system

Abstract

To provide a system capable of reducing the waiting time of patients by improving medical efficiency.SOLUTION: A system in an embodiment includes a collection unit, an analysis unit, and a follow-up unit. The collection unit collects information of patients. The analysis unit determines the necessity of treatment by analyzing the information collected by the collection unit. The follow-up unit provides follow-up after treatment based on the results obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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, there is a problem that the waiting time in a hospital is long and the efficiency of medical treatment is low.

[0005] The system according to the embodiment aims to improve the efficiency of medical treatment and reduce the waiting time of patients.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a follow-up unit. The collection unit collects patient information. The analysis unit analyzes the information collected by the collection unit and determines the necessity of medical treatment. The follow-up unit performs follow-up after medical treatment based on the result obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can improve the efficiency of medical treatment and reduce patient waiting times. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 medical interview system according to an embodiment of the present invention is an AI-powered system designed to reduce waiting times at hospitals and improve the efficiency of medical care. When a patient arrives at the hospital, the AI ​​conducts a medical interview. The patient uses a tablet or smartphone to input information such as symptoms, medical history, and current physical condition. This information is analyzed by the AI ​​to determine whether a doctor's consultation is actually necessary. For example, if a patient with cold symptoms arrives, the AI ​​analyzes the symptoms and, if they are common cold symptoms, can prescribe medication or provide lifestyle guidance before the patient sees a doctor. On the other hand, if the symptoms are severe or require a specific diagnosis, the AI ​​determines that a doctor's consultation is necessary. After the AI ​​determines the need for consultation, the patient receives a doctor's consultation as needed. The doctor can then conduct consultations efficiently based on the information collected by the AI. This shortens the doctor's consultation time and reduces patient waiting times. Furthermore, the AI ​​also provides follow-up after consultations. For example, it monitors whether the patient is taking prescribed medication correctly and whether their symptoms are improving, and can provide additional guidance or consultations as needed. This system improves the efficiency of hospital medical care and reduces patient waiting times. Furthermore, it reduces the burden on doctors, making it possible to provide high-quality medical care to more patients. As a result, the medical interview system can reduce waiting times at hospitals and improve the efficiency of medical consultations.

[0029] The patient interview system according to this embodiment comprises a data collection unit, an analysis unit, and a follow-up unit. The data collection unit collects patient information. Patient information includes, but is not limited to, symptoms, medical history, and current physical condition. The data collection unit collects information entered by the patient using, for example, a tablet or smartphone. The data collection unit can also collect the patient's biometric data using sensors. For example, the data collection unit collects data such as the patient's blood pressure, heart rate, and body temperature. Furthermore, the data collection unit can collect the patient's past medical history by referring to medical records. For example, the data collection unit obtains the patient's medical history from an electronic medical record system. The analysis unit analyzes the information collected by the data collection unit and determines the need for medical treatment. The analysis is performed using, for example, algorithms or analysis tools, but is not limited to these. For example, the analysis unit uses a machine learning algorithm to analyze the patient's symptoms and determine the need for medical treatment. The analysis unit can also use statistical analysis tools to analyze the collected data. For example, the analysis unit evaluates the need for medical treatment based on the severity of symptoms and medical history. The follow-up unit performs post-treatment follow-up based on the results obtained by the analysis unit. Follow-up may be conducted, for example, by telephone or email, but is not limited to such examples. For instance, the follow-up unit may conduct regular health checks for the patient. The follow-up unit may also provide rehabilitation programs. For example, the follow-up unit may provide lifestyle guidance or exercise guidance to the patient. This enables the medical interview system according to the embodiment to efficiently collect, analyze, and follow up on patient information. Some or all of the processing in the collection unit, analysis unit, and follow-up unit may be performed using, for example, AI, but may not be performed using AI.

[0030] The data collection unit collects patient information. This information includes, but is not limited to, symptoms, medical history, and current health condition. The data collection unit collects information entered by the patient, for example, using a tablet or smartphone. Specifically, the patient uses a dedicated application on their tablet or smartphone to answer questions about their symptoms, medical history, and current health condition. This application has a user-friendly interface and is designed to allow patients to easily input information. Furthermore, the data collection unit can also collect the patient's biometric data using sensors. For example, the data collection unit collects data such as the patient's blood pressure, heart rate, and body temperature. Wearable devices and home medical devices are used for this purpose. Wearable devices continuously collect data as the patient goes about their daily life and transmit it to the system in real time. Home medical devices are designed for easy use by patients at home, and the measurement results are automatically transmitted to the data collection unit. Furthermore, the data collection unit can also collect the patient's past medical history by referring to medical records. For example, the data collection unit retrieves the patient's medical history from an electronic medical record system. The electronic medical record system stores the patient's past medical information in digital format, and the data collection unit can retrieve this information quickly and accurately. This allows the data collection unit to understand not only the patient's current condition but also their past medical history and treatment progress. As a result, the data collection unit can comprehensively collect patient information and provide a foundation for improving the quality of medical care.

[0031] The analysis unit analyzes the information collected by the data collection unit to determine the need for medical treatment. Analysis is performed using algorithms and analytical tools, but is not limited to these examples. Specifically, the analysis unit uses machine learning algorithms to analyze a patient's symptoms and determine the need for medical treatment. Machine learning algorithms can learn from large amounts of medical data and analyze symptom patterns and relationships to determine the need for treatment with high accuracy. For example, the analysis unit takes patient symptom data as input, compares it with past medical data, and evaluates the need for treatment based on the treatment results of similar cases. The analysis unit can also analyze collected data using statistical analysis tools. For example, the analysis unit evaluates the need for treatment based on the severity of symptoms and medical history. Statistical analysis tools analyze collected data from multiple perspectives to clarify the relationship between symptom severity and medical history. This allows the analysis unit to comprehensively evaluate the patient's condition and determine the need for appropriate medical treatment. Furthermore, the analysis unit uses AI to analyze data in real time and support rapid medical decision-making. For example, AI can analyze collected biometric data in real time and detect abnormal patterns to determine the need for treatment early. This allows the analysis unit to quickly and accurately analyze the collected data and determine the necessity of medical treatment with high precision.

[0032] The Follow-up Department conducts post-treatment follow-up based on the results obtained by the Analysis Department. Follow-up is conducted, for example, by telephone or email, but is not limited to these methods. Specifically, the Follow-up Department conducts regular health checks for patients. For example, the Follow-up Department makes regular telephone calls to patients to check their current physical condition and any changes in symptoms. The Follow-up Department can also send health check reminders to patients via email to encourage regular health checks. Furthermore, the Follow-up Department can also provide rehabilitation programs. For example, the Follow-up Department provides lifestyle guidance and exercise guidance to patients. Lifestyle guidance includes improving diet, managing stress, and improving sleep quality. Exercise guidance provides exercise programs tailored to the patient's physical strength and health condition. This allows the Follow-up Department to continuously monitor the patient's health condition and provide appropriate follow-up. Furthermore, the Follow-up Department can collect patient feedback and continuously improve the content of the follow-up. For example, based on patient feedback, it can adjust the frequency and content of follow-up to provide more effective follow-up. Furthermore, the follow-up department can centrally manage patient data and collaborate with other medical institutions and specialists. This allows the follow-up department to comprehensively manage patients' health status and effectively conduct post-treatment follow-up.

[0033] The medical interview system includes a security unit that performs data encryption or access control. The security unit performs data encryption and access control. Data encryption may use, but is not limited to, encryption technologies such as AES (Advanced Encryption Standard) or RSA (Rivest-Shamir-Adleman). For example, the security unit encrypts patient information using AES-256. The security unit can also encrypt data communication using SSL / TLS (Secure Sockets Layer / Transport Layer Security). Access control may include, but is not limited to, user authentication and authorization management. For example, the security unit performs user authentication to access patient information. The security unit can also record access logs and detect unauthorized access. For example, the security unit monitors login history and detects abnormal access. This improves the data security of the medical interview system.

[0034] The medical interview system includes a criteria setting unit that sets the criteria and algorithms for determining the necessity of medical treatment. The criteria setting unit sets the criteria and algorithms for determining the necessity of medical treatment. Criteria include, but are not limited to, the severity of symptoms, medical history, and test results. For example, the criteria setting unit sets criteria for evaluating the severity of a patient's symptoms. The criteria setting unit can also set algorithms for determining the necessity of medical treatment based on medical history. For example, the criteria setting unit evaluates the necessity of medical treatment based on past diagnostic results and treatment history. Furthermore, the criteria setting unit can also set criteria for determining the necessity of medical treatment based on test results. For example, the criteria setting unit evaluates the necessity of medical treatment based on the results of blood tests and imaging diagnoses. This makes the criteria for determining medical treatment clear in the medical interview system.

[0035] The medical interview system includes a follow-up setting unit that defines specific methods for follow-up after a medical examination. These follow-up methods include, but are not limited to, regular health checks, rehabilitation programs, and lifestyle guidance. For example, the follow-up setting unit can define how to conduct regular health checks for patients. It can also define how to provide rehabilitation programs. For example, it can define how to provide exercise and lifestyle guidance to patients. Furthermore, the follow-up setting unit can define how to conduct follow-ups with patients via telephone or email. For example, it can define how to periodically check on patients' health status via email. This clarifies the follow-up methods within the medical interview system.

[0036] The data collection unit can collect information on the patient's symptoms or medical history, and their current physical condition. For example, the data collection unit can collect the patient's symptoms. Symptoms include, but are not limited to, pain, fever, and cough. The data collection unit can collect, for example, the patient's medical history. Medical history includes, but is not limited to, past diagnoses and treatments, and family history. The data collection unit can collect, for example, the patient's current physical condition. Current physical condition includes, but is not limited to, blood pressure, heart rate, and body temperature. This allows the data collection unit to collect detailed information on the patient.

[0037] The analysis unit can analyze the collected information and determine the necessity of medical treatment. For example, the analysis unit analyzes the collected information. The analysis may use, but is not limited to, algorithms and analytical tools. For example, the analysis unit may use machine learning algorithms to analyze the patient's symptoms and determine the necessity of medical treatment. For example, the analysis unit may evaluate the necessity of medical treatment based on the severity of symptoms and medical history. The analysis unit can also analyze the collected data using statistical analysis tools. For example, the analysis unit may evaluate the necessity of medical treatment based on the collected data. This allows the analysis unit to accurately determine the necessity of medical treatment. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit may input the collected information into AI and have the AI ​​perform the determination of the necessity of medical treatment.

[0038] The data collection unit can analyze a patient's past medical history and select the most appropriate method for collecting information. For example, the data collection unit analyzes a patient's past medical history. This medical history includes, but is not limited to, diagnoses, treatment history, and test results. For example, the data collection unit prioritizes collecting information from the medical departments the patient has frequently visited in the past. The data collection unit can also focus on collecting information related to specific symptoms from the patient's past medical history. Furthermore, the data collection unit can select the most effective method for collecting information (e.g., questionnaire format, checklist) based on the patient's past medical history. This enables the data collection unit to collect information optimally based on past medical history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input patient medical history data into a generating AI and have the generating AI select the most appropriate method for collecting information.

[0039] The data collection unit can filter information based on the patient's current living situation and areas of interest during the information collection process. For example, the data collection unit collects the patient's current living situation. This includes, but is not limited to, occupation, lifestyle, and family environment. The data collection unit also collects, for example, the patient's areas of interest. These include, but are not limited to, hobbies, interests, and activity history. The data collection unit prioritizes the collection of relevant information based on the patient's current living situation. The data collection unit can also collect relevant information based on the patient's areas of interest. Furthermore, the data collection unit can filter out unnecessary information based on the patient's living situation and areas of interest, thereby efficiently collecting information. This enables the data collection unit to collect information in accordance with the patient's living situation and areas of interest. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input patient living situation data into a generating AI and have the generating AI perform information filtering.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the patient's geographical location information during data collection. For example, the data collection unit collects the patient's geographical location information. This geographical location information includes, but is not limited to, GPS data, address information, and travel history. For example, the data collection unit can prioritize the collection of information related to region-specific diseases and symptoms based on the patient's place of residence. The data collection unit can also collect information about nearby medical institutions and pharmacies based on the patient's current location. Furthermore, the data collection unit can efficiently collect highly relevant information by considering the patient's geographical location information. This allows the data collection unit to collect highly relevant information based on geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the patient's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant information.

[0041] The data collection unit can analyze the patient's social media activity and collect relevant information during data collection. For example, the data collection unit analyzes the patient's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. For example, the data collection unit can analyze posts and comments related to health from the patient's social media activity and collect relevant information. The data collection unit can also collect information about specific symptoms or diseases from the patient's social media activity. Furthermore, the data collection unit can analyze the patient's social media activity and efficiently collect relevant information. This enables the data collection unit to collect information based on social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the patient's social media data into a generating AI and have the generating AI collect relevant information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the severity of the symptoms during the analysis. For example, the analysis unit evaluates the severity of the symptoms. Severity includes, but is not limited to, the intensity of pain, the degree of fever, and the degree of difficulty breathing. For example, the analysis unit performs a detailed analysis for symptoms with high severity and provides specific diagnostic results. The analysis unit can also perform a concise analysis for symptoms with low severity and provide only the minimum necessary information. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the severity of the symptoms, enabling efficient diagnosis. This allows the analysis unit to perform analysis according to the severity of the symptoms. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input symptom severity data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the symptom category during analysis. For example, the analysis unit classifies the symptom categories. These categories include, but are not limited to, respiratory, digestive, and circulatory systems. For example, the analysis unit can apply a dedicated analysis algorithm to respiratory symptoms to make an accurate diagnosis. Similarly, the analysis unit can apply a dedicated analysis algorithm to digestive symptoms to make an appropriate diagnosis. Furthermore, the analysis unit can apply the most suitable analysis algorithm according to the symptom category to make an efficient diagnosis. This enables the analysis unit to perform optimal analysis according to the symptom category. 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 symptom category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the timing of symptom onset during the analysis. For example, the analysis unit collects data on the timing of symptom onset. This includes, but is not limited to, the date of onset, the duration of symptoms, and the frequency of recurrence. For example, the analysis unit can prioritize the analysis of recently occurring symptoms to provide a rapid diagnosis. It can also postpone the analysis of symptoms that occurred in the past. Furthermore, the analysis unit can determine the priority of analysis based on the timing of symptom onset, enabling efficient diagnosis. This allows the analysis unit to perform analysis in a priority order based on the timing of symptom onset. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input symptom onset data into a generating AI and have the generating AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of symptoms during the analysis. For example, the analysis unit evaluates the relevance of symptoms. Relevance includes, but is not limited to, common causes, co-occurring symptoms, and past diagnostic results. For example, the analysis unit can prioritize the analysis of highly relevant symptoms to provide a rapid diagnosis. The analysis unit can also postpone the analysis of less relevant symptoms. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of symptoms to perform a diagnosis efficiently. This allows the analysis unit to perform analysis in an order based on the relevance of symptoms. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input symptom relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The follow-up unit can analyze a patient's past medical behavior during follow-up and select the optimal follow-up method. For example, the follow-up unit analyzes a patient's past medical behavior. Medical behavior includes, but is not limited to, medical history, treatment history, and follow-up history. For example, the follow-up unit can determine the optimal frequency of follow-up based on the patient's past medical behavior. The follow-up unit can also select the most effective follow-up method (telephone, email, etc.) based on the patient's past medical behavior. Furthermore, the follow-up unit can analyze the patient's past medical behavior and propose the optimal follow-up method. This enables the follow-up unit to provide optimal follow-up based on past medical behavior. Some or all of the above processing in the follow-up unit may be performed using, for example, AI, or not using AI. For example, the follow-up unit can input patient medical behavior data into a generating AI and have the generating AI select the optimal follow-up method.

[0047] The follow-up unit can customize follow-up methods based on the patient's current living situation during follow-up. For example, the follow-up unit collects information on the patient's current living situation. This information includes, but is not limited to, occupation, lifestyle, and home environment. The follow-up unit selects the most appropriate follow-up method based on the patient's current living situation. The follow-up unit can also adjust the frequency and method of follow-up according to the patient's living situation. Furthermore, the follow-up unit can propose less burdensome follow-up methods, taking into account the patient's living situation. This enables the follow-up unit to provide follow-up tailored to the patient's living situation. Some or all of the above-described processes in the follow-up unit may be performed using, for example, AI, or not. For example, the follow-up unit can input patient living situation data into a generating AI and have the generating AI customize the follow-up methods.

[0048] The follow-up unit can select the optimal follow-up method during follow-up, taking into account the patient's geographical location information. For example, the follow-up unit collects the patient's geographical location information. This geographical location information includes, but is not limited to, GPS data, address information, and travel history. For example, the follow-up unit provides region-specific medical information based on the patient's place of residence. The follow-up unit can also provide information on nearby medical institutions and pharmacies based on the patient's current location. Furthermore, the follow-up unit can select the optimal follow-up method, taking into account the patient's geographical location information. This enables the follow-up unit to provide optimal follow-up based on geographical location information. Some or all of the above-described processes in the follow-up unit may be performed using, for example, AI, or not using AI. For example, the follow-up unit can input the patient's geographical location data into a generating AI and have the generating AI select the optimal follow-up method.

[0049] The follow-up unit can analyze the patient's social media activity during follow-up and propose follow-up methods. For example, the follow-up unit analyzes the patient's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. For example, the follow-up unit analyzes health-related posts and comments from the patient's social media activity and proposes relevant follow-up methods. The follow-up unit can also collect information on specific symptoms or diseases from the patient's social media activity and propose follow-up methods. Furthermore, the follow-up unit can analyze the patient's social media activity and propose the most suitable follow-up method. This enables the follow-up unit to conduct follow-up based on social media activity. Some or all of the above processing in the follow-up unit may be performed using, for example, AI, or not using AI. For example, the follow-up unit can input the patient's social media data into a generating AI and have the generating AI propose follow-up methods.

[0050] The security department can analyze access control history and select the most appropriate security measures. For example, the security department can analyze access control history. Access control history includes, but is not limited to, login history, access logs, and permission change history. For example, the security department can apply enhanced security measures to frequently accessed data based on past access control history. The security department can also analyze access control history and implement additional security measures for data at high risk of unauthorized access. Furthermore, the security department can select the most appropriate security measures based on access control history to enhance data protection. This enables the security department to implement optimal security measures based on access control history. Some or all of the above processes in the security department may be performed using, for example, AI, or not using AI. For example, the security department can input access control history data into a generating AI and have the generating AI select the most appropriate security measures.

[0051] The security department can select the most appropriate security measures considering the patient's geographical location information. For example, the security department collects the patient's geographical location information. This geographical location information includes, but is not limited to, GPS data, address information, and travel history. For example, the security department applies measures to address region-specific security risks based on the patient's place of residence. The security department can also implement security measures to protect information about nearby medical facilities and pharmacies based on the patient's current location. Furthermore, the security department can select the most appropriate security measures and enhance data protection by considering the patient's geographical location information. This enables the security department to implement optimal security measures based on geographical location information. Some or all of the above processes in the security department may be performed using, for example, AI, or not using AI. For example, the security department can input the patient's geographical location data into a generating AI and have the generating AI select the most appropriate security measures.

[0052] The criteria setting unit can optimize criteria by referring to past medical data. For example, the criteria setting unit refers to past medical data. Medical data includes, but is not limited to, diagnostic results, treatment history, and test results. For example, the criteria setting unit sets optimal criteria for determining the necessity of medical treatment from past medical data. The criteria setting unit can also analyze past medical data and optimize the criteria for determining the necessity of medical treatment. Furthermore, the criteria setting unit can adjust the criteria for determining the necessity of medical treatment based on past medical data, enabling efficient medical treatment. This allows the criteria setting unit to set optimal criteria based on past medical data. Some or all of the above-described processes in the criteria setting unit may be performed using, for example, AI, or without AI. For example, the criteria setting unit can input past medical data into a generating AI and have the generating AI perform the optimization of criteria.

[0053] The criteria setting unit can set optimal criteria by considering the patient's geographical location information. For example, the criteria setting unit collects the patient's geographical location information. This geographical location information includes, but is not limited to, GPS data, address information, and travel history. For example, the criteria setting unit sets region-specific treatment criteria based on the patient's place of residence. The criteria setting unit can also set criteria based on the patient's current location, taking into account information about nearby medical institutions and pharmacies. Furthermore, by considering the patient's geographical location information, the criteria setting unit can set optimal treatment criteria and provide medical care efficiently. This enables the criteria setting unit to set optimal criteria based on geographical location information. Some or all of the above-described processes in the criteria setting unit may be performed using, for example, AI, or without AI. For example, the criteria setting unit can input the patient's geographical location data into a generating AI and have the generating AI perform the setting of optimal criteria.

[0054] The follow-up setting unit can set the optimal follow-up method by referring to past follow-up data. For example, the follow-up setting unit refers to past follow-up data. Follow-up data includes, but is not limited to, the frequency, method, and effectiveness of follow-up. For example, the follow-up setting unit sets the optimal follow-up frequency from past follow-up data. The follow-up setting unit can also analyze past follow-up data and set the most effective follow-up method (e.g., phone call, email). Furthermore, the follow-up setting unit can set the optimal follow-up method based on past follow-up data and perform follow-up efficiently. This enables the follow-up setting unit to set the optimal follow-up method based on past follow-up data. Some or all of the above processing in the follow-up setting unit may be performed using, for example, AI, or not using AI. For example, the follow-up setting unit can input past follow-up data into a generating AI and have the generating AI execute the setting of the optimal follow-up method.

[0055] The follow-up setting unit can set the optimal follow-up method considering the patient's geographical location information. For example, the follow-up setting unit collects the patient's geographical location information. This geographical location information includes, but is not limited to, GPS data, address information, and travel history. For example, the follow-up setting unit provides region-specific medical information based on the patient's place of residence. The follow-up setting unit can also provide information on nearby medical institutions and pharmacies based on the patient's current location. Furthermore, the follow-up setting unit can set the optimal follow-up method considering the patient's geographical location information and perform follow-up efficiently. This enables the follow-up setting unit to provide an optimal follow-up method based on geographical location information. Some or all of the above processing in the follow-up setting unit may be performed using, for example, AI, or not using AI. For example, the follow-up setting unit can input the patient's geographical location data into a generating AI and have the generating AI execute the setting of the optimal follow-up method.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The medical interview system can analyze a patient's lifestyle and consider it when determining the need for medical treatment. For example, the data collection unit collects lifestyle data such as the patient's diet, exercise, and sleep patterns. The analysis unit uses this data to evaluate the impact of lifestyle on symptoms and determine the need for medical treatment. The follow-up unit can also provide specific advice for improving lifestyle habits. This enables medical treatment that takes into account the patient's overall health condition.

[0058] The interview system can collect the genetic information of patients and consider it when determining the necessity of medical treatment. For example, the collection department collects the results of patients' genetic tests. The analysis department evaluates the genetic risks and determines the necessity of medical treatment based on this genetic information. In addition, the follow-up department can also provide preventive measures and lifestyle guidance based on the genetic risks. This enables personalized medicine that takes genetic factors into account.

[0059] The interview system can determine the necessity of medical treatment considering the patient's socioeconomic background. For example, the collection department collects socioeconomic data such as the patient's income, occupation, and education level. The analysis department evaluates the impact of the socioeconomic background on health and determines the necessity of medical treatment based on this data. In addition, the follow-up department can also provide support measures and resources according to the socioeconomic background. This provides comprehensive medical care that takes socioeconomic factors into account.

[0060] The interview system can evaluate the patient's psychological stress level and determine the necessity of medical treatment. For example, the collection department uses questionnaires or sensors to evaluate the patient's stress level. The analysis department evaluates the impact of stress on health and determines the necessity of medical treatment based on this data. In addition, the follow-up department can also provide counseling and relaxation techniques for stress management. This enables medical treatment that takes psychological factors into account.

[0061] The interview system can determine the necessity of medical treatment considering the environmental factors of the patient. For example, the collection department collects data related to the patient's living environment and workplace environment. The analysis department evaluates the impact of environmental factors on health and determines the necessity of medical treatment based on this data. In addition, the follow-up department can also provide specific advice for environmental improvement. This enables medical treatment that takes environmental factors into account.

[0062] The process flow of Form Example 1 is briefly described below.

[0063] Step 1: The data collection unit collects patient information. This information includes symptoms, medical history, and current health condition. The data collection unit collects information entered by the patient using a tablet or smartphone. The data collection unit can also collect the patient's biometric data (blood pressure, heart rate, body temperature, etc.) using sensors. Furthermore, the data collection unit retrieves the patient's medical history from the electronic medical record system. Step 2: The analysis unit analyzes the information collected by the data collection unit and determines the need for medical treatment. The analysis is performed using algorithms and analytical tools. For example, the analysis unit uses machine learning algorithms to analyze the patient's symptoms and determine the need for medical treatment. It also uses statistical analysis tools to evaluate the need for medical treatment based on the severity of symptoms and medical history. Step 3: The Follow-up Department conducts post-treatment follow-up based on the results obtained by the Analysis Department. Follow-up is carried out using telephone and email. For example, the Follow-up Department conducts regular health checks for patients. They also provide rehabilitation programs and offer lifestyle and exercise guidance to patients.

[0064] (Example of form 2) The medical interview system according to an embodiment of the present invention is an AI-powered system designed to reduce waiting times at hospitals and improve the efficiency of medical care. When a patient arrives at the hospital, the AI ​​conducts a medical interview. The patient uses a tablet or smartphone to input information such as symptoms, medical history, and current physical condition. This information is analyzed by the AI ​​to determine whether a doctor's consultation is actually necessary. For example, if a patient with cold symptoms arrives, the AI ​​analyzes the symptoms and, if they are common cold symptoms, can prescribe medication or provide lifestyle guidance before the patient sees a doctor. On the other hand, if the symptoms are severe or require a specific diagnosis, the AI ​​determines that a doctor's consultation is necessary. After the AI ​​determines the need for consultation, the patient receives a doctor's consultation as needed. The doctor can then conduct consultations efficiently based on the information collected by the AI. This shortens the doctor's consultation time and reduces patient waiting times. Furthermore, the AI ​​also provides follow-up after consultations. For example, it monitors whether the patient is taking prescribed medication correctly and whether their symptoms are improving, and can provide additional guidance or consultations as needed. This system improves the efficiency of hospital medical care and reduces patient waiting times. Furthermore, it reduces the burden on doctors, making it possible to provide high-quality medical care to more patients. As a result, the medical interview system can reduce waiting times at hospitals and improve the efficiency of medical consultations.

[0065] The patient interview system according to this embodiment comprises a data collection unit, an analysis unit, and a follow-up unit. The data collection unit collects patient information. Patient information includes, but is not limited to, symptoms, medical history, and current physical condition. The data collection unit collects information entered by the patient using, for example, a tablet or smartphone. The data collection unit can also collect the patient's biometric data using sensors. For example, the data collection unit collects data such as the patient's blood pressure, heart rate, and body temperature. Furthermore, the data collection unit can collect the patient's past medical history by referring to medical records. For example, the data collection unit obtains the patient's medical history from an electronic medical record system. The analysis unit analyzes the information collected by the data collection unit and determines the need for medical treatment. The analysis is performed using, for example, algorithms or analysis tools, but is not limited to these. For example, the analysis unit uses a machine learning algorithm to analyze the patient's symptoms and determine the need for medical treatment. The analysis unit can also use statistical analysis tools to analyze the collected data. For example, the analysis unit evaluates the need for medical treatment based on the severity of symptoms and medical history. The follow-up unit performs post-treatment follow-up based on the results obtained by the analysis unit. Follow-up may be conducted, for example, by telephone or email, but is not limited to such examples. For instance, the follow-up unit may conduct regular health checks for the patient. The follow-up unit may also provide rehabilitation programs. For example, the follow-up unit may provide lifestyle guidance or exercise guidance to the patient. This enables the medical interview system according to the embodiment to efficiently collect, analyze, and follow up on patient information. Some or all of the processing in the collection unit, analysis unit, and follow-up unit may be performed using, for example, AI, but may not be performed using AI.

[0066] The data collection unit collects patient information. This information includes, but is not limited to, symptoms, medical history, and current health condition. The data collection unit collects information entered by the patient, for example, using a tablet or smartphone. Specifically, the patient uses a dedicated application on their tablet or smartphone to answer questions about their symptoms, medical history, and current health condition. This application has a user-friendly interface and is designed to allow patients to easily input information. Furthermore, the data collection unit can also collect the patient's biometric data using sensors. For example, the data collection unit collects data such as the patient's blood pressure, heart rate, and body temperature. Wearable devices and home medical devices are used for this purpose. Wearable devices continuously collect data as the patient goes about their daily life and transmit it to the system in real time. Home medical devices are designed for easy use by patients at home, and the measurement results are automatically transmitted to the data collection unit. Furthermore, the data collection unit can also collect the patient's past medical history by referring to medical records. For example, the data collection unit retrieves the patient's medical history from an electronic medical record system. The electronic medical record system stores the patient's past medical information in digital format, and the data collection unit can retrieve this information quickly and accurately. This allows the data collection unit to understand not only the patient's current condition but also their past medical history and treatment progress. As a result, the data collection unit can comprehensively collect patient information and provide a foundation for improving the quality of medical care.

[0067] The analysis unit analyzes the information collected by the data collection unit to determine the need for medical treatment. Analysis is performed using algorithms and analytical tools, but is not limited to these examples. Specifically, the analysis unit uses machine learning algorithms to analyze a patient's symptoms and determine the need for medical treatment. Machine learning algorithms can learn from large amounts of medical data and analyze symptom patterns and relationships to determine the need for treatment with high accuracy. For example, the analysis unit takes patient symptom data as input, compares it with past medical data, and evaluates the need for treatment based on the treatment results of similar cases. The analysis unit can also analyze collected data using statistical analysis tools. For example, the analysis unit evaluates the need for treatment based on the severity of symptoms and medical history. Statistical analysis tools analyze collected data from multiple perspectives to clarify the relationship between symptom severity and medical history. This allows the analysis unit to comprehensively evaluate the patient's condition and determine the need for appropriate medical treatment. Furthermore, the analysis unit uses AI to analyze data in real time and support rapid medical decision-making. For example, AI can analyze collected biometric data in real time and detect abnormal patterns to determine the need for treatment early. This allows the analysis unit to quickly and accurately analyze the collected data and determine the necessity of medical treatment with high precision.

[0068] The Follow-up Department conducts post-treatment follow-up based on the results obtained by the Analysis Department. Follow-up is conducted, for example, by telephone or email, but is not limited to these methods. Specifically, the Follow-up Department conducts regular health checks for patients. For example, the Follow-up Department makes regular telephone calls to patients to check their current physical condition and any changes in symptoms. The Follow-up Department can also send health check reminders to patients via email to encourage regular health checks. Furthermore, the Follow-up Department can also provide rehabilitation programs. For example, the Follow-up Department provides lifestyle guidance and exercise guidance to patients. Lifestyle guidance includes improving diet, managing stress, and improving sleep quality. Exercise guidance provides exercise programs tailored to the patient's physical strength and health condition. This allows the Follow-up Department to continuously monitor the patient's health condition and provide appropriate follow-up. Furthermore, the Follow-up Department can collect patient feedback and continuously improve the content of the follow-up. For example, based on patient feedback, it can adjust the frequency and content of follow-up to provide more effective follow-up. Furthermore, the follow-up department can centrally manage patient data and collaborate with other medical institutions and specialists. This allows the follow-up department to comprehensively manage patients' health status and effectively conduct post-treatment follow-up.

[0069] The medical interview system includes a security unit that performs data encryption or access control. The security unit performs data encryption and access control. Data encryption may use, but is not limited to, encryption technologies such as AES (Advanced Encryption Standard) or RSA (Rivest-Shamir-Adleman). For example, the security unit encrypts patient information using AES-256. The security unit can also encrypt data communication using SSL / TLS (Secure Sockets Layer / Transport Layer Security). Access control may include, but is not limited to, user authentication and authorization management. For example, the security unit performs user authentication to access patient information. The security unit can also record access logs and detect unauthorized access. For example, the security unit monitors login history and detects abnormal access. This improves the data security of the medical interview system.

[0070] The medical interview system includes a criteria setting unit that sets the criteria and algorithms for determining the necessity of medical treatment. The criteria setting unit sets the criteria and algorithms for determining the necessity of medical treatment. Criteria include, but are not limited to, the severity of symptoms, medical history, and test results. For example, the criteria setting unit sets criteria for evaluating the severity of a patient's symptoms. The criteria setting unit can also set algorithms for determining the necessity of medical treatment based on medical history. For example, the criteria setting unit evaluates the necessity of medical treatment based on past diagnostic results and treatment history. Furthermore, the criteria setting unit can also set criteria for determining the necessity of medical treatment based on test results. For example, the criteria setting unit evaluates the necessity of medical treatment based on the results of blood tests and imaging diagnoses. This makes the criteria for determining medical treatment clear in the medical interview system.

[0071] The medical interview system includes a follow-up setting unit that defines specific methods for follow-up after a medical examination. These follow-up methods include, but are not limited to, regular health checks, rehabilitation programs, and lifestyle guidance. For example, the follow-up setting unit can define how to conduct regular health checks for patients. It can also define how to provide rehabilitation programs. For example, it can define how to provide exercise and lifestyle guidance to patients. Furthermore, the follow-up setting unit can define how to conduct follow-ups with patients via telephone or email. For example, it can define how to periodically check on patients' health status via email. This clarifies the follow-up methods within the medical interview system.

[0072] The data collection unit can collect information on the patient's symptoms or medical history, and their current physical condition. For example, the data collection unit can collect the patient's symptoms. Symptoms include, but are not limited to, pain, fever, and cough. The data collection unit can collect, for example, the patient's medical history. Medical history includes, but is not limited to, past diagnoses and treatments, and family history. The data collection unit can collect, for example, the patient's current physical condition. Current physical condition includes, but is not limited to, blood pressure, heart rate, and body temperature. This allows the data collection unit to collect detailed information on the patient.

[0073] The analysis unit can analyze the collected information and determine the necessity of medical treatment. For example, the analysis unit analyzes the collected information. The analysis may use, but is not limited to, algorithms and analytical tools. For example, the analysis unit may use machine learning algorithms to analyze the patient's symptoms and determine the necessity of medical treatment. For example, the analysis unit may evaluate the necessity of medical treatment based on the severity of symptoms and medical history. The analysis unit can also analyze the collected data using statistical analysis tools. For example, the analysis unit may evaluate the necessity of medical treatment based on the collected data. This allows the analysis unit to accurately determine the necessity of medical treatment. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit may input the collected information into AI and have the AI ​​perform the determination of the necessity of medical treatment.

[0074] The data collection unit can estimate the patient's emotions and adjust the timing of information collection based on the estimated emotions. For example, the data collection unit estimates the patient's emotions. Emotion estimation can be performed using, but is not limited to, facial expression analysis, voice analysis, or self-reporting. For example, if the patient is feeling anxious, the data collection unit can start collecting information immediately to provide reassurance. If the patient is relaxed, the data collection unit can wait a while before starting to collect information and conduct the interview in a natural flow. Furthermore, if the patient is in a hurry, the data collection unit can start collecting information quickly and collect the necessary information in a short time. This allows the data collection unit to collect information at an appropriate time according to the patient's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI can 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 data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input patient facial expression data into a generating AI, which can then perform emotion estimation.

[0075] The data collection unit can analyze a patient's past medical history and select the most appropriate method for collecting information. For example, the data collection unit analyzes a patient's past medical history. This medical history includes, but is not limited to, diagnoses, treatment history, and test results. For example, the data collection unit prioritizes collecting information from the medical departments the patient has frequently visited in the past. The data collection unit can also focus on collecting information related to specific symptoms from the patient's past medical history. Furthermore, the data collection unit can select the most effective method for collecting information (e.g., questionnaire format, checklist) based on the patient's past medical history. This enables the data collection unit to collect information optimally based on past medical history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input patient medical history data into a generating AI and have the generating AI select the most appropriate method for collecting information.

[0076] The data collection unit can filter information based on the patient's current living situation and areas of interest during the information collection process. For example, the data collection unit collects the patient's current living situation. This includes, but is not limited to, occupation, lifestyle, and family environment. The data collection unit also collects, for example, the patient's areas of interest. These include, but are not limited to, hobbies, interests, and activity history. The data collection unit prioritizes the collection of relevant information based on the patient's current living situation. The data collection unit can also collect relevant information based on the patient's areas of interest. Furthermore, the data collection unit can filter out unnecessary information based on the patient's living situation and areas of interest, thereby efficiently collecting information. This enables the data collection unit to collect information in accordance with the patient's living situation and areas of interest. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input patient living situation data into a generating AI and have the generating AI perform information filtering.

[0077] The data collection unit can estimate the patient's emotions and determine the priority of information to collect based on the estimated emotions. For example, the data collection unit estimates the patient's emotions. Emotion estimation may include, but is not limited to, facial expression analysis, voice analysis, or self-reporting. For example, if the patient is feeling anxious, the data collection unit will prioritize collecting information important to provide reassurance. If the patient is relaxed, the data collection unit can take more time to collect detailed information. Furthermore, if the patient is in a hurry, the data collection unit can prioritize collecting the most important information and proceed with the interview quickly. This allows the data collection unit to determine the priority of information according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input patient emotion data into a generative AI and have the generative AI determine the priority of information.

[0078] The data collection unit can prioritize the collection of highly relevant information by considering the patient's geographical location information during data collection. For example, the data collection unit collects the patient's geographical location information. This geographical location information includes, but is not limited to, GPS data, address information, and travel history. For example, the data collection unit can prioritize the collection of information related to region-specific diseases and symptoms based on the patient's place of residence. The data collection unit can also collect information about nearby medical institutions and pharmacies based on the patient's current location. Furthermore, the data collection unit can efficiently collect highly relevant information by considering the patient's geographical location information. This allows the data collection unit to collect highly relevant information based on geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the patient's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant information.

[0079] The data collection unit can analyze the patient's social media activity and collect relevant information during data collection. For example, the data collection unit analyzes the patient's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. For example, the data collection unit can analyze posts and comments related to health from the patient's social media activity and collect relevant information. The data collection unit can also collect information about specific symptoms or diseases from the patient's social media activity. Furthermore, the data collection unit can analyze the patient's social media activity and efficiently collect relevant information. This enables the data collection unit to collect information based on social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the patient's social media data into a generating AI and have the generating AI collect relevant information.

[0080] The analysis unit can estimate the patient's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit estimates the patient's emotions. Emotion estimation may use, but is not limited to, facial expression analysis, voice analysis, or self-reporting. For example, if the patient is feeling anxious, the analysis unit presents the analysis results in a way that provides reassurance. If the patient is relaxed, the analysis unit can provide detailed analysis results to deepen understanding. Furthermore, if the patient is in a hurry, the analysis unit can provide concise and to-the-point analysis results. This enables the analysis unit to express analysis results according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input patient emotional data into the generating AI and have the generating AI adjust how the analysis results are presented.

[0081] The analysis unit can adjust the level of detail of the analysis based on the severity of the symptoms during the analysis. For example, the analysis unit evaluates the severity of the symptoms. Severity includes, but is not limited to, the intensity of pain, the degree of fever, and the degree of difficulty breathing. For example, the analysis unit performs a detailed analysis for symptoms with high severity and provides specific diagnostic results. The analysis unit can also perform a concise analysis for symptoms with low severity and provide only the minimum necessary information. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the severity of the symptoms, enabling efficient diagnosis. This allows the analysis unit to perform analysis according to the severity of the symptoms. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input symptom severity data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the symptom category during analysis. For example, the analysis unit classifies the symptom categories. These categories include, but are not limited to, respiratory, digestive, and circulatory systems. For example, the analysis unit can apply a dedicated analysis algorithm to respiratory symptoms to make an accurate diagnosis. Similarly, the analysis unit can apply a dedicated analysis algorithm to digestive symptoms to make an appropriate diagnosis. Furthermore, the analysis unit can apply the most suitable analysis algorithm according to the symptom category to make an efficient diagnosis. This enables the analysis unit to perform optimal analysis according to the symptom category. 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 symptom category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0083] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit estimates the patient's emotions. Emotion estimation may use, but is not limited to, facial expression analysis, voice analysis, or self-reporting. For example, if the patient is feeling anxious, the analysis unit provides a short, concise analysis result. If the patient is relaxed, the analysis unit provides a detailed analysis result to deepen understanding. Furthermore, if the patient is in a hurry, the analysis unit can provide a brief, concise analysis result. This allows the analysis unit to adjust the length of the analysis according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, 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 length of the analysis.

[0084] The analysis unit can determine the priority of analysis based on the timing of symptom onset during the analysis. For example, the analysis unit collects data on the timing of symptom onset. This includes, but is not limited to, the date of onset, the duration of symptoms, and the frequency of recurrence. For example, the analysis unit can prioritize the analysis of recently occurring symptoms to provide a rapid diagnosis. It can also postpone the analysis of symptoms that occurred in the past. Furthermore, the analysis unit can determine the priority of analysis based on the timing of symptom onset, enabling efficient diagnosis. This allows the analysis unit to perform analysis in a priority order based on the timing of symptom onset. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input symptom onset data into a generating AI and have the generating AI determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of symptoms during the analysis. For example, the analysis unit evaluates the relevance of symptoms. Relevance includes, but is not limited to, common causes, co-occurring symptoms, and past diagnostic results. For example, the analysis unit can prioritize the analysis of highly relevant symptoms to provide a rapid diagnosis. The analysis unit can also postpone the analysis of less relevant symptoms. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of symptoms to perform a diagnosis efficiently. This allows the analysis unit to perform analysis in an order based on the relevance of symptoms. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input symptom relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The follow-up unit can estimate the patient's emotions and adjust the follow-up method based on the estimated emotions. For example, the follow-up unit estimates the patient's emotions. Emotion estimation may use, but is not limited to, facial expression analysis, voice analysis, or self-reporting. For example, if the patient is feeling anxious, the follow-up unit can provide frequent follow-ups to reassure them. If the patient is relaxed, the follow-up unit can provide follow-ups at an appropriate frequency to reduce their burden. Furthermore, if the patient is in a hurry, the follow-up unit can provide rapid follow-ups to provide necessary information. This enables the follow-up unit to provide follow-ups that are appropriate to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the follow-up unit may be performed using, for example, AI, or not using AI. For example, the follow-up unit can input patient emotional data into a generating AI and have the AI ​​adjust the follow-up method.

[0087] The follow-up unit can analyze a patient's past medical behavior during follow-up and select the optimal follow-up method. For example, the follow-up unit analyzes a patient's past medical behavior. Medical behavior includes, but is not limited to, medical history, treatment history, and follow-up history. For example, the follow-up unit can determine the optimal frequency of follow-up based on the patient's past medical behavior. The follow-up unit can also select the most effective follow-up method (telephone, email, etc.) based on the patient's past medical behavior. Furthermore, the follow-up unit can analyze the patient's past medical behavior and propose the optimal follow-up method. This enables the follow-up unit to provide optimal follow-up based on past medical behavior. Some or all of the above processing in the follow-up unit may be performed using, for example, AI, or not using AI. For example, the follow-up unit can input patient medical behavior data into a generating AI and have the generating AI select the optimal follow-up method.

[0088] The follow-up unit can customize follow-up methods based on the patient's current living situation during follow-up. For example, the follow-up unit collects information on the patient's current living situation. This information includes, but is not limited to, occupation, lifestyle, and home environment. The follow-up unit selects the most appropriate follow-up method based on the patient's current living situation. The follow-up unit can also adjust the frequency and method of follow-up according to the patient's living situation. Furthermore, the follow-up unit can propose less burdensome follow-up methods, taking into account the patient's living situation. This enables the follow-up unit to provide follow-up tailored to the patient's living situation. Some or all of the above-described processes in the follow-up unit may be performed using, for example, AI, or not. For example, the follow-up unit can input patient living situation data into a generating AI and have the generating AI customize the follow-up methods.

[0089] The follow-up unit can estimate the patient's emotions and determine the priority of follow-up based on the estimated emotions. For example, the follow-up unit estimates the patient's emotions. Emotion estimation may use, but is not limited to, facial expression analysis, voice analysis, or self-reporting. For example, if the patient is feeling anxious, the follow-up unit can prioritize follow-up to provide reassurance. Also, if the patient is relaxed, the follow-up unit can perform follow-up at an appropriate frequency to reduce the burden. Furthermore, if the patient is in a hurry, the follow-up unit can perform follow-up quickly to provide necessary information. This allows the follow-up unit to determine the priority of follow-up according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the follow-up unit may be performed using, for example, AI, or not using AI. For example, the follow-up unit can input patient emotional data into a generating AI and have the AI ​​determine the priority of follow-up care.

[0090] The follow-up unit can select the optimal follow-up method during follow-up, taking into account the patient's geographical location information. For example, the follow-up unit collects the patient's geographical location information. This geographical location information includes, but is not limited to, GPS data, address information, and travel history. For example, the follow-up unit provides region-specific medical information based on the patient's place of residence. The follow-up unit can also provide information on nearby medical institutions and pharmacies based on the patient's current location. Furthermore, the follow-up unit can select the optimal follow-up method, taking into account the patient's geographical location information. This enables the follow-up unit to provide optimal follow-up based on geographical location information. Some or all of the above-described processes in the follow-up unit may be performed using, for example, AI, or not using AI. For example, the follow-up unit can input the patient's geographical location data into a generating AI and have the generating AI select the optimal follow-up method.

[0091] The follow-up unit can analyze the patient's social media activity during follow-up and propose follow-up methods. For example, the follow-up unit analyzes the patient's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. For example, the follow-up unit analyzes health-related posts and comments from the patient's social media activity and proposes relevant follow-up methods. The follow-up unit can also collect information on specific symptoms or diseases from the patient's social media activity and propose follow-up methods. Furthermore, the follow-up unit can analyze the patient's social media activity and propose the most suitable follow-up method. This enables the follow-up unit to conduct follow-up based on social media activity. Some or all of the above processing in the follow-up unit may be performed using, for example, AI, or not using AI. For example, the follow-up unit can input the patient's social media data into a generating AI and have the generating AI propose follow-up methods.

[0092] The security unit can estimate the patient's emotions and adjust the data encryption level based on the estimated emotions. For example, the security unit estimates the patient's emotions. Emotion estimation may include, but is not limited to, facial expression analysis, voice analysis, or self-reporting. For example, if the patient is feeling anxious, the security unit may apply the highest level of encryption to provide reassurance. If the patient is relaxed, the security unit may apply a standard level of encryption to efficiently protect the data. Furthermore, if the patient is in a hurry, the security unit may select an appropriate encryption level to process the data quickly. This allows the security unit to adjust the encryption level according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security unit may be performed using AI or not using AI. For example, the security unit may input patient emotion data into a generative AI and have the generative AI perform the encryption level adjustment.

[0093] The security department can analyze access control history and select the most appropriate security measures. For example, the security department can analyze access control history. Access control history includes, but is not limited to, login history, access logs, and permission change history. For example, the security department can apply enhanced security measures to frequently accessed data based on past access control history. The security department can also analyze access control history and implement additional security measures for data at high risk of unauthorized access. Furthermore, the security department can select the most appropriate security measures based on access control history to enhance data protection. This enables the security department to implement optimal security measures based on access control history. Some or all of the above processes in the security department may be performed using, for example, AI, or not using AI. For example, the security department can input access control history data into a generating AI and have the generating AI select the most appropriate security measures.

[0094] The security unit can estimate the patient's emotions and determine the priority of access control based on the estimated emotions. For example, the security unit estimates the patient's emotions. Emotion estimation may be performed using, but is not limited to, facial expression analysis, voice analysis, or self-reporting. For example, if the patient is feeling anxious, the security unit can strengthen access control to important data to provide reassurance. If the patient is relaxed, the security unit can apply standard access control to efficiently protect the data. Furthermore, if the patient is in a hurry, the security unit can select appropriate access control to allow for quick access to the data. This allows the security unit to determine the priority of access control according to the patient's emotions. Emotion estimation is achieved using, for example, 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 security unit may be performed using, for example, AI, or not using AI. For example, the security department can input patient emotional data into a generating AI and have the AI ​​determine the priority of access control.

[0095] The security department can select the most appropriate security measures considering the patient's geographical location information. For example, the security department collects the patient's geographical location information. This geographical location information includes, but is not limited to, GPS data, address information, and travel history. For example, the security department applies measures to address region-specific security risks based on the patient's place of residence. The security department can also implement security measures to protect information about nearby medical facilities and pharmacies based on the patient's current location. Furthermore, the security department can select the most appropriate security measures and enhance data protection by considering the patient's geographical location information. This enables the security department to implement optimal security measures based on geographical location information. Some or all of the above processes in the security department may be performed using, for example, AI, or not using AI. For example, the security department can input the patient's geographical location data into a generating AI and have the generating AI select the most appropriate security measures.

[0096] The criteria setting unit can estimate the patient's emotions and adjust the criteria for determining the need for medical treatment based on the estimated emotions. For example, the criteria setting unit estimates the patient's emotions. For example, facial expression analysis, voice analysis, and self-reporting may be used for emotion estimation, but are not limited to these examples. For example, if the patient is feeling anxious, the criteria setting unit may set the need for medical treatment higher to provide reassurance. Also, if the patient is relaxed, the criteria setting unit may apply standard criteria to enable efficient medical treatment. Furthermore, if the patient is in a hurry, the criteria setting unit may adjust the criteria to quickly determine the need for medical treatment. In this way, the criteria setting unit can adjust the criteria for the need for medical treatment according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the criteria setting unit may be performed using AI, for example, or without using AI. For example, the criteria setting unit can input patient emotional data into the generating AI and have the generating AI adjust the criteria for determining the necessity of medical treatment.

[0097] The criteria setting unit can optimize criteria by referring to past medical data. For example, the criteria setting unit refers to past medical data. Medical data includes, but is not limited to, diagnostic results, treatment history, and test results. For example, the criteria setting unit sets optimal criteria for determining the necessity of medical treatment from past medical data. The criteria setting unit can also analyze past medical data and optimize the criteria for determining the necessity of medical treatment. Furthermore, the criteria setting unit can adjust the criteria for determining the necessity of medical treatment based on past medical data, enabling efficient medical treatment. This allows the criteria setting unit to set optimal criteria based on past medical data. Some or all of the above-described processes in the criteria setting unit may be performed using, for example, AI, or without AI. For example, the criteria setting unit can input past medical data into a generating AI and have the generating AI perform the optimization of criteria.

[0098] The criteria setting unit can estimate the patient's emotions and determine the priority of criteria setting based on the estimated emotions. For example, the criteria setting unit estimates the patient's emotions. For example, facial expression analysis, voice analysis, self-reporting, etc., may be used for emotion estimation, but are not limited to these examples. For example, if the patient is feeling anxious, the criteria setting unit will prioritize setting important criteria to provide reassurance. Also, if the patient is relaxed, the criteria setting unit will apply standard criteria to enable efficient treatment. Furthermore, if the patient is in a hurry, the criteria setting unit can adjust the priorities to set criteria quickly. In this way, the criteria setting unit can determine the priority of criteria setting according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the criteria setting unit may be performed using AI, for example, or without using AI. For example, the criteria setting unit can input patient emotional data into a generating AI and have the generating AI determine the priority of criteria setting.

[0099] The criteria setting unit can set optimal criteria by considering the patient's geographical location information. For example, the criteria setting unit collects the patient's geographical location information. This geographical location information includes, but is not limited to, GPS data, address information, and travel history. For example, the criteria setting unit sets region-specific treatment criteria based on the patient's place of residence. The criteria setting unit can also set criteria based on the patient's current location, taking into account information about nearby medical institutions and pharmacies. Furthermore, by considering the patient's geographical location information, the criteria setting unit can set optimal treatment criteria and provide medical care efficiently. This enables the criteria setting unit to set optimal criteria based on geographical location information. Some or all of the above-described processes in the criteria setting unit may be performed using, for example, AI, or without AI. For example, the criteria setting unit can input the patient's geographical location data into a generating AI and have the generating AI perform the setting of optimal criteria.

[0100] The follow-up setting unit can estimate the patient's emotions and adjust the specific follow-up method based on the estimated emotions. For example, the follow-up setting unit estimates the patient's emotions. For example, facial expression analysis, voice analysis, and self-reporting may be used for emotion estimation, but are not limited to these examples. For example, if the patient is feeling anxious, the follow-up setting unit can provide frequent follow-ups to reassure them. Also, if the patient is relaxed, the follow-up setting unit can provide follow-ups at an appropriate frequency to reduce their burden. Furthermore, if the patient is in a hurry, the follow-up setting unit can provide rapid follow-ups to provide necessary information. This allows the follow-up setting unit to adjust the follow-up method according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the follow-up setting unit may be performed using AI, for example, or without AI. For example, the follow-up setting unit can input patient emotional data into a generating AI and have the AI ​​adjust the follow-up method.

[0101] The follow-up setting unit can set the optimal follow-up method by referring to past follow-up data. For example, the follow-up setting unit refers to past follow-up data. Follow-up data includes, but is not limited to, the frequency, method, and effectiveness of follow-up. For example, the follow-up setting unit sets the optimal follow-up frequency from past follow-up data. The follow-up setting unit can also analyze past follow-up data and set the most effective follow-up method (e.g., phone call, email). Furthermore, the follow-up setting unit can set the optimal follow-up method based on past follow-up data and perform follow-up efficiently. This enables the follow-up setting unit to set the optimal follow-up method based on past follow-up data. Some or all of the above processing in the follow-up setting unit may be performed using, for example, AI, or not using AI. For example, the follow-up setting unit can input past follow-up data into a generating AI and have the generating AI execute the setting of the optimal follow-up method.

[0102] The follow-up setting unit can estimate the patient's emotions and determine the priority of follow-ups based on the estimated emotions. For example, the follow-up setting unit estimates the patient's emotions. For example, facial expression analysis, voice analysis, and self-reporting may be used for emotion estimation, but are not limited to these examples. For example, if the patient is feeling anxious, the follow-up setting unit can prioritize follow-ups to provide reassurance. Also, if the patient is relaxed, the follow-up setting unit can perform follow-ups at an appropriate frequency to reduce the burden. Furthermore, if the patient is in a hurry, the follow-up setting unit can perform follow-ups quickly to provide necessary information. In this way, the follow-up setting unit can determine the priority of follow-ups according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the follow-up setting unit may be performed using AI, for example, or without using AI. For example, the follow-up setting unit can input patient emotional data into a generating AI and have the generating AI determine the priority of follow-ups.

[0103] The follow-up setting unit can set the optimal follow-up method considering the patient's geographical location information. For example, the follow-up setting unit collects the patient's geographical location information. This geographical location information includes, but is not limited to, GPS data, address information, and travel history. For example, the follow-up setting unit provides region-specific medical information based on the patient's place of residence. The follow-up setting unit can also provide information on nearby medical institutions and pharmacies based on the patient's current location. Furthermore, the follow-up setting unit can set the optimal follow-up method considering the patient's geographical location information and perform follow-up efficiently. This enables the follow-up setting unit to provide an optimal follow-up method based on geographical location information. Some or all of the above processing in the follow-up setting unit may be performed using, for example, AI, or not using AI. For example, the follow-up setting unit can input the patient's geographical location data into a generating AI and have the generating AI execute the setting of the optimal follow-up method.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The medical interview system can analyze a patient's lifestyle and consider it when determining the need for medical treatment. For example, the data collection unit collects lifestyle data such as the patient's diet, exercise, and sleep patterns. The analysis unit uses this data to evaluate the impact of lifestyle on symptoms and determine the need for medical treatment. The follow-up unit can also provide specific advice for improving lifestyle habits. This enables medical treatment that takes into account the patient's overall health condition.

[0106] The medical interview system can collect patients' genetic information and consider it when determining the need for medical treatment. For example, the collection unit collects the patient's genetic test results. The analysis unit evaluates genetic risks based on this genetic information and determines the need for medical treatment. Furthermore, the follow-up unit can provide preventive measures and lifestyle guidance based on genetic risks. This enables personalized medicine that takes genetic factors into consideration.

[0107] The medical interview system can determine the need for medical treatment by considering the patient's socioeconomic background. For example, the data collection unit collects socioeconomic data such as the patient's income, occupation, and education level. The analysis unit uses this data to evaluate the impact of socioeconomic background on health and determine the need for medical treatment. Furthermore, the follow-up unit can provide support measures and resources tailored to the patient's socioeconomic background. This enables the provision of comprehensive medical care that takes socioeconomic factors into account.

[0108] The medical interview system can assess a patient's psychological stress level and determine the need for medical treatment. For example, the data collection unit uses questionnaires and sensors to assess the patient's stress level. The analysis unit uses this data to evaluate the impact of stress on health and determine the need for medical treatment. The follow-up unit can also provide counseling and relaxation techniques for stress management. This enables medical treatment that takes psychological factors into consideration.

[0109] The medical interview system can determine the need for medical treatment by considering the patient's environmental factors. For example, the data collection unit collects data on the patient's living and working environment. The analysis unit evaluates the impact of these environmental factors on health based on this data and determines the need for medical treatment. Furthermore, the follow-up unit can provide specific advice for improving the environment. This enables medical treatment that takes environmental factors into consideration.

[0110] The medical interview system can estimate a patient's emotions and determine treatment priorities based on those estimates. For example, the analysis unit prioritizes treatment if the patient is experiencing high levels of anxiety. Conversely, if the patient is relaxed, the system can follow the normal treatment schedule. Furthermore, if the patient is in a hurry, treatment can be provided quickly. This enables flexible treatment that is tailored to the patient's emotions.

[0111] The medical interview system can estimate the patient's emotions and adjust the treatment method based on those estimates. For example, if the analysis unit perceives the patient as anxious, it will provide reassuring explanations. If the patient is relaxed, it will provide more detailed explanations to deepen their understanding. Furthermore, if the patient is in a hurry, it can provide concise and to-the-point explanations. This enables appropriate medical care tailored to the patient's emotional state.

[0112] The medical interview system can estimate the patient's emotions and adjust the frequency of follow-ups based on those estimates. For example, if the patient is feeling anxious, the follow-up unit will conduct frequent follow-ups to provide reassurance. Conversely, if the patient is relaxed, follow-ups can be conducted at a moderate frequency to reduce their burden. Furthermore, if the patient is in a hurry, follow-ups can be conducted quickly to provide the necessary information. This enables follow-ups tailored to the patient's emotions.

[0113] The medical interview system can estimate the patient's emotions and adjust the follow-up content based on those estimates. For example, if the patient is feeling anxious, the follow-up section can provide reassuring content. If the patient is relaxed, it can provide detailed information to deepen their understanding. Furthermore, if the patient is in a hurry, it can provide concise and to-the-point information. This enables appropriate follow-up tailored to the patient's emotions.

[0114] The medical interview system can estimate the patient's emotions and select follow-up methods based on those estimates. For example, if the patient is feeling anxious, the follow-up department can provide reassurance through phone or in-person follow-up. If the patient is relaxed, follow-up can be done via email or message to reduce their burden. Furthermore, if the patient is in a hurry, the system can select a method that allows for a quick response. This ensures that the optimal follow-up method is provided according to the patient's emotions.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The data collection unit collects patient information. This information includes symptoms, medical history, and current health condition. The data collection unit collects information entered by the patient using a tablet or smartphone. The data collection unit can also collect the patient's biometric data (blood pressure, heart rate, body temperature, etc.) using sensors. Furthermore, the data collection unit retrieves the patient's medical history from the electronic medical record system. Step 2: The analysis unit analyzes the information collected by the data collection unit and determines the need for medical treatment. The analysis is performed using algorithms and analytical tools. For example, the analysis unit uses machine learning algorithms to analyze the patient's symptoms and determine the need for medical treatment. It also uses statistical analysis tools to evaluate the need for medical treatment based on the severity of symptoms and medical history. Step 3: The Follow-up Department conducts post-treatment follow-up based on the results obtained by the Analysis Department. Follow-up is carried out using telephone and email. For example, the Follow-up Department conducts regular health checks for patients. They also provide rehabilitation programs and offer lifestyle and exercise guidance to patients.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the data collection unit, analysis unit, follow-up unit, security unit, standard setting unit, and follow-up setting unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the data collection unit collects patient information using a tablet or smartphone of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the collected information and determines the necessity of medical treatment. The follow-up unit is implemented by the control unit 46A of the smart device 14, which performs follow-up after medical treatment. The security unit is implemented by the specific processing unit 290 of the data processing device 12, which performs data encryption and access control. The standard setting unit is implemented by the specific processing unit 290 of the data processing device 12, which sets the criteria and algorithms for determining the necessity of medical treatment. The follow-up setting unit is implemented by the control unit 46A of the smart device 14, which sets the specific methods for follow-up after medical treatment. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the data collection unit, analysis unit, follow-up unit, security unit, standard setting unit, and follow-up setting unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects patient information using the microphone and camera of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information and determines the necessity of medical treatment. The follow-up unit is implemented by the control unit 46A of the smart glasses 214, which performs follow-up after medical treatment. The security unit is implemented by the specific processing unit 290 of the data processing unit 12, which performs data encryption and access control. The standard setting unit is implemented by the specific processing unit 290 of the data processing unit 12, which sets the criteria and algorithms for determining the necessity of medical treatment. The follow-up setting unit is implemented by the control unit 46A of the smart glasses 214, which sets the specific method for follow-up after medical treatment. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

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

[0140] The 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.

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

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

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

[0144] Figure 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.

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

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

[0147] In the 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.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0151] The data processing system 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.

[0152] Each of the multiple elements described above, including the data collection unit, analysis unit, follow-up unit, security unit, standard setting unit, and follow-up setting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects patient information using the microphone and camera of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information and determines the necessity of medical treatment. The follow-up unit is implemented by the control unit 46A of the headset terminal 314, which performs follow-up after medical treatment. The security unit is implemented by the specific processing unit 290 of the data processing unit 12, which performs data encryption and access control. The standard setting unit is implemented by the specific processing unit 290 of the data processing unit 12, which sets the criteria and algorithms for determining the necessity of medical treatment. The follow-up setting unit is implemented by the control unit 46A of the headset terminal 314, which sets the specific methods for follow-up after medical treatment. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

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

[0156] The 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.

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

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the data collection unit, analysis unit, follow-up unit, security unit, standard setting unit, and follow-up setting unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects patient information using the microphone and camera of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information and determines the necessity of medical treatment. The follow-up unit is implemented by, for example, the control unit 46A of the robot 414, which performs follow-up after medical treatment. The security unit is implemented by the specific processing unit 290 of the data processing unit 12, which performs data encryption and access control. The standard setting unit is implemented by the specific processing unit 290 of the data processing unit 12, which sets the criteria and algorithms for determining the necessity of medical treatment. The follow-up setting unit is implemented by the control unit 46A of the robot 414, which sets the specific methods for follow-up after medical treatment. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) A collection unit that collects patient information, An analysis unit analyzes the information collected by the aforementioned collection unit and determines the necessity of medical treatment, The system includes a follow-up unit that performs post-treatment follow-up based on the results obtained by the analysis unit. A system characterized by the following features. (Note 2) It includes a security unit that performs data encryption or access control. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a criteria setting unit that sets the criteria and algorithms for determining the necessity of medical treatment. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a follow-up setting unit for configuring specific methods of follow-up after medical treatment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect information about the patient's symptoms or medical history and current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The collected information is analyzed to determine the need for medical treatment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the patient's emotions and adjusts the timing of information gathering based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the patient's past medical history and select the most appropriate method for gathering information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, filtering is performed based on the patient's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information, prioritize the collection of highly relevant information, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During information gathering, we analyze patients' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the severity of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the symptom category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the timing of symptom onset. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned follow-up unit is, Estimate the patient's emotions and adjust the follow-up method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned follow-up unit is, During follow-up, the patient's past medical behavior is analyzed to select the most appropriate follow-up method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned follow-up unit is, During follow-up, customize the follow-up methods based on the patient's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned follow-up unit is, The system estimates the patient's emotions and determines follow-up priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned follow-up unit is, During follow-up, the optimal follow-up method will be selected considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned follow-up unit is, During follow-up, we analyze the patient's social media activity and propose follow-up strategies. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned security unit is The system estimates the patient's emotions and adjusts the data encryption level based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned security unit is Analyze the access control history and select the most suitable security measures. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned security unit is The system estimates the patient's emotions and determines access control priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned security unit is Select the optimal security measures considering the patient's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned reference setting unit, We estimate the patient's emotions and adjust the criteria for determining the need for medical treatment based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned reference setting unit, Optimize the criteria by referring to past medical data. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned reference setting unit, The system estimates the patient's emotions and determines the priority of criteria setting based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned reference setting unit, Set optimal criteria considering the patient's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned follow-up setting unit is: We estimate the patient's emotions and adjust the specific follow-up methods based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned follow-up setting unit is: Refer to past follow-up data to determine the optimal follow-up method. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned follow-up setting unit is: The system estimates the patient's emotions and determines follow-up priorities based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned follow-up setting unit is: The optimal follow-up method is determined by considering the patient's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that uses a smart device or wearable device to collect patient information, including the patient's symptoms, medical history, current physical condition, and biometric information. An analysis unit analyzes patient information collected by the collection unit using a machine learning algorithm, and determines whether the patient requires direct medical treatment by a physician based on the analysis results, a pre-set severity level of symptoms, and criteria related to medical history. The system includes a follow-up unit that, when the analysis unit determines that the patient requires direct medical treatment by a physician, estimates the patient's emotional state based on the patient's facial expressions or voice after such treatment has been performed, and adjusts the frequency of regular health status checks, the content of rehabilitation programs including lifestyle guidance or exercise guidance, or the means of follow-up to provide post-treatment follow-up, such as increasing the frequency of follow-up if the estimated emotional state indicates anxiety, decreasing the frequency of follow-up if the estimated emotional state indicates relaxation, and providing rapid follow-up if the estimated emotional state indicates urgency. A system characterized by the following features.

2. The aforementioned collection unit is The patient's information is collected, including at least one of the following symptoms: pain, fever, and cough; at least one of the following medical history: past diagnostic history, treatment history, and family history; and at least one of the following information: blood pressure, heart rate, and body temperature. The system according to feature 1.

3. The aforementioned analysis unit, The level of detail of the analysis is adjusted based on the importance of the symptoms included in the patient's information, or different analysis algorithms are applied depending on the category of symptoms. The system according to feature 1.

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