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 technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology had issues such as long waiting times at hospitals and low efficiency of medical treatment.

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

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

[0007] Systems according to embodiments can improve clinical efficiency and reduce patient wait times. [Brief description of the drawings]

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

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

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

[0028] (Example 1) The medical interview system according to the embodiment of the present invention is a system that utilizes AI to reduce waiting times at hospitals and improve the efficiency of medical treatment. In this medical interview system, when a patient arrives at a hospital, AI conducts a medical interview. The patient uses a tablet or smartphone to input symptoms, medical history, current physical condition, etc. This information is analyzed by AI to determine whether or not a doctor's examination is actually required. For example, when a patient with cold symptoms comes to the hospital, AI analyzes the symptoms, and if the symptoms are those of a common cold, it can prescribe medicine and provide lifestyle guidance before seeing a doctor. On the other hand, if the symptoms are severe or a specific diagnosis is required, it is determined that a doctor's examination is required. Next, after AI determines the need for medical treatment, the patient sees a doctor as needed. The doctor can provide medical treatment efficiently based on the information collected by AI. This reduces the doctor's consultation time and reduces the patient's waiting time. In addition, AI also performs follow-up after the consultation. For example, it can monitor whether the patient is taking the prescribed medicine correctly and whether the symptoms are improving, and provide additional guidance and medical treatment as necessary. This system improves the medical treatment efficiency of the hospital and reduces the patient's waiting time. It also reduces the burden on doctors, enabling them to provide higher 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 treatment.

[0029] The interview system according to the embodiment includes a collection unit, an analysis unit, and a follow-up unit. The collection unit collects information about the patient. The patient's information includes, for example, symptoms, medical history, and current physical condition, but is not limited to such examples. The collection unit collects information input by the patient using, for example, a tablet or a smartphone. The collection unit can also collect biological data of the patient using a sensor. For example, the collection unit collects data such as the patient's blood pressure, heart rate, and body temperature. Furthermore, the collection unit can also collect the patient's past medical history by referring to the medical record. For example, the collection unit acquires the patient's medical history from an electronic medical record system. The analysis unit analyzes the information collected by the collection unit and determines the need for medical treatment. The analysis is performed using, for example, an algorithm or an analysis tool, but is not limited to such examples. For example, the analysis unit analyzes the patient's symptoms using a machine learning algorithm and determines the need for medical treatment. The analysis unit can also analyze the collected data using a statistical analysis tool. For example, the analysis unit evaluates the need for medical treatment based on the severity of the symptoms and the medical history. The follow-up unit performs follow-up after the medical treatment based on the results obtained by the analysis unit. The follow-up is performed, for example, by telephone or email, but is not limited to such examples. For example, the follow-up unit performs regular health checks on the patient. The follow-up unit can also provide a rehabilitation program. For example, the follow-up unit provides lifestyle and exercise guidance to the patient. This enables the 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 is performed, for example, using AI, but may also be performed without using AI.

[0030] The collection unit collects information about the patient. The patient's information includes, but is not limited to, symptoms, medical history, and current physical condition. The collection unit collects information entered by the patient using, for example, a tablet or a smartphone. Specifically, the patient answers questions about symptoms, medical history, and current physical condition using a dedicated application on the tablet or smartphone. This application has a user-friendly interface and is designed to allow the patient to easily enter information. Furthermore, the collection unit can also collect biological data of the patient using a sensor. For example, the collection unit collects data such as the patient's blood pressure, heart rate, and body temperature. For this purpose, a wearable device or a home medical device is used. The wearable device continuously collects data as the patient goes about their daily life and transmits the data to the system in real time. The home medical device is designed so that the patient can easily use it at home, and the measurement results are automatically transmitted to the collection unit. Furthermore, the collection unit can also collect the patient's past medical history by referring to the medical record. For example, the collection unit obtains 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 collection unit can obtain this information quickly and accurately. This allows the collection department to grasp not only the patient's current condition, but also the patient's past medical history and treatment progress, allowing the collection department to 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 collection unit and determines the need for medical treatment. The analysis is performed, for example, using an algorithm or an analysis tool, but is not limited to such examples. Specifically, the analysis unit analyzes the patient's symptoms using a machine learning algorithm and determines the need for medical treatment. The machine learning algorithm can determine the need for medical treatment with high accuracy by learning a large amount of medical data and analyzing the pattern and association of symptoms. For example, the analysis unit inputs the patient's symptom data, compares it with past medical data, and evaluates the need for medical treatment based on the medical results of similar cases. The analysis unit can also analyze the collected data using a statistical analysis tool. For example, the analysis unit evaluates the need for medical treatment based on the severity of symptoms and medical history. The statistical analysis tool analyzes the collected data from multiple angles and clarifies the association with the severity of symptoms 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 treatment decisions. For example, AI can analyze the collected biometric data in real time and detect abnormal patterns to determine the need for medical treatment early. This enables the analysis unit to quickly and accurately analyze the collected data and determine the need for medical treatment with a high degree of accuracy.

[0032] The follow-up unit performs a follow-up after medical treatment based on the results obtained by the analysis unit. The follow-up is performed, for example, by telephone or e-mail, but is not limited to such examples. Specifically, the follow-up unit performs a regular health check on the patient. For example, the follow-up unit periodically contacts the patient by telephone to check the current physical condition and changes in symptoms. The follow-up unit can also send a health check reminder to the patient by e-mail to encourage the patient to perform a regular health check. Furthermore, the follow-up unit can provide a rehabilitation program. For example, the follow-up unit provides lifestyle and exercise guidance to the patient. The lifestyle guidance includes improving diet, stress management, and improving the quality of sleep. The exercise guidance provides an exercise program according to the physical strength and health condition of the patient. This allows the follow-up unit to continuously monitor the health condition of the patient and perform appropriate follow-up. Furthermore, the follow-up unit can collect feedback from the patient and continuously improve the content of the follow-up. For example, the frequency and content of the follow-up can be adjusted based on the feedback from the patient to provide a more effective follow-up. The follow-up department can also centrally manage patient data and collaborate with other medical institutions and specialists, enabling it to comprehensively manage the health status of patients and effectively follow up after medical treatment.

[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. For example, encryption techniques such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) are used for data encryption, but are not limited to these examples. For example, the security unit encrypts patient information with AES-256. The security unit can also encrypt data communication using SSL / TLS (Secure Sockets Layer / Transport Layer Security). Access control includes, but is not limited to these examples. For example, the security unit performs user authentication to access the patient information. The security unit can also record an access log and detect unauthorized access. For example, the security unit monitors login history and detects abnormal access. This improves data security in the medical interview system.

[0034] The interview system includes a criteria setting unit that sets criteria and algorithms for determining the necessity of medical treatment. The criteria setting unit sets criteria and algorithms for determining the necessity of medical treatment. The criteria include, for example, the severity of symptoms, medical history, and test results, but are not limited to such examples. For example, the criteria setting unit sets criteria for evaluating the severity of the patient's symptoms. In addition, the criteria setting unit can also set an algorithm for determining the necessity of medical treatment based on the 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 a blood test or image diagnosis. This makes the medical treatment judgment criteria clear in the interview system.

[0035] The medical interview system includes a follow-up setting unit that sets a specific method of follow-up after medical examination. The follow-up setting unit sets a specific method of follow-up after medical examination. The follow-up method includes, but is not limited to, a regular health check, a rehabilitation program, and lifestyle guidance, for example. For example, the follow-up setting unit sets a method of conducting a regular health check for the patient. The follow-up setting unit can also set a method of providing a rehabilitation program. For example, the follow-up setting unit sets a method of providing exercise guidance and lifestyle guidance to the patient. Furthermore, the follow-up setting unit can also set a method of following up with the patient by telephone or email. For example, the follow-up setting unit sets a method of periodically checking the health condition of the patient by email. This makes the follow-up method clear in the medical interview system.

[0036] The collection unit can collect information on the symptoms or medical history of the patient, and the current physical condition. The collection unit, for example, collects the symptoms of the patient. The symptoms include, for example, pain, fever, cough, etc., but are not limited to such examples. The collection unit, for example, collects the medical history of the patient. The medical history includes, for example, past diagnosis and treatment history, family history, etc., but are not limited to such examples. The collection unit, for example, collects the current physical condition of the patient. The current physical condition includes, for example, blood pressure, heart rate, body temperature, etc., but are not limited to such examples. This allows the collection unit to collect detailed information of the patient.

[0037] The analysis unit can analyze the collected information and determine the need for medical treatment. The analysis unit, for example, analyzes the collected information. For example, an algorithm or an analysis tool is used for the analysis, but is not limited to such examples. The analysis unit, for example, uses a machine learning algorithm to analyze the patient's symptoms and determine the need for medical treatment. For example, the analysis unit evaluates the need for medical treatment based on the severity of the symptoms and the medical history. The analysis unit can also analyze the collected data using a statistical analysis tool. For example, the analysis unit evaluates the need for medical treatment based on the collected data. This allows the analysis unit to accurately determine the need for medical treatment. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collected information to AI and cause the AI ​​to execute the determination of the need for medical treatment.

[0038] The collection unit can analyze the patient's past medical history and select the optimal information collection method. The collection unit, for example, analyzes the patient's past medical history. The medical history includes, for example, diagnosis results, treatment history, and test results, but is not limited to such examples. The collection unit, for example, preferentially collects information on medical departments that the patient has frequently visited in the past. The collection unit can also focus on collecting information related to a specific symptom from the patient's past medical history. Furthermore, the collection unit can select the most effective information collection method (question format, checklist, etc.) based on the patient's past medical history. This enables the collection unit to collect optimal information based on the past medical history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the patient's medical history data to the generation AI and cause the generation AI to select the optimal information collection method.

[0039] The collection unit can perform filtering based on the patient's current living situation and field of interest when collecting information. The collection unit, for example, collects the patient's current living situation. The living situation includes, for example, occupation, lifestyle, home environment, etc., but is not limited to such examples. The collection unit, for example, collects the patient's field of interest. The field of interest includes, for example, hobbies, interests, activity history, etc., but is not limited to such examples. The collection unit, for example, preferentially collects related information based on the patient's current living situation. The collection unit can also collect related information based on the patient's field of interest. Furthermore, the collection unit can filter unnecessary information based on the patient's living situation and field of interest, and efficiently collect information. This enables the collection unit to collect information according to the patient's living situation and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the patient's living situation data to the generation AI and cause the generation AI to filter the information.

[0040] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the patient when collecting information. The collection unit, for example, collects geographical location information of the patient. The geographical location information includes, for example, GPS data, address information, movement history, etc., but is not limited to such examples. The collection unit, for example, preferentially collects information related to diseases and symptoms specific to the region based on the patient's place of residence. The collection unit can also collect information on nearby medical institutions and pharmacies based on the patient's current location. Furthermore, the collection unit can also efficiently collect highly relevant information in consideration of the geographical location information of the patient. This allows the collection unit to collect highly relevant information based on the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location data of the patient to the generation AI and cause the generation AI to execute collection of highly relevant information.

[0041] The collection unit can analyze the social media activity of the patient at the time of information collection and collect related information. The collection unit, for example, analyzes the social media activity of the patient. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. The collection unit, for example, analyzes posts and comments related to health from the social media activity of the patient and collects related information. The collection unit can also collect information related to a specific symptom or disease from the social media activity of the patient. Furthermore, the collection unit can analyze the social media activity of the patient and efficiently collect related information. This enables the collection unit to collect information based on the social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the social media data of the patient to the generation AI and cause the generation AI to collect related information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the symptom during the analysis. The analysis unit, for example, evaluates the importance of the symptom. The importance includes, for example, the intensity of pain, the degree of fever, the degree of dyspnea, and the like, but is not limited to such examples. The analysis unit, for example, performs a detailed analysis on a symptom with a high level of importance and provides a specific diagnosis result. The analysis unit can also perform a concise analysis on a symptom with a low level of importance and provide the minimum necessary information. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the symptom and perform an efficient diagnosis. This enables the analysis unit to perform an analysis according to the importance of the symptom. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the symptom during analysis. The analysis unit, for example, classifies the category of the symptom. The categories include, for example, the respiratory system, the digestive system, the circulatory system, and the like, but are not limited to such examples. The analysis unit, for example, applies a dedicated analysis algorithm to the symptoms of the respiratory system to perform an accurate diagnosis. The analysis unit can also apply a dedicated analysis algorithm to the symptoms of the digestive system to perform an appropriate diagnosis. Furthermore, the analysis unit can apply an optimal analysis algorithm depending on the category of the symptom to perform an efficient diagnosis. This enables the analysis unit to perform an optimal analysis depending on the category of the symptom. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom category data to the generation AI and cause the generation AI to execute the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of the analysis based on the occurrence time of the symptom during the analysis. The analysis unit, for example, collects the occurrence time of the symptom. The occurrence time includes, for example, the onset date, the duration of the symptom, the frequency of recurrence, etc., but is not limited to such examples. For example, the analysis unit performs analysis preferentially for a symptom that has recently occurred, and provides a quick diagnosis. The analysis unit can also perform analysis on a symptom that has occurred in the past at a later date. Furthermore, the analysis unit can determine the priority of the analysis based on the occurrence time of the symptom and perform an efficient diagnosis. This enables the analysis unit to perform analysis with the priority based on the occurrence time of the symptom. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the occurrence time data of the symptom to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of symptoms during analysis. The analysis unit, for example, evaluates the relevance of symptoms. The relevance includes, for example, common causes, concurrent symptoms, past diagnosis results, and the like, but is not limited to such examples. The analysis unit, for example, prioritizes analysis of highly related symptoms to provide a quick diagnosis. The analysis unit can also postpone analysis of less related symptoms. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of symptoms to perform efficient diagnosis. This enables the analysis unit to perform analysis in an order based on the relevance of symptoms. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The follow-up unit can select an optimal follow-up method by analyzing the patient's past medical behavior at the time of follow-up. The follow-up unit, for example, analyzes the patient's past medical behavior. The medical behavior includes, for example, a medical history, a treatment history, a follow-up history, and the like, but is not limited to such examples. The follow-up unit, for example, determines an optimal frequency of follow-up from 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 also analyze the patient's past medical behavior and propose an optimal follow-up method. This enables the follow-up unit to perform optimal follow-up based on the past medical behavior. A part or all of the above-mentioned processing in the follow-up unit may be performed, for example, using AI, or may be performed without using AI. For example, the follow-up unit can input the patient's medical behavior data to the generation AI and cause the generation AI to select the optimal follow-up method.

[0047] The follow-up unit can customize the follow-up means based on the patient's current living situation at the time of follow-up. The follow-up unit, for example, collects the patient's current living situation. The living situation includes, for example, occupation, lifestyle, home environment, etc., but is not limited to such examples. The follow-up unit selects the optimal follow-up means based on, for example, 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 also propose a follow-up means that is less burdensome, taking into account the patient's living situation. This enables the follow-up unit to follow up according to the patient's living situation. A part or all of the above-mentioned processing in the follow-up unit may be performed, for example, using AI, or may be performed without using AI. For example, the follow-up unit can input the patient's living situation data into the generation AI and cause the generation AI to execute customization of the follow-up means.

[0048] The follow-up unit can select the optimal follow-up method in consideration of the geographical location information of the patient at the time of follow-up. The follow-up unit, for example, collects the geographical location information of the patient. The geographical location information includes, for example, GPS data, address information, movement history, etc., but is not limited to such examples. The follow-up unit provides area-specific medical information, for example, 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 in consideration of the geographical location information of the patient. This enables the follow-up unit to perform optimal follow-up based on the geographical location information. A part or all of the above-mentioned processing in the follow-up unit may be performed, for example, using AI, or may be performed without using AI. For example, the follow-up unit can input the geographical location data of the patient to the generation AI and cause the generation AI to select the optimal follow-up method.

[0049] The follow-up unit can analyze the social media activity of the patient at the time of follow-up and suggest a follow-up means. The follow-up unit, for example, analyzes the social media activity of the patient. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. The follow-up unit, for example, analyzes health-related posts and comments from the social media activity of the patient and suggests related follow-up means. The follow-up unit can also collect information on specific symptoms or illnesses from the social media activity of the patient and suggest follow-up means. Furthermore, the follow-up unit can analyze the social media activity of the patient and suggest optimal follow-up means. This enables the follow-up unit to follow up based on the social media activity. A part or all of the above-mentioned processing in the follow-up unit may be performed, for example, using AI, or may be performed without using AI. For example, the follow-up unit can input the social media data of the patient to the generation AI and cause the generation AI to execute the suggestion of the follow-up means.

[0050] The security unit can analyze the access control history and select the optimal security measure. The security unit, for example, analyzes the access control history. The access control history includes, for example, a login history, an access log, and an authority change history, but is not limited to such examples. For example, the security unit applies enhanced security measures to data that is frequently accessed from the past access control history. The security unit can also analyze the access control history and implement additional security measures for data with a high risk of unauthorized access. Furthermore, the security unit can select optimal security measures based on the access control history and strengthen the protection of data. This enables the security unit to take optimal security measures based on the access control history. A part or all of the above-mentioned processing in the security unit may be performed, for example, using AI, or may be performed without using AI. For example, the security unit can input the access control history data to the generation AI and cause the generation AI to select the optimal security measure.

[0051] The security unit can select the optimal security measures in consideration of the geographical location information of the patient. The security unit, for example, collects the geographical location information of the patient. The geographical location information includes, for example, GPS data, address information, movement history, etc., but is not limited to such examples. The security unit applies measures to address security risks specific to the region, for example, based on the patient's place of residence. The security unit can also implement security measures to protect information about nearby medical institutions and pharmacies based on the patient's current location. Furthermore, the security unit can select optimal security measures and strengthen data protection in consideration of the geographical location information of the patient. This enables the security unit to take optimal security measures based on the geographical location information. Part or all of the above-mentioned processing in the security unit may be performed, for example, using AI, or may be performed without using AI. For example, the security unit can input the geographical location data of the patient to the generation AI and cause the generation AI to select the optimal security measures.

[0052] The criterion setting unit can optimize the criterion by referring to past medical data. The criterion setting unit, for example, refers to past medical data. The medical data includes, for example, diagnosis results, treatment history, and test results, but is not limited to such examples. The criterion setting unit, for example, sets optimal criteria for determining the necessity of medical treatment from past medical data. The criterion setting unit can also analyze past medical data and optimize the criteria for determining the necessity of medical treatment. Furthermore, the criterion setting unit can adjust the criteria for determining the necessity of medical treatment based on the past medical data, and perform medical treatment efficiently. This enables the criterion setting unit to set optimal criteria based on the past medical data. Part or all of the above-mentioned processing in the criterion setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the criterion setting unit can input past medical data to the generation AI and cause the generation AI to execute optimization of the criteria.

[0053] The standard setting unit can set optimal standards in consideration of the geographical location information of the patient. The standard setting unit, for example, collects geographical location information of the patient. The geographical location information includes, for example, GPS data, address information, movement history, etc., but is not limited to such examples. The standard setting unit, for example, sets area-specific medical treatment standards based on the patient's place of residence. The standard setting unit can also set standards that take into account information on nearby medical institutions and pharmacies based on the patient's current location. Furthermore, the standard setting unit can set optimal medical treatment standards in consideration of the geographical location information of the patient and perform medical treatment efficiently. This enables the standard setting unit to set optimal standards based on the geographical location information. Part or all of the above-mentioned processing in the standard setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the standard setting unit can input the geographical location data of the patient to the generation AI and cause the generation AI to execute the setting of optimal standards.

[0054] The follow-up setting unit can set an optimal follow-up method by referring to past follow-up data. The follow-up setting unit, for example, refers to past follow-up data. The follow-up data includes, for example, the frequency, method, effect, etc. of follow-up, but is not limited to such examples. The follow-up setting unit, for example, sets an optimal follow-up frequency from past follow-up data. In addition, the follow-up setting unit can also analyze past follow-up data and set the most effective follow-up method (telephone, email, etc.). Furthermore, the follow-up setting unit can set an optimal follow-up method based on past follow-up data and perform follow-up efficiently. This enables the follow-up setting unit to set an optimal follow-up method based on past follow-up data. A part or all of the above-mentioned processing in the follow-up setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the follow-up setting unit can input past follow-up data to the generation AI and cause the generation AI to execute the setting of the optimal follow-up method.

[0055] The follow-up setting unit can set an optimal follow-up method in consideration of the geographical location information of the patient. The follow-up setting unit, for example, collects geographical location information of the patient. The geographical location information includes, for example, GPS data, address information, movement history, etc., but is not limited to such examples. The follow-up setting unit provides area-specific medical information, for example, 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 an optimal follow-up method in consideration of the geographical location information of the patient and perform follow-up efficiently. This enables the follow-up setting unit to set an optimal follow-up method based on the geographical location information. A part or all of the above-mentioned processing in the follow-up setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the follow-up setting unit can input the geographical location data of the patient to the generation AI and cause the generation AI to execute the setting of the optimal follow-up method.

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

[0057] The interview system can analyze the patient's lifestyle habits and take them into consideration when determining whether medical treatment is necessary. For example, the 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 habits on symptoms and determine whether medical treatment is necessary. The follow-up unit can also provide specific advice for improving lifestyle habits. This allows medical treatment to take into account the patient's overall health condition.

[0058] The interview system can collect genetic information from patients and take it into consideration when determining whether medical treatment is necessary. For example, the collection unit collects the results of genetic tests on patients. The analysis unit evaluates genetic risks based on this genetic information and determines whether medical treatment is necessary. The follow-up unit can also provide preventive measures and lifestyle advice based on genetic risks. This makes it possible to provide personalized medicine that takes genetic factors into account.

[0059] The interview system can determine the need for medical treatment by taking into account 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 uses this data to evaluate the impact of socioeconomic background on health and determine the need for medical treatment. The follow-up department can also provide support measures and resources according to socioeconomic background. This allows for comprehensive medical care that takes socioeconomic factors into account.

[0060] The interview system can evaluate the patient's psychological stress level and determine the need for medical treatment. For example, the collection unit uses questionnaires and sensors to evaluate 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 makes it possible to provide medical treatment that takes psychological factors into account.

[0061] The interview system can determine the need for medical treatment by taking into account the patient's environmental factors. For example, the collection unit collects data on the patient's living and working environments. The analysis unit uses this data to evaluate the impact that environmental factors have on health and determine the need for medical treatment. The follow-up unit can also provide specific advice for improving the environment. This makes it possible to provide medical treatment that takes environmental factors into account.

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

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

[0064] (Example 2) The medical interview system according to the embodiment of the present invention is a system that utilizes AI to reduce waiting times at hospitals and improve the efficiency of medical treatment. In this medical interview system, when a patient arrives at a hospital, AI conducts a medical interview. The patient uses a tablet or smartphone to input symptoms, medical history, current physical condition, etc. This information is analyzed by AI to determine whether or not a doctor's examination is actually required. For example, when a patient with cold symptoms comes to the hospital, AI analyzes the symptoms, and if the symptoms are those of a common cold, it can prescribe medicine and provide lifestyle guidance before seeing a doctor. On the other hand, if the symptoms are severe or a specific diagnosis is required, it is determined that a doctor's examination is required. Next, after AI determines the need for medical treatment, the patient sees a doctor as needed. The doctor can provide medical treatment efficiently based on the information collected by AI. This reduces the doctor's consultation time and reduces the patient's waiting time. In addition, AI also performs follow-up after the consultation. For example, it can monitor whether the patient is taking the prescribed medicine correctly and whether the symptoms are improving, and provide additional guidance and medical treatment as necessary. This system improves the medical treatment efficiency of the hospital and reduces the patient's waiting time. It also reduces the burden on doctors, enabling them to provide higher 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 treatment.

[0065] The interview system according to the embodiment includes a collection unit, an analysis unit, and a follow-up unit. The collection unit collects information about the patient. The patient's information includes, for example, symptoms, medical history, and current physical condition, but is not limited to such examples. The collection unit collects information input by the patient using, for example, a tablet or a smartphone. The collection unit can also collect biological data of the patient using a sensor. For example, the collection unit collects data such as the patient's blood pressure, heart rate, and body temperature. Furthermore, the collection unit can also collect the patient's past medical history by referring to the medical record. For example, the collection unit acquires the patient's medical history from an electronic medical record system. The analysis unit analyzes the information collected by the collection unit and determines the need for medical treatment. The analysis is performed using, for example, an algorithm or an analysis tool, but is not limited to such examples. For example, the analysis unit analyzes the patient's symptoms using a machine learning algorithm and determines the need for medical treatment. The analysis unit can also analyze the collected data using a statistical analysis tool. For example, the analysis unit evaluates the need for medical treatment based on the severity of the symptoms and the medical history. The follow-up unit performs follow-up after the medical treatment based on the results obtained by the analysis unit. The follow-up is performed, for example, by telephone or email, but is not limited to such examples. For example, the follow-up unit performs regular health checks on the patient. The follow-up unit can also provide a rehabilitation program. For example, the follow-up unit provides lifestyle and exercise guidance to the patient. This enables the 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 is performed, for example, using AI, but may also be performed without using AI.

[0066] The collection unit collects information about the patient. The patient's information includes, but is not limited to, symptoms, medical history, and current physical condition. The collection unit collects information entered by the patient using, for example, a tablet or a smartphone. Specifically, the patient answers questions about symptoms, medical history, and current physical condition using a dedicated application on the tablet or smartphone. This application has a user-friendly interface and is designed to allow the patient to easily enter information. Furthermore, the collection unit can also collect biological data of the patient using a sensor. For example, the collection unit collects data such as the patient's blood pressure, heart rate, and body temperature. For this purpose, a wearable device or a home medical device is used. The wearable device continuously collects data as the patient goes about their daily life and transmits the data to the system in real time. The home medical device is designed so that the patient can easily use it at home, and the measurement results are automatically transmitted to the collection unit. Furthermore, the collection unit can also collect the patient's past medical history by referring to the medical record. For example, the collection unit obtains 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 collection unit can obtain this information quickly and accurately. This allows the collection department to grasp not only the patient's current condition, but also the patient's past medical history and treatment progress, allowing the collection department to 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 collection unit and determines the need for medical treatment. The analysis is performed, for example, using an algorithm or an analysis tool, but is not limited to such examples. Specifically, the analysis unit analyzes the patient's symptoms using a machine learning algorithm and determines the need for medical treatment. The machine learning algorithm can determine the need for medical treatment with high accuracy by learning a large amount of medical data and analyzing the pattern and association of symptoms. For example, the analysis unit inputs the patient's symptom data, compares it with past medical data, and evaluates the need for medical treatment based on the medical results of similar cases. The analysis unit can also analyze the collected data using a statistical analysis tool. For example, the analysis unit evaluates the need for medical treatment based on the severity of symptoms and medical history. The statistical analysis tool analyzes the collected data from multiple angles and clarifies the association with the severity of symptoms 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 treatment decisions. For example, AI can analyze the collected biometric data in real time and detect abnormal patterns to determine the need for medical treatment early. This enables the analysis unit to quickly and accurately analyze the collected data and determine the need for medical treatment with a high degree of accuracy.

[0068] The follow-up unit performs a follow-up after medical treatment based on the results obtained by the analysis unit. The follow-up is performed, for example, by telephone or e-mail, but is not limited to such examples. Specifically, the follow-up unit performs a regular health check on the patient. For example, the follow-up unit periodically contacts the patient by telephone to check the current physical condition and changes in symptoms. The follow-up unit can also send a health check reminder to the patient by e-mail to encourage the patient to perform a regular health check. Furthermore, the follow-up unit can provide a rehabilitation program. For example, the follow-up unit provides lifestyle and exercise guidance to the patient. The lifestyle guidance includes improving diet, stress management, and improving the quality of sleep. The exercise guidance provides an exercise program according to the physical strength and health condition of the patient. This allows the follow-up unit to continuously monitor the health condition of the patient and perform appropriate follow-up. Furthermore, the follow-up unit can collect feedback from the patient and continuously improve the content of the follow-up. For example, the frequency and content of the follow-up can be adjusted based on the feedback from the patient to provide a more effective follow-up. The follow-up department can also centrally manage patient data and collaborate with other medical institutions and specialists, enabling it to comprehensively manage the health status of patients and effectively follow up after medical treatment.

[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. For example, encryption techniques such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) are used for data encryption, but are not limited to these examples. For example, the security unit encrypts patient information with AES-256. The security unit can also encrypt data communication using SSL / TLS (Secure Sockets Layer / Transport Layer Security). Access control includes, but is not limited to these examples. For example, the security unit performs user authentication to access the patient information. The security unit can also record an access log and detect unauthorized access. For example, the security unit monitors login history and detects abnormal access. This improves data security in the medical interview system.

[0070] The interview system includes a criteria setting unit that sets criteria and algorithms for determining the necessity of medical treatment. The criteria setting unit sets criteria and algorithms for determining the necessity of medical treatment. The criteria include, for example, the severity of symptoms, medical history, and test results, but are not limited to such examples. For example, the criteria setting unit sets criteria for evaluating the severity of the patient's symptoms. In addition, the criteria setting unit can also set an algorithm for determining the necessity of medical treatment based on the 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 a blood test or image diagnosis. This makes the medical treatment judgment criteria clear in the interview system.

[0071] The medical interview system includes a follow-up setting unit that sets a specific method of follow-up after medical examination. The follow-up setting unit sets a specific method of follow-up after medical examination. The follow-up method includes, but is not limited to, a regular health check, a rehabilitation program, and lifestyle guidance, for example. For example, the follow-up setting unit sets a method of conducting a regular health check for the patient. The follow-up setting unit can also set a method of providing a rehabilitation program. For example, the follow-up setting unit sets a method of providing exercise guidance and lifestyle guidance to the patient. Furthermore, the follow-up setting unit can also set a method of following up with the patient by telephone or email. For example, the follow-up setting unit sets a method of periodically checking the health condition of the patient by email. This makes the follow-up method clear in the medical interview system.

[0072] The collection unit can collect information on the symptoms or medical history of the patient, and the current physical condition. The collection unit, for example, collects the symptoms of the patient. The symptoms include, for example, pain, fever, cough, etc., but are not limited to such examples. The collection unit, for example, collects the medical history of the patient. The medical history includes, for example, past diagnosis and treatment history, family history, etc., but are not limited to such examples. The collection unit, for example, collects the current physical condition of the patient. The current physical condition includes, for example, blood pressure, heart rate, body temperature, etc., but are not limited to such examples. This allows the collection unit to collect detailed information of the patient.

[0073] The analysis unit can analyze the collected information and determine the need for medical treatment. The analysis unit, for example, analyzes the collected information. For example, an algorithm or an analysis tool is used for the analysis, but is not limited to such examples. The analysis unit, for example, uses a machine learning algorithm to analyze the patient's symptoms and determine the need for medical treatment. For example, the analysis unit evaluates the need for medical treatment based on the severity of the symptoms and the medical history. The analysis unit can also analyze the collected data using a statistical analysis tool. For example, the analysis unit evaluates the need for medical treatment based on the collected data. This allows the analysis unit to accurately determine the need for medical treatment. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collected information to AI and cause the AI ​​to execute the determination of the need for medical treatment.

[0074] The collection unit can estimate the patient's emotion and adjust the timing of information collection based on the estimated emotion of the patient. The collection unit, for example, estimates the emotion of the patient. For example, facial expression analysis, voice analysis, self-reporting, etc. are used to estimate the emotion, but the example is not limited to these. For example, when the patient feels anxious, the collection unit immediately starts information collection to give the patient a sense of security. In addition, when the patient is relaxed, the collection unit can start information collection after a short time and conduct a natural interview. Furthermore, when the patient is in a hurry, the collection unit can quickly start information collection and collect necessary information in a short time. This allows the collection unit to collect information at an appropriate timing according to the emotion of the patient. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the example is not limited to these. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input facial expression data of a patient to the generation AI and have the generation AI perform emotion estimation.

[0075] The collection unit can analyze the patient's past medical history and select the optimal information collection method. The collection unit, for example, analyzes the patient's past medical history. The medical history includes, for example, diagnosis results, treatment history, and test results, but is not limited to such examples. The collection unit, for example, preferentially collects information on medical departments that the patient has frequently visited in the past. The collection unit can also focus on collecting information related to a specific symptom from the patient's past medical history. Furthermore, the collection unit can select the most effective information collection method (question format, checklist, etc.) based on the patient's past medical history. This enables the collection unit to collect optimal information based on the past medical history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the patient's medical history data to the generation AI and cause the generation AI to select the optimal information collection method.

[0076] The collection unit can perform filtering based on the patient's current living situation and field of interest when collecting information. The collection unit, for example, collects the patient's current living situation. The living situation includes, for example, occupation, lifestyle, home environment, etc., but is not limited to such examples. The collection unit, for example, collects the patient's field of interest. The field of interest includes, for example, hobbies, interests, activity history, etc., but is not limited to such examples. The collection unit, for example, preferentially collects related information based on the patient's current living situation. The collection unit can also collect related information based on the patient's field of interest. Furthermore, the collection unit can filter unnecessary information based on the patient's living situation and field of interest, and efficiently collect information. This enables the collection unit to collect information according to the patient's living situation and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the patient's living situation data to the generation AI and cause the generation AI to filter the information.

[0077] The collection unit can estimate the patient's emotion and determine the priority of information to be collected based on the estimated patient's emotion. The collection unit, for example, estimates the patient's emotion. For example, facial expression analysis, voice analysis, self-reporting, etc. are used to estimate the emotion, but the example is not limited to these. For example, when the patient feels anxious, the collection unit preferentially collects important information to give a sense of security. In addition, when the patient is relaxed, the collection unit can take time to collect detailed information. Furthermore, when the patient is in a hurry, the collection unit preferentially collects the most important information and can quickly proceed with the interview. This allows the collection unit to determine the priority of information according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the example is not limited to these. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the patient's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of information.

[0078] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the patient when collecting information. The collection unit, for example, collects geographical location information of the patient. The geographical location information includes, for example, GPS data, address information, movement history, etc., but is not limited to such examples. The collection unit, for example, preferentially collects information related to diseases and symptoms specific to the region based on the patient's place of residence. The collection unit can also collect information on nearby medical institutions and pharmacies based on the patient's current location. Furthermore, the collection unit can also efficiently collect highly relevant information in consideration of the geographical location information of the patient. This allows the collection unit to collect highly relevant information based on the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location data of the patient to the generation AI and cause the generation AI to execute collection of highly relevant information.

[0079] The collection unit can analyze the social media activity of the patient at the time of information collection and collect related information. The collection unit, for example, analyzes the social media activity of the patient. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. The collection unit, for example, analyzes posts and comments related to health from the social media activity of the patient and collects related information. The collection unit can also collect information related to a specific symptom or disease from the social media activity of the patient. Furthermore, the collection unit can analyze the social media activity of the patient and efficiently collect related information. This enables the collection unit to collect information based on the social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the social media data of the patient to the generation AI and cause the generation AI to collect related information.

[0080] The analysis unit can estimate the patient's emotion and adjust the expression method of the analysis based on the estimated emotion of the patient. The analysis unit, for example, estimates the emotion of the patient. For example, facial expression analysis, voice analysis, self-reporting, etc. are used to estimate the emotion, but the example is not limited to these. For example, when the patient feels anxious, the analysis unit presents the analysis result in an expression method that gives a sense of security. Furthermore, when the patient is relaxed, the analysis unit can provide a detailed analysis result and deepen the understanding. Furthermore, when the patient is in a hurry, the analysis unit can provide a concise analysis result that covers the main points. This enables the analysis unit to express the analysis result according to the emotion of the patient. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the example is not limited to these. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input patient emotion data into the generation AI and have the generation AI adjust the way the analysis results are expressed.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the symptom during the analysis. The analysis unit, for example, evaluates the importance of the symptom. The importance includes, for example, the intensity of pain, the degree of fever, the degree of dyspnea, and the like, but is not limited to such examples. The analysis unit, for example, performs a detailed analysis on a symptom with a high level of importance and provides a specific diagnosis result. The analysis unit can also perform a concise analysis on a symptom with a low level of importance and provide the minimum necessary information. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the symptom and perform an efficient diagnosis. This enables the analysis unit to perform an analysis according to the importance of the symptom. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the symptom during analysis. The analysis unit, for example, classifies the category of the symptom. The categories include, for example, the respiratory system, the digestive system, the circulatory system, and the like, but are not limited to such examples. The analysis unit, for example, applies a dedicated analysis algorithm to the symptoms of the respiratory system to perform an accurate diagnosis. The analysis unit can also apply a dedicated analysis algorithm to the symptoms of the digestive system to perform an appropriate diagnosis. Furthermore, the analysis unit can apply an optimal analysis algorithm depending on the category of the symptom to perform an efficient diagnosis. This enables the analysis unit to perform an optimal analysis depending on the category of the symptom. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom category data to the generation AI and cause the generation AI to execute the application of the analysis algorithm.

[0083] The analysis unit can estimate the patient's emotion and adjust the length of the analysis based on the estimated patient's emotion. The analysis unit, for example, estimates the patient's emotion. For example, facial expression analysis, voice analysis, self-reporting, etc. are used to estimate the emotion, but the example is not limited to these. For example, when the patient feels anxious, the analysis unit provides a short and to the point analysis result. Furthermore, when the patient is relaxed, the analysis unit can provide a detailed analysis result to deepen the understanding. Furthermore, when the patient is in a hurry, the analysis unit can provide a concise and to the point analysis result. This allows the analysis unit to adjust the length of the analysis according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the example is not limited to these. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the patient's emotion data to the generation AI and cause the generation AI to adjust the length of the analysis.

[0084] The analysis unit can determine the priority of the analysis based on the occurrence time of the symptom during the analysis. The analysis unit, for example, collects the occurrence time of the symptom. The occurrence time includes, for example, the onset date, the duration of the symptom, the frequency of recurrence, etc., but is not limited to such examples. For example, the analysis unit performs analysis preferentially for a symptom that has recently occurred, and provides a quick diagnosis. The analysis unit can also perform analysis on a symptom that has occurred in the past at a later date. Furthermore, the analysis unit can determine the priority of the analysis based on the occurrence time of the symptom and perform an efficient diagnosis. This enables the analysis unit to perform analysis with the priority based on the occurrence time of the symptom. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the occurrence time data of the symptom to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of symptoms during analysis. The analysis unit, for example, evaluates the relevance of symptoms. The relevance includes, for example, common causes, concurrent symptoms, past diagnosis results, and the like, but is not limited to such examples. The analysis unit, for example, prioritizes analysis of highly related symptoms to provide a quick diagnosis. The analysis unit can also postpone analysis of less related symptoms. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of symptoms to perform efficient diagnosis. This enables the analysis unit to perform analysis in an order based on the relevance of symptoms. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0086] The follow-up unit can estimate the patient's emotion and adjust the follow-up method based on the estimated patient's emotion. The follow-up unit, for example, estimates the patient's emotion. For example, facial expression analysis, voice analysis, self-reporting, etc. are used to estimate the emotion, but the example is not limited thereto. For example, when the patient feels anxious, the follow-up unit frequently follows up to give the patient a sense of security. Also, when the patient is relaxed, the follow-up unit can follow up at an appropriate frequency to reduce the burden. Furthermore, when the patient is in a hurry, the follow-up unit can quickly follow up and provide necessary information. This enables the follow-up unit to follow up according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the example is not limited thereto. A part or all of the above-mentioned processing in the follow-up unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the follow-up unit can input patient emotion data into the generation AI and have the generation AI adjust the follow-up method.

[0087] The follow-up unit can select an optimal follow-up method by analyzing the patient's past medical behavior at the time of follow-up. The follow-up unit, for example, analyzes the patient's past medical behavior. The medical behavior includes, for example, a medical history, a treatment history, a follow-up history, and the like, but is not limited to such examples. The follow-up unit, for example, determines an optimal frequency of follow-up from 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 also analyze the patient's past medical behavior and propose an optimal follow-up method. This enables the follow-up unit to perform optimal follow-up based on the past medical behavior. A part or all of the above-mentioned processing in the follow-up unit may be performed, for example, using AI, or may be performed without using AI. For example, the follow-up unit can input the patient's medical behavior data to the generation AI and cause the generation AI to select the optimal follow-up method.

[0088] The follow-up unit can customize the follow-up means based on the patient's current living situation at the time of follow-up. The follow-up unit, for example, collects the patient's current living situation. The living situation includes, for example, occupation, lifestyle, home environment, etc., but is not limited to such examples. The follow-up unit selects the optimal follow-up means based on, for example, 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 also propose a follow-up means that is less burdensome, taking into account the patient's living situation. This enables the follow-up unit to follow up according to the patient's living situation. A part or all of the above-mentioned processing in the follow-up unit may be performed, for example, using AI, or may be performed without using AI. For example, the follow-up unit can input the patient's living situation data into the generation AI and cause the generation AI to execute customization of the follow-up means.

[0089] The follow-up unit can estimate the patient's emotion and determine the priority of follow-up based on the estimated emotion of the patient. The follow-up unit, for example, estimates the emotion of the patient. For example, facial expression analysis, voice analysis, self-reporting, etc. are used for estimating the emotion, but the example is not limited thereto. For example, when the patient feels anxious, the follow-up unit performs follow-up with priority to provide a sense of security. Furthermore, when the patient is relaxed, the follow-up unit can perform follow-up at an appropriate frequency to reduce the burden on the patient. Furthermore, when the patient is in a hurry, the follow-up unit can perform follow-up quickly and provide necessary information. This allows the follow-up unit to determine the priority of follow-up according to the emotion of the patient. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the example is not limited thereto. A part or all of the above-mentioned processing in the follow-up unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the follow-up unit can input patient emotion data into the generation AI and have the generation AI determine the priority of follow-up.

[0090] The follow-up unit can select the optimal follow-up method in consideration of the geographical location information of the patient at the time of follow-up. The follow-up unit, for example, collects the geographical location information of the patient. The geographical location information includes, for example, GPS data, address information, movement history, etc., but is not limited to such examples. The follow-up unit provides area-specific medical information, for example, 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 in consideration of the geographical location information of the patient. This enables the follow-up unit to perform optimal follow-up based on the geographical location information. A part or all of the above-mentioned processing in the follow-up unit may be performed, for example, using AI, or may be performed without using AI. For example, the follow-up unit can input the geographical location data of the patient to the generation AI and cause the generation AI to select the optimal follow-up method.

[0091] The follow-up unit can analyze the social media activity of the patient at the time of follow-up and suggest a follow-up means. The follow-up unit, for example, analyzes the social media activity of the patient. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. The follow-up unit, for example, analyzes health-related posts and comments from the social media activity of the patient and suggests related follow-up means. The follow-up unit can also collect information on specific symptoms or illnesses from the social media activity of the patient and suggest follow-up means. Furthermore, the follow-up unit can analyze the social media activity of the patient and suggest optimal follow-up means. This enables the follow-up unit to follow up based on the social media activity. A part or all of the above-mentioned processing in the follow-up unit may be performed, for example, using AI, or may be performed without using AI. For example, the follow-up unit can input the social media data of the patient to the generation AI and cause the generation AI to execute the suggestion of the follow-up means.

[0092] The security unit can estimate the patient's emotion and adjust the encryption level of the data based on the estimated emotion of the patient. The security unit, for example, estimates the emotion of the patient. For example, facial expression analysis, voice analysis, self-reporting, etc. are used to estimate the emotion, but the example is not limited to these. For example, when the patient feels anxious, the security unit applies the highest level of encryption to give a sense of security. Also, when the patient is relaxed, the security unit can apply the standard level of encryption to efficiently protect the data. Furthermore, when the patient is in a hurry, the security unit can select an appropriate encryption level to quickly process the data. This enables the security unit to adjust the encryption level according to the emotion of the patient. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the example is not limited to these. A part or all of the above-mentioned processing in the security unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the security unit can input the emotion data of the patient to the generation AI and cause the generation AI to adjust the encryption level.

[0093] The security unit can analyze the access control history and select the optimal security measure. The security unit, for example, analyzes the access control history. The access control history includes, for example, a login history, an access log, and an authority change history, but is not limited to such examples. For example, the security unit applies enhanced security measures to data that is frequently accessed from the past access control history. The security unit can also analyze the access control history and implement additional security measures for data with a high risk of unauthorized access. Furthermore, the security unit can select optimal security measures based on the access control history and strengthen the protection of data. This enables the security unit to take optimal security measures based on the access control history. A part or all of the above-mentioned processing in the security unit may be performed, for example, using AI, or may be performed without using AI. For example, the security unit can input the access control history data to the generation AI and cause the generation AI to select the optimal security measure.

[0094] The security unit can estimate the patient's emotion and determine the priority of access control based on the estimated emotion of the patient. The security unit, for example, estimates the emotion of the patient. For example, facial expression analysis, voice analysis, self-reporting, etc. are used to estimate the emotion, but the example is not limited thereto. For example, when the patient feels anxious, the security unit strengthens access control to important data to give a sense of security. In addition, when the patient is relaxed, the security unit can apply standard access control to efficiently protect the data. Furthermore, when the patient is in a hurry, the security unit can select appropriate access control so that the data can be accessed quickly. This allows the security unit to determine the priority of access control according to the emotion of the patient. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the example is not limited thereto. A part or all of the above-mentioned processing in the security unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the security department can input patient emotion data into the generation AI and have the generation AI determine access control priorities.

[0095] The security unit can select the optimal security measures in consideration of the geographical location information of the patient. The security unit, for example, collects the geographical location information of the patient. The geographical location information includes, for example, GPS data, address information, movement history, etc., but is not limited to such examples. The security unit applies measures to address security risks specific to the region, for example, based on the patient's place of residence. The security unit can also implement security measures to protect information about nearby medical institutions and pharmacies based on the patient's current location. Furthermore, the security unit can select optimal security measures and strengthen data protection in consideration of the geographical location information of the patient. This enables the security unit to take optimal security measures based on the geographical location information. Part or all of the above-mentioned processing in the security unit may be performed, for example, using AI, or may be performed without using AI. For example, the security unit can input the geographical location data of the patient to the generation AI and cause the generation AI to select the optimal security measures.

[0096] The criterion setting unit can estimate the patient's emotion and adjust the criteria for determining the necessity of medical treatment based on the estimated patient's emotion. The criterion setting unit, for example, estimates the patient's emotion. For example, facial expression analysis, voice analysis, self-reporting, etc. are used to estimate the emotion, but the example is not limited thereto. For example, when the patient feels anxious, the criterion setting unit sets the necessity of medical treatment higher to give a sense of security. In addition, when the patient is relaxed, the criterion setting unit can apply standard criteria and perform medical treatment efficiently. Furthermore, when the patient is in a hurry, the criterion setting unit can adjust the criteria so that the necessity of medical treatment can be determined quickly. This allows the criterion setting unit to adjust the criteria for the necessity of medical treatment according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the example is not limited thereto. A part or all of the above-mentioned processing in the criterion setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the criteria setting unit can input patient emotion data to the generation AI and cause the generation AI to adjust the criteria for medical treatment needs.

[0097] The criterion setting unit can optimize the criterion by referring to past medical data. The criterion setting unit, for example, refers to past medical data. The medical data includes, for example, diagnosis results, treatment history, and test results, but is not limited to such examples. The criterion setting unit, for example, sets optimal criteria for determining the necessity of medical treatment from past medical data. The criterion setting unit can also analyze past medical data and optimize the criteria for determining the necessity of medical treatment. Furthermore, the criterion setting unit can adjust the criteria for determining the necessity of medical treatment based on the past medical data, and perform medical treatment efficiently. This enables the criterion setting unit to set optimal criteria based on the past medical data. Part or all of the above-mentioned processing in the criterion setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the criterion setting unit can input past medical data to the generation AI and cause the generation AI to execute optimization of the criteria.

[0098] The standard setting unit can estimate the patient's emotion and determine the priority of the standard setting based on the estimated emotion of the patient. The standard setting unit, for example, estimates the emotion of the patient. For example, facial expression analysis, voice analysis, self-reporting, etc. are used for estimating the emotion, but the example is not limited to these. For example, when the patient feels anxious, the standard setting unit sets important criteria with priority and gives a sense of security. Also, when the patient is relaxed, the standard setting unit can apply standard criteria and perform medical treatment efficiently. Furthermore, when the patient is in a hurry, the standard setting unit can adjust the priority so that the criteria can be set quickly. This allows the standard setting unit to determine the priority of the standard setting according to the emotion of the patient. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the example is not limited to these. A part or all of the above-mentioned processing in the standard setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the criteria setting unit can input patient emotion data to the generation AI and have the generation AI determine the priority of criteria setting.

[0099] The standard setting unit can set optimal standards in consideration of the geographical location information of the patient. The standard setting unit, for example, collects geographical location information of the patient. The geographical location information includes, for example, GPS data, address information, movement history, etc., but is not limited to such examples. The standard setting unit, for example, sets area-specific medical treatment standards based on the patient's place of residence. The standard setting unit can also set standards that take into account information on nearby medical institutions and pharmacies based on the patient's current location. Furthermore, the standard setting unit can set optimal medical treatment standards in consideration of the geographical location information of the patient and perform medical treatment efficiently. This enables the standard setting unit to set optimal standards based on the geographical location information. Part or all of the above-mentioned processing in the standard setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the standard setting unit can input the geographical location data of the patient to the generation AI and cause the generation AI to execute the setting of optimal standards.

[0100] The follow-up setting unit can estimate the patient's emotion and adjust a specific method of follow-up based on the estimated emotion of the patient. The follow-up setting unit, for example, estimates the emotion of the patient. For example, facial expression analysis, voice analysis, self-reporting, etc. are used to estimate the emotion, but the example is not limited thereto. For example, when the patient feels anxious, the follow-up setting unit performs frequent follow-up to give the patient a sense of security. In addition, when the patient is relaxed, the follow-up setting unit can perform follow-up at an appropriate frequency to reduce the burden on the patient. Furthermore, when the patient is in a hurry, the follow-up setting unit can perform quick follow-up and provide necessary information. This enables the follow-up setting unit to adjust the follow-up method according to the emotion of the patient. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the example is not limited thereto. A part or all of the above-mentioned processing in the follow-up setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the follow-up setting unit can input patient emotion data to the generation AI and cause the generation AI to adjust the follow-up method.

[0101] The follow-up setting unit can set an optimal follow-up method by referring to past follow-up data. The follow-up setting unit, for example, refers to past follow-up data. The follow-up data includes, for example, the frequency, method, effect, etc. of follow-up, but is not limited to such examples. The follow-up setting unit, for example, sets an optimal follow-up frequency from past follow-up data. In addition, the follow-up setting unit can also analyze past follow-up data and set the most effective follow-up method (telephone, email, etc.). Furthermore, the follow-up setting unit can set an optimal follow-up method based on past follow-up data and perform follow-up efficiently. This enables the follow-up setting unit to set an optimal follow-up method based on past follow-up data. A part or all of the above-mentioned processing in the follow-up setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the follow-up setting unit can input past follow-up data to the generation AI and cause the generation AI to execute the setting of the optimal follow-up method.

[0102] The follow-up setting unit can estimate the patient's emotion and determine the priority of follow-up based on the estimated emotion of the patient. The follow-up setting unit, for example, estimates the emotion of the patient. For example, facial expression analysis, voice analysis, self-reporting, etc. are used for estimating the emotion, but the example is not limited thereto. For example, when the patient feels anxious, the follow-up setting unit performs follow-up with priority to provide a sense of security. Furthermore, when the patient is relaxed, the follow-up setting unit can perform follow-up at an appropriate frequency to reduce the burden on the patient. Furthermore, when the patient is in a hurry, the follow-up setting unit can perform follow-up quickly and provide necessary information. This allows the follow-up setting unit to determine the priority of follow-up according to the emotion of the patient. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the example is not limited thereto. A part or all of the above-mentioned processing in the follow-up setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the follow-up setting unit can input patient emotion data to the generation AI and have the generation AI determine the priority of follow-up.

[0103] The follow-up setting unit can set an optimal follow-up method in consideration of the geographical location information of the patient. The follow-up setting unit, for example, collects geographical location information of the patient. The geographical location information includes, for example, GPS data, address information, movement history, etc., but is not limited to such examples. The follow-up setting unit provides area-specific medical information, for example, 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 an optimal follow-up method in consideration of the geographical location information of the patient and perform follow-up efficiently. This enables the follow-up setting unit to set an optimal follow-up method based on the geographical location information. A part or all of the above-mentioned processing in the follow-up setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the follow-up setting unit can input the geographical location data of the patient to the generation AI and cause the generation AI to execute the setting of the optimal follow-up method.

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

[0105] The interview system can analyze the patient's lifestyle habits and take them into consideration when determining whether medical treatment is necessary. For example, the 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 habits on symptoms and determine whether medical treatment is necessary. The follow-up unit can also provide specific advice for improving lifestyle habits. This allows medical treatment to take into account the patient's overall health condition.

[0106] The interview system can collect genetic information from patients and take it into consideration when determining whether medical treatment is necessary. For example, the collection unit collects the results of genetic tests on patients. The analysis unit evaluates genetic risks based on this genetic information and determines whether medical treatment is necessary. The follow-up unit can also provide preventive measures and lifestyle advice based on genetic risks. This makes it possible to provide personalized medicine that takes genetic factors into account.

[0107] The interview system can determine the need for medical treatment by taking into account 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 uses this data to evaluate the impact of socioeconomic background on health and determine the need for medical treatment. The follow-up department can also provide support measures and resources according to socioeconomic background. This allows for comprehensive medical care that takes socioeconomic factors into account.

[0108] The interview system can evaluate the patient's psychological stress level and determine the need for medical treatment. For example, the collection unit uses questionnaires and sensors to evaluate 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 makes it possible to provide medical treatment that takes psychological factors into account.

[0109] The interview system can determine the need for medical treatment by taking into account the patient's environmental factors. For example, the collection unit collects data on the patient's living and working environments. The analysis unit uses this data to evaluate the impact that environmental factors have on health and determine the need for medical treatment. The follow-up unit can also provide specific advice for improving the environment. This makes it possible to provide medical treatment that takes environmental factors into account.

[0110] The interview system can estimate the patient's emotions and determine the priority of medical treatment based on the estimated emotions. For example, if the patient is feeling strong anxiety, the analysis unit can prioritize medical treatment. If the patient is relaxed, the normal medical treatment schedule can be followed. Furthermore, if the patient is in a hurry, medical treatment can be expedited. This allows for flexible medical treatment according to the patient's emotions.

[0111] The interview system can estimate the patient's emotions and adjust the method of medical treatment based on the estimated emotions. For example, if the patient is feeling anxious, the analysis unit can provide an explanation that reassures the patient. If the patient is relaxed, the analysis unit can provide a detailed explanation to deepen the patient's understanding. Furthermore, if the patient is in a hurry, the analysis unit can provide a concise explanation that focuses on the main points. This makes it possible to provide appropriate medical treatment according to the patient's emotions.

[0112] The interview system can estimate the patient's emotions and adjust the frequency of follow-up based on the estimated emotions. For example, if the patient feels anxious, the follow-up unit can follow up frequently to reassure the patient. If the patient feels relaxed, the follow-up unit can follow up at an appropriate frequency to reduce the patient's burden. If the patient is in a hurry, the follow-up unit can quickly follow up and provide the patient with the necessary information. This makes it possible to follow up according to the patient's emotions.

[0113] The interview system can estimate the patient's emotions and adjust the content of follow-up based on the estimated emotions. For example, if the patient feels anxious, the follow-up unit can provide reassuring content. If the patient feels relaxed, the follow-up unit can provide detailed information to deepen understanding. If the patient is in a hurry, the follow-up unit can provide concise, to-the-point information. This makes it possible to provide appropriate follow-up according to the patient's emotions.

[0114] The interview system can estimate the patient's emotions and select a follow-up method based on the estimated emotions. For example, if the patient feels anxious, the follow-up unit can perform follow-up by phone or in person to reassure the patient. If the patient feels relaxed, the follow-up unit can perform follow-up by email or message to reduce the patient's burden. Furthermore, if the patient is in a hurry, a method that allows for a quick response can be selected. This provides the optimal follow-up method according to the patient's emotions.

[0115] The process flow of the second embodiment will be briefly described below.

[0116] Step 1: The collection unit collects patient information. The patient information includes symptoms, medical history, current physical condition, etc. The collection unit collects information entered by the patient using a tablet or smartphone. The collection unit can also collect the patient's biometric data (blood pressure, heart rate, body temperature, etc.) using sensors. Furthermore, the collection unit obtains the patient's medical history from the electronic medical record system. Step 2: The analysis unit analyzes the information collected by the collection unit and determines whether medical treatment is necessary. The analysis is performed using algorithms and analysis tools. For example, the analysis unit uses machine learning algorithms to analyze the patient's symptoms and determine whether medical treatment is necessary. It also uses statistical analysis tools to evaluate the necessity of medical treatment based on the severity of symptoms and medical history. Step 3: The follow-up department performs post-treatment follow-up based on the results obtained by the analysis department. Follow-up is performed by telephone or email. For example, the follow-up department performs regular health checks on patients. It also provides rehabilitation programs and gives 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0119] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

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

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

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0128] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

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

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

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

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0140] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0147] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

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

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

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

[0154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0164] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

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

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

[0170] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0171] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0175] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0180] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0181] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0182] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The above specific processes are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0186] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0188] (Appendix 1) A collection unit for collecting patient information; an analysis unit that analyzes the information collected by the collection unit and determines the need for medical treatment; A follow-up unit that performs follow-up after medical treatment based on the results obtained by the analysis unit. A system characterized by: (Appendix 2) Equipped with a security section that encrypts data or controls access 2. The system of claim 1, (Appendix 3) Equipped with a standard setting unit that sets standards and algorithms for determining the need for medical treatment 2. The system of claim 1, (Appendix 4) Equipped with a follow-up setting section for setting specific methods for follow-up after treatment 2. The system of claim 1, (Appendix 5) The collecting unit includes: Collect information about the patient's symptoms, medical history, and current condition 2. The system of claim 1, (Appendix 6) The analysis unit is Analyze the collected information and determine the need for treatment 2. The system of claim 1, (Appendix 7) The collecting unit includes: Inferring the patient's emotions and adjusting the timing of information collection based on the inferred patient emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze the patient's past medical history and choose the most appropriate method of collecting information 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting information, filter it based on the patient's current life situation and areas of interest 2. The system of claim 1, (Appendix 10) The collecting unit includes: Estimate the patient's emotions and prioritize the information to be collected based on the estimated patient's emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting information, prioritize collection of relevant information by taking into account the geographic location of the patient. 2. The system of claim 1, (Appendix 12) The collecting unit includes: At the time of collection, analyze the patient's social media activity and collect relevant information. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer patient emotion and adjust analysis presentation based on the inferred emotion 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the severity of symptoms 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the symptom category. 2. The system of claim 1, (Appendix 16) The analysis unit is Inferring patient sentiment and adjusting the length of the analysis based on the estimated patient sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when symptoms first appeared 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on symptom relevance 2. The system of claim 1, (Appendix 19) The follow-up unit: Estimate the patient's feelings and adjust follow-up methods based on the estimated patient feelings 2. The system of claim 1, (Appendix 20) The follow-up unit: During follow-up, the patient's past medical history is analyzed to select the most appropriate follow-up method. 2. The system of claim 1, (Appendix 21) The follow-up unit: At follow-up, tailor follow-up measures based on the patient's current living situation 2. The system of claim 1, (Appendix 22) The follow-up unit: Estimate patient emotions and prioritize follow-up based on estimated patient emotions 2. The system of claim 1, (Appendix 23) The follow-up unit: During follow-up, the geographic location of the patient is taken into account to determine the most appropriate follow-up method 2. The system of claim 1, (Appendix 24) The follow-up unit: At follow-up, analyze the patient's social media activity to suggest follow-up measures 2. The system of claim 1, (Appendix 25) The security unit includes: Inferring patient sentiment and adjusting the encryption level of data based on the inferred patient sentiment 3. The system of claim 2, (Appendix 26) The security unit includes: Analyze access control history and select optimal security measures 3. The system of claim 2, (Appendix 27) The security unit includes: Estimating a patient's emotions and determining access control priorities based on the estimated patient's emotions 3. The system of claim 2, (Appendix 28) The security unit includes: Select the most appropriate security measures based on the patient's geographic location 3. The system of claim 2, (Appendix 29) The reference setting unit is Estimate the patient's feelings and adjust the criteria for determining the need for medical treatment based on the estimated patient's feelings 4. The system of claim 3. (Appendix 30) The reference setting unit is Optimize standards by referencing past medical data 4. The system of claim 3. (Appendix 31) The reference setting unit is Estimating the patient's emotions and determining the priority of standard setting based on the estimated patient's emotions 4. The system of claim 3. (Appendix 32) The reference setting unit is Set optimal criteria based on patient geographic location 4. The system of claim 3. (Appendix 33) The follow-up setting unit, Estimate the patient's feelings and tailor the specifics of follow-up based on the patient's feelings. 5. The system of claim 4. (Appendix 34) The follow-up setting unit, Refer to past follow-up data to determine optimal follow-up methods 5. The system of claim 4. (Appendix 35) The follow-up setting unit, Estimate patient emotions and prioritize follow-up based on estimated patient emotions 5. The system of claim 4. (Appendix 36) The follow-up setting unit, Optimize follow-up by considering the patient's geographic location 5. The system of claim 4. [Explanation of symbols]

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

Claims

1. A collection unit for collecting patient information; an analysis unit that analyzes the information collected by the collection unit and determines the need for medical treatment; A follow-up unit that performs follow-up after medical treatment based on the results obtained by the analysis unit. A system characterized by:

2. Equipped with a security section that encrypts data or controls access 2. The system of claim 1 .

3. Equipped with a standard setting unit that sets standards and algorithms for determining the need for medical treatment 2. The system of claim 1 .

4. Equipped with a follow-up setting section for setting specific methods for follow-up after treatment 2. The system of claim 1 .

5. The collecting unit includes: Collect information about the patient's symptoms, medical history, and current condition 2. The system of claim 1 .

6. The analysis unit is Analyze the collected information and determine the need for treatment 2. The system of claim 1 .

7. The collecting unit includes: Estimate the patient's emotions and adjust the timing of information collection based on the estimated patient emotions 2. The system of claim 1 .

8. The collecting unit includes: Analyze the patient's past medical history and choose the most appropriate method of collecting information 2. The system of claim 1 .

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