical location information. Some or all of the above processing in the Home Care Proposal Department may be performed using AI, for example, or without AI. For example, the Home Care Proposal Department can input geographical location information into a generating AI and have the generating AI execute the proposal of a home care plan.

[0056] The collaboration unit can optimize the collaboration method by considering the patient's lifestyle and environmental data during collaboration. For example, the collaboration unit can propose an appropriate collaboration method based on the patient's lifestyle data. The collaboration unit can also propose an optimal collaboration method by considering the patient's environmental data. Furthermore, the collaboration unit can provide a customized collaboration method by integrating lifestyle and environmental data. For example, the collaboration unit can propose a collaboration method that includes health management advice based on the patient's dietary records and exercise habits. Environmental data includes information about the patient's living environment and lifestyle. By considering lifestyle and environmental data, a more appropriate collaboration method can be provided. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input lifestyle and environmental data into a generating AI and have the generating AI perform the optimization of the collaboration method.

[0057] The collaboration unit can select the optimal collaboration method when collaborating, taking into account the patient's geographical location information. For example, the collaboration unit can select a collaboration method by considering medical facilities in the area where the patient lives. The collaboration unit can also select the optimal collaboration method based on the patient's geographical location information. Furthermore, the collaboration unit can select a collaboration method that addresses region-specific health risks by considering geographical location information. For example, the collaboration unit can select an appropriate collaboration method based on information about medical facilities in the area where the patient lives. Geographical location information includes information about the patient's area of ​​residence and travel history. By considering geographical location information, a more appropriate collaboration method can be provided. This allows for the provision of a more appropriate collaboration method by considering geographical location information. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input geographical location information into a generating AI and have the generating AI select the collaboration method.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The analysis unit can generate more accurate care plans by combining and analyzing patients' past medical data and real-time data. For example, it can refer to a patient's past medical data and analyze it in comparison to their current symptoms. It can also collect real-time data and integrate it with past data for analysis. Furthermore, it can combine past medical data and real-time data to generate an optimal care plan. This allows for the provision of more accurate care plans by combining past medical data and real-time data.

[0060] The proposal department can adjust the level of detail in its proposals based on the severity of the patient's symptoms. For example, if the patient's symptoms are mild, a concise proposal can be made. If the patient's symptoms are moderate, a detailed proposal can be made. Furthermore, if the patient's symptoms are severe, a proposal including emergency response can be made. By adjusting the level of detail in the proposals based on the severity of the symptoms, a more appropriate care plan can be provided.

[0061] The detection unit can optimize its detection algorithm by referring to the patient's past abnormal data when detection occurs. For example, it can adjust the detection algorithm based on the patient's past abnormal data. It can also improve the accuracy of abnormality detection by referring to past abnormal data. Furthermore, it can integrate the patient's abnormal data to construct an optimal detection algorithm. This allows for improved accuracy of abnormality detection by referring to past abnormal data.

[0062] The generation unit can customize report content by considering the patient's lifestyle and environmental data during report generation. For example, it can generate an appropriate report based on the patient's lifestyle data. It can also generate an optimal report by considering the patient's environmental data. Furthermore, it can provide a customized report by integrating lifestyle and environmental data. This makes it possible to provide more appropriate information by considering lifestyle and environmental data.

[0063] The collaboration department can select the optimal collaboration method when collaborating, taking into account the patient's geographical location. For example, it can select a collaboration method considering the medical facilities in the patient's residential area. It can also select the optimal collaboration method based on the patient's geographical location. Furthermore, it can select a collaboration method that addresses region-specific health risks, taking geographical location into consideration. This allows for the provision of more appropriate collaboration methods by considering geographical location.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The analysis unit analyzes patient data. Patient data includes medical records, vital signs, and test results. The analysis unit analyzes the data using data mining techniques, statistical analysis techniques, and machine learning algorithms. For example, it extracts important patterns from medical records using data mining techniques, analyzes data distribution and correlations using statistical analysis techniques, and builds predictive models using machine learning algorithms. Step 2: The proposal department proposes a care plan based on the data analyzed by the analysis department. The care plan includes a treatment plan, rehabilitation plan, and lifestyle guidance. Based on the analysis results, the proposal department proposes the most suitable treatment plan, rehabilitation plan, and lifestyle guidance for the patient. Step 3: The detection unit detects abnormalities in real time based on the care plan proposed by the proposal unit. The detection unit detects abnormalities in vital signs, changes in behavioral patterns, and abnormalities in environmental data. For example, it monitors and detects abnormalities in heart rate and blood pressure, changes in patient movement and activity level, and changes in temperature and humidity in real time. Step 4: The generation unit automatically generates a report based on the care plan proposed by the proposal unit. The report includes a summary of the diagnosis, a record of the treatment history, and information about the patient's condition. The generation unit automatically generates a report that concisely summarizes the diagnosis, a report that records the details of the treatment history, and a report that provides information to understand the patient's condition.

[0066] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that reduces the burden on doctors and nurses and improves the quality of life for patients by providing emergency response and home care support for patients. This AI agent system analyzes the patient's past medical data and real-time data and proposes an optimal care plan. Next, it analyzes data from medical records and sensors and proposes specific countermeasures according to the symptoms. Furthermore, the AI ​​automatically generates reports that can be reviewed by doctors and nurses. Through this mechanism, the burden on doctors and nurses can be reduced and the quality of patient care can be improved by centrally managing and automatically analyzing patient data. For example, the AI ​​agent system can support home medical care by linking with devices that can be used at home (such as smart speakers and wearable devices) to assist in planning the patient's home care. This can strengthen early discharge and post-discharge care, and improve the quality of life for patients. As a specific example, the AI ​​agent analyzes the patient's past medical data, detects abnormalities in real time, and proposes countermeasures. In addition, the AI ​​agent automatically generates reports and shares them with doctors and nurses, which can reduce the time spent on recording medical records and creating reports. Furthermore, the AI ​​agent proposes home care plans and supports patients' home care in conjunction with smart speakers and wearable devices. This system improves efficiency in medical settings and patient satisfaction, and reduces the burden on doctors and nurses. In this way, the AI ​​agent system can reduce the burden on doctors and nurses and improve patients' quality of life by providing emergency response and home care support.

[0067] The AI ​​agent system according to this embodiment comprises an analysis unit, a proposal unit, a detection unit, and a generation unit. The analysis unit analyzes patient data. Patient data includes, but is not limited to, medical records, vital signs, and test results. The analysis unit analyzes patient data using, for example, data mining techniques. The analysis unit can also analyze data using statistical analysis techniques. The analysis unit can also analyze data using machine learning algorithms. For example, the analysis unit extracts important patterns from the patient's medical records using data mining techniques. Statistical analysis techniques are used to analyze the distribution and correlation of data. Machine learning algorithms are used to learn from large amounts of data and build predictive models. The proposal unit proposes a care plan based on the data analyzed by the analysis unit. The care plan includes, but is not limited to, treatment plans, rehabilitation plans, and lifestyle guidance. The proposal unit proposes, for example, a treatment plan. The proposal unit can also propose a rehabilitation plan. The proposal unit can also propose lifestyle guidance. For example, the proposal unit proposes the optimal treatment plan for the patient based on the analysis results. A rehabilitation plan is a specific plan to promote the patient's recovery. Lifestyle guidance is advice to support the patient's health management in their daily life. The detection unit detects abnormalities in real time based on the care plan proposed by the proposal unit. To detect abnormalities in real time, monitoring on a second-by-second basis is necessary, for example. The detection unit can detect abnormalities in vital signs, for example. The detection unit can also detect changes in behavioral patterns. The detection unit can also detect abnormalities in environmental data. For example, the detection unit can detect abnormalities in heart rate and blood pressure in real time. Changes in behavioral patterns are detected by monitoring changes in the patient's movements and activity levels. Abnormalities in environmental data are detected by monitoring changes in temperature and humidity. The generation unit automatically generates reports based on the care plan proposed by the proposal unit. Reports include, but are not limited to, summaries of diagnostic results and records of treatment history. The generation unit automatically generates summaries of diagnostic results, for example.Furthermore, the generation unit can automatically generate records of treatment history. It can also automatically generate reports on the patient's condition. For example, the generation unit generates a report that concisely summarizes the diagnostic results. Records of treatment history document the details of the treatments the patient received. Reports on the patient's condition provide information for doctors and nurses to understand the patient's condition. Thus, the AI ​​agent system according to this embodiment can reduce the burden on doctors and nurses and improve the patient's quality of life by analyzing patient data, proposing care plans, detecting abnormalities, and generating reports.

[0068] The analysis unit analyzes patient data. Patient data includes, but is not limited to, medical records, vital signs, and test results. The analysis unit analyzes patient data using, for example, data mining techniques. Data mining techniques are methods for extracting useful information from large amounts of data, and can identify important patterns from patients' medical records. For example, by finding common patterns before the appearance of specific symptoms, early diagnosis and preventive measures can be taken. The analysis unit can also analyze data using statistical analysis techniques. Statistical analysis techniques are used to analyze the distribution and correlation of data, and can identify trends in health status and risk factors from patient data. For example, it can analyze the incidence of diseases in specific age groups or genders and propose preventive measures for high-risk patients. Furthermore, the analysis unit can also analyze data using machine learning algorithms. Machine learning algorithms are used to learn from large amounts of data and build predictive models. For example, a model can be built to predict the risk of developing a specific disease based on past patient data, and future risks can be predicted by inputting new patient data. This allows the analysis unit to combine data mining techniques, statistical analysis techniques, and machine learning algorithms to analyze patient data from multiple perspectives, supporting more accurate diagnoses and treatment planning.

[0069] The proposal department proposes care plans based on data analyzed by the analysis department. These care plans may include, but are not limited to, treatment plans, rehabilitation plans, and lifestyle guidance. For example, the proposal department may propose a treatment plan. A treatment plan involves selecting the optimal treatment method and medication based on the patient's condition and treatment goals. For example, if the analysis results indicate that a particular medication is effective, the proposal department can propose a treatment plan using that medication. The proposal department can also propose a rehabilitation plan. A rehabilitation plan is a specific plan to promote the patient's recovery and may include exercise therapy and occupational therapy. For example, if the analysis results indicate that a particular exercise is effective, the proposal department can propose a rehabilitation plan that includes that exercise. The proposal department can also propose lifestyle guidance. Lifestyle guidance is advice to support the patient's health management in their daily life and includes guidance on diet, exercise, and sleep. For example, if the analysis results indicate that a particular diet is healthy, the proposal department can propose lifestyle guidance that includes that diet. In this way, the proposal department can propose the optimal treatment plan, rehabilitation plan, and lifestyle guidance to the patient based on the analysis results, thereby supporting the patient's health management.

[0070] The detection unit detects abnormalities in real time based on the care plan proposed by the proposal unit. Real-time detection of abnormalities requires, for example, monitoring on a second-by-second basis. The detection unit can detect abnormalities in vital signs, such as heart rate, blood pressure, body temperature, and respiratory rate. By monitoring this data in real time, abnormalities can be detected early. For example, a sudden increase in heart rate may indicate a cardiac abnormality, allowing for immediate notification to a doctor. The detection unit can also detect changes in behavioral patterns. These changes are detected by monitoring changes in the patient's movements and activity level. For example, if a normally active patient suddenly becomes inactive, it may indicate a deterioration in their health, requiring attention. Furthermore, the detection unit can detect abnormalities in environmental data. Environmental data includes temperature, humidity, and illuminance. By monitoring this data, changes in the patient's living environment can be detected. For example, a sudden increase in room temperature increases the risk of heatstroke, allowing for appropriate measures to be taken. This allows the detection unit to monitor vital signs, behavioral patterns, and environmental data in real time, enabling early detection of abnormalities, thereby ensuring patient safety and supporting a rapid response.

[0071] The generation unit automatically generates reports based on the care plan proposed by the proposal unit. These reports may include, but are not limited to, summaries of diagnostic results and records of treatment history. For example, the generation unit automatically generates summaries of diagnostic results. These summaries provide a concise overview of the diagnosis made by the physician, presented in a format easily understood by patients and other medical staff. For example, they may include the diagnosed illness, a summary of symptoms, and a treatment plan. The generation unit can also automatically generate records of treatment history. These records detail the treatments the patient received and are important for understanding the progress and effectiveness of the treatment. For example, they may include the type and dosage of medications used, the date of treatment, and the effectiveness of the treatment. The generation unit can also automatically generate reports on the patient's condition. These reports provide information for physicians and nurses to understand the patient's condition and may include vital signs, test results, and changes in behavioral patterns. For example, reports on a patient's condition include information such as fluctuations in heart rate and blood pressure, abnormal test results, and changes in behavioral patterns. This allows the generation unit to automatically generate summaries of diagnostic results, records of treatment history, and reports on the patient's condition, thereby reducing the burden on doctors and nurses and providing information to accurately understand the patient's state.

[0072] The system includes a Home Care Proposal Department that proposes home care plans. These plans may include, but are not limited to, home-based rehabilitation, dietary guidance, and medication management. For example, the Home Care Proposal Department may propose home-based rehabilitation. It may also propose dietary guidance. It may also propose medication management. For example, the Home Care Proposal Department may propose a home-based rehabilitation plan according to the patient's condition. Dietary guidance is advice to support the patient's nutritional management. Medication management is guidance to ensure the patient takes their medication appropriately. By proposing home care plans, the system can support home-based medical care and improve the patient's quality of life. Some or all of the above-described processes in the Home Care Proposal Department may be performed using, for example, AI, or not. For example, the Home Care Proposal Department may input patient data into a generating AI and have the generating AI generate a home care plan proposal.

[0073] The system includes a connectivity unit that interacts with smart speakers and wearable devices. The connectivity unit interacts with smart speakers and wearable devices. Smart speakers and wearable devices include, but are not limited to, devices with voice assistant functionality and devices that collect health data. The connectivity unit interacts with, for example, smart speakers with voice assistant functionality. The connectivity unit can also interact with wearable devices that collect health data. The connectivity unit can also interact with smartphones. For example, the connectivity unit provides care plans to patients through smart speakers with voice assistant functionality. Wearable devices that collect health data monitor patients' vital signs and activity levels. Smartphones are used to manage patient data and share information with doctors and nurses. This allows for support of patients' home care through interaction with smart speakers and wearable devices. Some or all of the above-described processes in the connectivity unit may be performed using, for example, AI, or not using AI. For example, the connectivity unit can input data acquired from smart speakers and wearable devices into a generating AI, and have the generating AI perform data analysis and propose care plans.

[0074] The detection unit, in cooperation with the home care proposal unit, can detect abnormalities in the home in real time and propose countermeasures. For example, the detection unit monitors vital signs to detect abnormalities in the home in real time. The detection unit can also detect changes in behavioral patterns. Furthermore, the detection unit can detect abnormalities in environmental data. For example, the detection unit can detect abnormalities in heart rate and blood pressure in real time and propose countermeasures. Changes in behavioral patterns are detected by monitoring changes in the patient's movements and activity levels. Abnormalities in environmental data are detected by monitoring changes in temperature and humidity. This allows for the detection of abnormalities in the home in real time and the proposal of countermeasures, thereby ensuring patient safety. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the care plan provided by the home care proposal unit into a generating AI, and have the generating AI perform abnormality detection and propose countermeasures.

[0075] The generation unit can generate reports based on the results of the proposal unit. For example, the generation unit can generate a report summarizing the contents of the care plan proposed by the proposal unit. The generation unit can also generate reports recording the patient's diagnosis and treatment history. Furthermore, the generation unit can generate detailed reports on the patient's condition. For example, the generation unit generates a report including a summary of the diagnosis based on the results of the proposal unit. Recording the treatment history records the details of the treatment the patient received. Detailed reports on the patient's condition provide information for doctors and nurses to understand the patient's condition. This reduces the burden on doctors and nurses by generating reports based on the results of the proposal unit. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the care plan data provided by the proposal unit into a generation AI and have the generation AI perform report generation.

[0076] The analysis unit estimates the patient's emotions and adjusts the analysis priority based on the estimated emotions. For example, if the patient is feeling anxious, the analysis unit prioritizes analyzing data of high urgency. If the patient is relaxed, the analysis unit can also analyze data according to the normal analysis procedure. Furthermore, if the patient is stressed, the analysis unit can prioritize analyzing stress-related data. For instance, the analysis unit can capture the patient's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The analysis unit can also record the patient's voice and estimate their emotions using voice analysis technology. Voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. An emotion score is calculated based on fluctuations in biometric data. This allows for more appropriate care to be provided by adjusting the analysis priority based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input patient emotion data into the generative AI and have the generative AI adjust the analysis priorities.

[0077] The analysis unit can generate a more accurate care plan by combining and analyzing the patient's past medical data and real-time data. For example, the analysis unit can refer to the patient's past medical data and analyze it in comparison to the current symptoms. The analysis unit can also collect real-time data and integrate it with past data for analysis. Furthermore, the analysis unit can combine past medical data and real-time data to generate an optimal care plan. For example, the analysis unit can propose an optimal treatment plan for the patient's current symptoms based on the patient's past medical data. Real-time data is used to monitor the patient's vital signs and activity level in real time. By combining past medical data and real-time data, a more accurate care plan can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past medical data and real-time data into a generation AI and have the generation AI perform the generation of the care plan.

[0078] The analysis unit can adjust the analysis results by considering the patient's lifestyle and environmental data during the analysis. For example, the analysis unit can collect the patient's lifestyle data and reflect it in the analysis results. The analysis unit can also adjust the analysis results by considering the patient's environmental data. Furthermore, the analysis unit can provide more accurate analysis results based on lifestyle and environmental data. For example, the analysis unit evaluates the patient's health status based on their dietary records and exercise habits. Environmental data includes information about the patient's living environment and lifestyle. By considering lifestyle and environmental data, more accurate analysis results can be provided. This allows for the provision of more accurate analysis results by considering lifestyle and environmental data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input lifestyle and environmental data into a generating AI and have the generating AI perform the adjustment of the analysis results.

[0079] The analysis unit can estimate the patient's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the patient is feeling anxious, the analysis unit provides a simple and easy-to-understand display method. If the patient is relaxed, the analysis unit can also display detailed analysis results. Furthermore, if the patient is stressed, the analysis unit can prioritize displaying information that helps reduce stress. For example, the analysis unit can capture the patient's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The analysis unit can also record the patient's voice and estimate their emotions using voice analysis technology. Voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The emotion score is calculated based on fluctuations in biometric data. This allows for more appropriate information to be provided by adjusting the display method of the analysis results based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit may input patient emotion data into the generating AI and have the generating AI adjust how the analysis results are displayed.

[0080] The analysis unit can perform analysis while considering the patient's family history and genetic information. For example, the analysis unit can refer to the patient's family history and perform analysis while considering genetic risk. The analysis unit can also analyze the risk of specific diseases based on the patient's genetic information. Furthermore, the analysis unit can integrate family history and genetic information to provide more accurate analysis results. For example, the analysis unit can assess genetic risk based on the patient's family history. Genetic information includes the patient's genetic test results. By considering family history and genetic information, more accurate analysis results can be provided. This allows for the provision of more accurate analysis results by considering family history and genetic information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input family history and genetic information into a generating AI and have the generating AI perform the analysis.

[0081] The analysis unit can analyze a patient's social media activity and obtain relevant health information during the analysis. For example, the analysis unit can analyze a patient's social media activity and extract posts related to health. The analysis unit can also incorporate health information obtained from social media into the analysis. Furthermore, the analysis unit can estimate a patient's lifestyle and health status based on their social media activity. For example, the analysis unit can analyze a patient's social media posts and extract information related to health. Social media activity provides information about a patient's lifestyle and health status. By incorporating information obtained from social media into the analysis, more accurate analysis results can be provided. Thus, by analyzing social media activity, relevant health information can be obtained and reflected in the analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input social media activity data into a generating AI and have the generating AI perform the acquisition of health information.

[0082] The suggestion unit can estimate the patient's emotions and adjust the way it presents suggestions based on those emotions. For example, if the patient is feeling anxious, the suggestion unit will use reassuring language. If the patient is relaxed, the suggestion unit can also provide suggestions that include more detailed information. If the patient is stressed, the suggestion unit can provide suggestions that help reduce stress. For example, the suggestion unit can capture the patient's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The suggestion unit can also record the patient's voice and estimate their emotions using voice analysis technology. Voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The suggestion unit can also collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The emotion score is calculated based on fluctuations in biometric data. This allows for the provision of more appropriate care plans by adjusting the way suggestions are presented based on the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the proposal unit may be performed using AI, or not using AI. For example, the proposal unit may input patient emotion data into the generation AI and have the generation AI adjust the way the proposal is expressed.

[0083] The suggestion unit can adjust the level of detail in its suggestions based on the severity of the patient's symptoms. For example, if the patient's symptoms are mild, the suggestion unit will provide a concise suggestion. If the patient's symptoms are moderate, the suggestion unit can provide a more detailed suggestion. If the patient's symptoms are severe, the suggestion unit can also provide suggestions that include emergency response. For example, the suggestion unit will assess the severity of the patient's symptoms and adjust the level of detail in its suggestions based on that assessment. The severity of symptoms will be assessed based on a disease score or a doctor's diagnosis. By adjusting the level of detail in suggestions based on the severity of symptoms, a more appropriate care plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input patient symptom data into a generating AI and have the generating AI perform the adjustment of the level of detail in its suggestions.

[0084] The proposal unit can customize the proposed plan by considering the patient's lifestyle and environmental data. For example, the proposal unit can propose an appropriate care plan based on the patient's lifestyle data. The proposal unit can also make optimal proposals by considering the patient's environmental data. Furthermore, the proposal unit can integrate lifestyle and environmental data to provide customized proposals. For example, the proposal unit can provide health management advice based on the patient's dietary records and exercise habits. Environmental data includes information about the patient's living environment. By considering lifestyle and environmental data, a more appropriate care plan can be provided. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input lifestyle and environmental data into a generating AI and have the generating AI customize the proposed plan.

[0085] The suggestion unit can estimate the patient's emotions and prioritize suggestions based on those emotions. For example, if the patient is feeling anxious, the suggestion unit will prioritize urgent suggestions. If the patient is relaxed, the suggestion unit can also make standard suggestions. Furthermore, if the patient is stressed, the suggestion unit can prioritize suggestions that help reduce stress. For example, the suggestion unit can capture the patient's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The suggestion unit can also record the patient's voice and estimate their emotions using voice analysis technology. Voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The suggestion unit can also collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The emotion score is calculated based on fluctuations in biometric data. This allows for the provision of more appropriate care plans by prioritizing suggestions based on the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, a text-generating AI (e.g., LLM) or a multimodal generative AI. Some or all of the processing described above in the proposal unit may be performed using AI, or not using AI. For example, the proposal unit may input patient emotion data into the generative AI and have the generative AI determine the priority of proposals.

[0086] The proposal unit can propose an optimal care plan by considering the patient's geographical location information. For example, the proposal unit considers medical facilities in the area where the patient lives when making a proposal. The proposal unit can also propose an optimal care plan based on the patient's geographical location information. Furthermore, the proposal unit can consider geographical location information to make proposals that address region-specific health risks. For example, the proposal unit proposes an appropriate care plan based on information about medical facilities in the area where the patient lives. Geographical location information includes information about the area where the patient lives and their travel history. By considering geographical location information, a more appropriate care plan can be provided. This allows for the provision of a more appropriate care plan by considering geographical location information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input geographical location information into a generating AI and have the generating AI execute the care plan proposal.

[0087] The proposal unit can analyze the patient's social media activity and propose a relevant care plan when making a proposal. For example, the proposal unit can analyze the patient's social media activity and make a proposal based on health-related posts. The proposal unit can also propose an appropriate care plan based on information obtained from social media. Furthermore, the proposal unit can make proposals tailored to the patient's lifestyle and health condition based on their social media activity. For example, the proposal unit can analyze the patient's social media posts and extract health-related information. Social media activity provides information about the patient's lifestyle and health condition. By proposing an appropriate care plan based on information obtained from social media, a more accurate care plan can be provided. Thus, by analyzing social media activity, a relevant care plan can be provided. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input social media activity data into a generating AI and have the generating AI execute the care plan proposal.

[0088] The detection unit can estimate the patient's emotions and adjust the anomaly detection criteria based on the estimated emotions. For example, if the patient is feeling anxious, the detection unit will set stricter criteria for anomaly detection. Conversely, if the patient is relaxed, the detection unit can perform anomaly detection using normal criteria. Furthermore, if the patient is stressed, the detection unit can prioritize detecting stress-related anomalies. For example, the detection unit can capture the patient's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The detection unit can also record the patient's voice and estimate their emotions using voice analysis technology. Voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The detection unit can also collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. An emotion score is calculated based on fluctuations in biometric data. This allows for more appropriate anomaly detection by adjusting the criteria for anomaly detection based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generating AI may be, but is not limited to, text generating AI (e.g., LLM) or multimodal generating AI. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit may input patient emotion data into the generating AI and have the generating AI adjust the criteria for anomaly detection.

[0089] The detection unit can optimize its detection algorithm by referring to the patient's past abnormal data when an abnormality is detected. For example, the detection unit adjusts the detection algorithm based on the patient's past abnormal data. The detection unit can also improve the accuracy of abnormality detection by referring to past abnormal data. Furthermore, the detection unit can build an optimal detection algorithm by integrating the patient's abnormal data. For example, the detection unit sets an abnormality detection threshold based on the patient's past abnormal data. Past abnormal data includes the patient's diagnosis results and abnormality detection history. By referring to past abnormal data, the accuracy of abnormality detection can be improved. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input past abnormal data into a generating AI and have the generating AI perform the optimization of the detection algorithm.

[0090] The detection unit can improve the accuracy of anomaly detection by considering the patient's lifestyle and environmental data when detecting an anomaly. For example, the detection unit can improve the accuracy of anomaly detection based on the patient's lifestyle data. The detection unit can also improve the accuracy of anomaly detection by considering the patient's environmental data. Furthermore, the detection unit can perform more accurate anomaly detection by integrating lifestyle and environmental data. For example, the detection unit sets an anomaly detection threshold based on the patient's dietary records and exercise habits. Environmental data includes information about the patient's living environment. By considering lifestyle and environmental data, the accuracy of anomaly detection can be improved. This allows for improved accuracy of anomaly detection by considering lifestyle and environmental data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input lifestyle and environmental data into a generating AI and have the generating AI perform the anomaly detection accuracy improvement.

[0091] The detection unit can estimate the patient's emotions and adjust the order in which it displays abnormality detection results based on the estimated emotions. For example, if the patient is feeling anxious, the detection unit will prioritize displaying highly urgent abnormalities. If the patient is relaxed, the detection unit can also display abnormalities in the normal order. Furthermore, if the patient is stressed, the detection unit can prioritize displaying stress-related abnormalities. For example, the detection unit can capture the patient's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The detection unit can also record the patient's voice and estimate their emotions using voice analysis technology. Voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The detection unit can also collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. It calculates an emotion score based on fluctuations in biometric data. This allows for more appropriate information to be provided by adjusting the order in which abnormality detection results are displayed based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generating AI may be, but is not limited to, text generating AI (e.g., LLM) or multimodal generating AI. Some or all of the above-described processing in the detection unit may be performed using AI, or not using AI. For example, the detection unit can input patient emotion data into the generating AI and have the generating AI adjust the display order of the anomaly detection results.

[0092] The detection unit can perform anomaly detection while considering the patient's geographical location information. For example, the detection unit can perform anomaly detection while considering medical facilities in the area where the patient lives. The detection unit can also perform anomaly detection based on the patient's geographical location information. Furthermore, the detection unit can perform anomaly detection that addresses region-specific health risks while considering geographical location information. For example, the detection unit sets an anomaly detection threshold based on information about medical facilities in the area where the patient lives. Geographical location information includes information about the area where the patient lives and their travel history. By considering geographical location information, more appropriate anomaly detection can be performed. This makes it possible to perform more appropriate anomaly detection by considering geographical location information. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input geographical location information into a generating AI and have the generating AI perform anomaly detection.

[0093] The detection unit can improve the accuracy of anomaly detection by referring to relevant literature on the patient when detection occurs. For example, the detection unit can improve the accuracy of anomaly detection by referring to relevant literature on the patient. The detection unit can also optimize the anomaly detection algorithm based on the relevant literature. Furthermore, the detection unit can perform more accurate anomaly detection by integrating relevant literature on the patient. For example, the detection unit sets an anomaly detection threshold based on relevant literature on the patient. Relevant literature includes medical papers, guidelines, and professional books. By referring to relevant literature, the accuracy of anomaly detection can be improved. This means that by referring to relevant literature, the accuracy of anomaly detection can be improved. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data from relevant literature into a generating AI and have the generating AI perform the anomaly detection accuracy improvement.

[0094] The generation unit can estimate the patient's emotions and adjust the report's presentation based on the estimated emotions. For example, if the patient is feeling anxious, the generation unit will use reassuring language. If the patient is relaxed, the generation unit can also generate a report with more detailed information. Furthermore, if the patient is stressed, the generation unit can prioritize including information that helps reduce stress in the report. For example, the generation unit can capture the patient's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The generation unit can also record the patient's voice and estimate their emotions using voice analysis technology. Voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The emotion score is calculated based on fluctuations in the biometric data. This allows for more appropriate information to be provided by adjusting the report's presentation based on the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the generation unit may be performed using AI, or not using AI. For example, the generation unit may input patient emotion data into the generation AI and have the generation AI adjust the way the report is presented.

[0095] The generation unit can adjust the level of detail in a report based on the severity of the patient's symptoms. For example, if the patient's symptoms are mild, the generation unit can generate a concise report. If the patient's symptoms are moderate, the generation unit can also generate a detailed report. Furthermore, if the patient's symptoms are severe, the generation unit can generate a detailed report including emergency response information. For example, the generation unit can assess the severity of the patient's symptoms and adjust the level of detail in the report accordingly. The severity of symptoms is assessed based on disease scoring or the doctor's diagnosis. By adjusting the level of detail in the report based on the severity of symptoms, it becomes possible to provide more appropriate information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input patient symptom data into a generation AI and have the generation AI perform the adjustment of the level of detail in the report.

[0096] The generation unit can customize report content by considering the patient's lifestyle and environmental data when generating reports. For example, the generation unit can generate an appropriate report based on the patient's lifestyle data. It can also generate an optimal report by considering the patient's environmental data. Furthermore, the generation unit can provide a customized report by integrating lifestyle and environmental data. For example, the generation unit can generate a report including health management advice based on the patient's dietary records and exercise habits. Environmental data includes information about the patient's living environment. By considering lifestyle and environmental data, a more appropriate report can be provided. This makes it possible to provide more appropriate information by considering lifestyle and environmental data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input lifestyle and environmental data into a generation AI and have the generation AI perform the customization of the report content.

[0097] The generation unit can estimate the patient's emotions and prioritize reports based on those emotions. For example, if the patient is feeling anxious, the generation unit will prioritize urgent reports. It can also generate normal reports if the patient is relaxed. Furthermore, if the patient is stressed, the generation unit can prioritize reports that help reduce stress. For instance, the generation unit can capture the patient's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The generation unit can also record the patient's voice and estimate their emotions using voice analysis technology. Voice analysis technology analyzes the tone and speed of the voice to calculate an emotion score. The generation unit can also collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. An emotion score is calculated based on fluctuations in the biometric data. This allows for more appropriate information to be provided by prioritizing reports based on the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit may input patient emotion data into the generation AI and have the generation AI determine the priority of reports.

[0098] The generation unit can generate reports while considering the patient's geographical location information. For example, the generation unit can generate reports while considering medical facilities in the area where the patient lives. The generation unit can also generate optimal reports based on the patient's geographical location information. Furthermore, the generation unit can generate reports that address region-specific health risks while considering geographical location information. For example, the generation unit generates appropriate reports based on information about medical facilities in the area where the patient lives. Geographical location information includes information about the area where the patient lives and their travel history. By considering geographical location information, more appropriate reports can be provided. This makes it possible to provide more appropriate information by considering geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input geographical location information into a generation AI and have the generation AI perform report generation.

[0099] The generation unit can analyze the patient's social media activity and include relevant information in the report during report generation. For example, the generation unit analyzes the patient's social media activity and generates a report based on health-related posts. The generation unit can also generate an appropriate report based on information obtained from social media. Furthermore, the generation unit can generate a report tailored to the patient's lifestyle and health status based on their social media activity. For example, the generation unit analyzes the patient's social media posts and extracts health-related information. Social media activity provides information about the patient's lifestyle and health status. By generating an appropriate report based on information obtained from social media, it becomes possible to provide more accurate information. This allows relevant information to be included in the report by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input social media activity data into a generation AI and have the generation AI perform report generation.

[0100] The Home Care Proposal Department can estimate a patient's emotions and adjust the way the home care plan is presented based on those estimated emotions. For example, if a patient is feeling anxious, the Home Care Proposal Department will use reassuring language. If a patient is relaxed, the Home Care Proposal Department can also propose a home care plan that includes detailed information. If a patient is stressed, the Home Care Proposal Department can also propose a home care plan that helps reduce stress. For example, the Home Care Proposal Department can capture the patient's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The Home Care Proposal Department can also record the patient's voice and estimate their emotions using voice analysis technology. The voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The Home Care Proposal Department can also collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The emotion score is calculated based on fluctuations in biometric data. This allows for the provision of more appropriate care plans by adjusting the way the home care plan is presented based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the home care proposal unit may be performed using AI, for example, or not using AI. For example, the home care proposal unit can input patient emotion data into a generative AI and have the generative AI adjust the way the home care plan is expressed.

[0101] The Home Care Proposal Department can customize the proposed home care plan by considering the patient's lifestyle and environmental data. For example, the Home Care Proposal Department can propose an appropriate home care plan based on the patient's lifestyle data. It can also propose an optimal home care plan by considering the patient's environmental data. Furthermore, the Home Care Proposal Department can provide a customized home care plan by integrating lifestyle and environmental data. For example, the Home Care Proposal Department can propose a home care plan that includes health management advice based on the patient's dietary records and exercise habits. Environmental data includes information about the patient's living environment. By considering lifestyle and environmental data, a more appropriate home care plan can be provided. This allows for the provision of a more appropriate care plan by considering lifestyle and environmental data. Some or all of the above processing in the Home Care Proposal Department may be performed using AI, for example, or without AI. For example, the Home Care Proposal Department can input lifestyle and environmental data into a generating AI and have the generating AI perform the customization of the proposed content.

[0102] The Home Care Proposal Department can estimate a patient's emotions and prioritize home care plans based on those estimated emotions. For example, if a patient is feeling anxious, the Home Care Proposal Department will prioritize urgent home care plans. If the patient is relaxed, the Home Care Proposal Department can also propose standard home care plans. Furthermore, if the patient is stressed, the Home Care Proposal Department can prioritize home care plans that help reduce stress. For example, the Home Care Proposal Department can capture the patient's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The Home Care Proposal Department can also record the patient's voice and estimate their emotions using voice analysis technology. The voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The Home Care Proposal Department can also collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The emotion score is calculated based on fluctuations in biometric data. This allows for the provision of more appropriate care plans by prioritizing home care plans based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the home care proposal unit may be performed using AI, for example, or not using AI. For example, the home care proposal unit can input patient emotion data into a generative AI and have the generative AI determine the priorities of the home care plan.

[0103] The Home Care Proposal Department can propose an optimal home care plan by considering the patient's geographical location information. For example, the Home Care Proposal Department proposes a home care plan by considering medical facilities in the area where the patient lives. The Home Care Proposal Department can also propose an optimal home care plan based on the patient's geographical location information. Furthermore, the Home Care Proposal Department can propose a home care plan that addresses region-specific health risks by considering geographical location information. For example, the Home Care Proposal Department proposes an appropriate home care plan based on information about medical facilities in the area where the patient lives. Geographical location information includes information about the area where the patient lives and their travel history. By considering geographical location information, a more appropriate home care plan can be provided. This allows for the provision of a more appropriate care plan by considering geographical location information. Some or all of the above processing in the Home Care Proposal Department may be performed using AI, for example, or without AI. For example, the Home Care Proposal Department can input geographical location information into a generating AI and have the generating AI execute the proposal of a home care plan.

[0104] The integration unit can estimate the patient's emotions and select an integration device based on those estimated emotions. For example, if the patient is feeling anxious, the integration unit can select a device that provides a sense of security. If the patient is relaxed, the integration unit can also select a device that provides detailed information. If the patient is stressed, the integration unit can also select a device that helps reduce stress. For example, the integration unit can capture the patient's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The integration unit can also record the patient's voice and estimate their emotions using voice analysis technology. The voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The integration unit can also collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The emotion score is calculated based on fluctuations in biometric data. This allows for the provision of more appropriate devices by selecting integration devices based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the collaboration unit may be performed using AI, or not using AI. For example, the collaboration unit may input patient emotion data into the generation AI and have the generation AI select a collaboration device.

[0105] The collaboration unit can optimize the collaboration method by considering the patient's lifestyle and environmental data during collaboration. For example, the collaboration unit can propose an appropriate collaboration method based on the patient's lifestyle data. The collaboration unit can also propose an optimal collaboration method by considering the patient's environmental data. Furthermore, the collaboration unit can provide a customized collaboration method by integrating lifestyle and environmental data. For example, the collaboration unit can propose a collaboration method that includes health management advice based on the patient's dietary records and exercise habits. Environmental data includes information about the patient's living environment and lifestyle. By considering lifestyle and environmental data, a more appropriate collaboration method can be provided. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input lifestyle and environmental data into a generating AI and have the generating AI perform the optimization of the collaboration method.

[0106] The connected unit can estimate the patient's emotions and prioritize connected devices based on those estimated emotions. For example, if the patient is feeling anxious, the unit will prioritize devices that provide a sense of security. It can also prioritize normal devices if the patient is relaxed. Furthermore, if the patient is stressed, it can prioritize devices that help reduce stress. For instance, the unit can capture the patient's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. The unit can also record the patient's voice and estimate their emotions using voice analysis technology. The voice analysis technology analyzes the tone and speed of the voice and calculates an emotion score. The unit can also collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. It calculates an emotion score based on fluctuations in biometric data. This allows for the provision of more appropriate devices by prioritizing connected devices based on the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generation AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the collaboration unit may be performed using AI, or not using AI. For example, the collaboration unit may input patient emotion data into the generation AI and have the generation AI determine the priority of the collaboration devices.

[0107] The collaboration unit can select the optimal collaboration method when collaborating, taking into account the patient's geographical location information. For example, the collaboration unit can select a collaboration method by considering medical facilities in the area where the patient lives. The collaboration unit can also select the optimal collaboration method based on the patient's geographical location information. Furthermore, the collaboration unit can select a collaboration method that addresses region-specific health risks by considering geographical location information. For example, the collaboration unit can select an appropriate collaboration method based on information about medical facilities in the area where the patient lives. Geographical location information includes information about the patient's area of ​​residence and travel history. By considering geographical location information, a more appropriate collaboration method can be provided. This allows for the provision of a more appropriate collaboration method by considering geographical location information. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input geographical location information into a generating AI and have the generating AI select the collaboration method.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The analysis unit can estimate the patient's emotions and adjust the analysis priority based on those estimates. For example, if the patient is feeling anxious, it will prioritize analyzing data with high urgency. If the patient is relaxed, it can analyze data according to the normal analysis procedure. Furthermore, if the patient is feeling stressed, it can prioritize analyzing stress-related data. This allows for more appropriate care to be provided by adjusting the analysis priority based on the patient's emotions.

[0110] The suggestion function can estimate the patient's emotions and adjust the way suggestions are presented based on those estimates. For example, if the patient is feeling anxious, it will use reassuring language. If the patient is relaxed, it can also provide suggestions that include more detailed information. Furthermore, if the patient is stressed, it can offer suggestions that help reduce stress. By adjusting the way suggestions are presented based on the patient's emotions, a more appropriate care plan can be provided.

[0111] The detection unit can estimate the patient's emotions and adjust the abnormality detection criteria based on the estimated emotions. For example, if the patient is feeling anxious, the abnormality detection criteria can be set more strictly. Conversely, if the patient is relaxed, abnormality detection can be performed using the normal criteria. Furthermore, if the patient is feeling stressed, stress-related abnormalities can be prioritized for detection. In this way, adjusting the abnormality detection criteria based on the patient's emotions enables more appropriate abnormality detection.

[0112] The generation unit can estimate the patient's emotions and adjust the report's presentation based on those estimates. For example, if the patient is feeling anxious, it will use reassuring language. If the patient is relaxed, it can generate a report with more detailed information. Furthermore, if the patient is stressed, it can prioritize including information that helps reduce stress in the report. This allows for more appropriate information to be provided by adjusting the report's presentation based on the patient's emotions.

[0113] The Home Care Proposal Department can estimate the patient's emotions and adjust the way the home care plan is presented based on those estimates. For example, if the patient is feeling anxious, it will use language that provides reassurance. If the patient is relaxed, it can propose a home care plan that includes detailed information. Furthermore, if the patient is feeling stressed, it can propose a home care plan that helps reduce stress. By adjusting the way the home care plan is presented based on the patient's emotions, a more appropriate care plan can be provided.

[0114] The analysis unit can generate more accurate care plans by combining and analyzing patients' past medical data and real-time data. For example, it can refer to a patient's past medical data and analyze it in comparison to their current symptoms. It can also collect real-time data and integrate it with past data for analysis. Furthermore, it can combine past medical data and real-time data to generate an optimal care plan. This allows for the provision of more accurate care plans by combining past medical data and real-time data.

[0115] The proposal department can adjust the level of detail in its proposals based on the severity of the patient's symptoms. For example, if the patient's symptoms are mild, a concise proposal can be made. If the patient's symptoms are moderate, a detailed proposal can be made. Furthermore, if the patient's symptoms are severe, a proposal including emergency response can be made. By adjusting the level of detail in the proposals based on the severity of the symptoms, a more appropriate care plan can be provided.

[0116] The detection unit can optimize its detection algorithm by referring to the patient's past abnormal data when detection occurs. For example, it can adjust the detection algorithm based on the patient's past abnormal data. It can also improve the accuracy of abnormality detection by referring to past abnormal data. Furthermore, it can integrate the patient's abnormal data to construct an optimal detection algorithm. This allows for improved accuracy of abnormality detection by referring to past abnormal data.

[0117] The generation unit can customize report content by considering the patient's lifestyle and environmental data during report generation. For example, it can generate an appropriate report based on the patient's lifestyle data. It can also generate an optimal report by considering the patient's environmental data. Furthermore, it can provide a customized report by integrating lifestyle and environmental data. This makes it possible to provide more appropriate information by considering lifestyle and environmental data.

[0118] The collaboration department can select the optimal collaboration method when collaborating, taking into account the patient's geographical location. For example, it can select a collaboration method considering the medical facilities in the patient's residential area. It can also select the optimal collaboration method based on the patient's geographical location. Furthermore, it can select a collaboration method that addresses region-specific health risks, taking geographical location into consideration. This allows for the provision of more appropriate collaboration methods by considering geographical location.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The analysis unit analyzes patient data. Patient data includes medical records, vital signs, and test results. The analysis unit analyzes the data using data mining techniques, statistical analysis techniques, and machine learning algorithms. For example, it extracts important patterns from medical records using data mining techniques, analyzes data distribution and correlations using statistical analysis techniques, and builds predictive models using machine learning algorithms. Step 2: The proposal department proposes a care plan based on the data analyzed by the analysis department. The care plan includes a treatment plan, rehabilitation plan, and lifestyle guidance. Based on the analysis results, the proposal department proposes the most suitable treatment plan, rehabilitation plan, and lifestyle guidance for the patient. Step 3: The detection unit detects abnormalities in real time based on the care plan proposed by the proposal unit. The detection unit detects abnormalities in vital signs, changes in behavioral patterns, and abnormalities in environmental data. For example, it monitors and detects abnormalities in heart rate and blood pressure, changes in patient movement and activity level, and changes in temperature and humidity in real time. Step 4: The generation unit automatically generates a report based on the care plan proposed by the proposal unit. The report includes a summary of the diagnosis, a record of the treatment history, and information about the patient's condition. The generation unit automatically generates a report that concisely summarizes the diagnosis, a report that records the details of the treatment history, and a report that provides information to understand the patient's condition.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the analysis unit, proposal unit, detection unit, generation unit, home care proposal unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes patient data. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes a care plan based on the analyzed data. The detection unit is implemented by the control unit 46A of the smart device 14 and detects abnormalities in real time. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically generates reports. The home care proposal unit is implemented by the control unit 46A of the smart device 14 and proposes a home care plan. The collaboration unit is implemented by the control unit 46A of the smart device 14 and collaborates with smart speakers and wearable devices. The analysis unit estimates the patient's emotions by the identification processing unit 290 of the data processing unit 12 and adjusts the analysis priority. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the analysis unit, proposal unit, detection unit, generation unit, home care proposal unit, and collaboration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and analyzes patient data. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes a care plan based on the analyzed data. The detection unit is implemented by the control unit 46A of the smart glasses 214 and detects abnormalities in real time. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically generates reports. The home care proposal unit is implemented by the control unit 46A of the smart glasses 214 and proposes a home care plan. The collaboration unit is implemented by the control unit 46A of the smart glasses 214 and collaborates with smart speakers and wearable devices. The analysis unit estimates the patient's emotions by the identification processing unit 290 of the data processing unit 12 and adjusts the analysis priority. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the analysis unit, proposal unit, detection unit, generation unit, home care proposal unit, and collaboration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and analyzes patient data. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes a care plan based on the analyzed data. The detection unit is implemented by the control unit 46A of the headset terminal 314 and detects abnormalities in real time. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically generates reports. The home care proposal unit is implemented by the control unit 46A of the headset terminal 314 and proposes a home care plan. The collaboration unit is implemented by the control unit 46A of the headset terminal 314 and collaborates with smart speakers and wearable devices. The analysis unit estimates the patient's emotions by the identification processing unit 290 of the data processing unit 12 and adjusts the analysis priority. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the analysis unit, proposal unit, detection unit, generation unit, home care proposal unit, and collaboration unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and analyzes patient data. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes a care plan based on the analyzed data. The detection unit is implemented by the control unit 46A of the robot 414 and detects abnormalities in real time. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically generates reports. The home care proposal unit is implemented by the control unit 46A of the robot 414 and proposes a home care plan. The collaboration unit is implemented by the control unit 46A of the robot 414 and collaborates with smart speakers and wearable devices. The analysis unit estimates the patient's emotions by the identification processing unit 290 of the data processing unit 12 and adjusts the analysis priority. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0174] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0184] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) The analysis unit analyzes patient data, A proposal unit proposes a care plan based on the data analyzed by the aforementioned analysis unit, A detection unit that detects abnormalities in real time based on the care plan proposed by the aforementioned proposal unit, A generation unit that automatically generates a report based on the care plan proposed by the aforementioned proposal unit, Equipped with A system characterized by the following features. (Note 2) We have a Home Care Proposal Department that proposes home care plans. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a connectivity unit that interacts with smart speakers and wearable devices. The system described in Appendix 1, characterized by the features described herein. (Note 4) The detection unit is In collaboration with the Home Care Proposal Department, we detect abnormalities in the home in real time and propose countermeasures. The system described in Appendix 2, characterized by the features described herein. (Note 5) The generating unit is Generate a report based on the results from the proposal department. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the analysis priorities based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, By combining and analyzing patients' past medical data with real-time data, we generate more accurate care plans. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, During analysis, the results are adjusted to take into account the patient's lifestyle and environmental data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During the analysis, the patient's family history and genetic information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During the analysis, the patient's social media activity is analyzed to obtain relevant health information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, The system estimates the patient's emotions and adjusts the way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the severity of the patient's symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, we customize the proposal content by taking into account the patient's lifestyle and environmental data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, The system estimates the patient's emotions and prioritizes proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, we will consider the patient's geographical location to suggest the most suitable care plan. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, we analyze the patient's social media activity and suggest a relevant care plan. The system described in Appendix 1, characterized by the features described herein. (Note 18) The detection unit is The system estimates the patient's emotions and adjusts the criteria for detecting anomalies based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit is When detection occurs, the detection algorithm is optimized by referring to the patient's past abnormal data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit is When detecting anomalies, the accuracy of the detection is improved by considering the patient's lifestyle and environmental data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The detection unit is The system estimates the patient's emotions and adjusts the order in which anomaly detection results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit is When detecting an anomaly, the patient's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The detection unit is When detecting an anomaly, we refer to relevant patient literature to improve the accuracy of anomaly detection. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is The system estimates the patient's emotions and adjusts the way the report is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating a report, adjust the level of detail in the report based on the severity of the patient's symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating reports, the report content is customized by taking into account the patient's lifestyle and environmental data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is The system estimates the patient's emotions and prioritizes reports based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is When generating reports, the report will be generated taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is When generating reports, the report analyzes patients' social media activity and includes relevant information in the report. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned home care proposal department, We estimate the patient's emotions and adjust the way the home care plan is expressed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned home care proposal department, When proposing a home care plan, the plan is customized by taking into account the patient's lifestyle and environmental data. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned home care proposal department, The system estimates the patient's emotions and prioritizes home care plans based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned home care proposal department, When proposing a home care plan, we will consider the patient's geographical location to propose the most suitable plan. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned linkage unit is, The system estimates the patient's emotions and selects a compatible device based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned linkage unit is, When collaborating, the method of collaboration is optimized by considering the patient's lifestyle and environmental data. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned linkage unit is, The system estimates the patient's emotions and prioritizes connected devices based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned linkage unit is, When collaborating, the optimal collaboration method will be selected considering the patient's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis unit analyzes patient data, A proposal unit proposes a care plan based on the data analyzed by the aforementioned analysis unit, A detection unit that detects abnormalities in real time based on the care plan proposed by the aforementioned proposal unit, A generation unit that automatically generates a report based on the care plan proposed by the aforementioned proposal unit, Equipped with A system characterized by the following features.

2. We have a Home Care Proposal Department that proposes home care plans. The system according to feature 1.

3. It features a connectivity unit that interacts with smart speakers and wearable devices. The system according to feature 1.

4. The detection unit, In collaboration with the Home Care Proposal Department, we detect abnormalities in the home in real time and propose countermeasures. The system according to feature 2.

5. The generating unit is A report is generated based on the results of the aforementioned proposal section. The system according to feature 1.

6. The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the analysis priorities based on the estimated emotions. The system according to feature 1.

7. The aforementioned analysis unit, By combining and analyzing patients' past medical data with real-time data, we generate more accurate care plans. The system according to feature 1.

8. The aforementioned analysis unit, During analysis, the results are adjusted to take into account the patient's lifestyle and environmental data. The system according to feature 1.

9. The aforementioned analysis unit, The system estimates the patient's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system according to feature 1.

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