system

Abstract

The system according to this embodiment aims to convert vague descriptions of a patient's symptoms into specific medical terms, thereby enabling appropriate diagnosis and guidance to a doctor. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a diagnosis unit, a guidance unit, and a generation unit. The reception unit inputs the patient's symptoms. The analysis unit analyzes the symptoms input by the reception unit and converts them into specific medical terms. The diagnosis unit performs a diagnosis based on the information converted by the analysis unit. The guidance unit guides the patient to an appropriate doctor based on the diagnosis results obtained by the diagnosis unit. The generation unit automatically generates a medical record based on the diagnosis results obtained by the diagnosis unit.

Description

Technical Field

[0004] ,

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[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 that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to convert ambiguous symptom expressions of patients into specific medical terms and provide appropriate diagnoses and doctor guidance.

[0005] The system according to the embodiment aims to convert ambiguous symptom expressions of patients into specific medical terms and provide appropriate diagnoses and doctor guidance.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a diagnosis unit, a guidance unit, and a generation unit. The reception unit inputs the patient's symptoms. The analysis unit analyzes the symptoms input by the reception unit and converts them into specific medical terms. The diagnosis unit performs a diagnosis based on the information converted by the analysis unit. The guidance unit guides the patient to an appropriate doctor based on the diagnosis results obtained by the diagnosis unit. The generation unit automatically generates a medical record based on the diagnosis results obtained by the diagnosis unit. [Effects of the Invention]

[0007] The system according to this embodiment can convert a patient's vague symptom descriptions into specific medical terms, enabling appropriate diagnosis and guidance to a doctor. [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 applicable 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 platform connecting doctors and patients according to an embodiment of the present invention is a system that utilizes generative AI to analyze a patient's symptoms, assist in diagnosis, and guide the patient to the most suitable doctor. This system begins with the patient inputting vague symptoms such as "my stomach hurts" or "I feel vaguely nauseous." The generative AI analyzes the input information and converts it into specific medical terms such as "stomach ache," "pain in the lower right abdomen," and "nausea." Next, the generative AI makes a diagnosis based on the converted information and guides the patient to the most suitable doctor. For example, in the case of stomach ache, it guides the patient to a gastroenterologist, and in the case of pain in the lower right abdomen, it guides the patient to a surgeon. After the diagnosis, the patient inputs symptoms again, and the generative AI analyzes the symptoms after the diagnosis and provides feedback on the accuracy of the diagnosis. For example, if the symptoms improve after the diagnosis, it determines that the diagnosis was correct, and conversely, if the symptoms worsen, it guides the patient to undergo another diagnosis. Furthermore, the generative AI has an automatic medical record generation function and automatically generates a medical record based on the information entered by the patient and the diagnosis results. This saves doctors the trouble of creating medical records and allows them to concentrate on medical treatment. Thus, the present invention provides a platform that connects doctors and patients using generative AI, reducing the workload of doctors and enabling the provision of appropriate medical care to patients by assisting with diagnosis and automatically generating medical records. As a result, the platform connecting doctors and patients can efficiently analyze patients' symptoms using generative AI, assist in diagnosis, guide them to the most suitable doctor, and automatically generate medical records.

[0029] The platform connecting doctors and patients according to this embodiment comprises a reception unit, an analysis unit, a diagnosis unit, a guidance unit, and a generation unit. The reception unit inputs the patient's symptoms. The patient's symptoms include, but are not limited to, vague expressions such as "my stomach hurts" or "I feel vaguely nauseous." The reception unit allows, for example, patients to input their symptoms using a smartphone or computer. The reception unit can also input the patient's symptoms using voice input. For example, if a patient says "my stomach hurts," the reception unit converts the voice into text and inputs it. The analysis unit uses generation AI to analyze the symptoms input by the reception unit and converts them into specific medical terms. The analysis unit uses, for example, natural language processing technology to convert vague expressions into specific medical terms. The analysis unit can also use machine learning algorithms to analyze symptoms based on past data. For example, the analysis unit converts the expression "my stomach hurts" to "stomach ache." The diagnosis unit uses generation AI to perform a diagnosis based on the information converted by the analysis unit. The diagnosis unit uses, for example, a diagnostic algorithm to identify the disease name corresponding to the symptoms. Furthermore, the diagnostic unit can also make a diagnosis by referring to a medical terminology dictionary. For example, the diagnostic unit may diagnose "gastritis" based on the information "stomach pain." The guidance unit guides the patient to the most suitable doctor based on the diagnosis obtained by the diagnostic unit. For example, the guidance unit may guide the patient to a gastroenterologist. The guidance unit can also guide the patient to a nearby doctor based on the patient's place of residence. For example, the guidance unit may guide the patient to the nearest gastroenterologist based on the patient's address information. The generation unit automatically generates a medical record based on the diagnosis obtained by the diagnostic unit. The generation unit generates a medical record using, for example, template-based generation. The generation unit can also generate a medical record using dynamic generation. For example, the generation unit automatically generates a medical record based on the patient's symptoms and diagnosis. As a result, the platform connecting doctors and patients according to this embodiment can efficiently analyze the patient's symptoms, make a diagnosis, guide the patient to the most suitable doctor, and automatically generate a medical record.

[0030] The feedback unit can analyze the symptoms after diagnosis and provide feedback on the accuracy of the diagnosis. For example, the feedback unit evaluates the accuracy of the diagnosis when the patient re-enters their symptoms after the diagnosis. For example, if the patient enters "symptoms have improved," the feedback unit determines that the diagnosis was correct. If the patient enters "symptoms have worsened," the feedback unit can also guide the patient to undergo another diagnosis. For example, the feedback unit displays a message to the patient saying, "Please undergo another diagnosis." This improves the accuracy of the diagnosis by providing feedback on its accuracy. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input the patient's input data into a generative AI and have the generative AI perform the evaluation of the accuracy of the diagnosis.

[0031] The reception desk can analyze the patient's past symptom input history and select the optimal input method. For example, the reception desk can automatically display symptoms that the patient has frequently entered in the past as suggestions. For instance, if the reception desk has frequently entered "stomach ache" in the past, it will display "stomach ache" as a suggestion the next time the patient enters a symptom. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the patient has used in the past. For example, if the reception desk has used voice input in the past, it will suggest voice input again the next time the patient enters a symptom. The reception desk can also predict and suggest symptoms that the patient will use at specific times of day based on their past input history. For example, if the reception desk has frequently entered "headache" at night, it will display "headache" as a suggestion when the patient enters a symptom at night. This allows the reception desk to select the optimal input method by analyzing the patient's past symptom input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the patient's past input data into a generative AI and have the generative AI select the optimal input method.

[0032] The reception unit can filter the input of symptoms based on the patient's current lifestyle and areas of interest. For example, if the patient is leading a stressful life, the reception unit will prioritize inputting stress-related symptoms. For example, the reception unit will display a message to the patient saying, "Please enter your stress-related symptoms." The reception unit can also prioritize inputting symptoms related to specific dietary restrictions if the patient has them. For example, the reception unit will display a message to the patient saying, "Please enter your dietary restrictions." The reception unit can also prioritize inputting symptoms related to specific exercise habits if the patient has them. For example, the reception unit will display a message to the patient saying, "Please enter your exercise habits." This allows for more appropriate symptom input by filtering based on the patient's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the patient's lifestyle data into a generative AI and have the generative AI perform the filtering.

[0033] The reception system can prioritize the input of highly relevant symptoms by considering the patient's geographical location when symptoms are entered. For example, if the patient lives at high altitude, the reception system will prioritize the input of symptoms specific to high altitude. For example, the reception system will display a message to the patient saying, "Please enter symptoms specific to high altitude." The reception system can also prioritize the input of symptoms specific to urban areas if the patient lives in an urban area. For example, the reception system will display a message to the patient saying, "Please enter symptoms specific to urban areas." The reception system can also prioritize the input of symptoms specific to coastal areas if the patient lives by the sea. For example, the reception system will display a message to the patient saying, "Please enter symptoms specific to coastal areas." This allows the system to prioritize the input of highly relevant symptoms by considering the patient's geographical location. Some or all of the above processing in the reception system may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception system can input the patient's geographical location data into a generative AI and have the generative AI select highly relevant symptoms.

[0034] The reception system can analyze the patient's social media activity when symptoms are entered and input relevant symptoms. For example, the reception system can prioritize inputting symptoms that the patient frequently mentions on social media. For instance, if the reception system frequently mentions "headache" on social media, it will display "headache" as a suggestion for the next input. The reception system can also input relevant symptoms based on lifestyle habits shared by the patient on social media. For example, if the reception system frequently shares "running" on social media, it will display "knee pain" as a suggestion for the next input. The reception system can also input relevant symptoms based on health information that the patient follows on social media. For example, if the reception system frequently follows information about "diet," it will display "loss of appetite" as a suggestion for the next input. This allows for the input of relevant symptoms by analyzing the patient's social media activity. Some or all of the above processing in the reception system may be performed using, for example, a generative AI, or not. For example, the reception system can input the patient's social media data into a generative AI and have the generative AI select relevant symptoms.

[0035] The analysis unit can adjust the level of detail of the analysis based on the severity of the symptoms. For example, in the case of severe symptoms, the analysis unit performs a detailed analysis. For example, the analysis unit displays a message to the patient stating, "Due to the severity of the symptoms, a detailed analysis will be performed." The analysis unit can also perform a concise analysis in the case of mild symptoms. For example, the analysis unit displays a message to the patient stating, "Due to the mild symptoms, a concise analysis will be performed." The analysis unit can also perform an analysis with a moderate level of detail in the case of moderate symptoms. For example, the analysis unit displays a message to the patient stating, "Due to the moderate symptoms, an analysis with a moderate level of detail will be performed." By adjusting the level of detail of the analysis based on the severity of the symptoms, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the patient's symptom data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0036] The analysis unit can apply different analysis algorithms depending on the symptom category during analysis. For example, in the case of digestive system symptoms, the analysis unit applies an analysis algorithm specialized for the digestive system. For example, the analysis unit displays a message to the patient stating, "Due to digestive system symptoms, a specialized analysis will be performed." The analysis unit can also apply a respiratory system analysis algorithm in the case of respiratory system symptoms. For example, the analysis unit displays a message to the patient stating, "Due to respiratory system symptoms, a specialized analysis will be performed." The analysis unit can also apply a nervous system analysis algorithm in the case of nervous system symptoms. For example, the analysis unit displays a message to the patient stating, "Due to nervous system symptoms, a specialized analysis will be performed." By applying different analysis algorithms depending on the symptom category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input patient symptom data into a generative AI and have the generative AI select an analysis algorithm.

[0037] The analysis unit can determine the priority of analysis based on when the symptoms were submitted. For example, the analysis unit may prioritize the analysis of recently submitted symptoms. For example, the analysis unit may display a message to the patient stating, "We will prioritize the analysis of recently submitted symptoms." The analysis unit can also prioritize the analysis of symptoms that have been left untreated for a long period. For example, the analysis unit may display a message to the patient stating, "We will prioritize the analysis of symptoms that have been left untreated for a long period." The analysis unit can also prioritize the analysis of symptoms submitted within a specific time period. For example, the analysis unit may display a message to the patient stating, "We will prioritize the analysis of symptoms submitted within a specific time period." By determining the priority of analysis based on when the symptoms were submitted, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the patient's symptom data into a generating AI and have the generating AI determine the priority of analysis.

[0038] The analysis unit can adjust the order of analysis based on the relevance of symptoms during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant symptoms. For example, the analysis unit can display a message to the patient saying, "We will prioritize the analysis of highly relevant symptoms." The analysis unit can also postpone the analysis of less relevant symptoms. For example, the analysis unit can display a message to the patient saying, "We will postpone the analysis of less relevant symptoms." Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of symptoms. For example, the analysis unit can display a message to the patient saying, "We will adjust the order of analysis based on the relevance of symptoms." By adjusting the order of analysis based on the relevance of symptoms, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the patient's symptom data into a generative AI and have the generative AI perform the adjustment of the analysis order.

[0039] The diagnostic unit can improve the accuracy of its diagnosis by considering the interrelationships between symptoms. For example, the diagnostic unit can make a diagnosis by considering the interrelationships between digestive system symptoms and respiratory system symptoms. For example, the diagnostic unit can display a message to the patient saying, "We will make a diagnosis considering the interrelationships between digestive and respiratory system symptoms." The diagnostic unit can also make a diagnosis by considering the interrelationships between nervous system symptoms and musculoskeletal system symptoms. For example, the diagnostic unit can display a message to the patient saying, "We will make a diagnosis considering the interrelationships between nervous system and musculoskeletal system symptoms." The diagnostic unit can also make a diagnosis by considering the interrelationships between circulatory system symptoms and digestive system symptoms. For example, the diagnostic unit can display a message to the patient saying, "We will make a diagnosis considering the interrelationships between circulatory and digestive system symptoms." By considering the interrelationships between symptoms, the accuracy of the diagnosis can be improved. Some or all of the above processing in the diagnostic unit may be performed using, for example, a generative AI, or without a generative AI. For example, the diagnostic unit can input the patient's symptom data into a generative AI and have the generative AI perform the analysis of the interrelationships.

[0040] The diagnostic unit can perform diagnoses while considering the patient's attribute information. For example, the diagnostic unit can perform diagnoses while considering the patient's age. For example, the diagnostic unit can display a message to the patient saying, "We will perform a diagnosis considering your age." The diagnostic unit can also perform diagnoses while considering the patient's gender. For example, the diagnostic unit can display a message to the patient saying, "We will perform a diagnosis considering your gender." The diagnostic unit can also perform diagnoses while considering the patient's medical history. For example, the diagnostic unit can display a message to the patient saying, "We will perform a diagnosis considering your medical history." By considering the patient's attribute information, a more appropriate diagnosis becomes possible. Some or all of the above processing in the diagnostic unit may be performed using, for example, a generative AI, or without a generative AI. For example, the diagnostic unit can input patient attribute data into a generative AI and have the generative AI perform adjustments to the diagnosis.

[0041] The diagnostic unit can perform a diagnosis while considering the geographical distribution of symptoms. For example, the diagnostic unit can perform a diagnosis while considering symptoms specific to the area where the patient lives. For example, the diagnostic unit can display a message to the patient saying, "We will perform a diagnosis while considering symptoms specific to your area." The diagnostic unit can also perform a diagnosis while considering symptoms that occurred while the patient was traveling. For example, the diagnostic unit can display a message to the patient saying, "We will perform a diagnosis while considering symptoms in your travel destination." The diagnostic unit can also perform a diagnosis while considering symptoms specific to the area the patient has moved to. For example, the diagnostic unit can display a message to the patient saying, "We will perform a diagnosis while considering symptoms in your new location." By considering the geographical distribution of symptoms, a more appropriate diagnosis becomes possible. Some or all of the above processing in the diagnostic unit may be performed using, for example, a generative AI, or without a generative AI. For example, the diagnostic unit can input the patient's geographical data into a generative AI and have the generative AI perform adjustments to the diagnosis.

[0042] The diagnostic unit can improve the accuracy of its diagnosis by referring to relevant literature on the symptoms during the diagnosis process. For example, the diagnostic unit can make a diagnosis by referring to the latest medical literature. For example, the diagnostic unit can display a message to the patient stating, "We will make a diagnosis by referring to the latest medical literature." The diagnostic unit can also make a diagnosis by referring to past case reports. For example, the diagnostic unit can display a message to the patient stating, "We will make a diagnosis by referring to past case reports." The diagnostic unit can also make a diagnosis by referring to specialized medical textbooks. For example, the diagnostic unit can display a message to the patient stating, "We will make a diagnosis by referring to specialized medical textbooks." By referring to relevant literature on the symptoms, the accuracy of the diagnosis can be improved. Some or all of the above processes in the diagnostic unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the diagnostic unit can input the patient's symptom data into a generative AI and have the generative AI perform the referencing of relevant literature.

[0043] The guidance unit can adjust the level of detail in the guidance based on the importance of the diagnostic result. For example, in the case of a serious diagnostic result, the guidance unit will provide detailed guidance. For example, the guidance unit will display a message to the patient stating, "Due to a serious diagnostic result, we will provide detailed guidance." The guidance unit can also provide concise guidance in the case of a minor diagnostic result. For example, the guidance unit will display a message to the patient stating, "Due to a minor diagnostic result, we will provide concise guidance." The guidance unit can also provide guidance with an appropriate level of detail in the case of a moderate diagnostic result. For example, the guidance unit will display a message to the patient stating, "Due to a moderate diagnostic result, we will provide guidance with an appropriate level of detail." By adjusting the level of detail in the guidance based on the importance of the diagnostic result, more appropriate guidance becomes possible. Some or all of the above processing in the guidance unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the guidance unit can input the patient's diagnostic result data into a generating AI and have the generating AI adjust the level of detail in the guidance.

[0044] The guidance unit can apply different guidance algorithms depending on the category of the diagnostic result. For example, if the diagnosis result is for the digestive system, the guidance unit will apply a guidance algorithm specialized for the digestive system. For example, the guidance unit will display a message to the patient saying, "Because the diagnosis result is for the digestive system, we will provide specialized guidance." The guidance unit can also apply a guidance algorithm specialized for the respiratory system if the diagnosis result is for the respiratory system. For example, the guidance unit will display a message to the patient saying, "Because the diagnosis result is for the respiratory system, we will provide specialized guidance." The guidance unit can also apply a guidance algorithm specialized for the nervous system if the diagnosis result is for the nervous system. For example, the guidance unit will display a message to the patient saying, "Because the diagnosis result is for the nervous system, we will provide specialized guidance." By applying different guidance algorithms depending on the category of the diagnostic result, more appropriate guidance becomes possible. Some or all of the above processing in the guidance unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the guidance unit can input the patient's diagnostic result data into a generative AI and have the generative AI select a guidance algorithm.

[0045] The guidance unit can determine the priority of guidance based on when the diagnostic results were submitted. For example, the guidance unit may prioritize recent diagnostic results. For example, the guidance unit may display a message to the patient stating, "We will prioritize guiding you with recent diagnostic results." The guidance unit can also prioritize guiding patients with diagnostic results that have been left unattended for a long time. For example, the guidance unit may display a message to the patient stating, "We will prioritize guiding you with diagnostic results that have been left unattended for a long time." The guidance unit can also prioritize guiding patients with diagnostic results submitted within a specific time period. For example, the guidance unit may display a message to the patient stating, "We will prioritize guiding you with diagnostic results submitted within a specific time period." By determining the priority of guidance based on when the diagnostic results were submitted, more appropriate guidance becomes possible. Some or all of the above processing in the guidance unit may be performed using, for example, a generative AI, or without a generative AI. For example, the guidance unit can input the patient's diagnostic result data into a generative AI and have the generative AI determine the priority of guidance.

[0046] The guidance unit can adjust the order of guidance based on the relevance of the diagnostic results. For example, the guidance unit can prioritize guiding users to highly relevant diagnostic results. For example, the guidance unit can display a message to the patient stating, "We will prioritize guiding you to highly relevant diagnostic results." The guidance unit can also postpone guiding users to less relevant diagnostic results. For example, the guidance unit can display a message to the patient stating, "We will postpone guiding users to less relevant diagnostic results." The guidance unit can also dynamically adjust the order of guidance based on the relevance of the diagnostic results. For example, the guidance unit can display a message to the patient stating, "We will adjust the order of guidance based on the relevance of the diagnostic results." This allows for more appropriate guidance by adjusting the order of guidance based on the relevance of the diagnostic results. Some or all of the above processing in the guidance unit may be performed using, for example, a generative AI, or without a generative AI. For example, the guidance unit can input the patient's diagnostic result data into a generative AI and have the generative AI adjust the order of guidance.

[0047] The generation unit can adjust the level of detail in the medical record based on the importance of the diagnosis when generating the medical record. For example, in the case of a serious diagnosis, the generation unit generates a detailed medical record. For example, the generation unit displays a message to the patient stating, "Due to a serious diagnosis, a detailed medical record will be generated." The generation unit can also generate a concise medical record in the case of a minor diagnosis. For example, the generation unit displays a message to the patient stating, "Due to a minor diagnosis, a concise medical record will be generated." The generation unit can also generate a medical record with appropriate detail in the case of a moderate diagnosis. For example, the generation unit displays a message to the patient stating, "Due to a moderate diagnosis, a medical record with appropriate detail will be generated." By adjusting the level of detail in the medical record based on the importance of the diagnosis, a more appropriate medical record can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the patient's diagnosis data into the generation AI and have the generation AI adjust the level of detail in the medical record.

[0048] The generation unit can apply different generation algorithms depending on the category of the diagnosis result when generating a medical record. For example, if the diagnosis result is for the digestive system, the generation unit will apply a generation algorithm specialized for the digestive system. For example, the generation unit will display a message to the patient stating, "Because the diagnosis result is for the digestive system, a specialized medical record will be generated." The generation unit can also apply a generation algorithm specialized for the respiratory system if the diagnosis result is for the respiratory system. For example, the generation unit will display a message to the patient stating, "Because the diagnosis result is for the respiratory system, a specialized medical record will be generated." The generation unit can also apply a generation algorithm specialized for the nervous system if the diagnosis result is for the nervous system. For example, the generation unit will display a message to the patient stating, "Because the diagnosis result is for the nervous system, a specialized medical record will be generated." By applying different generation algorithms depending on the category of the diagnosis result, a more appropriate medical record can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input the patient's diagnosis result data into a generation AI and have the generation AI select a generation algorithm.

[0049] The generation unit can determine the priority of medical records based on when the diagnostic results were submitted when generating them. For example, the generation unit can prioritize the inclusion of recent diagnostic results in the medical record. For example, the generation unit can display a message to the patient stating, "We will prioritize the inclusion of recent diagnostic results in your medical record." The generation unit can also prioritize the inclusion of diagnostic results that have been left unattended for a long time. For example, the generation unit can display a message to the patient stating, "We will prioritize the inclusion of diagnostic results that have been left unattended for a long time in your medical record." Furthermore, the generation unit can prioritize the inclusion of diagnostic results submitted within a specific time period. For example, the generation unit can display a message to the patient stating, "We will prioritize the inclusion of diagnostic results submitted within a specific time period in your medical record." By determining the priority of medical records based on when the diagnostic results were submitted, more appropriate medical records can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input patient diagnostic result data into a generation AI and have the generation AI determine the priority of the medical records.

[0050] The generation unit can adjust the order of medical records based on the relevance of the diagnostic results when generating medical records. For example, the generation unit can prioritize reflecting highly relevant diagnostic results in the medical record. For example, the generation unit can display a message to the patient stating, "We will prioritize reflecting highly relevant diagnostic results in your medical record." The generation unit can also postpone less relevant diagnostic results. For example, the generation unit can display a message to the patient stating, "We will postpone less relevant diagnostic results." The generation unit can also dynamically adjust the order of medical records based on the relevance of the diagnostic results. For example, the generation unit can display a message to the patient stating, "We will adjust the order of medical records based on the relevance of the diagnostic results." By adjusting the order of medical records based on the relevance of the diagnostic results, a more appropriate medical record can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input the patient's diagnostic result data into a generation AI and have the generation AI perform the adjustment of the order of medical records.

[0051] The feedback unit can adjust the level of detail of the feedback based on the importance of the diagnostic result. For example, in the case of a serious diagnosis, the feedback unit provides detailed feedback. For example, the feedback unit displays a message to the patient stating, "Due to the serious diagnosis, we will provide detailed feedback." The feedback unit can also provide concise feedback in the case of a minor diagnosis. For example, the feedback unit displays a message to the patient stating, "Due to the minor diagnosis, we will provide concise feedback." The feedback unit can also provide feedback with a moderate level of detail in the case of a moderate diagnosis. For example, the feedback unit displays a message to the patient stating, "Due to the moderate diagnosis, we will provide feedback with a moderate level of detail." By adjusting the level of detail of the feedback based on the importance of the diagnostic result, more appropriate feedback becomes possible. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input the patient's diagnostic result data into a generative AI and have the generative AI adjust the level of detail of the feedback.

[0052] The feedback unit can apply different feedback algorithms depending on the category of the diagnostic result when providing feedback. For example, if the diagnosis is related to the digestive system, the feedback unit will apply a feedback algorithm specifically for the digestive system. For example, the feedback unit will display a message to the patient stating, "Because the diagnosis is related to the digestive system, we will provide specialized feedback." The feedback unit can also apply a feedback algorithm specifically for the respiratory system if the diagnosis is related to the respiratory system. For example, the feedback unit will display a message to the patient stating, "Because the diagnosis is related to the respiratory system, we will provide specialized feedback." The feedback unit can also apply a feedback algorithm specifically for the nervous system if the diagnosis is related to the nervous system. For example, the feedback unit will display a message to the patient stating, "Because the diagnosis is related to the nervous system, we will provide specialized feedback." By applying different feedback algorithms depending on the category of the diagnostic result, more appropriate feedback becomes possible. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the feedback unit can input the patient's diagnostic result data into a generative AI and have the generative AI select a feedback algorithm.

[0053] The feedback unit can prioritize feedback based on when the diagnostic results were submitted. For example, the feedback unit can prioritize recent diagnostic results. For example, the feedback unit can display a message to the patient stating, "We will prioritize feedback on recent diagnostic results." The feedback unit can also prioritize feedback on diagnostic results that have been left unattended for a long time. For example, the feedback unit can display a message to the patient stating, "We will prioritize feedback on diagnostic results that have been left unattended for a long time." The feedback unit can also prioritize feedback on diagnostic results submitted within a specific time period. For example, the feedback unit can display a message to the patient stating, "We will prioritize feedback on diagnostic results submitted within a specific time period." By prioritizing feedback based on when the diagnostic results were submitted, more appropriate feedback becomes possible. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input the patient's diagnostic result data into a generative AI and have the generative AI determine the priority of feedback.

[0054] The feedback unit can adjust the order of feedback based on the relevance of the diagnostic results. For example, the feedback unit can prioritize providing feedback on highly relevant diagnostic results. For example, the feedback unit can display a message to the patient stating, "We will prioritize providing feedback on highly relevant diagnostic results." The feedback unit can also postpone providing feedback on less relevant diagnostic results. For example, the feedback unit can display a message to the patient stating, "We will postpone providing feedback on less relevant diagnostic results." Furthermore, the feedback unit can dynamically adjust the order of feedback based on the relevance of the diagnostic results. For example, the feedback unit can display a message to the patient stating, "We will adjust the order of feedback based on the relevance of the diagnostic results." This allows for more appropriate feedback by adjusting the order of feedback based on the relevance of the diagnostic results. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the feedback unit can input the patient's diagnostic result data into a generative AI and have the generative AI perform the adjustment of the feedback order.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can customize the input content when patients enter their symptoms, taking into account their lifestyle data. For example, if a patient exercises regularly, they can be prompted to prioritize entering exercise-related symptoms. Similarly, if a patient has specific dietary restrictions, they can be guided to prioritize entering symptoms related to those restrictions. Furthermore, if a patient leads a stressful life, they can be prompted to prioritize entering stress-related symptoms. By customizing symptom input based on the patient's lifestyle, a more accurate diagnosis becomes possible.

[0057] The feedback system can analyze patient feedback and incorporate it into future diagnoses. For example, if a patient provides feedback that the diagnosis was accurate, the diagnostic algorithm can be improved. If a patient provides feedback that the diagnosis was inaccurate, the diagnostic algorithm can be reviewed. Furthermore, if a patient provides feedback that the diagnosis was delayed, improvements can be made to expedite the diagnostic process. By incorporating patient feedback into future diagnoses, the accuracy and efficiency of diagnoses can be improved.

[0058] The reception desk can analyze a patient's past symptom input history and suggest the optimal input method. For example, it can automatically display symptoms that the patient has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the patient has used in the past (voice, text, etc.). Furthermore, it can predict and suggest symptoms that the patient will use at specific times based on their past input history. In this way, by analyzing a patient's past symptom input history, the optimal input method can be suggested.

[0059] The reception system can filter symptom input based on the patient's current lifestyle and areas of interest. For example, if a patient is leading a stressful life, they can be prompted to prioritize inputting stress-related symptoms. Similarly, if a patient has specific dietary restrictions, they can be guided to prioritize inputting symptoms related to those restrictions. Furthermore, if a patient has a specific exercise habit, they can be prompted to prioritize inputting symptoms related to that habit. This filtering based on the patient's lifestyle and areas of interest allows for more appropriate symptom input.

[0060] The reception system can prioritize the input of highly relevant symptoms by considering the patient's geographical location when symptoms are entered. For example, if the patient lives in a high-altitude area, it can prompt them to prioritize entering symptoms specific to high-altitude areas. Similarly, if the patient lives in an urban area, it can prompt them to prioritize entering symptoms specific to urban areas. Furthermore, if the patient lives by the sea, it can prompt them to prioritize entering symptoms specific to coastal areas. In this way, by considering the patient's geographical location, the system can prioritize the input of highly relevant symptoms.

[0061] The reception desk can analyze a patient's social media activity when they enter their symptoms and input relevant symptoms. For example, it can prompt patients to prioritize entering symptoms they frequently mention on social media. It can also prompt them to enter relevant symptoms based on lifestyle habits they share on social media. Furthermore, it can prompt them to enter relevant symptoms based on health information they follow on social media. This allows the system to input relevant symptoms by analyzing the patient's social media activity.

[0062] The analysis unit can adjust the level of detail of the analysis based on the severity of the symptoms. For example, a detailed analysis can be performed for severe symptoms. A simplified analysis can be performed for mild symptoms. Furthermore, an analysis with an appropriate level of detail can be performed for moderate symptoms. By adjusting the level of detail of the analysis based on the severity of the symptoms, more appropriate analysis results can be provided.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk enters the patient's symptoms. Patients can enter their symptoms using a smartphone or computer, or they can enter them using voice input. For example, if a patient says "My stomach hurts" aloud, the reception desk converts the voice into text and enters it. Step 2: The analysis unit analyzes the symptoms entered by the reception unit and converts them into specific medical terms. The analysis unit can use generative AI and natural language processing technology to convert ambiguous expressions into specific medical terms, and can also use machine learning algorithms to analyze symptoms based on past data. For example, it can convert the expression "my stomach hurts" to "stomach ache." Step 3: The diagnostic unit performs a diagnosis based on the information converted by the analysis unit. The diagnostic unit can also use generating AI or diagnostic algorithms to identify disease names corresponding to symptoms, and can refer to a medical terminology dictionary to perform a diagnosis. For example, based on the information "stomach pain," it may diagnose "gastritis." Step 4: The information desk will guide the patient to the most suitable physician based on the diagnostic results obtained by the diagnostic desk. The information desk will guide the patient to a gastroenterologist and can also guide the patient to a nearby physician based on the patient's place of residence. For example, it will guide the patient to the nearest gastroenterologist based on the patient's address information. Step 5: The generation unit automatically generates a medical record based on the diagnostic results obtained by the diagnostic unit. The generation unit can generate medical records using template-based generation or dynamic generation. For example, it can automatically generate a medical record based on the patient's symptoms and diagnostic results.

[0065] (Example of form 2) The platform connecting doctors and patients according to an embodiment of the present invention is a system that utilizes generative AI to analyze a patient's symptoms, assist in diagnosis, and guide the patient to the most suitable doctor. This system begins with the patient inputting vague symptoms such as "my stomach hurts" or "I feel vaguely nauseous." The generative AI analyzes the input information and converts it into specific medical terms such as "stomach ache," "pain in the lower right abdomen," and "nausea." Next, the generative AI makes a diagnosis based on the converted information and guides the patient to the most suitable doctor. For example, in the case of stomach ache, it guides the patient to a gastroenterologist, and in the case of pain in the lower right abdomen, it guides the patient to a surgeon. After the diagnosis, the patient inputs symptoms again, and the generative AI analyzes the symptoms after the diagnosis and provides feedback on the accuracy of the diagnosis. For example, if the symptoms improve after the diagnosis, it determines that the diagnosis was correct, and conversely, if the symptoms worsen, it guides the patient to undergo another diagnosis. Furthermore, the generative AI has an automatic medical record generation function and automatically generates a medical record based on the information entered by the patient and the diagnosis results. This saves doctors the trouble of creating medical records and allows them to concentrate on medical treatment. Thus, the present invention provides a platform that connects doctors and patients using generative AI, reducing the workload of doctors and enabling the provision of appropriate medical care to patients by assisting with diagnosis and automatically generating medical records. As a result, the platform connecting doctors and patients can efficiently analyze patients' symptoms using generative AI, assist in diagnosis, guide them to the most suitable doctor, and automatically generate medical records.

[0066] The platform connecting doctors and patients according to this embodiment comprises a reception unit, an analysis unit, a diagnosis unit, a guidance unit, and a generation unit. The reception unit inputs the patient's symptoms. The patient's symptoms include, but are not limited to, vague expressions such as "my stomach hurts" or "I feel vaguely nauseous." The reception unit allows, for example, patients to input their symptoms using a smartphone or computer. The reception unit can also input the patient's symptoms using voice input. For example, if a patient says "my stomach hurts," the reception unit converts the voice into text and inputs it. The analysis unit uses generation AI to analyze the symptoms input by the reception unit and converts them into specific medical terms. The analysis unit uses, for example, natural language processing technology to convert vague expressions into specific medical terms. The analysis unit can also use machine learning algorithms to analyze symptoms based on past data. For example, the analysis unit converts the expression "my stomach hurts" to "stomach ache." The diagnosis unit uses generation AI to perform a diagnosis based on the information converted by the analysis unit. The diagnosis unit uses, for example, a diagnostic algorithm to identify the disease name corresponding to the symptoms. Furthermore, the diagnostic unit can also make a diagnosis by referring to a medical terminology dictionary. For example, the diagnostic unit may diagnose "gastritis" based on the information "stomach pain." The guidance unit guides the patient to the most suitable doctor based on the diagnosis obtained by the diagnostic unit. For example, the guidance unit may guide the patient to a gastroenterologist. The guidance unit can also guide the patient to a nearby doctor based on the patient's place of residence. For example, the guidance unit may guide the patient to the nearest gastroenterologist based on the patient's address information. The generation unit automatically generates a medical record based on the diagnosis obtained by the diagnostic unit. The generation unit generates a medical record using, for example, template-based generation. The generation unit can also generate a medical record using dynamic generation. For example, the generation unit automatically generates a medical record based on the patient's symptoms and diagnosis. As a result, the platform connecting doctors and patients according to this embodiment can efficiently analyze the patient's symptoms, make a diagnosis, guide the patient to the most suitable doctor, and automatically generate a medical record.

[0067] The feedback unit can analyze the symptoms after diagnosis and provide feedback on the accuracy of the diagnosis. For example, the feedback unit evaluates the accuracy of the diagnosis when the patient re-enters their symptoms after the diagnosis. For example, if the patient enters "symptoms have improved," the feedback unit determines that the diagnosis was correct. If the patient enters "symptoms have worsened," the feedback unit can also guide the patient to undergo another diagnosis. For example, the feedback unit displays a message to the patient saying, "Please undergo another diagnosis." This improves the accuracy of the diagnosis by providing feedback on its accuracy. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input the patient's input data into a generative AI and have the generative AI perform the evaluation of the accuracy of the diagnosis.

[0068] The reception desk can estimate the patient's emotions and adjust the timing of symptom input based on the estimated emotions. For example, if the patient is feeling anxious, the reception desk can provide a relaxing environment and delay the timing of symptom input. For example, the reception desk can display a message to the patient saying, "Please relax before entering your symptoms." The reception desk can also simplify the interface to allow the patient to enter symptoms quickly if they are in a hurry. For example, the reception desk can display a message to the patient saying, "Please enter your symptoms quickly." The reception desk can also allow the patient to take their time to enter detailed symptoms if they are relaxed. For example, the reception desk can display a message to the patient saying, "Please enter your symptoms in detail." By adjusting the timing of symptom input according to the patient's emotions, more appropriate symptom input becomes possible. 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, for example, generative AI, or not using generative AI. For example, the reception desk can input patient facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0069] The reception desk can analyze the patient's past symptom input history and select the optimal input method. For example, the reception desk can automatically display symptoms that the patient has frequently entered in the past as suggestions. For instance, if the reception desk has frequently entered "stomach ache" in the past, it will display "stomach ache" as a suggestion the next time the patient enters a symptom. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the patient has used in the past. For example, if the reception desk has used voice input in the past, it will suggest voice input again the next time the patient enters a symptom. The reception desk can also predict and suggest symptoms that the patient will use at specific times of day based on their past input history. For example, if the reception desk has frequently entered "headache" at night, it will display "headache" as a suggestion when the patient enters a symptom at night. This allows the reception desk to select the optimal input method by analyzing the patient's past symptom input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the patient's past input data into a generative AI and have the generative AI select the optimal input method.

[0070] The reception unit can filter the input of symptoms based on the patient's current lifestyle and areas of interest. For example, if the patient is leading a stressful life, the reception unit will prioritize inputting stress-related symptoms. For example, the reception unit will display a message to the patient saying, "Please enter your stress-related symptoms." The reception unit can also prioritize inputting symptoms related to specific dietary restrictions if the patient has them. For example, the reception unit will display a message to the patient saying, "Please enter your dietary restrictions." The reception unit can also prioritize inputting symptoms related to specific exercise habits if the patient has them. For example, the reception unit will display a message to the patient saying, "Please enter your exercise habits." This allows for more appropriate symptom input by filtering based on the patient's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the patient's lifestyle data into a generative AI and have the generative AI perform the filtering.

[0071] The reception desk can estimate the patient's emotions and determine the priority of symptoms to be entered based on the estimated emotions. For example, if the patient is feeling anxious, the reception desk will prioritize the input of serious symptoms. For example, the reception desk will display a message to the patient saying, "Please prioritize entering serious symptoms." The reception desk can also prioritize the input of detailed symptoms if the patient is relaxed. For example, the reception desk will display a message to the patient saying, "Please enter detailed symptoms." The reception desk can also prioritize the input of major symptoms if the patient is in a hurry. For example, the reception desk will display a message to the patient saying, "Please prioritize entering major symptoms." This allows for more appropriate symptom input by prioritizing symptoms according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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, for example, generative AI, or not using generative AI. For example, the reception desk can input patient facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0072] The reception system can prioritize the input of highly relevant symptoms by considering the patient's geographical location when symptoms are entered. For example, if the patient lives at high altitude, the reception system will prioritize the input of symptoms specific to high altitude. For example, the reception system will display a message to the patient saying, "Please enter symptoms specific to high altitude." The reception system can also prioritize the input of symptoms specific to urban areas if the patient lives in an urban area. For example, the reception system will display a message to the patient saying, "Please enter symptoms specific to urban areas." The reception system can also prioritize the input of symptoms specific to coastal areas if the patient lives by the sea. For example, the reception system will display a message to the patient saying, "Please enter symptoms specific to coastal areas." This allows the system to prioritize the input of highly relevant symptoms by considering the patient's geographical location. Some or all of the above processing in the reception system may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception system can input the patient's geographical location data into a generative AI and have the generative AI select highly relevant symptoms.

[0073] The reception system can analyze the patient's social media activity when symptoms are entered and input relevant symptoms. For example, the reception system can prioritize inputting symptoms that the patient frequently mentions on social media. For instance, if the reception system frequently mentions "headache" on social media, it will display "headache" as a suggestion for the next input. The reception system can also input relevant symptoms based on lifestyle habits shared by the patient on social media. For example, if the reception system frequently shares "running" on social media, it will display "knee pain" as a suggestion for the next input. The reception system can also input relevant symptoms based on health information that the patient follows on social media. For example, if the reception system frequently follows information about "diet," it will display "loss of appetite" as a suggestion for the next input. This allows for the input of relevant symptoms by analyzing the patient's social media activity. Some or all of the above processing in the reception system may be performed using, for example, a generative AI, or not. For example, the reception system can input the patient's social media data into a generative AI and have the generative AI select relevant symptoms.

[0074] The analysis unit can estimate the patient's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the patient is feeling anxious, the analysis unit will use a simple and easy-to-understand presentation. For example, the analysis unit will display a message to the patient saying, "I will explain in simple terms." The analysis unit can also provide detailed analysis results if the patient is relaxed. For example, the analysis unit will display a message to the patient saying, "I will display detailed analysis results." The analysis unit can also provide concise analysis results that get straight to the point if the patient is in a hurry. For example, the analysis unit will display a message to the patient saying, "I will explain the key points concisely." By adjusting the presentation of the analysis according to the patient's emotions, more appropriate analysis results 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, for example, generative AI, or without generative AI. For example, the analysis unit can input patient facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0075] The analysis unit can adjust the level of detail of the analysis based on the severity of the symptoms. For example, in the case of severe symptoms, the analysis unit performs a detailed analysis. For example, the analysis unit displays a message to the patient stating, "Due to the severity of the symptoms, a detailed analysis will be performed." The analysis unit can also perform a concise analysis in the case of mild symptoms. For example, the analysis unit displays a message to the patient stating, "Due to the mild symptoms, a concise analysis will be performed." The analysis unit can also perform an analysis with a moderate level of detail in the case of moderate symptoms. For example, the analysis unit displays a message to the patient stating, "Due to the moderate symptoms, an analysis with a moderate level of detail will be performed." By adjusting the level of detail of the analysis based on the severity of the symptoms, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the patient's symptom data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the symptom category during analysis. For example, in the case of digestive system symptoms, the analysis unit applies an analysis algorithm specialized for the digestive system. For example, the analysis unit displays a message to the patient stating, "Due to digestive system symptoms, a specialized analysis will be performed." The analysis unit can also apply a respiratory system analysis algorithm in the case of respiratory system symptoms. For example, the analysis unit displays a message to the patient stating, "Due to respiratory system symptoms, a specialized analysis will be performed." The analysis unit can also apply a nervous system analysis algorithm in the case of nervous system symptoms. For example, the analysis unit displays a message to the patient stating, "Due to nervous system symptoms, a specialized analysis will be performed." By applying different analysis algorithms depending on the symptom category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input patient symptom data into a generative AI and have the generative AI select an analysis algorithm.

[0077] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the patient is feeling anxious, the analysis unit can perform a short, concise analysis. For example, the analysis unit can display a message to the patient saying, "We will perform a short, concise analysis." The analysis unit can also perform a detailed analysis if the patient is relaxed. For example, the analysis unit can display a message to the patient saying, "We will perform a detailed analysis." The analysis unit can also perform a brief analysis if the patient is in a hurry. For example, the analysis unit can display a message to the patient saying, "We will perform a brief analysis." By adjusting the length of the analysis according to the patient's emotions, more appropriate analysis results 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, for example, generative AI, or without generative AI. For example, the analysis unit can input patient facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0078] The analysis unit can determine the priority of analysis based on when the symptoms were submitted. For example, the analysis unit may prioritize the analysis of recently submitted symptoms. For example, the analysis unit may display a message to the patient stating, "We will prioritize the analysis of recently submitted symptoms." The analysis unit can also prioritize the analysis of symptoms that have been left untreated for a long period. For example, the analysis unit may display a message to the patient stating, "We will prioritize the analysis of symptoms that have been left untreated for a long period." The analysis unit can also prioritize the analysis of symptoms submitted within a specific time period. For example, the analysis unit may display a message to the patient stating, "We will prioritize the analysis of symptoms submitted within a specific time period." By determining the priority of analysis based on when the symptoms were submitted, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the patient's symptom data into a generating AI and have the generating AI determine the priority of analysis.

[0079] The analysis unit can adjust the order of analysis based on the relevance of symptoms during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant symptoms. For example, the analysis unit can display a message to the patient saying, "We will prioritize the analysis of highly relevant symptoms." The analysis unit can also postpone the analysis of less relevant symptoms. For example, the analysis unit can display a message to the patient saying, "We will postpone the analysis of less relevant symptoms." Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of symptoms. For example, the analysis unit can display a message to the patient saying, "We will adjust the order of analysis based on the relevance of symptoms." By adjusting the order of analysis based on the relevance of symptoms, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the patient's symptom data into a generative AI and have the generative AI perform the adjustment of the analysis order.

[0080] The diagnostic unit can estimate the patient's emotions and adjust the diagnostic criteria based on the estimated emotions. For example, if the patient is feeling anxious, the diagnostic unit can apply cautious diagnostic criteria. For example, the diagnostic unit can display a message to the patient stating, "Applying cautious diagnostic criteria." The diagnostic unit can also apply normal diagnostic criteria if the patient is relaxed. For example, the diagnostic unit can display a message to the patient stating, "Applying normal diagnostic criteria." The diagnostic unit can also apply rapid diagnostic criteria if the patient is in a hurry. For example, the diagnostic unit can display a message to the patient stating, "Applying rapid diagnostic criteria." By adjusting the diagnostic criteria according to the patient's emotions, a more appropriate diagnosis becomes possible. 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 diagnostic unit may be performed using, for example, generative AI, or without generative AI. For example, the diagnostic unit can input patient facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0081] The diagnostic unit can improve the accuracy of its diagnosis by considering the interrelationships between symptoms. For example, the diagnostic unit can make a diagnosis by considering the interrelationships between digestive system symptoms and respiratory system symptoms. For example, the diagnostic unit can display a message to the patient saying, "We will make a diagnosis considering the interrelationships between digestive and respiratory system symptoms." The diagnostic unit can also make a diagnosis by considering the interrelationships between nervous system symptoms and musculoskeletal system symptoms. For example, the diagnostic unit can display a message to the patient saying, "We will make a diagnosis considering the interrelationships between nervous system and musculoskeletal system symptoms." The diagnostic unit can also make a diagnosis by considering the interrelationships between circulatory system symptoms and digestive system symptoms. For example, the diagnostic unit can display a message to the patient saying, "We will make a diagnosis considering the interrelationships between circulatory and digestive system symptoms." By considering the interrelationships between symptoms, the accuracy of the diagnosis can be improved. Some or all of the above processing in the diagnostic unit may be performed using, for example, a generative AI, or without a generative AI. For example, the diagnostic unit can input the patient's symptom data into a generative AI and have the generative AI perform the analysis of the interrelationships.

[0082] The diagnostic unit can perform diagnoses while considering the patient's attribute information. For example, the diagnostic unit can perform diagnoses while considering the patient's age. For example, the diagnostic unit can display a message to the patient saying, "We will perform a diagnosis considering your age." The diagnostic unit can also perform diagnoses while considering the patient's gender. For example, the diagnostic unit can display a message to the patient saying, "We will perform a diagnosis considering your gender." The diagnostic unit can also perform diagnoses while considering the patient's medical history. For example, the diagnostic unit can display a message to the patient saying, "We will perform a diagnosis considering your medical history." By considering the patient's attribute information, a more appropriate diagnosis becomes possible. Some or all of the above processing in the diagnostic unit may be performed using, for example, a generative AI, or without a generative AI. For example, the diagnostic unit can input patient attribute data into a generative AI and have the generative AI perform adjustments to the diagnosis.

[0083] The diagnostic unit can estimate the patient's emotions and adjust the order in which the diagnostic results are displayed based on the estimated emotions. For example, if the patient is feeling anxious, the diagnostic unit may display important results first. For example, the diagnostic unit may display a message to the patient saying, "We will display important results first." The diagnostic unit may also display detailed results in order if the patient is relaxed. For example, the diagnostic unit may display a message to the patient saying, "We will display detailed results in order." The diagnostic unit may also display concise results first if the patient is in a hurry. For example, the diagnostic unit may display a message to the patient saying, "We will display concise results first." By adjusting the order in which the diagnostic results are displayed according to the patient's emotions, more appropriate diagnostic results 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 diagnostic unit may be performed using, for example, generative AI, or not using generative AI. For example, the diagnostic unit can input patient facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0084] The diagnostic unit can perform a diagnosis while considering the geographical distribution of symptoms. For example, the diagnostic unit can perform a diagnosis while considering symptoms specific to the area where the patient lives. For example, the diagnostic unit can display a message to the patient saying, "We will perform a diagnosis while considering symptoms specific to your area." The diagnostic unit can also perform a diagnosis while considering symptoms that occurred while the patient was traveling. For example, the diagnostic unit can display a message to the patient saying, "We will perform a diagnosis while considering symptoms in your travel destination." The diagnostic unit can also perform a diagnosis while considering symptoms specific to the area the patient has moved to. For example, the diagnostic unit can display a message to the patient saying, "We will perform a diagnosis while considering symptoms in your new location." By considering the geographical distribution of symptoms, a more appropriate diagnosis becomes possible. Some or all of the above processing in the diagnostic unit may be performed using, for example, a generative AI, or without a generative AI. For example, the diagnostic unit can input the patient's geographical data into a generative AI and have the generative AI perform adjustments to the diagnosis.

[0085] The diagnostic unit can improve the accuracy of its diagnosis by referring to relevant literature on the symptoms during the diagnosis process. For example, the diagnostic unit can make a diagnosis by referring to the latest medical literature. For example, the diagnostic unit can display a message to the patient stating, "We will make a diagnosis by referring to the latest medical literature." The diagnostic unit can also make a diagnosis by referring to past case reports. For example, the diagnostic unit can display a message to the patient stating, "We will make a diagnosis by referring to past case reports." The diagnostic unit can also make a diagnosis by referring to specialized medical textbooks. For example, the diagnostic unit can display a message to the patient stating, "We will make a diagnosis by referring to specialized medical textbooks." By referring to relevant literature on the symptoms, the accuracy of the diagnosis can be improved. Some or all of the above processes in the diagnostic unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the diagnostic unit can input the patient's symptom data into a generative AI and have the generative AI perform the referencing of relevant literature.

[0086] The guidance unit can estimate the patient's emotions and adjust the way guidance is displayed based on the estimated emotions. For example, if the patient is feeling anxious, the guidance unit can provide a simple and highly visible display. For example, the guidance unit can display the message "We provide a simple and highly visible display." If the patient is relaxed, the guidance unit can also provide a display that includes detailed information. For example, the guidance unit can display the message "We provide a display that includes detailed information." If the patient is in a hurry, the guidance unit can also provide a display that gets straight to the point. For example, the guidance unit can display the message "We provide a display that gets straight to the point." By adjusting the way guidance is displayed according to the patient's emotions, more appropriate guidance becomes possible. 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 guidance unit may be performed using, for example, generative AI, or not using generative AI. For example, the guidance unit can input patient facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0087] The guidance unit can adjust the level of detail in the guidance based on the importance of the diagnostic result. For example, in the case of a serious diagnostic result, the guidance unit will provide detailed guidance. For example, the guidance unit will display a message to the patient stating, "Due to a serious diagnostic result, we will provide detailed guidance." The guidance unit can also provide concise guidance in the case of a minor diagnostic result. For example, the guidance unit will display a message to the patient stating, "Due to a minor diagnostic result, we will provide concise guidance." The guidance unit can also provide guidance with an appropriate level of detail in the case of a moderate diagnostic result. For example, the guidance unit will display a message to the patient stating, "Due to a moderate diagnostic result, we will provide guidance with an appropriate level of detail." By adjusting the level of detail in the guidance based on the importance of the diagnostic result, more appropriate guidance becomes possible. Some or all of the above processing in the guidance unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the guidance unit can input the patient's diagnostic result data into a generating AI and have the generating AI adjust the level of detail in the guidance.

[0088] The guidance unit can apply different guidance algorithms depending on the category of the diagnostic result. For example, if the diagnosis result is for the digestive system, the guidance unit will apply a guidance algorithm specialized for the digestive system. For example, the guidance unit will display a message to the patient saying, "Because the diagnosis result is for the digestive system, we will provide specialized guidance." The guidance unit can also apply a guidance algorithm specialized for the respiratory system if the diagnosis result is for the respiratory system. For example, the guidance unit will display a message to the patient saying, "Because the diagnosis result is for the respiratory system, we will provide specialized guidance." The guidance unit can also apply a guidance algorithm specialized for the nervous system if the diagnosis result is for the nervous system. For example, the guidance unit will display a message to the patient saying, "Because the diagnosis result is for the nervous system, we will provide specialized guidance." By applying different guidance algorithms depending on the category of the diagnostic result, more appropriate guidance becomes possible. Some or all of the above processing in the guidance unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the guidance unit can input the patient's diagnostic result data into a generative AI and have the generative AI select a guidance algorithm.

[0089] The guidance unit can estimate the patient's emotions and adjust the length of the guidance based on the estimated emotions. For example, if the patient is feeling anxious, the guidance unit can provide short, concise guidance. For example, the guidance unit can display a message to the patient saying, "We will provide short, concise guidance." The guidance unit can also provide detailed guidance if the patient is relaxed. For example, the guidance unit can display a message to the patient saying, "We will provide detailed guidance." The guidance unit can also provide brief guidance if the patient is in a hurry. For example, the guidance unit can display a message to the patient saying, "We will provide brief guidance." By adjusting the length of the guidance according to the patient's emotions, more appropriate guidance becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, 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 guidance unit may be performed using, for example, generative AI, or without generative AI. For example, the guidance unit can input patient facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0090] The guidance unit can determine the priority of guidance based on when the diagnostic results were submitted. For example, the guidance unit may prioritize recent diagnostic results. For example, the guidance unit may display a message to the patient stating, "We will prioritize guiding you with recent diagnostic results." The guidance unit can also prioritize guiding patients with diagnostic results that have been left unattended for a long time. For example, the guidance unit may display a message to the patient stating, "We will prioritize guiding you with diagnostic results that have been left unattended for a long time." The guidance unit can also prioritize guiding patients with diagnostic results submitted within a specific time period. For example, the guidance unit may display a message to the patient stating, "We will prioritize guiding you with diagnostic results submitted within a specific time period." By determining the priority of guidance based on when the diagnostic results were submitted, more appropriate guidance becomes possible. Some or all of the above processing in the guidance unit may be performed using, for example, a generative AI, or without a generative AI. For example, the guidance unit can input the patient's diagnostic result data into a generative AI and have the generative AI determine the priority of guidance.

[0091] The guidance unit can adjust the order of guidance based on the relevance of the diagnostic results. For example, the guidance unit can prioritize guiding users to highly relevant diagnostic results. For example, the guidance unit can display a message to the patient stating, "We will prioritize guiding you to highly relevant diagnostic results." The guidance unit can also postpone guiding users to less relevant diagnostic results. For example, the guidance unit can display a message to the patient stating, "We will postpone guiding users to less relevant diagnostic results." The guidance unit can also dynamically adjust the order of guidance based on the relevance of the diagnostic results. For example, the guidance unit can display a message to the patient stating, "We will adjust the order of guidance based on the relevance of the diagnostic results." This allows for more appropriate guidance by adjusting the order of guidance based on the relevance of the diagnostic results. Some or all of the above processing in the guidance unit may be performed using, for example, a generative AI, or without a generative AI. For example, the guidance unit can input the patient's diagnostic result data into a generative AI and have the generative AI adjust the order of guidance.

[0092] The generation unit can estimate the patient's emotions and adjust the method of generating the medical record based on the estimated emotions. For example, if the patient is feeling anxious, the generation unit can generate a simple and easy-to-understand medical record. For example, the generation unit can display a message to the patient saying, "I will generate a simple and easy-to-understand medical record." The generation unit can also generate a detailed medical record if the patient is relaxed. For example, the generation unit can display a message to the patient saying, "I will generate a detailed medical record." The generation unit can also generate a concise medical record that gets straight to the point if the patient is in a hurry. For example, the generation unit can display a message to the patient saying, "I will generate a concise medical record that gets straight to the point." By adjusting the method of generating the medical record according to the patient's emotions, a more appropriate medical record can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input patient facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0093] The generation unit can adjust the level of detail in the medical record based on the importance of the diagnosis when generating the medical record. For example, in the case of a serious diagnosis, the generation unit generates a detailed medical record. For example, the generation unit displays a message to the patient stating, "Due to a serious diagnosis, a detailed medical record will be generated." The generation unit can also generate a concise medical record in the case of a minor diagnosis. For example, the generation unit displays a message to the patient stating, "Due to a minor diagnosis, a concise medical record will be generated." The generation unit can also generate a medical record with appropriate detail in the case of a moderate diagnosis. For example, the generation unit displays a message to the patient stating, "Due to a moderate diagnosis, a medical record with appropriate detail will be generated." By adjusting the level of detail in the medical record based on the importance of the diagnosis, a more appropriate medical record can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the patient's diagnosis data into the generation AI and have the generation AI adjust the level of detail in the medical record.

[0094] The generation unit can apply different generation algorithms depending on the category of the diagnosis result when generating a medical record. For example, if the diagnosis result is for the digestive system, the generation unit will apply a generation algorithm specialized for the digestive system. For example, the generation unit will display a message to the patient stating, "Because the diagnosis result is for the digestive system, a specialized medical record will be generated." The generation unit can also apply a generation algorithm specialized for the respiratory system if the diagnosis result is for the respiratory system. For example, the generation unit will display a message to the patient stating, "Because the diagnosis result is for the respiratory system, a specialized medical record will be generated." The generation unit can also apply a generation algorithm specialized for the nervous system if the diagnosis result is for the nervous system. For example, the generation unit will display a message to the patient stating, "Because the diagnosis result is for the nervous system, a specialized medical record will be generated." By applying different generation algorithms depending on the category of the diagnosis result, a more appropriate medical record can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input the patient's diagnosis result data into a generation AI and have the generation AI select a generation algorithm.

[0095] The generation unit can estimate the patient's emotions and adjust the length of the medical record based on the estimated emotions. For example, if the patient is feeling anxious, the generation unit can generate a short, concise medical record. For example, the generation unit can display a message to the patient saying, "Generating a short, concise medical record." The generation unit can also generate a detailed medical record if the patient is relaxed. For example, the generation unit can display a message to the patient saying, "Generating a detailed medical record." The generation unit can also generate a brief medical record if the patient is in a hurry. For example, the generation unit can display a message to the patient saying, "Generating a brief medical record." By adjusting the length of the medical record according to the patient's emotions, a more appropriate medical record can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input patient facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0096] The generation unit can determine the priority of medical records based on when the diagnostic results were submitted when generating them. For example, the generation unit can prioritize the inclusion of recent diagnostic results in the medical record. For example, the generation unit can display a message to the patient stating, "We will prioritize the inclusion of recent diagnostic results in your medical record." The generation unit can also prioritize the inclusion of diagnostic results that have been left unattended for a long time. For example, the generation unit can display a message to the patient stating, "We will prioritize the inclusion of diagnostic results that have been left unattended for a long time in your medical record." Furthermore, the generation unit can prioritize the inclusion of diagnostic results submitted within a specific time period. For example, the generation unit can display a message to the patient stating, "We will prioritize the inclusion of diagnostic results submitted within a specific time period in your medical record." By determining the priority of medical records based on when the diagnostic results were submitted, more appropriate medical records can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input patient diagnostic result data into a generation AI and have the generation AI determine the priority of the medical records.

[0097] The generation unit can adjust the order of medical records based on the relevance of the diagnostic results when generating medical records. For example, the generation unit can prioritize reflecting highly relevant diagnostic results in the medical record. For example, the generation unit can display a message to the patient stating, "We will prioritize reflecting highly relevant diagnostic results in your medical record." The generation unit can also postpone less relevant diagnostic results. For example, the generation unit can display a message to the patient stating, "We will postpone less relevant diagnostic results." The generation unit can also dynamically adjust the order of medical records based on the relevance of the diagnostic results. For example, the generation unit can display a message to the patient stating, "We will adjust the order of medical records based on the relevance of the diagnostic results." By adjusting the order of medical records based on the relevance of the diagnostic results, a more appropriate medical record can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input the patient's diagnostic result data into a generation AI and have the generation AI perform the adjustment of the order of medical records.

[0098] The feedback unit can estimate the patient's emotions and adjust the way feedback is displayed based on the estimated emotions. For example, if the patient is feeling anxious, the feedback unit can provide a simple and easily visible display. For example, the feedback unit can display the message "We are providing a simple and easily visible display." The feedback unit can also provide a display that includes detailed information if the patient is relaxed. For example, the feedback unit can display the message "We are providing a display that includes detailed information." The feedback unit can also provide a concise display if the patient is in a hurry. For example, the feedback unit can display the message "We are providing a concise display." By adjusting the way feedback is displayed according to the patient's emotions, more appropriate feedback becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, 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 feedback unit may be performed using, for example, generative AI, or without generative AI. For example, the feedback unit can input the patient's facial expression data into a generating AI, allowing the AI ​​to perform emotion estimation.

[0099] The feedback unit can adjust the level of detail of the feedback based on the importance of the diagnostic result. For example, in the case of a serious diagnosis, the feedback unit provides detailed feedback. For example, the feedback unit displays a message to the patient stating, "Due to the serious diagnosis, we will provide detailed feedback." The feedback unit can also provide concise feedback in the case of a minor diagnosis. For example, the feedback unit displays a message to the patient stating, "Due to the minor diagnosis, we will provide concise feedback." The feedback unit can also provide feedback with a moderate level of detail in the case of a moderate diagnosis. For example, the feedback unit displays a message to the patient stating, "Due to the moderate diagnosis, we will provide feedback with a moderate level of detail." By adjusting the level of detail of the feedback based on the importance of the diagnostic result, more appropriate feedback becomes possible. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input the patient's diagnostic result data into a generative AI and have the generative AI adjust the level of detail of the feedback.

[0100] The feedback unit can apply different feedback algorithms depending on the category of the diagnostic result when providing feedback. For example, if the diagnosis is related to the digestive system, the feedback unit will apply a feedback algorithm specifically for the digestive system. For example, the feedback unit will display a message to the patient stating, "Because the diagnosis is related to the digestive system, we will provide specialized feedback." The feedback unit can also apply a feedback algorithm specifically for the respiratory system if the diagnosis is related to the respiratory system. For example, the feedback unit will display a message to the patient stating, "Because the diagnosis is related to the respiratory system, we will provide specialized feedback." The feedback unit can also apply a feedback algorithm specifically for the nervous system if the diagnosis is related to the nervous system. For example, the feedback unit will display a message to the patient stating, "Because the diagnosis is related to the nervous system, we will provide specialized feedback." By applying different feedback algorithms depending on the category of the diagnostic result, more appropriate feedback becomes possible. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the feedback unit can input the patient's diagnostic result data into a generative AI and have the generative AI select a feedback algorithm.

[0101] The feedback unit can estimate the patient's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the patient is feeling anxious, the feedback unit can provide short, concise feedback. For example, the feedback unit can display a message to the patient saying, "We will provide short, concise feedback." The feedback unit can also provide detailed feedback if the patient is relaxed. For example, the feedback unit can display a message to the patient saying, "We will provide detailed feedback." The feedback unit can also provide brief feedback if the patient is in a hurry. For example, the feedback unit can display a message to the patient saying, "We will provide brief feedback." By adjusting the length of the feedback according to the patient's emotions, more appropriate feedback 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 feedback unit may be performed using, for example, generative AI, or without generative AI. For example, the feedback unit can input the patient's facial expression data into a generating AI, allowing the AI ​​to perform emotion estimation.

[0102] The feedback unit can prioritize feedback based on when the diagnostic results were submitted. For example, the feedback unit can prioritize recent diagnostic results. For example, the feedback unit can display a message to the patient stating, "We will prioritize feedback on recent diagnostic results." The feedback unit can also prioritize feedback on diagnostic results that have been left unattended for a long time. For example, the feedback unit can display a message to the patient stating, "We will prioritize feedback on diagnostic results that have been left unattended for a long time." The feedback unit can also prioritize feedback on diagnostic results submitted within a specific time period. For example, the feedback unit can display a message to the patient stating, "We will prioritize feedback on diagnostic results submitted within a specific time period." By prioritizing feedback based on when the diagnostic results were submitted, more appropriate feedback becomes possible. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input the patient's diagnostic result data into a generative AI and have the generative AI determine the priority of feedback.

[0103] The feedback unit can adjust the order of feedback based on the relevance of the diagnostic results. For example, the feedback unit can prioritize providing feedback on highly relevant diagnostic results. For example, the feedback unit can display a message to the patient stating, "We will prioritize providing feedback on highly relevant diagnostic results." The feedback unit can also postpone providing feedback on less relevant diagnostic results. For example, the feedback unit can display a message to the patient stating, "We will postpone providing feedback on less relevant diagnostic results." Furthermore, the feedback unit can dynamically adjust the order of feedback based on the relevance of the diagnostic results. For example, the feedback unit can display a message to the patient stating, "We will adjust the order of feedback based on the relevance of the diagnostic results." This allows for more appropriate feedback by adjusting the order of feedback based on the relevance of the diagnostic results. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the feedback unit can input the patient's diagnostic result data into a generative AI and have the generative AI perform the adjustment of the feedback order. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, diagnosis unit, guidance unit, and generation 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 is implemented by the reception device 38 of the smart device 14, allowing patients to input their symptoms using a smartphone or personal computer. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the input symptoms using a generation AI and converts them into specific medical terms. The diagnosis unit is implemented by the identification processing unit 290 of the data processing unit 12, which makes a diagnosis based on the analyzed information. The guidance unit is implemented by the identification processing unit 290 of the data processing unit 12, which guides the patient to the most suitable doctor based on the diagnosis results. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which automatically generates a medical record based on the diagnosis results. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, diagnosis unit, guidance unit, and generation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the patient to input symptoms by voice. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the input symptoms using generation AI and converts them into specific medical terms. The diagnosis unit is implemented by the identification processing unit 290 of the data processing unit 12, which makes a diagnosis based on the analyzed information. The guidance unit is implemented by the identification processing unit 290 of the data processing unit 12, which guides the patient to the most suitable doctor based on the diagnosis results. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which automatically generates a medical record based on the diagnosis results. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, analysis unit, diagnosis unit, guidance unit, and generation 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 is implemented by the microphone 238 of the headset terminal 314, allowing the patient to input symptoms by voice. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the input symptoms using generation AI and converts them into specific medical terms. The diagnosis unit is implemented by the identification processing unit 290 of the data processing unit 12, which makes a diagnosis based on the analyzed information. The guidance unit is implemented by the identification processing unit 290 of the data processing unit 12, which guides the patient to the most suitable doctor based on the diagnosis results. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which automatically generates a medical record based on the diagnosis results. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, analysis unit, diagnosis unit, guidance unit, and generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing patients to input their symptoms by voice. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the input symptoms using a generation AI and converts them into specific medical terms. The diagnosis unit is implemented by the identification processing unit 290 of the data processing unit 12, which makes a diagnosis based on the analyzed information. The guidance unit is implemented by the identification processing unit 290 of the data processing unit 12, which guides the patient to the most suitable doctor based on the diagnosis results. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which automatically generates a medical record based on the diagnosis results.

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

[0105] The reception desk can customize the input content when patients enter their symptoms, taking into account their lifestyle data. For example, if a patient exercises regularly, they can be prompted to prioritize entering exercise-related symptoms. Similarly, if a patient has specific dietary restrictions, they can be guided to prioritize entering symptoms related to those restrictions. Furthermore, if a patient leads a stressful life, they can be prompted to prioritize entering stress-related symptoms. By customizing symptom input based on the patient's lifestyle, a more accurate diagnosis becomes possible.

[0106] The feedback system can analyze patient feedback and incorporate it into future diagnoses. For example, if a patient provides feedback that the diagnosis was accurate, the diagnostic algorithm can be improved. If a patient provides feedback that the diagnosis was inaccurate, the diagnostic algorithm can be reviewed. Furthermore, if a patient provides feedback that the diagnosis was delayed, improvements can be made to expedite the diagnostic process. By incorporating patient feedback into future diagnoses, the accuracy and efficiency of diagnoses can be improved.

[0107] The reception desk can estimate the patient's emotions and customize the symptom input interface based on those emotions. For example, if the patient is feeling anxious, a simple and intuitive interface can be provided. If the patient is relaxed, an interface that allows for detailed input can be provided. Furthermore, if the patient is in a hurry, an interface that allows for quick input can be provided. By customizing the interface according to the patient's emotions, more appropriate symptom input becomes possible.

[0108] The reception desk can analyze a patient's past symptom input history and suggest the optimal input method. For example, it can automatically display symptoms that the patient has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the patient has used in the past (voice, text, etc.). Furthermore, it can predict and suggest symptoms that the patient will use at specific times based on their past input history. In this way, by analyzing a patient's past symptom input history, the optimal input method can be suggested.

[0109] The reception system can filter symptom input based on the patient's current lifestyle and areas of interest. For example, if a patient is leading a stressful life, they can be prompted to prioritize inputting stress-related symptoms. Similarly, if a patient has specific dietary restrictions, they can be guided to prioritize inputting symptoms related to those restrictions. Furthermore, if a patient has a specific exercise habit, they can be prompted to prioritize inputting symptoms related to that habit. This filtering based on the patient's lifestyle and areas of interest allows for more appropriate symptom input.

[0110] The reception desk can estimate the patient's emotions and determine the priority of symptoms to be entered based on those emotions. For example, if the patient is feeling anxious, it can prompt them to prioritize entering major symptoms. If the patient is relaxed, it can prompt them to prioritize entering detailed symptoms. Furthermore, if the patient is in a hurry, it can prompt them to prioritize entering major symptoms. This allows for more appropriate symptom entry by prioritizing symptoms according to the patient's emotions.

[0111] The reception system can prioritize the input of highly relevant symptoms by considering the patient's geographical location when symptoms are entered. For example, if the patient lives in a high-altitude area, it can prompt them to prioritize entering symptoms specific to high-altitude areas. Similarly, if the patient lives in an urban area, it can prompt them to prioritize entering symptoms specific to urban areas. Furthermore, if the patient lives by the sea, it can prompt them to prioritize entering symptoms specific to coastal areas. In this way, by considering the patient's geographical location, the system can prioritize the input of highly relevant symptoms.

[0112] The reception desk can analyze a patient's social media activity when they enter their symptoms and input relevant symptoms. For example, it can prompt patients to prioritize entering symptoms they frequently mention on social media. It can also prompt them to enter relevant symptoms based on lifestyle habits they share on social media. Furthermore, it can prompt them to enter relevant symptoms based on health information they follow on social media. This allows the system to input relevant symptoms by analyzing the patient's social media activity.

[0113] The analysis unit can estimate the patient's emotions and adjust the presentation of the analysis based on those emotions. For example, if the patient is feeling anxious, a simple and easy-to-understand presentation can be used. If the patient is relaxed, detailed analysis results can be provided. Furthermore, if the patient is in a hurry, concise analysis results that get straight to the point can be provided. In this way, by adjusting the presentation of the analysis according to the patient's emotions, more appropriate analysis results can be provided.

[0114] The analysis unit can adjust the level of detail of the analysis based on the severity of the symptoms. For example, a detailed analysis can be performed for severe symptoms. A simplified analysis can be performed for mild symptoms. Furthermore, an analysis with an appropriate level of detail can be performed for moderate symptoms. By adjusting the level of detail of the analysis based on the severity of the symptoms, more appropriate analysis results can be provided.

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

[0116] Step 1: The reception desk enters the patient's symptoms. Patients can enter their symptoms using a smartphone or computer, or they can enter them using voice input. For example, if a patient says "My stomach hurts" aloud, the reception desk converts the voice into text and enters it. Step 2: The analysis unit analyzes the symptoms entered by the reception unit and converts them into specific medical terms. The analysis unit can use generative AI and natural language processing technology to convert ambiguous expressions into specific medical terms, and can also use machine learning algorithms to analyze symptoms based on past data. For example, it can convert the expression "my stomach hurts" to "stomach ache." Step 3: The diagnostic unit performs a diagnosis based on the information converted by the analysis unit. The diagnostic unit can also use generating AI or diagnostic algorithms to identify disease names corresponding to symptoms, and can refer to a medical terminology dictionary to perform a diagnosis. For example, based on the information "stomach pain," it may diagnose "gastritis." Step 4: The information desk will guide the patient to the most suitable physician based on the diagnostic results obtained by the diagnostic desk. The information desk will guide the patient to a gastroenterologist and can also guide the patient to a nearby physician based on the patient's place of residence. For example, it will guide the patient to the nearest gastroenterologist based on the patient's address information. Step 5: The generation unit automatically generates a medical record based on the diagnostic results obtained by the diagnostic unit. The generation unit can generate medical records using template-based generation or dynamic generation. For example, it can automatically generate a medical record based on the patient's symptoms and diagnostic results.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

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

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

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

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

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

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

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

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

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

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

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

[0180] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

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

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] [Explanation of Symbols]

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

Claims

1. A reception area where patients' symptoms are entered, An analysis unit analyzes the symptoms entered by the reception unit and converts them into specific medical terms, A diagnostic unit that performs a diagnosis based on the information converted by the analysis unit, A guidance unit that guides the appropriate physician based on the diagnostic results obtained by the aforementioned diagnostic unit, The system includes a generation unit that automatically generates a medical record based on the diagnostic results obtained by the diagnostic unit. A system characterized by the following features.

2. It also includes a feedback unit that analyzes symptoms after diagnosis and provides feedback on the accuracy of the diagnosis. The system according to feature 1.

3. The aforementioned reception unit is The system estimates the patient's emotions and adjusts the timing of symptom input based on the estimated emotions. The system according to feature 1.

4. The aforementioned reception unit is Analyze the patient's past symptom input history and select the appropriate input method. The system according to feature 1.

5. The aforementioned reception unit is When entering symptoms, filtering is performed based on the patient's current lifestyle and areas of interest. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the patient's emotions and determines the priority of symptoms to input based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is When entering symptoms, the system prioritizes entering symptoms that are highly relevant to the patient's geographical location. The system according to feature 1.

8. The aforementioned reception unit is When entering symptoms, the system analyzes the patient's social media activity and enters relevant symptoms. The system according to feature 1.

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

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