system

Abstract

The system according to this embodiment aims to determine the appropriate medical department when a person is feeling unwell, select a nearby hospital, and appropriately communicate their symptoms during the consultation. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a selection unit, and a creation unit. The reception unit receives the user's symptoms. The analysis unit analyzes the symptoms received by the reception unit and determines the medical department. The selection unit selects a hospital near the user's residence based on the medical department determined by the analysis unit. The creation unit creates a medical condition declaration form based on the information of the hospital selected by the selection unit so that the user can communicate their medical condition during the consultation.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to determine an appropriate medical department when in poor health, select a nearby hospital, and appropriately convey the medical condition during examination.

[0005] The system according to the embodiment aims to determine an appropriate medical department when in poor health, select a nearby hospital, and appropriately convey the medical condition during examination.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a selection unit, and a creation unit. The reception unit receives input from the user regarding their symptoms. The analysis unit analyzes the symptoms entered by the reception unit and determines the appropriate medical department. The selection unit selects a hospital near the user's residence based on the medical department determined by the analysis unit. The creation unit creates a medical condition declaration form based on the information of the hospital selected by the selection unit, so that the user can communicate their symptoms during their consultation. [Effects of the Invention]

[0007] The system according to this embodiment can determine the appropriate medical department when a person is feeling unwell, select a nearby hospital, and appropriately communicate their symptoms during the consultation. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". 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, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example of form 1) The medical support system according to an embodiment of the present invention is a system that allows a user to select an appropriate medical department when feeling unwell, find a nearby hospital, and appropriately communicate their symptoms during a consultation. In this medical support system, the user reports feeling unwell, the AI ​​analyzes the reported information to determine the appropriate medical department, selects an appropriate hospital near the user's residence, and creates a medical condition report form so that the user can appropriately communicate their symptoms during a consultation. For example, when a user reports feeling unwell, the user inputs details of their symptoms. For example, they input specific symptoms such as "I have a headache," "I have a fever," or "I have a cough." This information is input into the AI. Next, the AI ​​analyzes the input information and determines the appropriate medical department. Based on past medical data and case data, the AI ​​identifies the medical department that is most suitable for the user's symptoms. For example, if there is a headache or fever, it determines the medical department according to the symptoms, such as internal medicine, or if there is a cough, it determines the medical department according to the symptoms. Furthermore, the AI ​​selects an appropriate hospital near the user's residence. Based on the user's location information, the AI ​​searches for nearby hospitals and lists hospitals with matching medical departments. For example, if a user needs to see an internal medicine specialist, the system will list nearby hospitals with internal medicine departments. Finally, the AI ​​will create a medical declaration form so that the user can properly communicate their symptoms during the consultation. The declaration form will include the symptoms entered by the user, the medical department determined by the AI, and information about the selected hospitals. This allows the user to smoothly communicate their symptoms during the consultation. In other words, when a user is unwell, they can choose the appropriate medical department, find a nearby hospital, and properly communicate their symptoms during the consultation. For example, if a user has a headache and a fever, the AI ​​will determine that internal medicine is necessary, select a nearby hospital with an internal medicine department, and create a medical declaration form to properly communicate their symptoms during the consultation, allowing them to receive treatment smoothly. In this way, the medical support system enables users to choose the appropriate medical department when they are unwell, find a nearby hospital, and properly communicate their symptoms during the consultation.

[0029] The medical support system according to this embodiment comprises a reception unit, an analysis unit, a selection unit, and a creation unit. The reception unit inputs the user's symptoms. The user's symptoms include, for example, headaches, fever, and abdominal pain, but are not limited to such examples. The reception unit allows the user to input symptoms in a free format, for example, by text input, voice input, or image attachment. The analysis unit analyzes the symptoms entered by the reception unit and determines the appropriate medical department. Based on past medical data and case data, the analysis unit identifies the medical department best suited to the user's symptoms. For example, it determines the medical department according to the symptoms, such as internal medicine for headaches and fevers, and respiratory medicine for coughs. The analysis unit refers to past medical data such as electronic medical records, medical records, and test results, and identifies the medical department based on case data. Based on the medical department determined by the analysis unit, the selection unit selects an appropriate hospital near the user's residence. Based on the user's location information, the selection unit searches for nearby hospitals and lists hospitals with matching medical departments. For example, if a user needs to see an internal medicine specialist, the system lists nearby hospitals with internal medicine departments. The selection unit obtains location information using, for example, GPS data, address information, or location services, and searches for hospitals. Furthermore, the selection unit can sort the search results by criteria such as distance, consultation time, and rating. For example, it can sort by criteria such as straight-line distance, walking distance, or driving time. The creation unit creates a medical condition declaration form based on the hospital information selected by the selection unit, so that the user can appropriately communicate their symptoms during the consultation. The creation unit creates a medical condition declaration form that includes the symptoms entered by the user, the medical department determined by the AI, and the information of the selected hospitals. For example, it creates a declaration form that includes information such as detailed symptoms, onset date, and medical history. As a result, the medical support system according to this embodiment allows the user to select the appropriate medical department when feeling unwell, find a nearby hospital, and appropriately communicate their symptoms during the consultation.

[0030] The reception desk receives the user's symptoms. These symptoms may include, but are not limited to, headaches, fever, or stomachaches. The reception desk allows users to enter symptoms in a free-form format, such as text input, voice input, or image attachment. Specifically, text input allows users to describe detailed symptoms using a keyboard. Voice input allows users to speak into a microphone, which is automatically converted to text using speech recognition technology. Image attachment allows users to upload photos of visual symptoms, such as skin rashes or swelling. This provides the reception desk with diverse input methods, allowing users to report symptoms in the most convenient way. Furthermore, the reception desk can automatically classify the entered symptoms and pre-process them before sending them to the analysis department. For example, natural language processing technology can be used to extract keywords from the entered text and categorize them. In the case of voice input, algorithms are applied to remove noise and ensure accurate speech recognition when converting the audio data to text. This allows the reception desk to provide the user-entered information to the analysis department accurately and efficiently.

[0031] The analysis unit analyzes the symptoms entered by the reception unit and determines the appropriate medical department. Based on past medical data and case data, the analysis unit identifies the medical department best suited to the user's symptoms. For example, it determines the appropriate department based on the symptoms, such as internal medicine for headaches and fevers, and respiratory medicine for coughs. The analysis unit refers to past medical data such as electronic medical records, medical records, and test results, and identifies the medical department based on case data. Specifically, it utilizes machine learning algorithms using AI and trains them with past case data to predict the optimal medical department for the entered symptoms with high accuracy. For example, it uses a neural network to analyze symptom patterns and identify the medical department by matching them with similar cases. The analysis unit also considers the severity and urgency of the symptoms, and can recommend a visit to the emergency department or a specialist if emergency treatment is necessary. Furthermore, the analysis unit can consider the user's medical history and allergy information to provide more individualized medical department suggestions. As a result, the analysis unit can quickly and accurately determine the appropriate medical department for the user's symptoms and support them in visiting the appropriate medical institution.

[0032] The selection unit selects appropriate hospitals near the user's residence based on the medical specialty determined by the analysis unit. The selection unit searches for nearby hospitals based on the user's location information and lists hospitals with matching medical specialties. For example, if the user needs to see an internal medicine specialist, it lists nearby hospitals with internal medicine departments. The selection unit obtains location information using, for example, GPS data, address information, and location services to search for hospitals. Furthermore, the selection unit can sort search results by criteria such as distance, consultation time, and ratings. For example, it can sort by straight-line distance, walking distance, and driving time. Specifically, the selection unit not only prioritizes displaying the closest hospital based on the user's current location, but also suggests the optimal hospital by considering consultation time, hospital ratings, and feedback from past patients. The selection unit also checks hospital congestion and appointment availability in real time to support users in receiving medical care smoothly. For example, if a particular hospital is crowded, it can suggest other nearby hospitals to reduce waiting times. In this way, the selection unit helps users find appropriate medical institutions quickly and efficiently.

[0033] The creation unit creates a medical condition declaration form based on the hospital information selected by the selection unit, enabling users to appropriately communicate their symptoms during consultations. The creation unit creates a medical condition declaration form that includes the symptoms entered by the user, the medical department determined by AI, and the information of the selected hospital. For example, it creates a declaration form that includes information such as detailed symptoms, onset date, and medical history. Specifically, the creation unit describes the symptoms entered by the user in detail, including the onset date, progression of symptoms, past treatment history, and allergy information, enabling doctors to make a quick diagnosis. The declaration form also includes information on the selected hospital and medical department, so that users can see at a glance the information they need when visiting a doctor. Furthermore, the creation unit provides the declaration form in PDF format or a printable format, allowing users to view it on their smartphones or tablets, or print it out and bring it with them. This allows users to accurately communicate their symptoms during consultations, and enables doctors to make quick and appropriate diagnoses. In addition, the creation unit implements measures to protect user privacy, such as data encryption and access control, to prevent the leakage of personal information. This allows the development team to provide users with an environment where they can use the system with peace of mind, thereby improving the overall reliability of the medical support system.

[0034] The reception desk allows users to input symptoms in a free-form manner. For example, users can input symptoms in a free-form manner, such as through text input, voice input, or image attachment. This allows users to input symptoms in a free-form manner, enabling the provision of detailed symptom information. Some or all of the above-described processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the text data entered by the user into a generating AI, which can then analyze the text data to extract symptom information.

[0035] The analysis unit can identify the most appropriate medical department for the user's symptoms based on past medical data and case data. For example, it can refer to past medical data such as electronic medical records, medical records, and test results, and identify the appropriate medical department based on case data. This allows for the selection of the appropriate medical department based on past medical data and case data. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input past medical data into a generating AI, which can then analyze the data to identify the appropriate medical department.

[0036] The selection unit can search for nearby hospitals based on the user's location information and list hospitals with matching medical departments. For example, it can obtain location information using GPS data, address information, location services, etc., and search for hospitals. This allows the user to find a suitable nearby hospital by searching based on their location information. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's location information into a generating AI, which can then analyze the location information and list nearby hospitals.

[0037] The selection unit can sort search results by distance, consultation time, and evaluation criteria. The selection unit sorts the search results by distance, consultation time, and evaluation criteria. For example, it sorts by criteria such as straight-line distance, walking distance, and travel time by car. This allows the user to select the most suitable hospital by sorting the search results. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the search results into a generating AI, which can then sort them by distance, consultation time, and evaluation criteria.

[0038] The creation unit can create a medical condition declaration form that includes the symptoms entered by the user, the medical department determined by the AI, and information about the selected hospital. For example, it can create a declaration form that includes details of the symptoms, the time of onset, and medical history. This allows the user to appropriately communicate their medical condition during a medical examination by creating a medical condition declaration form. Some or all of the above-described processes in the creation unit may be performed using AI, or not. For example, the creation unit can input the user-entered symptom data into a generating AI, which can then create the declaration form.

[0039] The reception desk can analyze the user's past symptom input history and suggest the optimal input method. For example, it can automatically display symptoms that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest symptoms that the user will use at specific times based on their past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input data into a generating AI, which can then analyze the data and suggest the optimal input method.

[0040] The reception unit can filter the input content based on the user's current health status and lifestyle when symptoms are entered. For example, it can prioritize displaying relevant symptoms based on the user's current health status. It can also filter relevant symptoms by considering the user's lifestyle (smoking, drinking, etc.). Furthermore, it can prioritize displaying relevant symptoms based on the user's medical history. This allows users to enter more relevant symptoms by filtering the input content based on their current health status and lifestyle. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's health data into a generating AI, which can then analyze the data and filter the input content.

[0041] The reception unit can prioritize the input of highly relevant symptoms by considering the user's geographical location when symptoms are entered. For example, if the user is in a specific region, symptoms of diseases prevalent in that region will be displayed preferentially. Also, if the user is traveling, symptoms based on the health risks at the travel destination can be displayed preferentially. Furthermore, if the user is in a specific environment (such as a factory or farm), symptoms related to that environment can be displayed preferentially. In this way, highly relevant symptoms can be prioritized by considering geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's location information into a generating AI, which can analyze the location information and prioritize the display of highly relevant symptoms.

[0042] The reception desk can analyze the user's social media activity and input relevant symptoms when symptoms are entered. For example, it can input relevant symptoms based on health information shared by the user on social media. It can also analyze the user's social media activity (posts, comments, etc.) and input relevant symptoms. Furthermore, it can input relevant symptoms based on information from health-related accounts that the user follows on social media. In this way, relevant symptoms can be entered by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI, and the generating AI can analyze the data and input relevant symptoms.

[0043] The analysis unit can adjust the level of detail of the analysis based on the severity of the symptoms during the analysis. For example, a detailed analysis can be performed for severe symptoms, while a simplified analysis can be performed for minor symptoms. Furthermore, if there are multiple symptoms, the unit can prioritize the analysis of the symptoms with the highest severity. This allows for a detailed analysis of important symptoms by adjusting the level of detail based on the severity of the symptoms. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom data into a generating AI, which can then analyze the data and adjust the level of detail.

[0044] The analysis unit can apply different analysis algorithms depending on the symptom category during analysis. For example, in the case of respiratory symptoms, a specialized respiratory analysis algorithm can be applied. Similarly, in the case of digestive symptoms, a specialized digestive analysis algorithm can be applied. Furthermore, in the case of neurological symptoms, a specialized neurological analysis algorithm can be applied. By applying different analysis algorithms depending on the symptom category, more accurate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom data into a generating AI, which can then analyze the data and apply different algorithms.

[0045] The analysis unit can determine the priority of analysis based on when the symptoms were submitted. For example, it can prioritize the analysis of recently submitted symptoms. It can also prioritize the analysis of symptoms that have been left untreated for a long time. Furthermore, it can dynamically adjust the analysis priority based on the submission time. This allows for the provision of timely analysis results by determining the analysis priority based on the submission time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom data into a generating AI, and the generating AI can analyze the data and determine the priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of symptoms during the analysis. For example, it can prioritize the analysis of highly relevant symptoms. It can also postpone the analysis of less relevant symptoms. Furthermore, if multiple symptoms are related, it can adjust the order of analysis based on their relevance. This allows for prioritizing the analysis of highly relevant symptoms by adjusting the order of analysis based on the relevance of symptoms. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom data into a generating AI, which can then analyze the data and adjust the order.

[0047] The selection unit can improve the accuracy of hospital selection by considering the interrelationships between hospitals. For example, it can select the optimal hospital based on inter-hospital collaboration information. It can also select hospitals by considering their specialized fields and departments. Furthermore, it can improve the accuracy of selection based on the hospitals' past treatment performance. In this way, the accuracy of selection can be improved by considering the interrelationships between hospitals. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input hospital collaboration information into a generating AI, and the generating AI can analyze the data to improve the accuracy of selection.

[0048] The selection unit can make hospital selections while considering hospital evaluation information. For example, it can prioritize hospitals with high user ratings. It can also improve the accuracy of selection based on online hospital reviews. Furthermore, it can also consider past patient satisfaction data of hospitals during the selection process. This allows for the selection of more reliable hospitals by considering hospital evaluation information. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input hospital evaluation data into a generating AI, which can then analyze the data and make selections.

[0049] The selection unit can select hospitals while considering their geographical distribution. For example, it can select the most suitable hospital based on the distance from the user's residence. It can also select hospitals that are easily accessible by considering their geographical distribution. Furthermore, it can prioritize hospitals along the user's commute route. This allows for the selection of easily accessible hospitals by considering their geographical distribution. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input geographical data of hospitals into a generating AI, which can then analyze the data and make a selection.

[0050] The selection unit can improve the accuracy of hospital selection by referring to relevant literature on hospitals. For example, it can select the most suitable hospital based on relevant literature on the hospital's specialty. It can also select hospitals by referring to literature on the hospital's clinical performance. Furthermore, it can improve the accuracy of selection based on literature on the hospital's research activities. In this way, the accuracy of selection can be improved by referring to relevant literature on hospitals. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input hospital-related literature data into a generating AI, which can then analyze the data and perform the selection.

[0051] The preparation unit can adjust the level of detail in the declaration form based on the severity of the symptoms when preparing the declaration form. For example, in the case of serious symptoms, a declaration form with detailed information can be prepared. Conversely, in the case of minor symptoms, a concise declaration form can be prepared. Furthermore, if there are multiple symptoms, the unit can prioritize the description of the symptoms with the highest severity. This allows for detailed description of important symptoms by adjusting the level of detail in the declaration form based on the severity of the symptoms. Some or all of the above processing in the preparation unit may be performed using AI, for example, or not using AI. For example, the preparation unit can input symptom data into a generating AI, which can then analyze the data and adjust the level of detail.

[0052] The creation unit can apply different declaration form formats depending on the symptom category when creating the declaration form. For example, for respiratory symptoms, a specialized respiratory declaration form format can be applied. Similarly, for digestive symptoms, a specialized digestive declaration form format can be applied. Furthermore, for neurological symptoms, a specialized neurological declaration form format can be applied. This allows for the creation of more appropriate declaration forms by applying different declaration form formats depending on the symptom category. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input symptom data into a generating AI, which can then analyze the data and apply different formats.

[0053] The preparation unit can determine the priority of declarations based on when the symptoms were reported when preparing the declaration. For example, it can prioritize reporting recently reported symptoms. It can also prioritize reporting symptoms that have been left untreated for a long time. Furthermore, it can dynamically adjust the priority of declarations based on the reporting date. This allows for the creation of timely declarations by prioritizing declarations based on the reporting date. Some or all of the above processing in the preparation unit may be performed using AI, for example, or not using AI. For example, the preparation unit can input symptom data into a generating AI, which can then analyze the data and determine the priority.

[0054] The preparation unit can adjust the order of the declaration forms based on the relevance of the symptoms when preparing the declaration form. For example, it can prioritize listing highly relevant symptoms in the declaration form, and postpone listing less relevant symptoms. Furthermore, if multiple symptoms are related, it can adjust the order of the declaration forms based on their relevance. This allows for prioritizing the listing of highly relevant symptoms by adjusting the order of the declaration forms based on the relevance of the symptoms. Some or all of the above processing in the preparation unit may be performed using AI, for example, or not using AI. For example, the preparation unit can input symptom data into a generating AI, which can then analyze the data and adjust the 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 supplement the user's input by referring to their past medical history when they enter their symptoms. For example, it can supplement the current symptom input based on the symptoms and diagnoses the user received during past consultations. It can also automatically suggest related symptoms from past medical history. Furthermore, it can automatically generate appropriate questions about the symptoms entered by the user based on past medical history and collect detailed information. This allows for the provision of more accurate and detailed symptom information by utilizing past medical history.

[0057] The analysis unit can consider the influence of seasons and weather when analyzing a user's symptoms. For example, during allergy season, it prioritizes analyzing symptoms related to pollen allergies. It can also prioritize symptoms related to colds and influenza during colder periods. Furthermore, based on weather data, it can analyze the impact of temperature and humidity changes on symptoms and identify the appropriate medical department. This allows for the selection of a more appropriate medical department by considering the influence of seasons and weather.

[0058] The selection function can recommend specific specialists based on the user's symptoms. For example, if a user enters a specific symptom, it will recommend a doctor with specialized knowledge in that symptom. It can also select highly reliable specialists based on past treatment records and patient reviews. Furthermore, it can suggest the most suitable treatment schedule for the user, taking into account the specialist's consultation hours and appointment availability. This allows users to receive higher quality medical care through specialist recommendations.

[0059] The creation unit can generate a declaration form when the user inputs their symptoms, taking into account the user's lifestyle and occupational information. For example, if the user has a desk job, it can consider symptoms caused by prolonged sitting. Similarly, if the user exercises frequently, it can consider symptoms caused by the strain of exercise. Furthermore, it can reflect related symptoms in the declaration form based on the user's diet and sleep habits. This allows for the creation of a more detailed and relevant declaration form by considering lifestyle and occupational information.

[0060] The analysis unit can consider the user's genetic information when analyzing their symptoms. For example, it can assess the risk of specific diseases based on the user's family history and genetic risk. It can also identify the appropriate medical department for specific symptoms based on genetic information. Furthermore, it can suggest preventive measures and treatments suitable for the user based on genetic information. In this way, by considering genetic information, more personalized medical support can be provided.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk enters the user's symptoms. These symptoms may include, but are not limited to, headaches, fever, or stomachaches. The reception desk allows users to enter their symptoms in a free-form manner, such as through text input, voice input, or image attachments. Step 2: The analysis unit analyzes the symptoms entered by the reception unit and determines the appropriate medical department. Based on past medical data and case data, the analysis unit identifies the medical department best suited to the user's symptoms. For example, if there is a headache or fever, it will determine the medical department to be internal medicine, and if there is a cough, it will determine the medical department to be respiratory medicine, etc. The analysis unit refers to past medical data such as electronic medical records, medical records, and test results, and identifies the medical department based on case data. Step 3: The selection unit selects appropriate hospitals near the user's residence based on the medical specialty determined by the analysis unit. The selection unit searches for nearby hospitals based on the user's location information and lists hospitals with matching medical specialties. For example, if the user needs to see an internal medicine specialist, it will list nearby hospitals with internal medicine departments. The selection unit obtains location information using, for example, GPS data, address information, and location services to search for hospitals. Furthermore, the selection unit can sort the search results by criteria such as distance, consultation time, and rating. For example, it can sort by criteria such as straight-line distance, walking distance, and driving time. Step 4: The creation unit creates a medical condition declaration form based on the hospital information selected by the selection unit, so that the patient can properly communicate their condition during the consultation. The creation unit creates a medical condition declaration form that includes the symptoms entered by the user, the medical department determined by the AI, and the information of the selected hospital. For example, it creates a declaration form that includes information such as details of the symptoms, the time of onset, and medical history.

[0063] (Example of form 2) The medical support system according to an embodiment of the present invention is a system that allows a user to select an appropriate medical department when feeling unwell, find a nearby hospital, and appropriately communicate their symptoms during a consultation. In this medical support system, the user reports feeling unwell, the AI ​​analyzes the reported information to determine the appropriate medical department, selects an appropriate hospital near the user's residence, and creates a medical condition report form so that the user can appropriately communicate their symptoms during a consultation. For example, when a user reports feeling unwell, the user inputs details of their symptoms. For example, they input specific symptoms such as "I have a headache," "I have a fever," or "I have a cough." This information is input into the AI. Next, the AI ​​analyzes the input information and determines the appropriate medical department. Based on past medical data and case data, the AI ​​identifies the medical department that is most suitable for the user's symptoms. For example, if there is a headache or fever, it determines the medical department according to the symptoms, such as internal medicine, or if there is a cough, it determines the medical department according to the symptoms. Furthermore, the AI ​​selects an appropriate hospital near the user's residence. Based on the user's location information, the AI ​​searches for nearby hospitals and lists hospitals with matching medical departments. For example, if a user needs to see an internal medicine specialist, the system will list nearby hospitals with internal medicine departments. Finally, the AI ​​will create a medical declaration form so that the user can properly communicate their symptoms during the consultation. The declaration form will include the symptoms entered by the user, the medical department determined by the AI, and information about the selected hospitals. This allows the user to smoothly communicate their symptoms during the consultation. In other words, when a user is unwell, they can choose the appropriate medical department, find a nearby hospital, and properly communicate their symptoms during the consultation. For example, if a user has a headache and a fever, the AI ​​will determine that internal medicine is necessary, select a nearby hospital with an internal medicine department, and create a medical declaration form to properly communicate their symptoms during the consultation, allowing them to receive treatment smoothly. In this way, the medical support system enables users to choose the appropriate medical department when they are unwell, find a nearby hospital, and properly communicate their symptoms during the consultation.

[0064] The medical support system according to this embodiment comprises a reception unit, an analysis unit, a selection unit, and a creation unit. The reception unit inputs the user's symptoms. The user's symptoms include, for example, headaches, fever, and abdominal pain, but are not limited to such examples. The reception unit allows the user to input symptoms in a free format, for example, by text input, voice input, or image attachment. The analysis unit analyzes the symptoms entered by the reception unit and determines the appropriate medical department. Based on past medical data and case data, the analysis unit identifies the medical department best suited to the user's symptoms. For example, it determines the medical department according to the symptoms, such as internal medicine for headaches and fevers, and respiratory medicine for coughs. The analysis unit refers to past medical data such as electronic medical records, medical records, and test results, and identifies the medical department based on case data. Based on the medical department determined by the analysis unit, the selection unit selects an appropriate hospital near the user's residence. Based on the user's location information, the selection unit searches for nearby hospitals and lists hospitals with matching medical departments. For example, if a user needs to see an internal medicine specialist, the system lists nearby hospitals with internal medicine departments. The selection unit obtains location information using, for example, GPS data, address information, or location services, and searches for hospitals. Furthermore, the selection unit can sort the search results by criteria such as distance, consultation time, and rating. For example, it can sort by criteria such as straight-line distance, walking distance, or driving time. The creation unit creates a medical condition declaration form based on the hospital information selected by the selection unit, so that the user can appropriately communicate their symptoms during the consultation. The creation unit creates a medical condition declaration form that includes the symptoms entered by the user, the medical department determined by the AI, and the information of the selected hospitals. For example, it creates a declaration form that includes information such as detailed symptoms, onset date, and medical history. As a result, the medical support system according to this embodiment allows the user to select the appropriate medical department when feeling unwell, find a nearby hospital, and appropriately communicate their symptoms during the consultation.

[0065] The reception desk receives the user's symptoms. These symptoms may include, but are not limited to, headaches, fever, or stomachaches. The reception desk allows users to enter symptoms in a free-form format, such as text input, voice input, or image attachment. Specifically, text input allows users to describe detailed symptoms using a keyboard. Voice input allows users to speak into a microphone, which is automatically converted to text using speech recognition technology. Image attachment allows users to upload photos of visual symptoms, such as skin rashes or swelling. This provides the reception desk with diverse input methods, allowing users to report symptoms in the most convenient way. Furthermore, the reception desk can automatically classify the entered symptoms and pre-process them before sending them to the analysis department. For example, natural language processing technology can be used to extract keywords from the entered text and categorize them. In the case of voice input, algorithms are applied to remove noise and ensure accurate speech recognition when converting the audio data to text. This allows the reception desk to provide the user-entered information to the analysis department accurately and efficiently.

[0066] The analysis unit analyzes the symptoms entered by the reception unit and determines the appropriate medical department. Based on past medical data and case data, the analysis unit identifies the medical department best suited to the user's symptoms. For example, it determines the appropriate department based on the symptoms, such as internal medicine for headaches and fevers, and respiratory medicine for coughs. The analysis unit refers to past medical data such as electronic medical records, medical records, and test results, and identifies the medical department based on case data. Specifically, it utilizes machine learning algorithms using AI and trains them with past case data to predict the optimal medical department for the entered symptoms with high accuracy. For example, it uses a neural network to analyze symptom patterns and identify the medical department by matching them with similar cases. The analysis unit also considers the severity and urgency of the symptoms, and can recommend a visit to the emergency department or a specialist if emergency treatment is necessary. Furthermore, the analysis unit can consider the user's medical history and allergy information to provide more individualized medical department suggestions. As a result, the analysis unit can quickly and accurately determine the appropriate medical department for the user's symptoms and support them in visiting the appropriate medical institution.

[0067] The selection unit selects appropriate hospitals near the user's residence based on the medical specialty determined by the analysis unit. The selection unit searches for nearby hospitals based on the user's location information and lists hospitals with matching medical specialties. For example, if the user needs to see an internal medicine specialist, it lists nearby hospitals with internal medicine departments. The selection unit obtains location information using, for example, GPS data, address information, and location services to search for hospitals. Furthermore, the selection unit can sort search results by criteria such as distance, consultation time, and ratings. For example, it can sort by straight-line distance, walking distance, and driving time. Specifically, the selection unit not only prioritizes displaying the closest hospital based on the user's current location, but also suggests the optimal hospital by considering consultation time, hospital ratings, and feedback from past patients. The selection unit also checks hospital congestion and appointment availability in real time to support users in receiving medical care smoothly. For example, if a particular hospital is crowded, it can suggest other nearby hospitals to reduce waiting times. In this way, the selection unit helps users find appropriate medical institutions quickly and efficiently.

[0068] The creation unit creates a medical condition declaration form based on the hospital information selected by the selection unit, enabling users to appropriately communicate their symptoms during consultations. The creation unit creates a medical condition declaration form that includes the symptoms entered by the user, the medical department determined by AI, and the information of the selected hospital. For example, it creates a declaration form that includes information such as detailed symptoms, onset date, and medical history. Specifically, the creation unit describes the symptoms entered by the user in detail, including the onset date, progression of symptoms, past treatment history, and allergy information, enabling doctors to make a quick diagnosis. The declaration form also includes information on the selected hospital and medical department, so that users can see at a glance the information they need when visiting a doctor. Furthermore, the creation unit provides the declaration form in PDF format or a printable format, allowing users to view it on their smartphones or tablets, or print it out and bring it with them. This allows users to accurately communicate their symptoms during consultations, and enables doctors to make quick and appropriate diagnoses. In addition, the creation unit implements measures to protect user privacy, such as data encryption and access control, to prevent the leakage of personal information. This allows the development team to provide users with an environment where they can use the system with peace of mind, thereby improving the overall reliability of the medical support system.

[0069] The reception desk allows users to input symptoms in a free-form manner. For example, users can input symptoms in a free-form manner, such as through text input, voice input, or image attachment. This allows users to input symptoms in a free-form manner, enabling the provision of detailed symptom information. Some or all of the above-described processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the text data entered by the user into a generating AI, which can then analyze the text data to extract symptom information.

[0070] The analysis unit can identify the most appropriate medical department for the user's symptoms based on past medical data and case data. For example, it can refer to past medical data such as electronic medical records, medical records, and test results, and identify the appropriate medical department based on case data. This allows for the selection of the appropriate medical department based on past medical data and case data. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input past medical data into a generating AI, which can then analyze the data to identify the appropriate medical department.

[0071] The selection unit can search for nearby hospitals based on the user's location information and list hospitals with matching medical departments. For example, it can obtain location information using GPS data, address information, location services, etc., and search for hospitals. This allows the user to find a suitable nearby hospital by searching based on their location information. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's location information into a generating AI, which can then analyze the location information and list nearby hospitals.

[0072] The selection unit can sort search results by distance, consultation time, and evaluation criteria. The selection unit sorts the search results by distance, consultation time, and evaluation criteria. For example, it sorts by criteria such as straight-line distance, walking distance, and travel time by car. This allows the user to select the most suitable hospital by sorting the search results. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the search results into a generating AI, which can then sort them by distance, consultation time, and evaluation criteria.

[0073] The creation unit can create a medical condition declaration form that includes the symptoms entered by the user, the medical department determined by the AI, and information about the selected hospital. For example, it can create a declaration form that includes details of the symptoms, the time of onset, and medical history. This allows the user to appropriately communicate their medical condition during a medical examination by creating a medical condition declaration form. Some or all of the above-described processes in the creation unit may be performed using AI, or not. For example, the creation unit can input the user-entered symptom data into a generating AI, which can then create the declaration form.

[0074] The reception unit can estimate the user's emotions and adjust the symptom input interface based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick symptom input. This provides a more comfortable input experience by adjusting the interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 unit may be performed using AI or not. For example, the reception unit can input the user's facial expression data into a generative AI, which can estimate emotions and adjust the interface.

[0075] The reception desk can analyze the user's past symptom input history and suggest the optimal input method. For example, it can automatically display symptoms that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest symptoms that the user will use at specific times based on their past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input data into a generating AI, which can then analyze the data and suggest the optimal input method.

[0076] The reception unit can filter the input content based on the user's current health status and lifestyle when symptoms are entered. For example, it can prioritize displaying relevant symptoms based on the user's current health status. It can also filter relevant symptoms by considering the user's lifestyle (smoking, drinking, etc.). Furthermore, it can prioritize displaying relevant symptoms based on the user's medical history. This allows users to enter more relevant symptoms by filtering the input content based on their current health status and lifestyle. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's health data into a generating AI, which can then analyze the data and filter the input content.

[0077] The reception desk can estimate the user's emotions and determine the priority of symptoms to be entered based on the estimated emotions. For example, if the user is feeling anxious, it may prompt them to prioritize entering serious symptoms. If the user is relaxed, it may prompt them to enter detailed symptoms. Furthermore, if the user is in a hurry, it may prompt them to prioritize entering major symptoms. This ensures that important symptoms are entered first by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI, which can estimate emotions and determine the priority of symptoms.

[0078] The reception unit can prioritize the input of highly relevant symptoms by considering the user's geographical location when symptoms are entered. For example, if the user is in a specific region, symptoms of diseases prevalent in that region will be displayed preferentially. Also, if the user is traveling, symptoms based on the health risks at the travel destination can be displayed preferentially. Furthermore, if the user is in a specific environment (such as a factory or farm), symptoms related to that environment can be displayed preferentially. In this way, highly relevant symptoms can be prioritized by considering geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's location information into a generating AI, which can analyze the location information and prioritize the display of highly relevant symptoms.

[0079] The reception desk can analyze the user's social media activity and input relevant symptoms when symptoms are entered. For example, it can input relevant symptoms based on health information shared by the user on social media. It can also analyze the user's social media activity (posts, comments, etc.) and input relevant symptoms. Furthermore, it can input relevant symptoms based on information from health-related accounts that the user follows on social media. In this way, relevant symptoms can be entered by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI, and the generating AI can analyze the data and input relevant symptoms.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is feeling anxious, an algorithm that performs a quick and accurate analysis can be applied. If the user is relaxed, an algorithm that performs a detailed analysis can be applied. Furthermore, if the user is in a hurry, an algorithm that performs a concise and quick analysis can be applied. By adjusting the analysis algorithm based on the user'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 AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generative AI, which can estimate emotions and adjust the analysis algorithm.

[0081] The analysis unit can adjust the level of detail of the analysis based on the severity of the symptoms during the analysis. For example, a detailed analysis can be performed for severe symptoms, while a simplified analysis can be performed for minor symptoms. Furthermore, if there are multiple symptoms, the unit can prioritize the analysis of the symptoms with the highest severity. This allows for a detailed analysis of important symptoms by adjusting the level of detail based on the severity of the symptoms. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom data into a generating AI, which can then analyze the data and adjust the level of detail.

[0082] The analysis unit can apply different analysis algorithms depending on the symptom category during analysis. For example, in the case of respiratory symptoms, a specialized respiratory analysis algorithm can be applied. Similarly, in the case of digestive symptoms, a specialized digestive analysis algorithm can be applied. Furthermore, in the case of neurological symptoms, a specialized neurological analysis algorithm can be applied. By applying different analysis algorithms depending on the symptom category, more accurate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom data into a generating AI, which can then analyze the data and apply different algorithms.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, it can provide a simple and highly visible display method. If the user is relaxed, it can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. By adjusting the display method based on the user's emotions, it is possible to provide analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI, which can estimate the emotions and adjust the display method.

[0084] The analysis unit can determine the priority of analysis based on when the symptoms were submitted. For example, it can prioritize the analysis of recently submitted symptoms. It can also prioritize the analysis of symptoms that have been left untreated for a long time. Furthermore, it can dynamically adjust the analysis priority based on the submission time. This allows for the provision of timely analysis results by determining the analysis priority based on the submission time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom data into a generating AI, and the generating AI can analyze the data and determine the priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of symptoms during the analysis. For example, it can prioritize the analysis of highly relevant symptoms. It can also postpone the analysis of less relevant symptoms. Furthermore, if multiple symptoms are related, it can adjust the order of analysis based on their relevance. This allows for prioritizing the analysis of highly relevant symptoms by adjusting the order of analysis based on the relevance of symptoms. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom data into a generating AI, which can then analyze the data and adjust the order.

[0086] The selection unit can estimate the user's emotions and adjust the hospital selection criteria based on those estimated emotions. For example, if the user is feeling anxious, it can prioritize hospitals with high ratings. If the user is relaxed, it can prioritize hospitals based on distance and consultation time. Furthermore, if the user is in a hurry, it can prioritize the nearest hospital. In this way, by adjusting the hospital selection criteria based on the user's emotions, a more appropriate hospital can be selected. 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 selection unit may be performed using AI, or not using AI. For example, the selection unit can input user facial expression data into a generative AI, which can estimate emotions and adjust the selection criteria.

[0087] The selection unit can improve the accuracy of hospital selection by considering the interrelationships between hospitals. For example, it can select the optimal hospital based on inter-hospital collaboration information. It can also select hospitals by considering their specialized fields and departments. Furthermore, it can improve the accuracy of selection based on the hospitals' past treatment performance. In this way, the accuracy of selection can be improved by considering the interrelationships between hospitals. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input hospital collaboration information into a generating AI, and the generating AI can analyze the data to improve the accuracy of selection.

[0088] The selection unit can make hospital selections while considering hospital evaluation information. For example, it can prioritize hospitals with high user ratings. It can also improve the accuracy of selection based on online hospital reviews. Furthermore, it can also consider past patient satisfaction data of hospitals during the selection process. This allows for the selection of more reliable hospitals by considering hospital evaluation information. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input hospital evaluation data into a generating AI, which can then analyze the data and make selections.

[0089] The selection unit can estimate the user's emotions and adjust the display order of hospitals based on the estimated emotions. For example, if the user is feeling anxious, hospitals with high ratings will be displayed higher. If the user is relaxed, the display order can be prioritized based on distance and consultation time. Furthermore, if the user is in a hurry, the nearest hospitals can be displayed higher. By adjusting the display order based on the user's emotions, more appropriate hospital information 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 selection unit may be performed using AI, or not using AI. For example, the selection unit can input the user's facial expression data into the generative AI, which can estimate emotions and adjust the display order.

[0090] The selection unit can select hospitals while considering their geographical distribution. For example, it can select the most suitable hospital based on the distance from the user's residence. It can also select hospitals that are easily accessible by considering their geographical distribution. Furthermore, it can prioritize hospitals along the user's commute route. This allows for the selection of easily accessible hospitals by considering their geographical distribution. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input geographical data of hospitals into a generating AI, which can then analyze the data and make a selection.

[0091] The selection unit can improve the accuracy of hospital selection by referring to relevant literature on hospitals. For example, it can select the most suitable hospital based on relevant literature on the hospital's specialty. It can also select hospitals by referring to literature on the hospital's clinical performance. Furthermore, it can improve the accuracy of selection based on literature on the hospital's research activities. In this way, the accuracy of selection can be improved by referring to relevant literature on hospitals. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input hospital-related literature data into a generating AI, which can then analyze the data and perform the selection.

[0092] The creation unit can estimate the user's emotions and adjust the expression of the declaration form based on the estimated emotions. For example, if the user is feeling anxious, the unit can use concise and easy-to-understand language. If the user is relaxed, it can also use language that includes detailed information. Furthermore, if the user is in a hurry, it can use language that gets straight to the point. By adjusting the expression based on the user's emotions, a more appropriate declaration form can be created. Emotion estimation is achieved using an emotion estimation function, such as 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 creation unit may be performed using AI or not. For example, the creation unit can input user facial expression data into a generative AI, which can estimate emotions and adjust the expression.

[0093] The preparation unit can adjust the level of detail in the declaration form based on the severity of the symptoms when preparing the declaration form. For example, in the case of serious symptoms, a declaration form with detailed information can be prepared. Conversely, in the case of minor symptoms, a concise declaration form can be prepared. Furthermore, if there are multiple symptoms, the unit can prioritize the description of the symptoms with the highest severity. This allows for detailed description of important symptoms by adjusting the level of detail in the declaration form based on the severity of the symptoms. Some or all of the above processing in the preparation unit may be performed using AI, for example, or not using AI. For example, the preparation unit can input symptom data into a generating AI, which can then analyze the data and adjust the level of detail.

[0094] The creation unit can apply different declaration form formats depending on the symptom category when creating the declaration form. For example, for respiratory symptoms, a specialized respiratory declaration form format can be applied. Similarly, for digestive symptoms, a specialized digestive declaration form format can be applied. Furthermore, for neurological symptoms, a specialized neurological declaration form format can be applied. This allows for the creation of more appropriate declaration forms by applying different declaration form formats depending on the symptom category. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input symptom data into a generating AI, which can then analyze the data and apply different formats.

[0095] The creation unit can estimate the user's emotions and adjust the length of the declaration based on those emotions. For example, if the user is feeling anxious, it can create a concise and to-the-point declaration. If the user is relaxed, it can create a longer declaration with more detailed information. Furthermore, if the user is in a hurry, it can create a short and to-the-point declaration. By adjusting the length of the declaration based on the user's emotions, a more appropriate declaration can be created. Emotion estimation is achieved using an emotion estimation function, such as 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 creation unit may be performed using AI or not. For example, the creation unit can input user facial expression data into a generative AI, which can estimate emotions and adjust the length of the declaration.

[0096] The preparation unit can determine the priority of declarations based on when the symptoms were reported when preparing the declaration. For example, it can prioritize reporting recently reported symptoms. It can also prioritize reporting symptoms that have been left untreated for a long time. Furthermore, it can dynamically adjust the priority of declarations based on the reporting date. This allows for the creation of timely declarations by prioritizing declarations based on the reporting date. Some or all of the above processing in the preparation unit may be performed using AI, for example, or not using AI. For example, the preparation unit can input symptom data into a generating AI, which can then analyze the data and determine the priority.

[0097] The preparation unit can adjust the order of the declaration forms based on the relevance of the symptoms when preparing the declaration form. For example, it can prioritize listing highly relevant symptoms in the declaration form, and postpone listing less relevant symptoms. Furthermore, if multiple symptoms are related, it can adjust the order of the declaration forms based on their relevance. This allows for prioritizing the listing of highly relevant symptoms by adjusting the order of the declaration forms based on the relevance of the symptoms. Some or all of the above processing in the preparation unit may be performed using AI, for example, or not using AI. For example, the preparation unit can input symptom data into a generating AI, which can then analyze the data and adjust the order.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception desk can supplement the user's input by referring to their past medical history when they enter their symptoms. For example, it can supplement the current symptom input based on the symptoms and diagnoses the user received during past consultations. It can also automatically suggest related symptoms from past medical history. Furthermore, it can automatically generate appropriate questions about the symptoms entered by the user based on past medical history and collect detailed information. This allows for the provision of more accurate and detailed symptom information by utilizing past medical history.

[0100] The analysis unit can consider the influence of seasons and weather when analyzing a user's symptoms. For example, during allergy season, it prioritizes analyzing symptoms related to pollen allergies. It can also prioritize symptoms related to colds and influenza during colder periods. Furthermore, based on weather data, it can analyze the impact of temperature and humidity changes on symptoms and identify the appropriate medical department. This allows for the selection of a more appropriate medical department by considering the influence of seasons and weather.

[0101] The selection function can recommend specific specialists based on the user's symptoms. For example, if a user enters a specific symptom, it will recommend a doctor with specialized knowledge in that symptom. It can also select highly reliable specialists based on past treatment records and patient reviews. Furthermore, it can suggest the most suitable treatment schedule for the user, taking into account the specialist's consultation hours and appointment availability. This allows users to receive higher quality medical care through specialist recommendations.

[0102] The creation unit can generate a declaration form when the user inputs their symptoms, taking into account the user's lifestyle and occupational information. For example, if the user has a desk job, it can consider symptoms caused by prolonged sitting. Similarly, if the user exercises frequently, it can consider symptoms caused by the strain of exercise. Furthermore, it can reflect related symptoms in the declaration form based on the user's diet and sleep habits. This allows for the creation of a more detailed and relevant declaration form by considering lifestyle and occupational information.

[0103] The reception desk can estimate the user's emotions and provide support for symptom input based on those emotions. For example, if the user is feeling anxious, it can support the input process with encouraging words. If the user is relaxed, it can provide guidance to encourage more detailed input. Furthermore, if the user is in a hurry, it can suggest a concise input method to enable quick symptom entry. In this way, a more comfortable input experience can be provided by offering support tailored to the user's emotions.

[0104] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, it can provide reassuring feedback. If the user is relaxed, it can provide detailed analysis results to deepen their understanding. Furthermore, if the user is in a hurry, it can provide concise and to-the-point feedback. In this way, by adjusting the feedback method based on the user's emotions, more appropriate analysis results can be provided.

[0105] The selection function can estimate the user's emotions and adjust the hospital selection criteria based on those emotions. For example, if the user is feeling anxious, it can prioritize hospitals with high ratings. If the user is relaxed, it can prioritize hospitals based on distance and consultation time. Furthermore, if the user is in a hurry, it can prioritize the nearest hospital. In this way, by adjusting the hospital selection criteria based on the user's emotions, a more appropriate hospital can be selected.

[0106] The creation process can estimate the user's emotions and adjust the wording of the declaration based on those emotions. For example, if the user is feeling anxious, it will use concise and easy-to-understand language. If the user is relaxed, it may use language that includes detailed information. Furthermore, if the user is in a hurry, it may use language that gets straight to the point. By adjusting the wording based on the user's emotions, a more appropriate declaration can be created.

[0107] The reception desk can estimate the user's emotions and adjust the symptom input interface based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick symptom entry. In this way, by adjusting the interface according to the user's emotions, a more comfortable input experience can be provided.

[0108] The analysis unit can consider the user's genetic information when analyzing their symptoms. For example, it can assess the risk of specific diseases based on the user's family history and genetic risk. It can also identify the appropriate medical department for specific symptoms based on genetic information. Furthermore, it can suggest preventive measures and treatments suitable for the user based on genetic information. In this way, by considering genetic information, more personalized medical support can be provided.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk enters the user's symptoms. These symptoms may include, but are not limited to, headaches, fever, or stomachaches. The reception desk allows users to enter symptoms in a free-form format, such as text input, voice input, or image attachments. Step 2: The analysis unit analyzes the symptoms entered by the reception unit and determines the appropriate medical department. Based on past medical data and case data, the analysis unit identifies the medical department best suited to the user's symptoms. For example, if there is a headache or fever, it will determine the medical department to be internal medicine, and if there is a cough, it will determine the medical department to be respiratory medicine, etc. The analysis unit refers to past medical data such as electronic medical records, medical records, and test results, and identifies the medical department based on case data. Step 3: The selection unit selects appropriate hospitals near the user's residence based on the medical specialty determined by the analysis unit. The selection unit searches for nearby hospitals based on the user's location information and lists hospitals with matching medical specialties. For example, if the user needs to see an internal medicine specialist, it will list nearby hospitals with internal medicine departments. The selection unit obtains location information using, for example, GPS data, address information, and location services to search for hospitals. Furthermore, the selection unit can sort the search results by criteria such as distance, consultation time, and rating. For example, it can sort by criteria such as straight-line distance, walking distance, and driving time. Step 4: The creation unit creates a medical condition declaration form based on the hospital information selected by the selection unit, so that the patient can properly communicate their condition during the consultation. The creation unit creates a medical condition declaration form that includes the symptoms entered by the user, the medical department determined by the AI, and the information of the selected hospital. For example, it creates a declaration form that includes information such as details of the symptoms, the time of onset, and medical history.

[0111] 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.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] 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.

[0114] For example, the reception unit is implemented by the reception device 38 of the smart device 14, which receives the user's symptoms via text input or voice input. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the user's symptoms based on past medical data and case data and determines the appropriate medical department. The selection unit is implemented by the specific processing unit 290 of the data processing device 12, which searches for appropriate hospitals in the vicinity of the user's residence and lists hospitals with matching medical departments. The creation unit is implemented by the specific processing unit 290 of the data processing device 12, which creates a medical condition declaration form based on the symptoms entered by the user and the information of the selected hospitals. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] 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.

[0130] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, which receives the user's symptoms via voice input. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the user's symptoms based on past medical data and case data and determines the appropriate medical department. The selection unit is implemented by the identification processing unit 290 of the data processing device 12, which searches for appropriate hospitals in the vicinity of the user's residence and lists hospitals with matching medical departments. The creation unit is implemented by the identification processing unit 290 of the data processing device 12, which creates a medical condition declaration form based on the symptoms entered by the user and the information of the selected hospitals. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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.

[0146] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, which receives the user's symptoms via voice input. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the user's symptoms based on past medical data and case data and determines the appropriate medical department. The selection unit is implemented by the identification processing unit 290 of the data processing device 12, which searches for appropriate hospitals in the vicinity of the user's residence and lists hospitals with matching medical departments. The creation unit is implemented by the identification processing unit 290 of the data processing device 12, which creates a medical condition declaration form based on the symptoms entered by the user and the information of the selected hospitals. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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.

[0163] For example, the reception unit is implemented by the microphone 238 of the robot 414, which receives the user's symptoms via voice input. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the user's symptoms based on past medical data and case data and determines the appropriate medical department. The selection unit is implemented by the identification processing unit 290 of the data processing unit 12, which searches for appropriate hospitals in the vicinity of the user's residence and lists hospitals with matching medical departments. The creation unit is implemented by the identification processing unit 290 of the data processing unit 12, which creates a medical condition declaration form based on the symptoms entered by the user and the information of the selected hospitals. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A reception area where users input their symptoms, An analysis unit analyzes the symptoms entered by the reception unit and determines the medical department, Based on the medical department determined by the aforementioned analysis unit, a selection unit selects a hospital in the vicinity of the user's residence, The system includes a creation unit that, based on the hospital information selected by the aforementioned selection unit, creates a medical condition declaration form so that the patient can communicate their medical condition during the consultation. A system characterized by the following features. (Note 2) The aforementioned reception unit is Users enter their symptoms in a free-form format. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on past medical data and case data, the system identifies the appropriate medical department for the user's symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned select unit is Based on the user's location information, the system searches for nearby hospitals and lists hospitals with matching medical departments. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned select unit is Sort search results by distance, consultation time, and rating criteria. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned creation unit, The system creates a medical condition declaration form that includes the symptoms entered by the user, the medical department determined by the AI, and information about the selected hospital. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the symptom input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past symptom input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter symptoms, the system filters the input based on their current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of symptoms to input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering symptoms, the system prioritizes the input of symptoms that are most relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When a user enters their symptoms, the system analyzes their social media activity and inputs relevant symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the severity of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the symptom category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis will be determined based on when the symptoms were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned select unit is The system estimates user sentiment and adjusts hospital selection criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned select unit is When selecting a hospital, consider the interrelationships between hospitals to improve the accuracy of the selection process. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned select unit is When selecting a hospital, we will take into consideration hospital evaluation information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned select unit is The system estimates the user's emotions and adjusts the display order of hospitals based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned select unit is When selecting a hospital, the geographical distribution of the hospitals should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned select unit is When selecting a hospital, refer to relevant literature to improve the accuracy of the selection. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned creation unit, The system estimates the user's emotions and adjusts the wording of the declaration form based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned creation unit, When preparing your tax return, adjust the level of detail based on the severity of your symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned creation unit, When preparing your tax return, apply a different tax return format depending on the category of symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned creation unit, The system estimates the user's emotions and adjusts the length of the declaration based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned creation unit, When preparing your tax return, prioritize the reports based on when you reported your symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned creation unit, When preparing your tax return, adjust the order of the return items based on the relevance of your symptoms. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 users input their symptoms, An analysis unit analyzes the symptoms entered by the reception unit and determines the medical department, Based on the medical department determined by the aforementioned analysis unit, a selection unit selects a hospital in the vicinity of the user's residence. The system includes a creation unit that, based on the hospital information selected by the selection unit, creates a medical condition declaration form so that the patient can communicate their medical condition during the consultation. A system characterized by the following features.

2. The aforementioned reception unit is Users enter their symptoms in a free-form format. The system according to feature 1.

3. The aforementioned analysis unit, Based on past medical data and case data, the system identifies the appropriate medical department for the user's symptoms. The system according to feature 1.

4. The aforementioned select unit is Based on the user's location information, the system searches for nearby hospitals and lists hospitals with matching medical departments. The system according to feature 1.

5. The aforementioned select unit is Sort search results by distance, consultation time, and rating criteria. The system according to feature 1.

6. The aforementioned creation unit, The system creates a medical condition declaration form that includes the symptoms entered by the user, the medical department determined by the AI, and information about the selected hospital. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the symptom input interface based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past symptom input history and suggests the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When users enter symptoms, the system filters the input based on their current health status and lifestyle. The system according to feature 1.

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

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