system

Abstract

The system according to this embodiment aims to suggest the most suitable medical department and treatment method based on the patient's diagnosis and symptoms. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, and a treatment proposal unit. The reception unit inputs the patient's disease name and symptoms. The analysis unit analyzes the information input by the reception unit. The proposal unit proposes the most suitable medical department and examination method based on the information analyzed by the analysis unit. The treatment proposal unit proposes the most suitable treatment method based on the examination results proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a risk that the proposal of the most appropriate medical department and treatment method for a patient's disease name and symptoms may be biased depending on the doctor's field and level.

[0005] The system according to the embodiment aims to propose the most appropriate medical department and treatment method based on the patient's disease name and symptoms.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a treatment proposal unit. The reception unit inputs the patient's disease name and symptoms. The analysis unit analyzes the information entered by the reception unit. The proposal unit proposes the most suitable medical department and examination method based on the information analyzed by the analysis unit. The treatment proposal unit proposes the most suitable treatment method based on the examination results proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the most suitable medical department and treatment method based on the patient's diagnosis and symptoms. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The medical support system according to an embodiment of the present invention is a system for providing patients with the optimal treatment approach. This medical support system works by having the patient input their disease name and symptoms, which are then analyzed by AI, which proposes the most suitable medical department and examination methods, and further proposes the most suitable treatment method based on the examination results. This eliminates bias based on the doctor's field of expertise or level, and realizes the optimal treatment approach for the patient. For example, the patient only needs to input their disease name and symptoms into the system. For example, they might input information such as "I have a headache" or "I have diabetes." This information is then input into the AI. Next, the AI ​​analyzes the input information and proposes the most suitable medical department and examination methods. The AI ​​analyzes medical big data to identify the most suitable medical department and examination methods for the patient. For example, if the patient has a headache, it might suggest neurology or neurosurgery and indicate the necessary examination methods. Furthermore, the AI ​​proposes the most suitable treatment method based on the examination results. The AI ​​analyzes the examination results and selects the most suitable treatment method from countless options. For example, for a patient with diabetes, it might suggest treatment methods such as diet therapy, exercise therapy, or drug therapy. This mechanism eliminates bias based on the doctor's field of expertise or level, and realizes the optimal treatment approach for the patient. Patients can find the optimal treatment method through a single system without having to visit multiple doctors. Furthermore, medical professionals can efficiently provide the best possible treatment to patients. For example, a dedicated chatbot can instantly suggest the most suitable medical department and examination methods for each patient. This improves patient satisfaction and increases efficiency in the medical field. Ultimately, the medical support system can provide patients with the most appropriate treatment approach.

[0029] The medical support system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a treatment proposal unit. The reception unit inputs the patient's illness and symptoms. The patient's illness and symptoms include, for example, headaches, diabetes, and abdominal pain, but are not limited to such examples. The reception unit provides, for example, an interface for the patient to input their illness and symptoms into the system. The analysis unit analyzes the information input by the reception unit. The analysis unit analyzes the input information using, for example, AI, and identifies the optimal medical department and examination method. The proposal unit proposes the optimal medical department and examination method based on the information analyzed by the analysis unit. The proposal unit proposes the optimal medical department and examination method based on the analysis results using, for example, AI. The treatment proposal unit proposes the optimal treatment method based on the examination results proposed by the proposal unit. The treatment proposal unit analyzes the examination results using, for example, AI, and selects the optimal one from countless treatment methods. As a result, the medical support system according to this embodiment can provide the patient with the optimal treatment approach. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without using AI. For example, the reception unit provides an interface for inputting the patient's diagnosis and symptoms, and can analyze the input information using AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can analyze the input information using AI and identify the optimal medical department and examination method. Some or all of the above-described processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can use AI to propose the optimal medical department and examination method based on the analysis results. Some or all of the above-described processes in the treatment proposal unit may be performed using AI, or not. For example, the treatment proposal unit can use AI to analyze the examination results and select the optimal one from countless treatment methods. As a result, the medical support system according to the embodiment can provide the patient with the optimal treatment approach.

[0030] The reception desk inputs the patient's diagnosis and symptoms. These include, but are not limited to, headaches, diabetes, and abdominal pain. The reception desk provides an interface for patients to input their diagnosis and symptoms into the system. Specifically, it offers various input methods such as touchscreens, voice input, and keyboard input to allow patients to easily enter information. Furthermore, the reception desk has a function to automatically classify the entered information and convert it into an appropriate format. For example, if a patient enters "I have a headache," the system extracts the keyword "headache" and lists related symptoms and diagnoses. The reception desk can also input the patient's past medical history and allergy information, allowing for the collection of more detailed information. This enables the reception desk to accurately understand the patient's condition and provide necessary information to the subsequent analysis and recommendation departments. Additionally, the reception desk can monitor the patient's input in real time and ask additional questions as needed. For example, if a patient enters "abdominal pain," the system displays additional questions such as "Which part hurts?" and "How severe is the pain?" to collect more detailed information. This allows the reception desk to more accurately understand the patient's symptoms and provide the necessary basic information to identify the appropriate medical department and examination methods.

[0031] The analysis department analyzes the information entered by the reception department. For example, the analysis department uses AI to analyze the entered information and identify the most suitable medical department and examination method. Specifically, the AI ​​uses natural language processing technology to analyze the patient's input information and automatically classify symptoms and disease names. For example, if a patient enters "I have a headache," the AI ​​extracts the keyword "headache" and identifies the relevant medical department (e.g., neurology or otolaryngology). The AI ​​can also suggest the most suitable examination method for a specific symptom based on past medical data and statistical information. For example, if a patient has a headache, the AI ​​will suggest examination methods such as MRI or CT scans. Furthermore, the analysis department also considers the patient's past medical history and allergy information to identify the most suitable medical department and examination method. For example, if a patient has a history of allergies to a particular drug, the AI ​​will consider this information and suggest a treatment method that will not trigger an allergic reaction. In this way, the analysis department can comprehensively evaluate the patient's condition and identify the most suitable medical department and examination method. In addition, the analysis department can utilize a real-time updated medical database to perform analysis based on the latest medical information. This allows the analysis unit to perform highly accurate analyses based on the latest information at all times, enabling it to propose the most suitable medical department and examination methods for each patient.

[0032] The Proposal Department suggests the most suitable medical department and examination method based on the information analyzed by the Analysis Department. For example, the Proposal Department uses AI to suggest the most suitable medical department and examination method based on the analysis results. Specifically, the Proposal Department presents patients with specific medical departments and examination methods based on the information provided by the Analysis Department. For example, for a patient with headache symptoms, the Proposal Department recommends a visit to a neurologist and suggests an MRI examination. The Proposal Department can also provide information such as the nearest medical institution and its operating hours, taking patient convenience into consideration. This allows patients to receive appropriate medical services quickly and efficiently. Furthermore, the Proposal Department can collect patient feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can confirm with the patient whether the suggested medical department and examination method were appropriate and adjust the suggestion algorithm based on the results. The Proposal Department can also compare multiple medical departments and examination methods and present the best option. For example, for headache symptoms, it can suggest both neurology and otolaryngology, allowing the patient to choose. This enables the Proposal Department to provide patients with flexible and appropriate medical suggestions and the optimal treatment approach.

[0033] The Treatment Proposal Department proposes the most suitable treatment method based on the test results suggested by the Proposal Department. For example, the Treatment Proposal Department uses AI to analyze test results and select the best option from countless treatment methods. Specifically, the AI ​​evaluates the patient's condition in detail based on the test results and identifies the optimal treatment method. For example, if an abnormality is found in the brain as a result of an MRI scan, the AI ​​compares treatment methods such as surgery, drug therapy, and rehabilitation and proposes the optimal treatment plan. The Treatment Proposal Department also considers the patient's past treatment history and allergy information to propose individually optimized treatment methods. For example, for a patient allergic to a specific drug, it will suggest an alternative drug. Furthermore, the Treatment Proposal Department can propose cutting-edge treatment methods by utilizing data from the latest medical research and clinical trials. This ensures that patients always receive the best possible treatment based on the latest medical information. The Treatment Proposal Department can also monitor the effectiveness of the proposed treatment methods and modify the treatment plan as needed. For example, it monitors the progress of treatment and the patient's response in real time, and if the treatment method is not sufficiently effective, it will propose an alternative treatment method. This allows the Treatment Proposal Department to continuously provide patients with the best possible treatment and maximize treatment effectiveness.

[0034] The system includes a chatbot unit that uses a dedicated chatbot to suggest the most suitable medical department and examination methods to patients. For example, the chatbot unit suggests the most suitable medical department and examination methods based on the disease name and symptoms entered by the patient into the system. The chatbot unit can also use AI to analyze the patient's input information and suggest the most suitable medical department and examination methods. For example, if the patient enters "I have a headache," the chatbot unit will suggest neurology or neurosurgery and indicate the necessary examination methods. This allows for the rapid suggestion of the most suitable medical department and examination methods to patients. Some or all of the above processing in the chatbot unit may be performed using AI, or not using AI. For example, the chatbot unit can input the patient's input information into the AI, and the AI ​​can suggest the most suitable medical department and examination methods based on the analysis results. This allows for the rapid suggestion of the most suitable medical department and examination methods to patients.

[0035] The system includes a data analysis department that analyzes medical big data. The data analysis department, for example, uses AI to analyze medical big data and identify the optimal medical department and examination methods. Medical big data includes, but is not limited to, electronic medical record data, medical records, and test results. For example, the data analysis department can use AI to analyze electronic medical record data and propose the most suitable medical department and examination methods for a patient. For example, the data analysis department can use AI to analyze medical records and propose the most suitable medical department and examination methods for a patient. For example, the data analysis department can use AI to analyze test results and propose the most suitable medical department and examination methods for a patient. This allows for the proposal of more accurate medical departments and examination methods by analyzing medical big data. Some or all of the above-described processes in the data analysis department may be performed using AI, or not. For example, the data analysis department can input medical big data into AI, which can then propose the most suitable medical department and examination methods based on the analysis results. This allows for the proposal of more accurate medical departments and examination methods by analyzing medical big data.

[0036] The system includes an examination analysis unit that analyzes examination results. The examination analysis unit, for example, uses AI to analyze examination results and proposes the optimal treatment method. Examination results include, but are not limited to, blood test results and imaging diagnostic results. The examination analysis unit can, for example, use AI to analyze blood test results and propose the optimal treatment method. The examination analysis unit can, for example, use AI to analyze imaging diagnostic results and propose the optimal treatment method. The examination analysis unit can, for example, use AI to analyze other examination results and propose the optimal treatment method. In this way, by analyzing examination results, a more appropriate treatment method can be proposed. Some or all of the above-described processes in the examination analysis unit may be performed using AI, for example, or without AI. For example, the examination analysis unit can input examination results into AI, and the AI ​​can propose the optimal treatment method based on the analysis results. In this way, by analyzing examination results, a more appropriate treatment method can be proposed.

[0037] The suggestion unit can identify the optimal medical department and examination method for a patient. For example, the suggestion unit can use AI to identify the optimal medical department and examination method based on the analysis results. For example, the suggestion unit can use AI to identify the optimal medical department and examination method based on the patient's diagnosis and symptoms. For example, the suggestion unit can use AI to identify the optimal medical department and examination method based on the patient's diagnosis and symptoms. For example, the suggestion unit can use AI to identify the optimal medical department and examination method based on the patient's diagnosis and symptoms. This improves the accuracy of medical care by identifying the optimal medical department and examination method for the patient. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input the patient's diagnosis and symptoms into the AI, and the AI ​​can identify the optimal medical department and examination method based on the analysis results. This improves the accuracy of medical care by identifying the optimal medical department and examination method for the patient.

[0038] The treatment suggestion unit can select the optimal treatment from a multitude of options. For example, the treatment suggestion unit uses AI to analyze test results and select the optimal treatment from a multitude of options. These multitude of treatment options include, but are not limited to, drug therapy, surgical therapy, and rehabilitation. For example, the treatment suggestion unit can suggest drug therapy using AI. For example, the treatment suggestion unit can suggest surgical therapy using AI. For example, the treatment suggestion unit can suggest rehabilitation using AI. By selecting the optimal treatment from a multitude of options, the effectiveness of the treatment is improved. Some or all of the above-described processes in the treatment suggestion unit may be performed using AI, or not. For example, the treatment suggestion unit can input test results into AI, which can then select the optimal treatment method based on the analysis results. By selecting the optimal treatment from a multitude of options, the effectiveness of the treatment is improved.

[0039] The reception desk can refer to the patient's past medical history and evaluate the reliability of the entered information. For example, the reception desk may use AI to refer to the patient's past medical history and evaluate the reliability of the entered information. For example, it may refer to the patient's past medical records to check whether the entered disease name or symptoms match the past medical history. For example, the reception desk may evaluate the reliability of the entered information based on the patient's past treatment history and perform additional verification as needed. For example, the reception desk may analyze the patient's past medical data to determine whether the entered information is reliable. This improves the reliability of the entered information by referring to past medical history. Some or all of the above processes in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk may input the patient's past medical history into AI, and the AI ​​may evaluate the reliability of the entered information based on the analysis results. This improves the reliability of the entered information by referring to past medical history.

[0040] The reception desk can acquire additional information about the patient's lifestyle and environment when the patient enters their illness or symptoms. For example, the reception desk can use AI to acquire additional information about the patient's lifestyle and environment when the patient enters their illness or symptoms. For example, it can acquire information about the patient's lifestyle (diet, exercise, sleep, etc.) when the patient enters their information and use it to evaluate the illness or symptoms. For example, the reception desk can acquire information about the patient's environment (living environment, work environment, etc.) when the patient enters their information and use it as a reference to identify the cause of the illness or symptoms. For example, the reception desk can supplement the entered information about the illness or symptoms based on the patient's lifestyle and environment information, providing more accurate information. This makes it possible to make a more accurate diagnosis by acquiring lifestyle and environmental information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the patient's lifestyle and environment information into the AI, and the AI ​​can evaluate the illness or symptoms based on the analysis results. This makes it possible to make a more accurate diagnosis by acquiring lifestyle and environmental information.

[0041] The reception desk can prioritize the acquisition of highly relevant information by considering the patient's geographical location when inputting disease names and symptoms. For example, the reception desk can use AI to prioritize the acquisition of highly relevant information by considering the patient's geographical location when inputting disease names and symptoms. For example, it can prioritize the acquisition of information on region-specific diseases and symptoms based on the patient's current location. For example, the reception desk can acquire information on nearby medical institutions based on the patient's geographical location and suggest appropriate medical departments and examination methods. For example, the reception desk can acquire information related to regional environmental factors (climate, air quality, etc.) by considering the patient's geographical location. This allows for addressing region-specific diseases and symptoms by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the patient's geographical location into AI, and the AI ​​can prioritize the acquisition of highly relevant information based on the analysis results. This allows for addressing region-specific diseases and symptoms by considering geographical location.

[0042] The reception desk can analyze the patient's social media activity and obtain relevant information when the patient enters their illness or symptoms. For example, the reception desk can use AI to analyze the patient's social media activity and obtain relevant information when the patient enters their illness or symptoms. For example, it can analyze the patient's social media posts and obtain information related to the illness or symptoms. The reception desk can, for example, obtain lifestyle and environmental information from the patient's social media activity and use it to evaluate the illness or symptoms. For example, the reception desk can analyze posts from the patient's friends and followers on social media and obtain relevant information. This allows for the acquisition of lifestyle and environmental information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the patient's social media activity into the AI, which can then acquire relevant information based on the analysis results. This allows for the acquisition of lifestyle and environmental information by analyzing social media activity.

[0043] The analysis unit can optimize the analysis algorithm by referring to the patient's past medical data during analysis. For example, the analysis unit can use AI to optimize the analysis algorithm by referring to the patient's past medical data during analysis. For example, it can optimize the analysis algorithm based on the patient's past medical data to improve accuracy. The analysis unit can adjust the analysis algorithm by referring to the patient's past treatment history. For example, the analysis unit can analyze the patient's past medical data and select the optimal algorithm. This improves the accuracy of the analysis by referring to past medical data. Some or all of the above processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the patient's past medical data into the AI, which can then optimize the analysis algorithm based on the analysis results. This improves the accuracy of the analysis by referring to past medical data.

[0044] The analysis unit can perform analysis while considering the patient's lifestyle and environmental information. For example, the analysis unit can use AI to perform analysis while considering the patient's lifestyle and environmental information. For example, it can perform analysis while considering the patient's lifestyle (diet, exercise, sleep, etc.). The analysis unit can perform analysis while considering the patient's environmental information (living environment, work environment, etc.). For example, the analysis unit can supplement the analysis results based on the patient's lifestyle and environmental information. This makes it possible to perform more accurate analysis by considering lifestyle and environmental information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the patient's lifestyle and environmental information into the AI, and the AI ​​can perform analysis based on the analysis results. This makes it possible to perform more accurate analysis by considering lifestyle and environmental information.

[0045] The analysis unit can perform analysis while considering the patient's geographical location information. For example, the analysis unit can use AI to perform analysis while considering the patient's geographical location information. For example, it can analyze information on region-specific diseases and symptoms based on the patient's current location. For example, the analysis unit can analyze information on nearby medical institutions based on the patient's geographical location information. For example, the analysis unit can analyze information related to regional environmental factors (climate, air quality, etc.) while considering the patient's geographical location information. This allows for addressing region-specific diseases and symptoms by considering geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the patient's geographical location information into AI, and the AI ​​can perform analysis based on the analysis results. This allows for addressing region-specific diseases and symptoms by considering geographical location information.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the patient during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the patient during the analysis using AI. For example, it can improve the accuracy of its analysis by referring to literature related to the patient's disease name and symptoms. For example, the analysis unit can supplement the analysis results by referring to literature related to the patient's treatment methods. For example, the analysis unit can improve the accuracy of its analysis by comparing the patient's medical data with relevant literature. In this way, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature on the patient into the AI, and the AI ​​can improve the accuracy of the analysis based on the analysis results. In this way, the accuracy of the analysis is improved by referring to relevant literature.

[0047] The suggestion unit can identify the optimal medical department and examination method by referring to the patient's past medical data when making a suggestion. The suggestion unit can, for example, use AI to identify the optimal medical department and examination method by referring to the patient's past medical data when making a suggestion. For example, it can identify the optimal medical department based on the patient's past medical data. The suggestion unit can, for example, refer to the patient's past treatment history to identify the optimal examination method. The suggestion unit can, for example, analyze the patient's past medical data and suggest the optimal medical department and examination method. This allows the optimal medical department and examination method to be identified by referring to past medical data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input the patient's past medical data into AI, and the AI ​​can identify the optimal medical department and examination method based on the analysis results. This allows the optimal medical department and examination method to be identified by referring to past medical data.

[0048] The suggestion unit can make suggestions while considering the patient's lifestyle and environmental information. For example, the suggestion unit can use AI to make suggestions while considering the patient's lifestyle and environmental information. For example, it can make suggestions while considering the patient's lifestyle (diet, exercise, sleep, etc.). The suggestion unit can make suggestions while considering the patient's environmental information (living environment, work environment, etc.). For example, the suggestion unit can suggest the most suitable medical department and examination method based on the patient's lifestyle and environmental information. This makes it possible to make more appropriate suggestions by considering lifestyle and environmental information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the patient's lifestyle and environmental information into AI, and the AI ​​can make suggestions based on the analysis results. This makes it possible to make more appropriate suggestions by considering lifestyle and environmental information.

[0049] The suggestion unit can propose the most suitable medical department and examination method when making a suggestion, taking into account the patient's geographical location. For example, the suggestion unit can use AI to propose the most suitable medical department and examination method when making a suggestion, taking into account the patient's geographical location. For example, it can propose information on region-specific diseases and symptoms based on the patient's current location. For example, the suggestion unit can propose information on nearby medical institutions based on the patient's geographical location. For example, the suggestion unit can propose information related to regional environmental factors (climate, air quality, etc.) taking into account the patient's geographical location. This allows for addressing region-specific diseases and symptoms by considering geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the patient's geographical location into AI, and the AI ​​can propose the most suitable medical department and examination method based on the analysis results. This allows for addressing region-specific diseases and symptoms by considering geographical location.

[0050] The suggestion unit can analyze the patient's social media activity and suggest relevant medical departments and examination methods when making suggestions. For example, the suggestion unit can use AI to analyze the patient's social media activity and suggest relevant medical departments and examination methods when making suggestions. For example, it can analyze the patient's social media posts and suggest information related to medical departments and examination methods. For example, the suggestion unit can obtain lifestyle and environmental information from the patient's social media activity and use it to suggest medical departments and examination methods. For example, the suggestion unit can analyze posts from the patient's friends and followers on social media and suggest relevant medical departments and examination methods. In this way, lifestyle and environmental information can be obtained by analyzing social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the patient's social media activity into AI, and the AI ​​can suggest relevant medical departments and examination methods based on the analysis results. In this way, lifestyle and environmental information can be obtained by analyzing social media activity.

[0051] The treatment suggestion unit can select the optimal treatment method by referring to the patient's past treatment history when suggesting a treatment. For example, the treatment suggestion unit can use AI to select the optimal treatment method by referring to the patient's past treatment history when suggesting a treatment. For example, it can select the optimal treatment method based on the patient's past treatment history. For example, the treatment suggestion unit can select an effective treatment method by referring to the patient's past treatment results. For example, the treatment suggestion unit can analyze the patient's past treatment history and suggest the optimal treatment method. This allows the optimal treatment method to be selected by referring to the past treatment history. Some or all of the above processes in the treatment suggestion unit may be performed using AI, for example, or without using AI. For example, the treatment suggestion unit can input the patient's past treatment history into AI, and the AI ​​can select the optimal treatment method based on the analysis results. This allows the optimal treatment method to be selected by referring to the past treatment history.

[0052] The treatment proposal unit can customize treatment methods when proposing treatment, taking into account the patient's lifestyle and environmental information. For example, the treatment proposal unit can use AI to customize treatment methods when proposing treatment, taking into account the patient's lifestyle and environmental information. For example, it can customize treatment methods by taking into account the patient's lifestyle (diet, exercise, sleep, etc.). The treatment proposal unit can customize treatment methods by taking into account the patient's environmental information (living environment, work environment, etc.). For example, the treatment proposal unit can individually adjust treatment methods based on the patient's lifestyle and environmental information. This allows for the proposal of more appropriate treatment methods by taking into account lifestyle and environmental information. Some or all of the above processing in the treatment proposal unit may be performed using AI, for example, or without using AI. For example, the treatment proposal unit can input the patient's lifestyle and environmental information into AI, and the AI ​​can customize treatment methods based on the analysis results. This allows for the proposal of more appropriate treatment methods by taking into account lifestyle and environmental information.

[0053] The treatment suggestion unit can select the optimal treatment method when suggesting treatment, taking into account the patient's geographical location information. For example, the treatment suggestion unit can use AI to select the optimal treatment method when suggesting treatment, taking into account the patient's geographical location information. For example, it can suggest a region-specific treatment method based on the patient's current location. For example, the treatment suggestion unit can suggest a treatment method from a nearby medical institution based on the patient's geographical location information. For example, the treatment suggestion unit can suggest a treatment method related to regional environmental factors (climate, air quality, etc.), taking into account the patient's geographical location information. In this way, by taking geographical location information into account, it is possible to suggest a region-specific treatment method. Some or all of the above processing in the treatment suggestion unit may be performed using AI, for example, or without using AI. For example, the treatment suggestion unit can input the patient's geographical location information into AI, and the AI ​​can select the optimal treatment method based on the analysis results. In this way, by taking geographical location information into account, it is possible to suggest a region-specific treatment method.

[0054] The treatment suggestion unit can analyze a patient's social media activity and propose relevant treatment methods when proposing treatment. For example, the treatment suggestion unit can use AI to analyze a patient's social media activity and propose relevant treatment methods when proposing treatment. For example, it can analyze a patient's social media posts and propose information related to treatment methods. For example, the treatment suggestion unit can obtain lifestyle and environmental information from a patient's social media activity and use it to propose treatment methods. For example, the treatment suggestion unit can analyze posts from a patient's friends and followers on social media and propose relevant treatment methods. In this way, lifestyle and environmental information can be obtained by analyzing social media activity. Some or all of the above processing in the treatment suggestion unit may be performed using AI, for example, or without AI. For example, the treatment suggestion unit can input a patient's social media activity into AI, and the AI ​​can propose relevant treatment methods based on the analysis results. In this way, lifestyle and environmental information can be obtained by analyzing social media activity.

[0055] The chatbot unit can provide the optimal response by referring to the patient's past dialogue history when responding to a chatbot. The chatbot unit can, for example, use AI to provide the optimal response by referring to the patient's past dialogue history when responding to a chatbot. For example, it can provide the optimal response based on the patient's past dialogue history. The chatbot unit can, for example, refer to the patient's past questions and provide relevant information. The chatbot unit can, for example, analyze the patient's past dialogue history and select the optimal response method. This allows it to provide the optimal response by referring to past dialogue history. Some or all of the above processing in the chatbot unit may be performed using AI, for example, or without AI. For example, the chatbot unit can input the patient's past dialogue history into AI, and the AI ​​can provide the optimal response based on the analysis results. This allows it to provide the optimal response by referring to past dialogue history.

[0056] The chatbot unit can provide the optimal response by considering the patient's device information when responding to a chatbot. For example, the chatbot unit can use AI to provide the optimal response by considering the patient's device information when responding to a chatbot. For example, if the patient is using a smartphone, it can provide a response that is appropriate for the screen size. For example, if the patient is using a tablet, the chatbot unit can provide a response optimized for a large screen. For example, if the patient is using a smartwatch, the chatbot unit can provide a concise and highly visible response. In this way, the optimal response can be provided by considering the device information. Some or all of the above processing in the chatbot unit may be performed using AI, for example, or without AI. For example, the chatbot unit can input the patient's device information into the AI, and the AI ​​can provide the optimal response based on the analysis results. In this way, the optimal response can be provided by considering the device information.

[0057] The data analysis unit can optimize its analysis algorithm by referring to past medical data during data analysis. For example, the data analysis unit can use AI to optimize its analysis algorithm by referring to past medical data during data analysis. For example, it can optimize the analysis algorithm based on past medical data to improve accuracy. The data analysis unit can adjust its analysis algorithm by referring to past treatment history. For example, the data analysis unit can analyze past medical data and select the optimal algorithm. This improves the accuracy of the analysis by referring to past medical data. Some or all of the above processes in the data analysis unit may be performed using AI, or without AI. For example, the data analysis unit can input past medical data into AI, and the AI ​​can optimize the analysis algorithm based on the analysis results. This improves the accuracy of the analysis by referring to past medical data.

[0058] The data analysis unit can perform data analysis while considering the patient's geographical location. For example, the data analysis unit can use AI to perform data analysis while considering the patient's geographical location. For example, it can analyze data related to region-specific diseases and symptoms based on the patient's current location. For example, the data analysis unit can analyze data from nearby medical institutions based on the patient's geographical location. For example, the data analysis unit can analyze data related to regional environmental factors (climate, air quality, etc.) while considering the patient's geographical location. This allows for addressing region-specific diseases and symptoms by considering geographical location. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input the patient's geographical location into AI, and the AI ​​can perform analysis based on the analysis results. This allows for addressing region-specific diseases and symptoms by considering geographical location.

[0059] The examination analysis unit can optimize the analysis algorithm by referring to the patient's past examination data when analyzing examination results. For example, the examination analysis unit can use AI to optimize the analysis algorithm by referring to the patient's past examination data when analyzing examination results. For example, it can optimize the analysis algorithm based on the patient's past examination data to improve accuracy. The examination analysis unit can adjust the analysis algorithm by referring to the patient's past examination results. For example, the examination analysis unit can analyze the patient's past examination data and select the optimal algorithm. This improves the accuracy of the analysis by referring to past examination data. Some or all of the above processes in the examination analysis unit may be performed using AI, or without AI. For example, the examination analysis unit can input the patient's past examination data into AI, and the AI ​​can optimize the analysis algorithm based on the analysis results. This improves the accuracy of the analysis by referring to past examination data.

[0060] The test analysis unit can perform analysis of test results while considering the patient's geographical location information. For example, the test analysis unit can use AI to perform analysis of test results while considering the patient's geographical location information. For example, it can analyze test results related to region-specific diseases and symptoms based on the patient's current location. For example, the test analysis unit can analyze test results from nearby medical institutions based on the patient's geographical location information. For example, the test analysis unit can analyze test results related to regional environmental factors (climate, air quality, etc.) while considering the patient's geographical location information. This allows for addressing region-specific diseases and symptoms by considering geographical location information. Some or all of the above processing in the test analysis unit may be performed using AI, for example, or without AI. For example, the test analysis unit can input the patient's geographical location information into AI, and the AI ​​can perform analysis based on the analysis results. This allows for addressing region-specific diseases and symptoms by considering geographical location information.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The reception desk can refer to the patient's past medical history and evaluate the reliability of the entered information. For example, it can refer to the patient's past medical records to check if the entered diagnosis and symptoms match the past medical history. Based on the patient's past treatment history, it can evaluate the reliability of the entered information and perform additional verification as needed. It can analyze the patient's past medical data to determine whether the entered information is reliable. This improves the reliability of the entered information by referring to past medical history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the patient's past medical history into the AI, and the AI ​​can evaluate the reliability of the entered information based on the analysis results. This improves the reliability of the entered information by referring to past medical history.

[0063] The reception desk can acquire additional information about the patient's lifestyle and environment when inputting disease names and symptoms. For example, information about the patient's lifestyle (diet, exercise, sleep, etc.) can be acquired during input to help evaluate the disease name and symptoms. Information about the patient's environment (living environment, work environment, etc.) can be acquired during input to help identify the cause of the disease name and symptoms. Based on the patient's lifestyle and environmental information, the system can supplement the input information about the disease name and symptoms, providing more accurate information. As a result, acquiring lifestyle and environmental information enables a more accurate diagnosis. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the patient's lifestyle and environmental information into the AI, and the AI ​​can evaluate the disease name and symptoms based on the analysis results. As a result, acquiring lifestyle and environmental information enables a more accurate diagnosis.

[0064] The reception desk can prioritize retrieving highly relevant information by considering the patient's geographical location when inputting disease names and symptoms. For example, it can prioritize retrieving information on region-specific diseases and symptoms based on the patient's current location. Based on the patient's geographical location, it can retrieve information on nearby medical institutions and suggest appropriate departments and examination methods. It can also retrieve information related to regional environmental factors (climate, air quality, etc.) by considering the patient's geographical location. This allows for addressing region-specific diseases and symptoms by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the patient's geographical location into the AI, and the AI ​​can prioritize retrieving highly relevant information based on the analysis results. This allows for addressing region-specific diseases and symptoms by considering geographical location.

[0065] The analysis unit can optimize the analysis algorithm by referring to the patient's past medical data during analysis. For example, it can optimize the analysis algorithm based on the patient's past medical data to improve accuracy. It can adjust the analysis algorithm by referring to the patient's past treatment history. It can analyze the patient's past medical data and select the optimal algorithm. As a result, the accuracy of the analysis is improved by referring to past medical data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the patient's past medical data into the AI, and the AI ​​can optimize the analysis algorithm based on the analysis results. As a result, the accuracy of the analysis is improved by referring to past medical data.

[0066] The analysis unit can perform analysis while considering the patient's lifestyle and environmental information. For example, it can perform analysis while considering the patient's lifestyle (diet, exercise, sleep, etc.). It can also perform analysis while considering the patient's environmental information (living environment, work environment, etc.). The analysis results can be supplemented based on the patient's lifestyle and environmental information. This makes it possible to perform more accurate analysis by considering lifestyle and environmental information. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input the patient's lifestyle and environmental information into the AI, and the AI ​​can perform analysis based on the analysis results. This makes it possible to perform more accurate analysis by considering lifestyle and environmental information.

[0067] The analysis unit can perform analysis while considering the patient's geographical location. For example, it can analyze information on region-specific diseases and symptoms based on the patient's current location. It can analyze information on nearby medical institutions based on the patient's geographical location. It can analyze information related to regional environmental factors (climate, air quality, etc.) while considering the patient's geographical location. This allows for addressing region-specific diseases and symptoms by considering geographical location. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the patient's geographical location into the AI, and the AI ​​can perform analysis based on the analysis results. This allows for addressing region-specific diseases and symptoms by considering geographical location.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk enters the patient's diagnosis and symptoms. These include, but are not limited to, headaches, diabetes, and stomach aches. The reception desk provides an interface for the patient to enter their diagnosis and symptoms into the system. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses AI, for example, to analyze the entered information and identify the most suitable medical department and examination method. Step 3: The proposal department proposes the most suitable medical department and examination method based on the information analyzed by the analysis department. For example, the proposal department may use AI to propose the most suitable medical department and examination method based on the analysis results. Step 4: The treatment proposal department proposes the optimal treatment method based on the test results proposed by the proposal department. For example, the treatment proposal department uses AI to analyze the test results and select the best option from countless treatment methods.

[0070] (Example of form 2) The medical support system according to an embodiment of the present invention is a system for providing patients with the optimal treatment approach. This medical support system works by having the patient input their disease name and symptoms, which are then analyzed by AI, which proposes the most suitable medical department and examination methods, and further proposes the most suitable treatment method based on the examination results. This eliminates bias based on the doctor's field of expertise or level, and realizes the optimal treatment approach for the patient. For example, the patient only needs to input their disease name and symptoms into the system. For example, they might input information such as "I have a headache" or "I have diabetes." This information is then input into the AI. Next, the AI ​​analyzes the input information and proposes the most suitable medical department and examination methods. The AI ​​analyzes medical big data to identify the most suitable medical department and examination methods for the patient. For example, if the patient has a headache, it might suggest neurology or neurosurgery and indicate the necessary examination methods. Furthermore, the AI ​​proposes the most suitable treatment method based on the examination results. The AI ​​analyzes the examination results and selects the most suitable treatment method from countless options. For example, for a patient with diabetes, it might suggest treatment methods such as diet therapy, exercise therapy, or drug therapy. This mechanism eliminates bias based on the doctor's field of expertise or level, and realizes the optimal treatment approach for the patient. Patients can find the optimal treatment method through a single system without having to visit multiple doctors. Furthermore, medical professionals can efficiently provide the best possible treatment to patients. For example, a dedicated chatbot can instantly suggest the most suitable medical department and examination methods for each patient. This improves patient satisfaction and increases efficiency in the medical field. Ultimately, the medical support system can provide patients with the most appropriate treatment approach.

[0071] The medical support system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a treatment proposal unit. The reception unit inputs the patient's illness and symptoms. The patient's illness and symptoms include, for example, headaches, diabetes, and abdominal pain, but are not limited to such examples. The reception unit provides, for example, an interface for the patient to input their illness and symptoms into the system. The analysis unit analyzes the information input by the reception unit. The analysis unit analyzes the input information using, for example, AI, and identifies the optimal medical department and examination method. The proposal unit proposes the optimal medical department and examination method based on the information analyzed by the analysis unit. The proposal unit proposes the optimal medical department and examination method based on the analysis results using, for example, AI. The treatment proposal unit proposes the optimal treatment method based on the examination results proposed by the proposal unit. The treatment proposal unit analyzes the examination results using, for example, AI, and selects the optimal one from countless treatment methods. As a result, the medical support system according to this embodiment can provide the patient with the optimal treatment approach. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without using AI. For example, the reception unit provides an interface for inputting the patient's diagnosis and symptoms, and can analyze the input information using AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can analyze the input information using AI and identify the optimal medical department and examination method. Some or all of the above-described processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can use AI to propose the optimal medical department and examination method based on the analysis results. Some or all of the above-described processes in the treatment proposal unit may be performed using AI, or not. For example, the treatment proposal unit can use AI to analyze the examination results and select the optimal one from countless treatment methods. As a result, the medical support system according to the embodiment can provide the patient with the optimal treatment approach.

[0072] The reception desk inputs the patient's diagnosis and symptoms. These include, but are not limited to, headaches, diabetes, and abdominal pain. The reception desk provides an interface for patients to input their diagnosis and symptoms into the system. Specifically, it offers various input methods such as touchscreens, voice input, and keyboard input to allow patients to easily enter information. Furthermore, the reception desk has a function to automatically classify the entered information and convert it into an appropriate format. For example, if a patient enters "I have a headache," the system extracts the keyword "headache" and lists related symptoms and diagnoses. The reception desk can also input the patient's past medical history and allergy information, allowing for the collection of more detailed information. This enables the reception desk to accurately understand the patient's condition and provide necessary information to the subsequent analysis and recommendation departments. Additionally, the reception desk can monitor the patient's input in real time and ask additional questions as needed. For example, if a patient enters "abdominal pain," the system displays additional questions such as "Which part hurts?" and "How severe is the pain?" to collect more detailed information. This allows the reception desk to more accurately understand the patient's symptoms and provide the necessary basic information to identify the appropriate medical department and examination methods.

[0073] The analysis department analyzes the information entered by the reception department. For example, the analysis department uses AI to analyze the entered information and identify the most suitable medical department and examination method. Specifically, the AI ​​uses natural language processing technology to analyze the patient's input information and automatically classify symptoms and disease names. For example, if a patient enters "I have a headache," the AI ​​extracts the keyword "headache" and identifies the relevant medical department (e.g., neurology or otolaryngology). The AI ​​can also suggest the most suitable examination method for a specific symptom based on past medical data and statistical information. For example, if a patient has a headache, the AI ​​will suggest examination methods such as MRI or CT scans. Furthermore, the analysis department also considers the patient's past medical history and allergy information to identify the most suitable medical department and examination method. For example, if a patient has a history of allergies to a particular drug, the AI ​​will consider this information and suggest a treatment method that will not trigger an allergic reaction. In this way, the analysis department can comprehensively evaluate the patient's condition and identify the most suitable medical department and examination method. In addition, the analysis department can utilize a real-time updated medical database to perform analysis based on the latest medical information. This allows the analysis unit to perform highly accurate analyses based on the latest information at all times, enabling it to propose the most suitable medical department and examination methods for each patient.

[0074] The Proposal Department suggests the most suitable medical department and examination method based on the information analyzed by the Analysis Department. For example, the Proposal Department uses AI to suggest the most suitable medical department and examination method based on the analysis results. Specifically, the Proposal Department presents patients with specific medical departments and examination methods based on the information provided by the Analysis Department. For example, for a patient with headache symptoms, the Proposal Department recommends a visit to a neurologist and suggests an MRI examination. The Proposal Department can also provide information such as the nearest medical institution and its operating hours, taking patient convenience into consideration. This allows patients to receive appropriate medical services quickly and efficiently. Furthermore, the Proposal Department can collect patient feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can confirm with the patient whether the suggested medical department and examination method were appropriate and adjust the suggestion algorithm based on the results. The Proposal Department can also compare multiple medical departments and examination methods and present the best option. For example, for headache symptoms, it can suggest both neurology and otolaryngology, allowing the patient to choose. This enables the Proposal Department to provide patients with flexible and appropriate medical suggestions and the optimal treatment approach.

[0075] The Treatment Proposal Department proposes the most suitable treatment method based on the test results suggested by the Proposal Department. For example, the Treatment Proposal Department uses AI to analyze test results and select the best option from countless treatment methods. Specifically, the AI ​​evaluates the patient's condition in detail based on the test results and identifies the optimal treatment method. For example, if an abnormality is found in the brain as a result of an MRI scan, the AI ​​compares treatment methods such as surgery, drug therapy, and rehabilitation and proposes the optimal treatment plan. The Treatment Proposal Department also considers the patient's past treatment history and allergy information to propose individually optimized treatment methods. For example, for a patient allergic to a specific drug, it will suggest an alternative drug. Furthermore, the Treatment Proposal Department can propose cutting-edge treatment methods by utilizing data from the latest medical research and clinical trials. This ensures that patients always receive the best possible treatment based on the latest medical information. The Treatment Proposal Department can also monitor the effectiveness of the proposed treatment methods and modify the treatment plan as needed. For example, it monitors the progress of treatment and the patient's response in real time, and if the treatment method is not sufficiently effective, it will propose an alternative treatment method. This allows the Treatment Proposal Department to continuously provide patients with the best possible treatment and maximize treatment effectiveness.

[0076] The system includes a chatbot unit that uses a dedicated chatbot to suggest the most suitable medical department and examination methods to patients. For example, the chatbot unit suggests the most suitable medical department and examination methods based on the disease name and symptoms entered by the patient into the system. The chatbot unit can also use AI to analyze the patient's input information and suggest the most suitable medical department and examination methods. For example, if the patient enters "I have a headache," the chatbot unit will suggest neurology or neurosurgery and indicate the necessary examination methods. This allows for the rapid suggestion of the most suitable medical department and examination methods to patients. Some or all of the above processing in the chatbot unit may be performed using AI, or not using AI. For example, the chatbot unit can input the patient's input information into the AI, and the AI ​​can suggest the most suitable medical department and examination methods based on the analysis results. This allows for the rapid suggestion of the most suitable medical department and examination methods to patients.

[0077] The system includes a data analysis department that analyzes medical big data. The data analysis department, for example, uses AI to analyze medical big data and identify the optimal medical department and examination methods. Medical big data includes, but is not limited to, electronic medical record data, medical records, and test results. For example, the data analysis department can use AI to analyze electronic medical record data and propose the most suitable medical department and examination methods for a patient. For example, the data analysis department can use AI to analyze medical records and propose the most suitable medical department and examination methods for a patient. For example, the data analysis department can use AI to analyze test results and propose the most suitable medical department and examination methods for a patient. This allows for the proposal of more accurate medical departments and examination methods by analyzing medical big data. Some or all of the above-described processes in the data analysis department may be performed using AI, or not. For example, the data analysis department can input medical big data into AI, which can then propose the most suitable medical department and examination methods based on the analysis results. This allows for the proposal of more accurate medical departments and examination methods by analyzing medical big data.

[0078] The system includes an examination analysis unit that analyzes examination results. The examination analysis unit, for example, uses AI to analyze examination results and proposes the optimal treatment method. Examination results include, but are not limited to, blood test results and imaging diagnostic results. The examination analysis unit can, for example, use AI to analyze blood test results and propose the optimal treatment method. The examination analysis unit can, for example, use AI to analyze imaging diagnostic results and propose the optimal treatment method. The examination analysis unit can, for example, use AI to analyze other examination results and propose the optimal treatment method. In this way, by analyzing examination results, a more appropriate treatment method can be proposed. Some or all of the above-described processes in the examination analysis unit may be performed using AI, for example, or without AI. For example, the examination analysis unit can input examination results into AI, and the AI ​​can propose the optimal treatment method based on the analysis results. In this way, by analyzing examination results, a more appropriate treatment method can be proposed.

[0079] The suggestion unit can identify the optimal medical department and examination method for a patient. For example, the suggestion unit can use AI to identify the optimal medical department and examination method based on the analysis results. For example, the suggestion unit can use AI to identify the optimal medical department and examination method based on the patient's diagnosis and symptoms. For example, the suggestion unit can use AI to identify the optimal medical department and examination method based on the patient's diagnosis and symptoms. For example, the suggestion unit can use AI to identify the optimal medical department and examination method based on the patient's diagnosis and symptoms. This improves the accuracy of medical care by identifying the optimal medical department and examination method for the patient. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input the patient's diagnosis and symptoms into the AI, and the AI ​​can identify the optimal medical department and examination method based on the analysis results. This improves the accuracy of medical care by identifying the optimal medical department and examination method for the patient.

[0080] The treatment suggestion unit can select the optimal treatment from a multitude of options. For example, the treatment suggestion unit uses AI to analyze test results and select the optimal treatment from a multitude of options. These multitude of treatment options include, but are not limited to, drug therapy, surgical therapy, and rehabilitation. For example, the treatment suggestion unit can suggest drug therapy using AI. For example, the treatment suggestion unit can suggest surgical therapy using AI. For example, the treatment suggestion unit can suggest rehabilitation using AI. By selecting the optimal treatment from a multitude of options, the effectiveness of the treatment is improved. Some or all of the above-described processes in the treatment suggestion unit may be performed using AI, or not. For example, the treatment suggestion unit can input test results into AI, which can then select the optimal treatment method based on the analysis results. By selecting the optimal treatment from a multitude of options, the effectiveness of the treatment is improved.

[0081] The reception desk can estimate the patient's emotions and adjust the input method for diagnoses and symptoms based on the estimated emotions. For example, the reception desk can use AI to estimate the patient's emotions and adjust the input method based on the estimated emotions. For example, if the patient is feeling anxious, it can provide a simple and intuitive interface and minimize the input steps. For example, if the patient is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the patient is in a hurry, the reception desk can prioritize voice input to allow for quick input of diagnoses and symptoms. This reduces the burden on the patient by adjusting the input method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the patient's emotions into the AI, and the AI ​​can adjust the input method based on the analysis results. This reduces the burden on patients by adjusting the input method according to their emotions.

[0082] The reception desk can refer to the patient's past medical history and evaluate the reliability of the entered information. For example, the reception desk may use AI to refer to the patient's past medical history and evaluate the reliability of the entered information. For example, it may refer to the patient's past medical records to check whether the entered disease name or symptoms match the past medical history. For example, the reception desk may evaluate the reliability of the entered information based on the patient's past treatment history and perform additional verification as needed. For example, the reception desk may analyze the patient's past medical data to determine whether the entered information is reliable. This improves the reliability of the entered information by referring to past medical history. Some or all of the above processes in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk may input the patient's past medical history into AI, and the AI ​​may evaluate the reliability of the entered information based on the analysis results. This improves the reliability of the entered information by referring to past medical history.

[0083] The reception desk can acquire additional information about the patient's lifestyle and environment when the patient enters their illness or symptoms. For example, the reception desk can use AI to acquire additional information about the patient's lifestyle and environment when the patient enters their illness or symptoms. For example, it can acquire information about the patient's lifestyle (diet, exercise, sleep, etc.) when the patient enters their information and use it to evaluate the illness or symptoms. For example, the reception desk can acquire information about the patient's environment (living environment, work environment, etc.) when the patient enters their information and use it as a reference to identify the cause of the illness or symptoms. For example, the reception desk can supplement the entered information about the illness or symptoms based on the patient's lifestyle and environment information, providing more accurate information. This makes it possible to make a more accurate diagnosis by acquiring lifestyle and environmental information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the patient's lifestyle and environment information into the AI, and the AI ​​can evaluate the illness or symptoms based on the analysis results. This makes it possible to make a more accurate diagnosis by acquiring lifestyle and environmental information.

[0084] The reception desk can estimate the patient's emotions and prioritize the input information based on the estimated emotions. For example, the reception desk can use AI to estimate the patient's emotions and prioritize the input information based on the estimated emotions. For example, if the patient is experiencing severe pain, that information will be processed preferentially and a quick response will be given. For example, if the patient is reporting mild symptoms, the reception desk can postpone that information and prioritize information of higher severity. For example, the reception desk can evaluate the importance of the input information based on the patient's emotional state and set appropriate priorities. This enables a quick response by prioritizing information based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the patient's emotions into the AI, and the AI ​​can determine the priority of the information based on the analysis results. This allows for quicker responses by prioritizing information based on emotions.

[0085] The reception desk can prioritize the acquisition of highly relevant information by considering the patient's geographical location when inputting disease names and symptoms. For example, the reception desk can use AI to prioritize the acquisition of highly relevant information by considering the patient's geographical location when inputting disease names and symptoms. For example, it can prioritize the acquisition of information on region-specific diseases and symptoms based on the patient's current location. For example, the reception desk can acquire information on nearby medical institutions based on the patient's geographical location and suggest appropriate medical departments and examination methods. For example, the reception desk can acquire information related to regional environmental factors (climate, air quality, etc.) by considering the patient's geographical location. This allows for addressing region-specific diseases and symptoms by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the patient's geographical location into AI, and the AI ​​can prioritize the acquisition of highly relevant information based on the analysis results. This allows for addressing region-specific diseases and symptoms by considering geographical location.

[0086] The reception desk can analyze the patient's social media activity and obtain relevant information when the patient enters their illness or symptoms. For example, the reception desk can use AI to analyze the patient's social media activity and obtain relevant information when the patient enters their illness or symptoms. For example, it can analyze the patient's social media posts and obtain information related to the illness or symptoms. The reception desk can, for example, obtain lifestyle and environmental information from the patient's social media activity and use it to evaluate the illness or symptoms. For example, the reception desk can analyze posts from the patient's friends and followers on social media and obtain relevant information. This allows for the acquisition of lifestyle and environmental information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the patient's social media activity into the AI, which can then acquire relevant information based on the analysis results. This allows for the acquisition of lifestyle and environmental information by analyzing social media activity.

[0087] The analysis unit can estimate the patient's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, the analysis unit can use AI to estimate the patient's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the patient is feeling anxious, the accuracy of the analysis can be increased to provide more detailed information. For example, if the patient is relaxed, the analysis unit can adjust the accuracy of the analysis to provide only the necessary information. For example, if the patient is in a hurry, the analysis unit can perform the analysis quickly and prioritize providing important information. This allows for the provision of more appropriate information by adjusting the accuracy of the analysis based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the patient's emotions into the AI, and the AI ​​can adjust the accuracy of the analysis based on the analysis results. This allows for the provision of more appropriate information by adjusting the accuracy of the analysis based on emotions.

[0088] The analysis unit can optimize the analysis algorithm by referring to the patient's past medical data during analysis. For example, the analysis unit can use AI to optimize the analysis algorithm by referring to the patient's past medical data during analysis. For example, it can optimize the analysis algorithm based on the patient's past medical data to improve accuracy. The analysis unit can adjust the analysis algorithm by referring to the patient's past treatment history. For example, the analysis unit can analyze the patient's past medical data and select the optimal algorithm. This improves the accuracy of the analysis by referring to past medical data. Some or all of the above processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the patient's past medical data into the AI, which can then optimize the analysis algorithm based on the analysis results. This improves the accuracy of the analysis by referring to past medical data.

[0089] The analysis unit can perform analysis while considering the patient's lifestyle and environmental information. For example, the analysis unit can use AI to perform analysis while considering the patient's lifestyle and environmental information. For example, it can perform analysis while considering the patient's lifestyle (diet, exercise, sleep, etc.). The analysis unit can perform analysis while considering the patient's environmental information (living environment, work environment, etc.). For example, the analysis unit can supplement the analysis results based on the patient's lifestyle and environmental information. This makes it possible to perform more accurate analysis by considering lifestyle and environmental information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the patient's lifestyle and environmental information into the AI, and the AI ​​can perform analysis based on the analysis results. This makes it possible to perform more accurate analysis by considering lifestyle and environmental information.

[0090] The analysis unit can estimate the patient's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit can use AI to estimate the patient's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the patient is tense, it can provide a simple and highly visible display method. For example, if the patient is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the patient is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method based on emotions, information that is easy for the patient to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the patient's emotions into the AI, and the AI ​​can adjust the display method based on the analysis results. By adjusting the display method based on emotions, information that is easy for the patient to understand can be provided.

[0091] The analysis unit can perform analysis while considering the patient's geographical location information. For example, the analysis unit can use AI to perform analysis while considering the patient's geographical location information. For example, it can analyze information on region-specific diseases and symptoms based on the patient's current location. For example, the analysis unit can analyze information on nearby medical institutions based on the patient's geographical location information. For example, the analysis unit can analyze information related to regional environmental factors (climate, air quality, etc.) while considering the patient's geographical location information. This allows for addressing region-specific diseases and symptoms by considering geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the patient's geographical location information into AI, and the AI ​​can perform analysis based on the analysis results. This allows for addressing region-specific diseases and symptoms by considering geographical location information.

[0092] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the patient during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the patient during the analysis using AI. For example, it can improve the accuracy of its analysis by referring to literature related to the patient's disease name and symptoms. For example, the analysis unit can supplement the analysis results by referring to literature related to the patient's treatment methods. For example, the analysis unit can improve the accuracy of its analysis by comparing the patient's medical data with relevant literature. In this way, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature on the patient into the AI, and the AI ​​can improve the accuracy of the analysis based on the analysis results. In this way, the accuracy of the analysis is improved by referring to relevant literature.

[0093] The suggestion unit can estimate the patient's emotions and adjust the way the suggestion is expressed based on the estimated emotions. For example, the suggestion unit can use AI to estimate the patient's emotions and adjust the way the suggestion is expressed based on the estimated emotions. For example, if the patient is feeling anxious, the suggestion unit can make suggestions using gentle language. For example, if the patient is relaxed, the suggestion unit can make suggestions that include detailed explanations. For example, if the patient is in a hurry, the suggestion unit can make suggestions that are concise and to the point. By adjusting the expression based on emotions, it becomes possible to make suggestions that are easy for the patient to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the patient's emotions into the AI, and the AI ​​can adjust the expression based on the analysis results. By adjusting the expression based on emotions, it becomes possible to make suggestions that are easy for the patient to understand.

[0094] The suggestion unit can identify the optimal medical department and examination method by referring to the patient's past medical data when making a suggestion. The suggestion unit can, for example, use AI to identify the optimal medical department and examination method by referring to the patient's past medical data when making a suggestion. For example, it can identify the optimal medical department based on the patient's past medical data. The suggestion unit can, for example, refer to the patient's past treatment history to identify the optimal examination method. The suggestion unit can, for example, analyze the patient's past medical data and suggest the optimal medical department and examination method. This allows the optimal medical department and examination method to be identified by referring to past medical data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input the patient's past medical data into AI, and the AI ​​can identify the optimal medical department and examination method based on the analysis results. This allows the optimal medical department and examination method to be identified by referring to past medical data.

[0095] The suggestion unit can make suggestions while considering the patient's lifestyle and environmental information. For example, the suggestion unit can use AI to make suggestions while considering the patient's lifestyle and environmental information. For example, it can make suggestions while considering the patient's lifestyle (diet, exercise, sleep, etc.). The suggestion unit can make suggestions while considering the patient's environmental information (living environment, work environment, etc.). For example, the suggestion unit can suggest the most suitable medical department and examination method based on the patient's lifestyle and environmental information. This makes it possible to make more appropriate suggestions by considering lifestyle and environmental information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the patient's lifestyle and environmental information into AI, and the AI ​​can make suggestions based on the analysis results. This makes it possible to make more appropriate suggestions by considering lifestyle and environmental information.

[0096] The suggestion unit can estimate the patient's emotions and determine the priority of suggestions based on the estimated emotions. For example, the suggestion unit can use AI to estimate the patient's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the patient is experiencing severe pain, the suggestion unit can prioritize processing that information and respond quickly. For example, if the patient is reporting mild symptoms, the suggestion unit can postpone that information and prioritize information indicating a higher severity. For example, the suggestion unit can evaluate the importance of suggestions based on the patient's emotional state and set appropriate priorities. This enables a quick response by determining priorities based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the patient's emotions into AI, and the AI ​​can determine priorities based on the analysis results. This enables a quick response by determining priorities based on emotions.

[0097] The suggestion unit can propose the most suitable medical department and examination method when making a suggestion, taking into account the patient's geographical location. For example, the suggestion unit can use AI to propose the most suitable medical department and examination method when making a suggestion, taking into account the patient's geographical location. For example, it can propose information on region-specific diseases and symptoms based on the patient's current location. For example, the suggestion unit can propose information on nearby medical institutions based on the patient's geographical location. For example, the suggestion unit can propose information related to regional environmental factors (climate, air quality, etc.) taking into account the patient's geographical location. This allows for addressing region-specific diseases and symptoms by considering geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the patient's geographical location into AI, and the AI ​​can propose the most suitable medical department and examination method based on the analysis results. This allows for addressing region-specific diseases and symptoms by considering geographical location.

[0098] The suggestion unit can analyze the patient's social media activity and suggest relevant medical departments and examination methods when making suggestions. For example, the suggestion unit can use AI to analyze the patient's social media activity and suggest relevant medical departments and examination methods when making suggestions. For example, it can analyze the patient's social media posts and suggest information related to medical departments and examination methods. For example, the suggestion unit can obtain lifestyle and environmental information from the patient's social media activity and use it to suggest medical departments and examination methods. For example, the suggestion unit can analyze posts from the patient's friends and followers on social media and suggest relevant medical departments and examination methods. In this way, lifestyle and environmental information can be obtained by analyzing social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the patient's social media activity into AI, and the AI ​​can suggest relevant medical departments and examination methods based on the analysis results. In this way, lifestyle and environmental information can be obtained by analyzing social media activity.

[0099] The treatment suggestion unit can estimate the patient's emotions and adjust the suggested treatment methods based on the estimated emotions. For example, the treatment suggestion unit can use AI to estimate the patient's emotions and adjust the suggested treatment methods based on the estimated emotions. For example, if the patient is feeling anxious, the treatment suggestion unit can suggest treatment methods using gentle language. For example, if the patient is relaxed, the treatment suggestion unit can suggest treatment methods that include detailed explanations. For example, if the patient is in a hurry, the treatment suggestion unit can suggest concise and to-the-point treatment methods. By adjusting the treatment methods based on emotions, it becomes possible to make suggestions that are easy for the patient to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the treatment suggestion unit may be performed using AI, for example, or without AI. For example, the treatment suggestion unit can input the patient's emotions into AI, and the AI ​​can adjust the treatment methods based on the analysis results. This allows for treatment methods to be adjusted based on emotions, making it possible to offer suggestions that are easier for patients to understand.

[0100] The treatment suggestion unit can select the optimal treatment method by referring to the patient's past treatment history when suggesting a treatment. For example, the treatment suggestion unit can use AI to select the optimal treatment method by referring to the patient's past treatment history when suggesting a treatment. For example, it can select the optimal treatment method based on the patient's past treatment history. For example, the treatment suggestion unit can select an effective treatment method by referring to the patient's past treatment results. For example, the treatment suggestion unit can analyze the patient's past treatment history and suggest the optimal treatment method. This allows the optimal treatment method to be selected by referring to the past treatment history. Some or all of the above processes in the treatment suggestion unit may be performed using AI, for example, or without using AI. For example, the treatment suggestion unit can input the patient's past treatment history into AI, and the AI ​​can select the optimal treatment method based on the analysis results. This allows the optimal treatment method to be selected by referring to the past treatment history.

[0101] The treatment proposal unit can customize treatment methods when proposing treatment, taking into account the patient's lifestyle and environmental information. For example, the treatment proposal unit can use AI to customize treatment methods when proposing treatment, taking into account the patient's lifestyle and environmental information. For example, it can customize treatment methods by taking into account the patient's lifestyle (diet, exercise, sleep, etc.). The treatment proposal unit can customize treatment methods by taking into account the patient's environmental information (living environment, work environment, etc.). For example, the treatment proposal unit can individually adjust treatment methods based on the patient's lifestyle and environmental information. This allows for the proposal of more appropriate treatment methods by taking into account lifestyle and environmental information. Some or all of the above processing in the treatment proposal unit may be performed using AI, for example, or without using AI. For example, the treatment proposal unit can input the patient's lifestyle and environmental information into AI, and the AI ​​can customize treatment methods based on the analysis results. This allows for the proposal of more appropriate treatment methods by taking into account lifestyle and environmental information.

[0102] The treatment suggestion unit can estimate the patient's emotions and determine the priority of treatment methods based on the estimated emotions. For example, the treatment suggestion unit can use AI to estimate the patient's emotions and determine the priority of treatment methods based on the estimated emotions. For example, if the patient is experiencing severe pain, the treatment method for that condition will be suggested first. For example, if the patient is complaining of mild symptoms, the treatment suggestion unit can postpone that treatment method and prioritize treatment methods for more severe conditions. For example, the treatment suggestion unit can evaluate the importance of treatment methods based on the patient's emotional state and set appropriate priorities. This allows for a quick response by determining priorities based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the treatment suggestion unit may be performed using AI, for example, or without AI. For example, the treatment suggestion unit can input the patient's emotions into the AI, and the AI ​​can determine the priority of treatment methods based on the analysis results. This allows for quicker responses by prioritizing based on emotions.

[0103] The treatment suggestion unit can select the optimal treatment method when suggesting treatment, taking into account the patient's geographical location information. For example, the treatment suggestion unit can use AI to select the optimal treatment method when suggesting treatment, taking into account the patient's geographical location information. For example, it can suggest a region-specific treatment method based on the patient's current location. For example, the treatment suggestion unit can suggest a treatment method from a nearby medical institution based on the patient's geographical location information. For example, the treatment suggestion unit can suggest a treatment method related to regional environmental factors (climate, air quality, etc.), taking into account the patient's geographical location information. In this way, by taking geographical location information into account, it is possible to suggest a region-specific treatment method. Some or all of the above processing in the treatment suggestion unit may be performed using AI, for example, or without using AI. For example, the treatment suggestion unit can input the patient's geographical location information into AI, and the AI ​​can select the optimal treatment method based on the analysis results. In this way, by taking geographical location information into account, it is possible to suggest a region-specific treatment method.

[0104] The treatment suggestion unit can analyze a patient's social media activity and propose relevant treatment methods when proposing treatment. For example, the treatment suggestion unit can use AI to analyze a patient's social media activity and propose relevant treatment methods when proposing treatment. For example, it can analyze a patient's social media posts and propose information related to treatment methods. For example, the treatment suggestion unit can obtain lifestyle and environmental information from a patient's social media activity and use it to propose treatment methods. For example, the treatment suggestion unit can analyze posts from a patient's friends and followers on social media and propose relevant treatment methods. In this way, lifestyle and environmental information can be obtained by analyzing social media activity. Some or all of the above processing in the treatment suggestion unit may be performed using AI, for example, or without AI. For example, the treatment suggestion unit can input a patient's social media activity into AI, and the AI ​​can propose relevant treatment methods based on the analysis results. In this way, lifestyle and environmental information can be obtained by analyzing social media activity.

[0105] The chatbot unit can estimate the patient's emotions and adjust its response method based on the estimated emotions. For example, the chatbot unit can use AI to estimate the patient's emotions and adjust its response method based on the estimated emotions. For example, if the patient is feeling anxious, it will respond using gentle language. For example, if the patient is relaxed, the chatbot unit can provide a response that includes detailed explanations. For example, if the patient is in a hurry, the chatbot unit can provide a concise and to-the-point response. By adjusting the response method based on emotions, it becomes possible to provide responses that are easy for the patient to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the chatbot unit may be performed using AI, for example, or without AI. For example, the chatbot unit can input the patient's emotions into the AI, and the AI ​​can adjust the response method based on the analysis results. This allows for responses that are easier for patients to understand by adjusting the response method based on their emotions.

[0106] The chatbot unit can provide the optimal response by referring to the patient's past dialogue history when responding to a chatbot. The chatbot unit can, for example, use AI to provide the optimal response by referring to the patient's past dialogue history when responding to a chatbot. For example, it can provide the optimal response based on the patient's past dialogue history. The chatbot unit can, for example, refer to the patient's past questions and provide relevant information. The chatbot unit can, for example, analyze the patient's past dialogue history and select the optimal response method. This allows it to provide the optimal response by referring to past dialogue history. Some or all of the above processing in the chatbot unit may be performed using AI, for example, or without AI. For example, the chatbot unit can input the patient's past dialogue history into AI, and the AI ​​can provide the optimal response based on the analysis results. This allows it to provide the optimal response by referring to past dialogue history.

[0107] The chatbot unit can estimate the patient's emotions and determine the chatbot's response priority based on the estimated emotions. For example, the chatbot unit can use AI to estimate the patient's emotions and determine the chatbot's response priority based on the estimated emotions. For example, if the patient is experiencing severe pain, that response will be processed preferentially. For example, if the patient is reporting mild symptoms, the chatbot unit can postpone that response and prioritize responses of higher severity. For example, the chatbot unit can evaluate the importance of responses based on the patient's emotional state and set appropriate priorities. This enables a rapid response by determining response priorities based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the chatbot unit may be performed using AI, for example, or without AI. For example, the chatbot unit can input the patient's emotions into the AI, and the AI ​​can determine the response priority based on the analysis results. This allows for quicker responses by prioritizing responses based on emotions.

[0108] The chatbot unit can provide the optimal response by considering the patient's device information when responding to a chatbot. For example, the chatbot unit can use AI to provide the optimal response by considering the patient's device information when responding to a chatbot. For example, if the patient is using a smartphone, it can provide a response that is appropriate for the screen size. For example, if the patient is using a tablet, the chatbot unit can provide a response optimized for a large screen. For example, if the patient is using a smartwatch, the chatbot unit can provide a concise and highly visible response. In this way, the optimal response can be provided by considering the device information. Some or all of the above processing in the chatbot unit may be performed using AI, for example, or without AI. For example, the chatbot unit can input the patient's device information into the AI, and the AI ​​can provide the optimal response based on the analysis results. In this way, the optimal response can be provided by considering the device information.

[0109] The data analysis unit can estimate the patient's emotions and adjust the accuracy of the data analysis based on the estimated emotions. For example, the data analysis unit can use AI to estimate the patient's emotions and adjust the accuracy of the data analysis based on the estimated emotions. For example, if the patient is feeling anxious, the accuracy of the data analysis can be increased to provide more detailed information. For example, if the patient is relaxed, the data analysis unit can adjust the accuracy of the data analysis to provide only the necessary information. For example, if the patient is in a hurry, the data analysis unit can perform data analysis quickly and prioritize providing important information. This allows for the provision of more appropriate information by adjusting the accuracy of the data analysis based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data analysis unit may be performed using AI or not. For example, the data analysis unit can input the patient's emotions into the AI, which can then adjust the accuracy of the data analysis based on the analysis results. This allows us to adjust the accuracy of data analysis based on emotions, thereby providing more relevant information.

[0110] The data analysis unit can optimize its analysis algorithm by referring to past medical data during data analysis. For example, the data analysis unit can use AI to optimize its analysis algorithm by referring to past medical data during data analysis. For example, it can optimize the analysis algorithm based on past medical data to improve accuracy. The data analysis unit can adjust its analysis algorithm by referring to past treatment history. For example, the data analysis unit can analyze past medical data and select the optimal algorithm. This improves the accuracy of the analysis by referring to past medical data. Some or all of the above processes in the data analysis unit may be performed using AI, or without AI. For example, the data analysis unit can input past medical data into AI, and the AI ​​can optimize the analysis algorithm based on the analysis results. This improves the accuracy of the analysis by referring to past medical data.

[0111] The data analysis unit can estimate the patient's emotions and determine the priority of data analysis based on the estimated emotions. For example, the data analysis unit can use AI to estimate the patient's emotions and determine the priority of data analysis based on the estimated emotions. For example, if a patient is experiencing severe pain, that data analysis will be prioritized. The data analysis unit can, for example, postpone data analysis if the patient is reporting mild symptoms and prioritize data analysis of more severe symptoms. The data analysis unit can, for example, evaluate the importance of data analysis based on the patient's emotional state and set appropriate priorities. This allows for a quicker response by prioritizing based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data analysis unit may be performed using AI or not. For example, the data analysis unit can input the patient's emotions into an AI, which can then determine the priority of data analysis based on the analysis results. This allows for quicker responses by prioritizing based on emotions.

[0112] The data analysis unit can perform data analysis while considering the patient's geographical location. For example, the data analysis unit can use AI to perform data analysis while considering the patient's geographical location. For example, it can analyze data related to region-specific diseases and symptoms based on the patient's current location. For example, the data analysis unit can analyze data from nearby medical institutions based on the patient's geographical location. For example, the data analysis unit can analyze data related to regional environmental factors (climate, air quality, etc.) while considering the patient's geographical location. This allows for addressing region-specific diseases and symptoms by considering geographical location. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input the patient's geographical location into AI, and the AI ​​can perform analysis based on the analysis results. This allows for addressing region-specific diseases and symptoms by considering geographical location.

[0113] The test analysis unit can estimate the patient's emotions and adjust the analysis method of the test results based on the estimated emotions. For example, the test analysis unit can use AI to estimate the patient's emotions and adjust the analysis method of the test results based on the estimated emotions. For example, if the patient is feeling anxious, it can provide detailed test results. For example, if the patient is relaxed, the test analysis unit can provide only the necessary information. For example, if the patient is in a hurry, the test analysis unit can quickly analyze the test results and prioritize providing important information. This allows for the provision of more appropriate information by adjusting the analysis method based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the test analysis unit may be performed using AI, for example, or without AI. For example, the test analysis unit can input the patient's emotions into AI, and the AI ​​can adjust the analysis method based on the analysis results. This allows for the provision of more appropriate information by adjusting the analysis method based on emotions.

[0114] The examination analysis unit can optimize the analysis algorithm by referring to the patient's past examination data when analyzing examination results. For example, the examination analysis unit can use AI to optimize the analysis algorithm by referring to the patient's past examination data when analyzing examination results. For example, it can optimize the analysis algorithm based on the patient's past examination data to improve accuracy. The examination analysis unit can adjust the analysis algorithm by referring to the patient's past examination results. For example, the examination analysis unit can analyze the patient's past examination data and select the optimal algorithm. This improves the accuracy of the analysis by referring to past examination data. Some or all of the above processes in the examination analysis unit may be performed using AI, or without AI. For example, the examination analysis unit can input the patient's past examination data into AI, and the AI ​​can optimize the analysis algorithm based on the analysis results. This improves the accuracy of the analysis by referring to past examination data.

[0115] The test analysis unit can estimate the patient's emotions and prioritize test results based on the estimated emotions. For example, the test analysis unit can use AI to estimate the patient's emotions and prioritize test results based on the estimated emotions. For example, if a patient is experiencing severe pain, that test result will be processed first. For example, if a patient is reporting mild symptoms, the test analysis unit can postpone that test result and prioritize test results indicating higher severity. For example, the test analysis unit can evaluate the importance of test results based on the patient's emotional state and set appropriate priorities. This enables a quick response by prioritizing based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the test analysis unit may be performed using AI, for example, or without AI. For example, the test analysis unit can input the patient's emotions into the AI, and the AI ​​can determine the priority of test results based on the analysis results. This allows for quicker responses by prioritizing based on emotions.

[0116] The test analysis unit can perform analysis of test results while considering the patient's geographical location information. For example, the test analysis unit can use AI to perform analysis of test results while considering the patient's geographical location information. For example, it can analyze test results related to region-specific diseases and symptoms based on the patient's current location. For example, the test analysis unit can analyze test results from nearby medical institutions based on the patient's geographical location information. For example, the test analysis unit can analyze test results related to regional environmental factors (climate, air quality, etc.) while considering the patient's geographical location information. This allows for addressing region-specific diseases and symptoms by considering geographical location information. Some or all of the above processing in the test analysis unit may be performed using AI, for example, or without AI. For example, the test analysis unit can input the patient's geographical location information into AI, and the AI ​​can perform analysis based on the analysis results. This allows for addressing region-specific diseases and symptoms by considering geographical location information.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The reception desk can estimate the patient's emotions and adjust the input method for diagnoses and symptoms based on the estimated emotions. For example, if the patient is anxious, a simple and intuitive interface can be provided, minimizing the input steps. If the patient is relaxed, detailed input options can be provided, and a customizable input method can be suggested. If the patient is in a hurry, voice input can be prioritized, allowing for quick input of diagnoses and symptoms. This reduces the burden on the patient by adjusting the input method according to their emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the patient's emotions into the AI, which can then adjust the input method based on the analysis results. This reduces the burden on the patient by adjusting the input method according to their emotions.

[0119] The reception desk can refer to the patient's past medical history and evaluate the reliability of the entered information. For example, it can refer to the patient's past medical records to check if the entered diagnosis and symptoms match the past medical history. Based on the patient's past treatment history, it can evaluate the reliability of the entered information and perform additional verification as needed. It can analyze the patient's past medical data to determine whether the entered information is reliable. This improves the reliability of the entered information by referring to past medical history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the patient's past medical history into the AI, and the AI ​​can evaluate the reliability of the entered information based on the analysis results. This improves the reliability of the entered information by referring to past medical history.

[0120] The reception desk can acquire additional information about the patient's lifestyle and environment when inputting disease names and symptoms. For example, information about the patient's lifestyle (diet, exercise, sleep, etc.) can be acquired during input to help evaluate the disease name and symptoms. Information about the patient's environment (living environment, work environment, etc.) can be acquired during input to help identify the cause of the disease name and symptoms. Based on the patient's lifestyle and environmental information, the system can supplement the input information about the disease name and symptoms, providing more accurate information. As a result, acquiring lifestyle and environmental information enables a more accurate diagnosis. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the patient's lifestyle and environmental information into the AI, and the AI ​​can evaluate the disease name and symptoms based on the analysis results. As a result, acquiring lifestyle and environmental information enables a more accurate diagnosis.

[0121] The reception desk can estimate the patient's emotions and prioritize the input information based on the estimated emotions. For example, if the patient is experiencing severe pain, that information can be processed preferentially and a quick response can be given. If the patient is reporting mild symptoms, that information can be postponed, and information of higher severity can be prioritized. Based on the patient's emotional state, the importance of the input information can be evaluated and appropriate priorities can be set. This enables a quick response by prioritizing information based on emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the patient's emotions into the AI, and the AI ​​can determine the priority of information based on the analysis results. This enables a quick response by prioritizing information based on emotions.

[0122] The reception desk can prioritize retrieving highly relevant information by considering the patient's geographical location when inputting disease names and symptoms. For example, it can prioritize retrieving information on region-specific diseases and symptoms based on the patient's current location. Based on the patient's geographical location, it can retrieve information on nearby medical institutions and suggest appropriate departments and examination methods. It can also retrieve information related to regional environmental factors (climate, air quality, etc.) by considering the patient's geographical location. This allows for addressing region-specific diseases and symptoms by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the patient's geographical location into the AI, and the AI ​​can prioritize retrieving highly relevant information based on the analysis results. This allows for addressing region-specific diseases and symptoms by considering geographical location.

[0123] The analysis unit can estimate the patient's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the patient is feeling anxious, the accuracy of the analysis can be increased to provide more detailed information. If the patient is relaxed, the accuracy of the analysis can be adjusted to provide only the necessary information. If the patient is in a hurry, the analysis can be performed quickly, and important information can be prioritized. In this way, more appropriate information can be provided by adjusting the accuracy of the analysis based on emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the patient's emotions into the AI, and the AI ​​can adjust the accuracy of the analysis based on the analysis results. In this way, more appropriate information can be provided by adjusting the accuracy of the analysis based on emotions.

[0124] The analysis unit can optimize the analysis algorithm by referring to the patient's past medical data during analysis. For example, it can optimize the analysis algorithm based on the patient's past medical data to improve accuracy. It can adjust the analysis algorithm by referring to the patient's past treatment history. It can analyze the patient's past medical data and select the optimal algorithm. As a result, the accuracy of the analysis is improved by referring to past medical data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the patient's past medical data into the AI, and the AI ​​can optimize the analysis algorithm based on the analysis results. As a result, the accuracy of the analysis is improved by referring to past medical data.

[0125] The analysis unit can perform analysis while considering the patient's lifestyle and environmental information. For example, it can perform analysis while considering the patient's lifestyle (diet, exercise, sleep, etc.). It can also perform analysis while considering the patient's environmental information (living environment, work environment, etc.). The analysis results can be supplemented based on the patient's lifestyle and environmental information. This makes it possible to perform more accurate analysis by considering lifestyle and environmental information. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input the patient's lifestyle and environmental information into the AI, and the AI ​​can perform analysis based on the analysis results. This makes it possible to perform more accurate analysis by considering lifestyle and environmental information.

[0126] The analysis unit can estimate the patient's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the patient is tense, a simple and highly visible display method can be provided. If the patient is relaxed, a display method containing detailed information can be provided. If the patient is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method based on emotions, information that is easy for the patient to understand can be provided. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the patient's emotions into the AI, and the AI ​​can adjust the display method based on the analysis results. In this way, by adjusting the display method based on emotions, information that is easy for the patient to understand can be provided.

[0127] The analysis unit can perform analysis while considering the patient's geographical location. For example, it can analyze information on region-specific diseases and symptoms based on the patient's current location. It can analyze information on nearby medical institutions based on the patient's geographical location. It can analyze information related to regional environmental factors (climate, air quality, etc.) while considering the patient's geographical location. This allows for addressing region-specific diseases and symptoms by considering geographical location. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the patient's geographical location into the AI, and the AI ​​can perform analysis based on the analysis results. This allows for addressing region-specific diseases and symptoms by considering geographical location.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The reception desk enters the patient's diagnosis and symptoms. These include, but are not limited to, headaches, diabetes, and stomach aches. The reception desk provides an interface for the patient to enter their diagnosis and symptoms into the system. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses AI, for example, to analyze the entered information and identify the most suitable medical department and examination method. Step 3: The proposal department proposes the most suitable medical department and examination method based on the information analyzed by the analysis department. For example, the proposal department may use AI to propose the most suitable medical department and examination method based on the analysis results. Step 4: The treatment proposal department proposes the optimal treatment method based on the test results proposed by the proposal department. For example, the treatment proposal department uses AI to analyze the test results and select the best option from countless treatment methods.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0132] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, treatment proposal unit, chatbot unit, data analysis unit, and test analysis unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit provides an interface for inputting the patient's disease name and symptoms using the reception device 38 of the smart device 14. The analysis unit analyzes the input information using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the most suitable medical department and test method based on the analysis results using the specific processing unit 290 of the data processing unit 12. The treatment proposal unit analyzes the test results using the specific processing unit 290 of the data processing unit 12 and proposes the most suitable treatment method. The chatbot unit suggests the most suitable medical department and test method to the patient using the control unit 46A of the smart device 14. The data analysis unit analyzes medical big data using the specific processing unit 290 of the data processing unit 12 and identifies the most suitable medical department and test method. The test analysis unit analyzes the test results using the specific processing unit 290 of the data processing unit 12 and proposes the most suitable treatment method. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, treatment proposal unit, chatbot unit, data analysis unit, and test analysis unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit provides an interface for inputting the patient's disease name and symptoms using the microphone 238 of the smart glasses 214. The analysis unit analyzes the input information using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the most suitable medical department and examination method based on the analysis results using the specific processing unit 290 of the data processing unit 12. The treatment proposal unit analyzes the test results using the specific processing unit 290 of the data processing unit 12 and proposes the most suitable treatment method. The chatbot unit suggests the most suitable medical department and examination method to the patient using the control unit 46A of the smart glasses 214. The data analysis unit analyzes medical big data using the specific processing unit 290 of the data processing unit 12 and identifies the most suitable medical department and examination method. The test analysis unit analyzes the test results using the specific processing unit 290 of the data processing unit 12 and proposes the most suitable treatment method. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, treatment proposal unit, chatbot unit, data analysis unit, and test analysis unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit provides an interface for inputting the patient's disease name and symptoms using the microphone 238 of the headset terminal 314. The analysis unit analyzes the input information using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal medical department and test method based on the analysis results using the specific processing unit 290 of the data processing unit 12. The treatment proposal unit analyzes the test results using the specific processing unit 290 of the data processing unit 12 and proposes the optimal treatment method. The chatbot unit suggests the optimal medical department and test method to the patient using the control unit 46A of the headset terminal 314. The data analysis unit analyzes medical big data using the specific processing unit 290 of the data processing unit 12 and identifies the optimal medical department and test method. The test analysis unit analyzes the test results using the specific processing unit 290 of the data processing unit 12 and proposes the optimal treatment method. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0168] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0169] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0170] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0172] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0173] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0174] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0175] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0176] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0177] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0179] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0181] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, treatment proposal unit, chatbot unit, data analysis unit, and test analysis unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit provides an interface for inputting the patient's disease name and symptoms using the microphone 238 of the robot 414. The analysis unit analyzes the input information using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the most suitable medical department and test method based on the analysis results using the specific processing unit 290 of the data processing unit 12. The treatment proposal unit analyzes the test results using the specific processing unit 290 of the data processing unit 12 and proposes the most suitable treatment method. The chatbot unit suggests the most suitable medical department and test method to the patient using the control unit 46A of the robot 414. The data analysis unit analyzes medical big data using the specific processing unit 290 of the data processing unit 12 and identifies the most suitable medical department and test method. The test analysis unit analyzes the test results using the specific processing unit 290 of the data processing unit 12 and proposes the most suitable treatment method. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0183] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0184] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0185] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0186] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0187] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0188] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0190] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0191] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0192] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0193] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0194] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0195] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0196] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0197] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0198] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0199] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0200] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0201] (Note 1) A reception desk where the patient's diagnosis and symptoms are entered, An analysis unit analyzes the information input by the reception unit, Based on the information analyzed by the aforementioned analysis unit, the proposal unit proposes the most suitable medical department and examination method. A treatment proposal unit proposes the optimal treatment method based on the test results proposed by the aforementioned proposal unit, Equipped with A system characterized by the following features. (Note 2) It features a chatbot section that uses a dedicated chatbot to suggest the most suitable medical department and examination methods to patients. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a data analysis department that analyzes medical big data. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with an inspection analysis unit that analyzes the inspection results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Identifying the most appropriate medical department and examination methods for the patient. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned treatment proposal unit, Selecting the optimal treatment method from countless options. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the patient's emotions and adjusts the input method for disease names and symptoms based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The reliability of the entered information is evaluated by referring to the patient's past medical history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering the disease name and symptoms, additional information about the patient's lifestyle and environment is acquired. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the patient's emotions and prioritizes the input information based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering disease names and symptoms, the system prioritizes retrieving highly relevant information by considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When patients enter their illness or symptoms, the system analyzes their social media activity to retrieve relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the accuracy of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to the patient's past medical data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the patient's lifestyle and environmental information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the patient's geographical location information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant patient literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, The system estimates the patient's emotions and adjusts the way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we refer to the patient's past medical data to identify the most suitable medical department and examination method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, we will take into account the patient's lifestyle and environmental information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, The system estimates the patient's emotions and prioritizes proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we will consider the patient's geographical location to suggest the most suitable medical department and examination methods. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, we analyze the patient's social media activity and suggest relevant medical departments and examination methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned treatment proposal unit, The system estimates the patient's emotions and adjusts treatment suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned treatment proposal unit, When proposing treatment, the optimal treatment method is selected by referring to the patient's past treatment history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned treatment proposal unit, When proposing treatment, we customize the treatment method by taking into account the patient's lifestyle and environmental information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned treatment proposal unit, The system estimates the patient's emotions and prioritizes treatment methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned treatment proposal unit, When proposing treatment, the most suitable treatment method will be selected considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned treatment proposal unit, When proposing treatment, we analyze the patient's social media activity and suggest relevant treatment methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned chatbot unit is It estimates the patient's emotions and adjusts the chatbot's response based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned chatbot unit is When the chatbot responds, it refers to the patient's past conversation history to provide the most appropriate response. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned chatbot unit is The system estimates the patient's emotions and determines the chatbot's response priority based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned chatbot unit is When the chatbot responds, it takes the patient's device information into consideration to provide the most appropriate response. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned data analysis unit, The system estimates the patient's emotions and adjusts the accuracy of the data analysis based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned data analysis unit, During data analysis, we optimize the analysis algorithm by referring to past medical data. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned data analysis unit, The system estimates the patient's emotions and prioritizes data analysis based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned data analysis unit, When analyzing data, the analysis should take into account the patient's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned inspection and analysis unit is The system estimates the patient's emotions and adjusts the analysis method of the test results based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned inspection and analysis unit is When analyzing test results, the analysis algorithm is optimized by referring to the patient's past test data. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned inspection and analysis unit is The system estimates the patient's emotions and prioritizes test results based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned inspection and analysis unit is When analyzing test results, the analysis will take into account the patient's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where the patient's diagnosis and symptoms are entered, An analysis unit analyzes the information input by the reception unit, Based on the information analyzed by the aforementioned analysis unit, the proposal unit proposes the most suitable medical department and examination method. A treatment proposal unit proposes the optimal treatment method based on the test results proposed by the aforementioned proposal unit, Equipped with A system characterized by the following features.

2. It features a chatbot section that uses a dedicated chatbot to suggest the most suitable medical department and examination methods to patients. The system according to feature 1.

3. It has a data analysis department that analyzes medical big data. The system according to feature 1.

4. It is equipped with an inspection analysis unit that analyzes the inspection results. The system according to feature 1.

5. The aforementioned proposal section is, Identifying the most appropriate medical department and examination methods for the patient. The system according to feature 1.

6. The aforementioned treatment proposal unit, Selecting the optimal treatment method from countless options. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the patient's emotions and adjusts the input method for disease names and symptoms based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is The reliability of the entered information is evaluated by referring to the patient's past medical history. The system according to feature 1.

9. The aforementioned reception unit is When entering the disease name and symptoms, additional information about the patient's lifestyle and environment is acquired. The system according to feature 1.

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