System

Abstract

To provide a system capable of identifying an appropriate medical department when falling into poor health and providing information about nearby medical institutions.SOLUTION: The system in an embodiment includes a reception unit, an analysis unit, an identification unit, a providing unit, and an advice unit. The reception unit receives an input of symptoms. The analysis unit analyzes the information input by the reception unit. The identification unit identifies the clinical department based on the information analyzed by the analysis unit. The providing unit provides information about nearby medical institutions with a clinical department identified by the identification unit. The advice unit provides specific advice on recovery when no medical treatment is necessary.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] With conventional technology, it was difficult to determine which medical department to visit when feeling unwell, and it took a long time to find an appropriate medical institution.

[0005] The system according to the embodiment aims to identify an appropriate medical department when a patient feels unwell and provide information on nearby medical institutions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an identification unit, a provision unit, and an advice unit. The reception unit inputs symptoms. The analysis unit analyzes the information input by the reception unit. The identification unit identifies a medical department based on the information analyzed by the analysis unit. The provision unit provides information on nearby medical institutions that have the medical department identified by the identification unit. The advice unit provides specific advice for recovering health when no medical treatment is necessary. Effect of the Invention

[0007] The system according to the embodiment can identify an appropriate medical department when a user feels unwell and provide information on nearby medical institutions. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The health management system according to the embodiment of the present invention is a system that, when a user is feeling unwell, inputs the user's current symptoms and health condition, and then provides a simple judgment of which medical department the user should visit before going to the hospital, and provides information on nearby medical institutions with medical departments that can handle the patient. Furthermore, if the simple judgment is made that no medical treatment is necessary, the system provides hints on diet, sleep, etc. to improve the user's health. First, the user inputs the user's current symptoms and health condition. For example, specific symptoms such as "I have a headache," "I have a sore throat," and "I have a fever" are input. This information is input to the generation AI. Next, the generation AI analyzes the input information and makes a simple judgment of which medical department the user should visit. The generation AI identifies the medical department that is most suitable for the input symptoms based on past medical data and case data. For example, for the information "I have a headache," it suggests a neurology department or an internal medicine department. Furthermore, the generation AI provides information on nearby medical institutions with medical departments that can handle the patient. The generation AI searches for the nearest medical institution based on the user's current location information, and provides the information. For example, it provides information such as "The nearest hospital with a neurology department is XX Hospital." If a simplified judgment is made that medical treatment is not necessary, the generating AI will give hints on diet, sleep, etc. to improve health. For example, it will provide advice such as "drink plenty of fluids," "try to eat a nutritionally balanced diet," and "get enough sleep." This system allows users to know the appropriate medical department before going to the hospital, and avoid unnecessary visits. In addition, even if medical treatment is not necessary, users can receive specific advice to improve their health, allowing them to manage their health with peace of mind. This allows the health management system to know the appropriate medical department before going to the hospital, and avoid unnecessary visits. In addition, even if medical treatment is not necessary, users can receive specific advice to improve their health, allowing them to manage their health with peace of mind.

[0029] The health management system according to the embodiment includes a reception unit, an analysis unit, a specification unit, a provision unit, and an advice unit. The reception unit provides an interface for the user to input his / her current symptoms and health condition. For example, the reception unit can accept text input or voice input. The user can input specific symptoms such as "I have a headache," "I have a sore throat," and "I have a fever." The analysis unit uses a generation AI to analyze the information input by the reception unit. The generation AI analyzes the input symptoms based on past medical data and case data. For example, the generation AI uses a text generation AI (for example, LLM) to analyze the input symptoms and generate information for identifying an appropriate medical department. The specification unit uses the generation AI to specify a medical department based on the information analyzed by the analysis unit. The generation AI specifies the most appropriate medical department based on the analyzed information. For example, for information such as "I have a headache," a neurology department or an internal medicine department is suggested. The provision unit searches for the nearest medical institution based on the user's current location information and provides the information. The providing unit, for example, identifies the user's current location using GPS data or an IP address, and searches for the nearest medical institution. For example, it provides information such as "The nearest hospital with a neurology department is XX Hospital." The advising unit provides specific advice on diet, sleep, etc. for physical recovery when a simplified judgment is made that medical treatment is not necessary. The advising unit provides advice such as "Drink enough water," "Try to eat a nutritionally balanced diet," and "Get enough sleep." In this way, the physical condition management system according to the embodiment allows the user to know the appropriate medical department before going to the hospital, and to avoid unnecessary medical examinations. In addition, even if medical treatment is not necessary, the user can receive specific advice for physical recovery, so that he or she can manage his or her physical condition with peace of mind.

[0030] The reception unit provides an interface for the user to input his / her current symptoms and physical condition. For example, the reception unit can accept text input or voice input. The user can input specific symptoms such as "I have a headache," "I have a sore throat," and "I have a fever." Specifically, the reception unit is provided as an application for a smartphone or a computer, and is designed to be easily accessible by the user. In the case of text input, the user inputs symptoms using a keyboard, and in the case of voice input, the system automatically converts the voice into text by simply speaking the symptoms through a microphone. In addition, the reception unit also has a function to store the information entered by the user and to enable the user to refer to the history of past symptoms and physical condition. This allows the user to check past changes in physical condition and use it when consulting a doctor. In addition, the reception unit also takes security measures to encrypt and store the information entered by the user and protect privacy. This allows the user to input his / her symptoms and physical condition with peace of mind.

[0031] The analysis unit uses the generation AI to analyze the information entered by the reception unit. The generation AI analyzes the entered symptoms based on past medical data and case data. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the entered symptoms and generate information to identify the appropriate medical department. Specifically, the generation AI uses natural language processing technology to analyze the text and voice data entered by the user and extracts symptom patterns and associations. For example, in response to the input "I have a headache," the generation AI refers to past case data and identifies multiple factors that may be the cause of the headache (e.g., tension headache, migraine, brain tumor, etc.). In addition, the generation AI also takes into account personal information such as the user's age, gender, and medical history, and performs analysis to provide more accurate diagnostic information. Based on this information, the analysis unit can suggest specific medical departments and the types of tests required to the user. This allows the user to obtain information to select an appropriate medical institution for their symptoms.

[0032] The identification unit uses the generation AI to identify a medical department based on the information analyzed by the analysis unit. The generation AI identifies the most appropriate medical department based on the analyzed information. For example, for the information "I have a headache," it suggests neurology or internal medicine. Specifically, the identification unit selects the most appropriate medical department based on the symptom data analyzed by the generation AI, taking into account the expertise and treatment track record of each medical department. For example, if there is a headache symptom, past medical data and medical guidelines are referred to to determine whether neurology or internal medicine is appropriate. In addition, the identification unit can also suggest multiple medical departments if the user's symptoms span multiple medical departments. For example, if the symptoms "sore throat" and "fever" are entered at the same time, both otolaryngology and internal medicine are suggested. This allows the user to obtain information to select the most appropriate medical department for their symptoms, and avoid unnecessary visits to the doctor.

[0033] The providing unit searches for the nearest medical institution based on the user's current location information and provides the information. The providing unit, for example, uses GPS data or an IP address to identify the user's current location and searches for the nearest medical institution. For example, information such as "The nearest hospital with a neurology department is XX Hospital" is provided. Specifically, the providing unit accurately identifies the current location using the location information service of the user's smartphone or computer. Furthermore, the providing unit cooperates with a database of medical institutions to obtain detailed information such as the medical department, consultation hours, and access method of each medical institution. This allows the user to quickly find the nearest medical institution that corresponds to his or her symptoms. The providing unit also provides route guidance to the medical institution selected by the user. For example, the providing unit cooperates with a map app to display the optimal route by foot, public transportation, or car. This allows the user to arrive at the medical institution without getting lost. Furthermore, the providing unit also provides information such as the congestion status of the medical institution and the presence or absence of reservations, thereby supporting the user to receive treatment smoothly.

[0034] When the advice unit determines that medical treatment is not necessary, it provides specific advice on diet, sleep, etc., to improve physical condition. For example, the advice unit provides advice such as "take sufficient fluids," "try to eat a nutritionally balanced diet," and "get sufficient sleep." Specifically, the advice unit uses a generation AI to generate customized advice according to the user's symptoms and physical condition. For example, if the user has mild cold symptoms, it provides specific advice such as "eat foods that are rich in vitamin C" and "drink warm drinks." In addition, the advice unit also provides advice that is useful for long-term health management, taking into account the user's lifestyle habits and past health data. For example, it provides advice such as "try to exercise regularly" and "try relaxation methods to reduce stress." In addition, the advice unit also has a function to feed back the results of the user's practice of the advice, and continuously supports the user's improvement of his or her health condition. This allows the user to manage his or her physical condition with peace of mind by receiving specific advice, even if medical treatment is not necessary.

[0035] The analysis unit can analyze symptoms based on past specific medical data and case data. The analysis unit, for example, analyzes symptoms using past electronic medical record data and medical records. For example, the analysis unit collects past electronic medical record data and analyzes symptom patterns. The analysis unit can also analyze the severity and frequency of symptoms based on past medical records. For example, the analysis unit analyzes past medical records and evaluates the severity and frequency of symptoms. This enables the analysis unit to more accurately identify a medical department based on past medical data and case data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past electronic medical record data to the generation AI and have the generation AI execute symptom analysis.

[0036] The identification unit can identify an appropriate medical department based on the analyzed information. The identification unit, for example, identifies the most appropriate medical department based on the analyzed information. For example, the identification unit suggests a neurology department or an internal medicine department for the information "having a headache." The identification unit can also suggest an otolaryngology department or an internal medicine department for the information "having a sore throat." Furthermore, the identification unit can also suggest an internal medicine department or a pediatrics department for the information "having a fever." In this way, the identification unit enables the user to visit an appropriate medical department based on the analyzed information. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the identification unit can input the analyzed information to the generation AI and cause the generation AI to identify the medical department.

[0037] The providing unit can search for the nearest medical institution based on the user's current location information and provide the information. The providing unit can, for example, use GPS data or an IP address to identify the user's current location and search for the nearest medical institution. For example, the providing unit can identify the user's current location based on GPS data and search for the nearest medical institution. The providing unit can also identify the user's current location based on an IP address and search for the nearest medical institution. Furthermore, the providing unit can provide information on the nearest medical institution based on the user's current location information. For example, information such as "the nearest hospital with a neurology department is XX Hospital" is provided. This allows the providing unit to enable the user to quickly find a medical institution. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generating AI, or may be performed without using a generating AI. For example, the providing unit can input the user's current location information to the generating AI and cause the generating AI to search for the nearest medical institution.

[0038] The advice unit can provide advice on specific diet and sleep for physical recovery. The advice unit provides advice such as, for example, "take sufficient water," "try to eat a nutritionally balanced diet," and "get sufficient sleep." For example, the advice unit recommends the user to take 2 liters of water per day. The advice unit can also recommend the user to eat a balanced diet that includes a lot of vegetables and fruits. Furthermore, the advice unit can also recommend the user to sleep 7 to 8 hours every night. In this way, the advice unit enables the user to properly manage his or her physical condition. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's physical condition information into the generation AI and cause the generation AI to generate advice for physical recovery.

[0039] The reception unit can analyze the user's past symptom input history and suggest the optimal input method. For example, the reception unit automatically displays symptoms that the user frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest symptoms to be input in a specific time period from the user's past input history. This allows the reception unit to suggest the optimal input method for the user. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past input data to the generation AI and have the generation AI execute a suggestion of the optimal input method.

[0040] The reception unit can filter the input contents based on the user's current activity status and environment when inputting symptoms. For example, when the user is exercising, the reception unit preferentially inputs symptoms related to exercise. In addition, when the user is at work, the reception unit can also preferentially input symptoms of stress and fatigue related to work. Furthermore, when the user is at home, the reception unit can also preferentially input symptoms related to the home environment. This allows the reception unit to filter the input contents according to the user's activity status and environment. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's activity log and environmental sensor data to the generation AI and cause the generation AI to filter the input contents.

[0041] The reception unit can preferentially input highly relevant symptoms in consideration of the geographical location information of the user when inputting symptoms. For example, when the user is at high altitude, the reception unit preferentially inputs symptoms related to altitude sickness. In addition, when the user is in an urban area, the reception unit can also preferentially input stress and allergy symptoms specific to the city. Furthermore, when the user is at the seaside, the reception unit can also preferentially input symptoms related to sunburn and heat stroke. This allows the reception unit to preferentially input highly relevant symptoms in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the priority order of highly relevant symptoms.

[0042] The reception unit can analyze the social media activity of the user and input related symptoms when inputting symptoms. For example, if the user frequently mentions "headache" on social media, the reception unit preferentially inputs symptoms related to headache. In addition, if the user frequently mentions "fatigue" on social media, the reception unit can also preferentially input symptoms related to fatigue. In addition, if the user frequently mentions "stress" on social media, the reception unit can also preferentially input symptoms related to stress. In this way, the reception unit can preferentially input symptoms related to the user by analyzing social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the social media data of the user to the generation AI and cause the generation AI to execute input of related symptoms.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the symptom during the analysis. For example, in the case of a serious symptom, the analysis unit performs a detailed analysis to provide a highly accurate result. In addition, in the case of a mild symptom, the analysis unit can perform a simple analysis to provide a quick result. Furthermore, in the case of multiple symptoms, the analysis unit can also prioritize the analysis of a symptom with a high importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the symptom. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input symptom importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the symptom during analysis. For example, in the case of a respiratory symptom, the analysis unit applies an analysis algorithm specialized for the respiratory system. In addition, in the case of a digestive symptom, the analysis unit can also apply an analysis algorithm specialized for the digestive system. Furthermore, in the case of a nervous symptom, the analysis unit can also apply an analysis algorithm specialized for the nervous system. This allows the analysis unit to apply different analysis algorithms depending on the category of the symptom. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input symptom category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time of symptom submission. For example, the analysis unit preferentially analyzes symptoms that have been submitted recently. The analysis unit can also analyze symptoms that have been submitted over a long period of time in detail. Furthermore, the analysis unit can also preferentially analyze symptoms with high urgency based on the time of submission. This allows the analysis unit to determine the priority of analysis based on the time of symptom submission. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input symptom submission time data to the generation AI and have the generation AI execute the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of symptoms during analysis. For example, the analysis unit prioritizes analysis of highly related symptoms. The analysis unit can also analyze less related symptoms later. Furthermore, when multiple symptoms are related, the analysis unit can analyze them in order of relevance. This allows the analysis unit to adjust the order of analysis based on the relevance of symptoms. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input symptom relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0047] The identification unit can improve the accuracy of identifying the medical department by considering the interrelationships of the symptoms at the time of identification. For example, when multiple symptoms are related, the identification unit identifies the medical department by considering the interrelationships. The identification unit can also analyze the interrelationships of the symptoms and identify the optimal medical department. Furthermore, the identification unit can improve the accuracy of identifying the medical department based on the interrelationships of the symptoms. In this way, the identification unit can improve the accuracy of identifying the medical department by considering the interrelationships of the symptoms. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input symptom interrelationship data to the generation AI and cause the generation AI to identify the medical department.

[0048] At the time of identification, the identification unit can identify the medical department by considering attribute information of the person who submitted the symptoms. The identification unit can identify an appropriate medical department by considering, for example, the age of the person who submitted the symptoms. The identification unit can also identify an appropriate medical department by considering the gender of the person who submitted the symptoms. Furthermore, the identification unit can also identify an appropriate medical department by considering the medical history of the person who submitted the symptoms. In this way, the identification unit can identify a more appropriate medical department by considering the attribute information of the person who submitted the symptoms. A part or all of the above-mentioned processing in the identification unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the identification unit can input the attribute information of the person who submitted the symptoms to the generation AI and cause the generation AI to identify the medical department.

[0049] The identification unit can identify the medical department in consideration of the geographical distribution of the symptoms at the time of identification. The identification unit can identify the medical department based on, for example, symptoms that occur frequently in a specific geographical area. The identification unit can also analyze the geographical distribution and identify the most suitable medical department. Furthermore, the identification unit can improve the accuracy of identifying the medical department based on the geographical distribution. In this way, the identification unit can identify the medical department corresponding to the symptoms specific to the area by considering the geographical distribution of the symptoms. A part or all of the above-mentioned processing in the identification unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the identification unit can input the geographical distribution data of the symptoms to the generation AI and cause the generation AI to identify the medical department.

[0050] The identification unit can improve the accuracy of identifying the medical department by referring to literature related to the symptoms at the time of identification. The identification unit, for example, refers to related literature and identifies the most suitable medical department. The identification unit can also identify the medical department based on the latest research results related to the symptoms. Furthermore, the identification unit can analyze related literature and improve the accuracy of identifying the medical department. As a result, the identification unit can improve the accuracy of identifying the medical department by referring to related literature. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the literature data related to the symptoms to the generation AI and cause the generation AI to identify the medical department.

[0051] The providing unit can provide optimal medical institution information by referring to the user's past medical institution use history at the time of providing. The providing unit provides optimal medical institution information based on, for example, medical institutions used by the user in the past. The providing unit can also provide highly reliable medical institutions preferentially based on the user's past medical institution use history. Furthermore, the providing unit can analyze the user's past medical institution use history and provide the most suitable medical institution information. As a result, the providing unit can provide optimal medical institution information for the user by referring to the past medical institution use history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the providing unit can input the user's past medical institution use history data to the generating AI and cause the generating AI to provide optimal medical institution information.

[0052] The providing unit can customize the medical institution information based on the user's current living situation at the time of providing. For example, when the user is at work, the providing unit provides medical institution information close to the workplace. In addition, when the user is at home, the providing unit can also provide medical institution information close to the home. Furthermore, when the user is traveling, the providing unit can also provide medical institution information close to the travel destination. This allows the providing unit to customize the medical institution information according to the user's living situation. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's living situation data into the generation AI and cause the generation AI to customize the medical institution information.

[0053] The providing unit can provide optimal medical institution information by considering the geographical location information of the user at the time of providing. The providing unit provides, for example, medical institution information closest to the user's current location. The providing unit can also provide easily accessible medical institution information based on the user's geographical location information. Furthermore, the providing unit can provide medical institution information according to a means of transportation by considering the user's geographical location information. In this way, the providing unit can provide easily accessible medical institution information by considering the user's geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generating AI, or may be performed without using a generating AI. For example, the providing unit can input the user's geographical location information to the generating AI and cause the generating AI to provide optimal medical institution information.

[0054] The providing unit can provide medical institution information by analyzing the social media activity of the user at the time of providing. The providing unit provides, for example, medical institution information mentioned by the user on social media. The providing unit can also provide reliable medical institution information from the user's social media activity. Furthermore, the providing unit can analyze the user's social media activity and provide the most suitable medical institution information. As a result, the providing unit can provide medical institution information that is highly relevant to the user by analyzing the social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the providing unit can input the user's social media data to the generating AI and cause the generating AI to provide the medical institution information.

[0055] The advice unit can provide optimal advice by referring to the user's past health history when giving advice. The advice unit can provide optimal diet and sleep advice based on the user's past health history, for example. The advice unit can also provide advice for a specific symptom based on the user's past health history. Furthermore, the advice unit can analyze the user's past health history and provide the most effective advice. In this way, the advice unit can provide optimal advice for the user by referring to the past health history. A part or all of the above-mentioned processing in the advice unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the advice unit can input the user's past health history data into the generation AI and cause the generation AI to provide optimal advice.

[0056] The advice unit can customize the means of advice based on the user's current living situation when giving advice. For example, when the user is at work, the advice unit provides advice that can be practiced at the workplace. In addition, when the user is at home, the advice unit can also provide advice that can be practiced at home. Furthermore, when the user is traveling, the advice unit can also provide advice that can be practiced at the travel destination. This allows the advice unit to customize the means of advice according to the user's living situation. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's living situation data into the generation AI and cause the generation AI to customize the means of advice.

[0057] The advice unit can provide optimal advice by taking into account the geographical location information of the user when providing advice. For example, when the user is at high altitude, the advice unit provides advice related to altitude sickness. Furthermore, when the user is in an urban area, the advice unit can also provide advice related to stress and allergies specific to the city. Furthermore, when the user is at the seaside, the advice unit can also provide advice related to sunburn and heat stroke. In this way, the advice unit can provide advice corresponding to symptoms specific to the region by taking into account the geographical location information of the user. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the geographical location information of the user to the generation AI and cause the generation AI to provide optimal advice.

[0058] When providing advice, the advice unit can analyze the user's social media activity and suggest a means of advice. For example, if the user frequently mentions "fatigue" on social media, the advice unit can provide advice related to fatigue recovery. In addition, if the user frequently mentions "stress" on social media, the advice unit can also provide advice related to stress relief. In addition, if the user frequently mentions "insomnia" on social media, the advice unit can also provide advice related to sleep improvement. In this way, the advice unit can provide advice that is highly relevant to the user by analyzing social media activity. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's social media data into the generation AI and cause the generation AI to execute the suggestion of a means of advice.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can analyze the user's past symptom input history and suggest the optimal input method. For example, symptoms that the user frequently input in the past are automatically displayed as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest symptoms to be input in a specific time period from the user's past input history. This allows the reception unit to suggest the optimal input method for the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's past input data to the generation AI and have the generation AI execute the suggestion of the optimal input method.

[0061] The analysis unit can adjust the level of detail of the analysis based on the importance of the symptoms during the analysis. For example, in the case of a serious symptom, a detailed analysis is performed to provide a highly accurate result. In addition, in the case of a mild symptom, the analysis unit can also perform a simple analysis to provide a quick result. Furthermore, in the case of multiple symptoms, the analysis unit can also prioritize the analysis of symptoms with a high importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the symptoms. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input symptom importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0062] The identification unit can improve the accuracy of identifying the medical department by considering the interrelationships of the symptoms at the time of identification. For example, when multiple symptoms are related, the identification unit can identify the medical department by considering the interrelationships. The identification unit can also analyze the interrelationships of the symptoms and identify the most suitable medical department. Furthermore, the identification unit can improve the accuracy of identifying the medical department based on the interrelationships of the symptoms. In this way, the identification unit can improve the accuracy of identifying the medical department by considering the interrelationships of the symptoms. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input symptom interrelationship data to the generation AI and cause the generation AI to identify the medical department.

[0063] The providing unit can provide optimal medical institution information by referring to the user's past medical institution use history at the time of providing. For example, the providing unit provides optimal medical institution information based on medical institutions used by the user in the past. The providing unit can also provide highly reliable medical institutions preferentially based on the user's past medical institution use history. Furthermore, the providing unit can analyze the user's past medical institution use history and provide the most suitable medical institution information. As a result, the providing unit can provide optimal medical institution information for the user by referring to the past medical institution use history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past medical institution use history data to the generation AI and cause the generation AI to provide optimal medical institution information.

[0064] When giving advice, the advice unit can provide optimal advice by referring to the user's past health history. For example, the advice unit can provide optimal diet and sleep advice based on the user's past health history. The advice unit can also provide advice for a specific symptom based on the user's past health history. Furthermore, the advice unit can analyze the user's past health history and provide the most effective advice. In this way, the advice unit can provide optimal advice for the user by referring to the past health history. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's past health history data into the generation AI and cause the generation AI to provide optimal advice.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The reception unit provides an interface for the user to input their current symptoms and physical condition. For example, the reception unit can accept text input or voice input. The user can input specific symptoms such as "I have a headache," "I have a sore throat," and "I have a fever." Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The generation AI analyzes the entered symptoms based on past medical data and case data. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the entered symptoms and generate information to identify the appropriate medical department. Step 3: The identification unit uses the generation AI to identify a medical specialty based on the information analyzed by the analysis unit. The generation AI identifies the most appropriate medical specialty based on the analyzed information. For example, for the information "I have a headache," it would suggest a neurologist or an internal medicine specialist. Step 4: The providing unit searches for the nearest medical institution based on the user's current location information and provides the information. The providing unit identifies the user's current location using, for example, GPS data or an IP address, and searches for the nearest medical institution. For example, it provides information such as "The nearest hospital with a neurology department is X Hospital." Step 5: If the advice unit makes a simplified judgment that medical treatment is not necessary, it will provide specific advice on diet, sleep, etc. to help the patient recover. For example, the advice unit will provide advice such as "drink plenty of fluids," "try to eat a nutritionally balanced diet," and "get enough sleep."

[0067] (Example 2) The health management system according to the embodiment of the present invention is a system that, when a user is feeling unwell, inputs the user's current symptoms and health condition, and then provides a simple judgment of which medical department the user should visit before going to the hospital, and provides information on nearby medical institutions with medical departments that can handle the patient. Furthermore, if the simple judgment is made that no medical treatment is necessary, the system provides hints on diet, sleep, etc. to improve the user's health. First, the user inputs the user's current symptoms and health condition. For example, specific symptoms such as "I have a headache," "I have a sore throat," and "I have a fever" are input. This information is input to the generation AI. Next, the generation AI analyzes the input information and makes a simple judgment of which medical department the user should visit. The generation AI identifies the medical department that is most suitable for the input symptoms based on past medical data and case data. For example, for the information "I have a headache," it suggests a neurology department or an internal medicine department. Furthermore, the generation AI provides information on nearby medical institutions with medical departments that can handle the patient. The generation AI searches for the nearest medical institution based on the user's current location information, and provides the information. For example, it provides information such as "The nearest hospital with a neurology department is XX Hospital." If a simplified judgment is made that medical treatment is not necessary, the generating AI will give hints on diet, sleep, etc. to improve health. For example, it will provide advice such as "drink plenty of fluids," "try to eat a nutritionally balanced diet," and "get enough sleep." This system allows users to know the appropriate medical department before going to the hospital, and avoid unnecessary visits. In addition, even if medical treatment is not necessary, users can receive specific advice to improve their health, allowing them to manage their health with peace of mind. This allows the health management system to know the appropriate medical department before going to the hospital, and avoid unnecessary visits. In addition, even if medical treatment is not necessary, users can receive specific advice to improve their health, allowing them to manage their health with peace of mind.

[0068] The health management system according to the embodiment includes a reception unit, an analysis unit, a specification unit, a provision unit, and an advice unit. The reception unit provides an interface for the user to input his / her current symptoms and health condition. For example, the reception unit can accept text input or voice input. The user can input specific symptoms such as "I have a headache," "I have a sore throat," and "I have a fever." The analysis unit uses a generation AI to analyze the information input by the reception unit. The generation AI analyzes the input symptoms based on past medical data and case data. For example, the generation AI uses a text generation AI (for example, LLM) to analyze the input symptoms and generate information for identifying an appropriate medical department. The specification unit uses the generation AI to specify a medical department based on the information analyzed by the analysis unit. The generation AI specifies the most appropriate medical department based on the analyzed information. For example, for information such as "I have a headache," a neurology department or an internal medicine department is suggested. The provision unit searches for the nearest medical institution based on the user's current location information and provides the information. The providing unit, for example, identifies the user's current location using GPS data or an IP address, and searches for the nearest medical institution. For example, it provides information such as "The nearest hospital with a neurology department is XX Hospital." The advising unit provides specific advice on diet, sleep, etc. for physical recovery when a simplified judgment is made that medical treatment is not necessary. The advising unit provides advice such as "Drink enough water," "Try to eat a nutritionally balanced diet," and "Get enough sleep." In this way, the physical condition management system according to the embodiment allows the user to know the appropriate medical department before going to the hospital, and to avoid unnecessary medical examinations. In addition, even if medical treatment is not necessary, the user can receive specific advice for physical recovery, so that he or she can manage his or her physical condition with peace of mind.

[0069] The reception unit provides an interface for the user to input his / her current symptoms and physical condition. For example, the reception unit can accept text input or voice input. The user can input specific symptoms such as "I have a headache," "I have a sore throat," and "I have a fever." Specifically, the reception unit is provided as an application for a smartphone or a computer, and is designed to be easily accessible by the user. In the case of text input, the user inputs symptoms using a keyboard, and in the case of voice input, the system automatically converts the voice into text by simply speaking the symptoms through a microphone. In addition, the reception unit also has a function to store the information entered by the user and to enable the user to refer to the history of past symptoms and physical condition. This allows the user to check past changes in physical condition and use it when consulting a doctor. In addition, the reception unit also takes security measures to encrypt and store the information entered by the user and protect privacy. This allows the user to input his / her symptoms and physical condition with peace of mind.

[0070] The analysis unit uses the generation AI to analyze the information entered by the reception unit. The generation AI analyzes the entered symptoms based on past medical data and case data. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the entered symptoms and generate information to identify the appropriate medical department. Specifically, the generation AI uses natural language processing technology to analyze the text and voice data entered by the user and extracts symptom patterns and associations. For example, in response to the input "I have a headache," the generation AI refers to past case data and identifies multiple factors that may be the cause of the headache (e.g., tension headache, migraine, brain tumor, etc.). In addition, the generation AI also takes into account personal information such as the user's age, gender, and medical history, and performs analysis to provide more accurate diagnostic information. Based on this information, the analysis unit can suggest specific medical departments and the types of tests required to the user. This allows the user to obtain information to select an appropriate medical institution for their symptoms.

[0071] The identification unit uses the generation AI to identify a medical department based on the information analyzed by the analysis unit. The generation AI identifies the most appropriate medical department based on the analyzed information. For example, for the information "I have a headache," it suggests neurology or internal medicine. Specifically, the identification unit selects the most appropriate medical department based on the symptom data analyzed by the generation AI, taking into account the expertise and treatment track record of each medical department. For example, if there is a headache symptom, past medical data and medical guidelines are referred to to determine whether neurology or internal medicine is appropriate. In addition, the identification unit can also suggest multiple medical departments if the user's symptoms span multiple medical departments. For example, if the symptoms "sore throat" and "fever" are entered at the same time, both otolaryngology and internal medicine are suggested. This allows the user to obtain information to select the most appropriate medical department for their symptoms, and avoid unnecessary visits to the doctor.

[0072] The providing unit searches for the nearest medical institution based on the user's current location information and provides the information. The providing unit, for example, uses GPS data or an IP address to identify the user's current location and searches for the nearest medical institution. For example, information such as "The nearest hospital with a neurology department is XX Hospital" is provided. Specifically, the providing unit accurately identifies the current location using the location information service of the user's smartphone or computer. Furthermore, the providing unit cooperates with a database of medical institutions to obtain detailed information such as the medical department, consultation hours, and access method of each medical institution. This allows the user to quickly find the nearest medical institution that corresponds to his or her symptoms. The providing unit also provides route guidance to the medical institution selected by the user. For example, the providing unit cooperates with a map app to display the optimal route by foot, public transportation, or car. This allows the user to arrive at the medical institution without getting lost. Furthermore, the providing unit also provides information such as the congestion status of the medical institution and the presence or absence of reservations, thereby supporting the user to receive treatment smoothly.

[0073] When the advice unit determines that medical treatment is not necessary, it provides specific advice on diet, sleep, etc., to improve physical condition. For example, the advice unit provides advice such as "take sufficient fluids," "try to eat a nutritionally balanced diet," and "get sufficient sleep." Specifically, the advice unit uses a generation AI to generate customized advice according to the user's symptoms and physical condition. For example, if the user has mild cold symptoms, it provides specific advice such as "eat foods that are rich in vitamin C" and "drink warm drinks." In addition, the advice unit also provides advice that is useful for long-term health management, taking into account the user's lifestyle habits and past health data. For example, it provides advice such as "try to exercise regularly" and "try relaxation methods to reduce stress." In addition, the advice unit also has a function to feed back the results of the user's practice of the advice, and continuously supports the user's improvement of his or her health condition. This allows the user to manage his or her physical condition with peace of mind by receiving specific advice, even if medical treatment is not necessary.

[0074] The analysis unit can analyze symptoms based on past specific medical data and case data. The analysis unit, for example, analyzes symptoms using past electronic medical record data and medical records. For example, the analysis unit collects past electronic medical record data and analyzes symptom patterns. The analysis unit can also analyze the severity and frequency of symptoms based on past medical records. For example, the analysis unit analyzes past medical records and evaluates the severity and frequency of symptoms. This enables the analysis unit to more accurately identify a medical department based on past medical data and case data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past electronic medical record data to the generation AI and have the generation AI execute symptom analysis.

[0075] The identification unit can identify an appropriate medical department based on the analyzed information. The identification unit, for example, identifies the most appropriate medical department based on the analyzed information. For example, the identification unit suggests a neurology department or an internal medicine department for the information "having a headache." The identification unit can also suggest an otolaryngology department or an internal medicine department for the information "having a sore throat." Furthermore, the identification unit can also suggest an internal medicine department or a pediatrics department for the information "having a fever." In this way, the identification unit enables the user to visit an appropriate medical department based on the analyzed information. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the identification unit can input the analyzed information to the generation AI and cause the generation AI to identify the medical department.

[0076] The providing unit can search for the nearest medical institution based on the user's current location information and provide the information. The providing unit can, for example, use GPS data or an IP address to identify the user's current location and search for the nearest medical institution. For example, the providing unit can identify the user's current location based on GPS data and search for the nearest medical institution. The providing unit can also identify the user's current location based on an IP address and search for the nearest medical institution. Furthermore, the providing unit can provide information on the nearest medical institution based on the user's current location information. For example, information such as "the nearest hospital with a neurology department is XX Hospital" is provided. This allows the providing unit to enable the user to quickly find a medical institution. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generating AI, or may be performed without using a generating AI. For example, the providing unit can input the user's current location information to the generating AI and cause the generating AI to search for the nearest medical institution.

[0077] The advice unit can provide advice on specific diet and sleep for physical recovery. The advice unit provides advice such as, for example, "take sufficient water," "try to eat a nutritionally balanced diet," and "get sufficient sleep." For example, the advice unit recommends the user to take 2 liters of water per day. The advice unit can also recommend the user to eat a balanced diet that includes a lot of vegetables and fruits. Furthermore, the advice unit can also recommend the user to sleep 7 to 8 hours every night. In this way, the advice unit enables the user to properly manage his or her physical condition. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's physical condition information into the generation AI and cause the generation AI to generate advice for physical recovery.

[0078] The reception unit can estimate the user's emotion and adjust the symptom input interface based on the estimated user's emotion. For example, when the user is stressed, the reception unit provides a simple and intuitive interface and minimizes input procedures. In addition, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to input symptoms quickly. In this way, the reception unit enables the user to input symptoms without stress. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0079] The reception unit can analyze the user's past symptom input history and suggest the optimal input method. For example, the reception unit automatically displays symptoms that the user frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest symptoms to be input in a specific time period from the user's past input history. This allows the reception unit to suggest the optimal input method for the user. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past input data to the generation AI and have the generation AI execute a suggestion of the optimal input method.

[0080] The reception unit can filter the input contents based on the user's current activity status and environment when inputting symptoms. For example, when the user is exercising, the reception unit preferentially inputs symptoms related to exercise. In addition, when the user is at work, the reception unit can also preferentially input symptoms of stress and fatigue related to work. Furthermore, when the user is at home, the reception unit can also preferentially input symptoms related to the home environment. This allows the reception unit to filter the input contents according to the user's activity status and environment. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's activity log and environmental sensor data to the generation AI and cause the generation AI to filter the input contents.

[0081] The reception unit can estimate the user's emotion and determine the priority of symptoms to be input based on the estimated user's emotion. For example, when the user feels anxious, the reception unit inputs serious symptoms with priority. In addition, the reception unit can also prompt the user to input detailed symptoms when the user is relaxed. Furthermore, the reception unit can input only major symptoms when the user is in a hurry. In this way, the reception unit can determine the priority of symptoms according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0082] The reception unit can preferentially input highly relevant symptoms in consideration of the geographical location information of the user when inputting symptoms. For example, when the user is at high altitude, the reception unit preferentially inputs symptoms related to altitude sickness. In addition, when the user is in an urban area, the reception unit can also preferentially input stress and allergy symptoms specific to the city. Furthermore, when the user is at the seaside, the reception unit can also preferentially input symptoms related to sunburn and heat stroke. This allows the reception unit to preferentially input highly relevant symptoms in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the priority order of highly relevant symptoms.

[0083] The reception unit can analyze the social media activity of the user and input related symptoms when inputting symptoms. For example, if the user frequently mentions "headache" on social media, the reception unit preferentially inputs symptoms related to headache. In addition, if the user frequently mentions "fatigue" on social media, the reception unit can also preferentially input symptoms related to fatigue. In addition, if the user frequently mentions "stress" on social media, the reception unit can also preferentially input symptoms related to stress. In this way, the reception unit can preferentially input symptoms related to the user by analyzing social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the social media data of the user to the generation AI and cause the generation AI to execute input of related symptoms.

[0084] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. For example, when the user feels anxious, the analysis unit quickly performs an analysis and provides a result. In addition, when the user is relaxed, the analysis unit can also perform a detailed analysis and provide a highly accurate result. Furthermore, when the user is in a hurry, the analysis unit can also perform an analysis by focusing on the main symptoms. This allows the analysis unit to adjust the analysis algorithm according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the symptom during the analysis. For example, in the case of a serious symptom, the analysis unit performs a detailed analysis to provide a highly accurate result. In addition, in the case of a mild symptom, the analysis unit can perform a simple analysis to provide a quick result. Furthermore, in the case of multiple symptoms, the analysis unit can also prioritize the analysis of a symptom with a high importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the symptom. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input symptom importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the category of the symptom during analysis. For example, in the case of a respiratory symptom, the analysis unit applies an analysis algorithm specialized for the respiratory system. In addition, in the case of a digestive symptom, the analysis unit can also apply an analysis algorithm specialized for the digestive system. Furthermore, in the case of a nervous symptom, the analysis unit can also apply an analysis algorithm specialized for the nervous system. This allows the analysis unit to apply different analysis algorithms depending on the category of the symptom. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input symptom category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0087] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user feels anxious, the analysis unit provides a simple and easy-to-understand display method. Furthermore, when the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This allows the analysis unit to adjust the display method of the analysis result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0088] During analysis, the analysis unit can determine the priority of analysis based on the time of symptom submission. For example, the analysis unit preferentially analyzes symptoms that have been submitted recently. The analysis unit can also analyze symptoms that have been submitted over a long period of time in detail. Furthermore, the analysis unit can also preferentially analyze symptoms with high urgency based on the time of submission. This allows the analysis unit to determine the priority of analysis based on the time of symptom submission. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input symptom submission time data to the generation AI and have the generation AI execute the determination of the analysis priority.

[0089] The analysis unit can adjust the order of analysis based on the relevance of symptoms during analysis. For example, the analysis unit prioritizes analysis of highly related symptoms. The analysis unit can also analyze less related symptoms later. Furthermore, when multiple symptoms are related, the analysis unit can analyze them in order of relevance. This allows the analysis unit to adjust the order of analysis based on the relevance of symptoms. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input symptom relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0090] The identification unit can estimate the user's emotion and adjust the identification criteria of the medical department based on the estimated user's emotion. For example, when the user feels anxious, the identification unit quickly identifies the medical department. Also, when the user is relaxed, the identification unit can identify the medical department based on detailed information. Furthermore, when the user is in a hurry, the identification unit can identify the medical department based on the main symptoms. This allows the identification unit to adjust the identification criteria of the medical department according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the emotion estimation.

[0091] The identification unit can improve the accuracy of identifying the medical department by considering the interrelationships of the symptoms at the time of identification. For example, when multiple symptoms are related, the identification unit identifies the medical department by considering the interrelationships. The identification unit can also analyze the interrelationships of the symptoms and identify the optimal medical department. Furthermore, the identification unit can improve the accuracy of identifying the medical department based on the interrelationships of the symptoms. In this way, the identification unit can improve the accuracy of identifying the medical department by considering the interrelationships of the symptoms. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input symptom interrelationship data to the generation AI and cause the generation AI to identify the medical department.

[0092] At the time of identification, the identification unit can identify the medical department by considering attribute information of the person who submitted the symptoms. The identification unit can identify an appropriate medical department by considering, for example, the age of the person who submitted the symptoms. The identification unit can also identify an appropriate medical department by considering the gender of the person who submitted the symptoms. Furthermore, the identification unit can also identify an appropriate medical department by considering the medical history of the person who submitted the symptoms. In this way, the identification unit can identify a more appropriate medical department by considering the attribute information of the person who submitted the symptoms. A part or all of the above-mentioned processing in the identification unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the identification unit can input the attribute information of the person who submitted the symptoms to the generation AI and cause the generation AI to identify the medical department.

[0093] The identification unit can estimate the user's emotion and adjust the order of displaying the identified results of the medical department based on the estimated emotion of the user. For example, when the user feels anxious, the identification unit first displays the most relevant medical department. Also, when the user is relaxed, the identification unit can sequentially display medical departments including detailed information. Furthermore, when the user is in a hurry, the identification unit can display the main medical department first. This allows the identification unit to adjust the order of displaying the identified results of the medical department according to the emotion of the user. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the emotion.

[0094] The identification unit can identify the medical department in consideration of the geographical distribution of the symptoms at the time of identification. The identification unit can identify the medical department based on, for example, symptoms that occur frequently in a specific geographical area. The identification unit can also analyze the geographical distribution and identify the most suitable medical department. Furthermore, the identification unit can improve the accuracy of identifying the medical department based on the geographical distribution. In this way, the identification unit can identify the medical department corresponding to the symptoms specific to the area by considering the geographical distribution of the symptoms. A part or all of the above-mentioned processing in the identification unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the identification unit can input the geographical distribution data of the symptoms to the generation AI and cause the generation AI to identify the medical department.

[0095] The identification unit can improve the accuracy of identifying the medical department by referring to literature related to the symptoms at the time of identification. The identification unit, for example, refers to related literature and identifies the most suitable medical department. The identification unit can also identify the medical department based on the latest research results related to the symptoms. Furthermore, the identification unit can analyze related literature and improve the accuracy of identifying the medical department. As a result, the identification unit can improve the accuracy of identifying the medical department by referring to related literature. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the literature data related to the symptoms to the generation AI and cause the generation AI to identify the medical department.

[0096] The providing unit can estimate the user's emotion and adjust the method of providing medical institution information based on the estimated user's emotion. For example, when the user feels anxious, the providing unit provides simple and easy-to-understand information. In addition, the providing unit can also provide detailed information when the user is relaxed. Furthermore, when the user is in a hurry, the providing unit can quickly provide only the main information. This allows the providing unit to adjust the method of providing medical institution information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0097] The providing unit can provide optimal medical institution information by referring to the user's past medical institution use history at the time of providing. The providing unit provides optimal medical institution information based on, for example, medical institutions used by the user in the past. The providing unit can also provide highly reliable medical institutions preferentially based on the user's past medical institution use history. Furthermore, the providing unit can analyze the user's past medical institution use history and provide the most suitable medical institution information. As a result, the providing unit can provide optimal medical institution information for the user by referring to the past medical institution use history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the providing unit can input the user's past medical institution use history data to the generating AI and cause the generating AI to provide optimal medical institution information.

[0098] The providing unit can customize the medical institution information based on the user's current living situation at the time of providing. For example, when the user is at work, the providing unit provides medical institution information close to the workplace. In addition, when the user is at home, the providing unit can also provide medical institution information close to the home. Furthermore, when the user is traveling, the providing unit can also provide medical institution information close to the travel destination. This allows the providing unit to customize the medical institution information according to the user's living situation. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's living situation data into the generation AI and cause the generation AI to customize the medical institution information.

[0099] The providing unit can estimate the user's emotion and determine the priority of medical institution information based on the estimated user's emotion. For example, when the user feels anxious, the providing unit preferentially provides highly reliable medical institution information. In addition, the providing unit can also provide detailed medical institution information when the user is relaxed. Furthermore, when the user is in a hurry, the providing unit can quickly provide only major medical institution information. This allows the providing unit to determine the priority of medical institution information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0100] The providing unit can provide optimal medical institution information by considering the geographical location information of the user at the time of providing. The providing unit provides, for example, medical institution information closest to the user's current location. The providing unit can also provide easily accessible medical institution information based on the user's geographical location information. Furthermore, the providing unit can provide medical institution information according to a means of transportation by considering the user's geographical location information. In this way, the providing unit can provide easily accessible medical institution information by considering the user's geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generating AI, or may be performed without using a generating AI. For example, the providing unit can input the user's geographical location information to the generating AI and cause the generating AI to provide optimal medical institution information.

[0101] The providing unit can provide medical institution information by analyzing the social media activity of the user at the time of providing. The providing unit provides, for example, medical institution information mentioned by the user on social media. The providing unit can also provide reliable medical institution information from the user's social media activity. Furthermore, the providing unit can analyze the user's social media activity and provide the most suitable medical institution information. As a result, the providing unit can provide medical institution information that is highly relevant to the user by analyzing the social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the providing unit can input the user's social media data to the generating AI and cause the generating AI to provide the medical institution information.

[0102] The advice unit can estimate the user's emotion and adjust the content of the advice based on the estimated user's emotion. For example, when the user feels anxious, the advice unit provides advice that gives a sense of security. In addition, when the user is relaxed, the advice unit can also provide detailed advice. Furthermore, when the user is in a hurry, the advice unit can also provide concise advice that focuses on the main points. This allows the advice unit to adjust the content of the advice according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0103] The advice unit can provide optimal advice by referring to the user's past health history when giving advice. The advice unit can provide optimal diet and sleep advice based on the user's past health history, for example. The advice unit can also provide advice for a specific symptom based on the user's past health history. Furthermore, the advice unit can analyze the user's past health history and provide the most effective advice. In this way, the advice unit can provide optimal advice for the user by referring to the past health history. A part or all of the above-mentioned processing in the advice unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the advice unit can input the user's past health history data into the generation AI and cause the generation AI to provide optimal advice.

[0104] The advice unit can customize the means of advice based on the user's current living situation when giving advice. For example, when the user is at work, the advice unit provides advice that can be practiced at the workplace. In addition, when the user is at home, the advice unit can also provide advice that can be practiced at home. Furthermore, when the user is traveling, the advice unit can also provide advice that can be practiced at the travel destination. This allows the advice unit to customize the means of advice according to the user's living situation. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's living situation data into the generation AI and cause the generation AI to customize the means of advice.

[0105] The advice unit can estimate the user's emotion and determine the priority of advice based on the estimated user's emotion. For example, when the user feels anxious, the advice unit preferentially provides advice that gives a sense of security. In addition, the advice unit can also provide detailed advice when the user is relaxed. Furthermore, when the user is in a hurry, the advice unit can also preferentially provide concise advice that focuses on the main points. This allows the advice unit to determine the priority of advice according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0106] The advice unit can provide optimal advice by taking into account the geographical location information of the user when providing advice. For example, when the user is at high altitude, the advice unit provides advice related to altitude sickness. Furthermore, when the user is in an urban area, the advice unit can also provide advice related to stress and allergies specific to the city. Furthermore, when the user is at the seaside, the advice unit can also provide advice related to sunburn and heat stroke. In this way, the advice unit can provide advice corresponding to symptoms specific to the region by taking into account the geographical location information of the user. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the geographical location information of the user to the generation AI and cause the generation AI to provide optimal advice.

[0107] When providing advice, the advice unit can analyze the user's social media activity and suggest a means of advice. For example, if the user frequently mentions "fatigue" on social media, the advice unit can provide advice related to fatigue recovery. In addition, if the user frequently mentions "stress" on social media, the advice unit can also provide advice related to stress relief. In addition, if the user frequently mentions "insomnia" on social media, the advice unit can also provide advice related to sleep improvement. In this way, the advice unit can provide advice that is highly relevant to the user by analyzing social media activity. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's social media data into the generation AI and cause the generation AI to execute the suggestion of a means of advice.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The reception unit can analyze the user's past symptom input history and suggest the optimal input method. For example, symptoms that the user frequently input in the past are automatically displayed as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest symptoms to be input in a specific time period from the user's past input history. This allows the reception unit to suggest the optimal input method for the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's past input data to the generation AI and have the generation AI execute the suggestion of the optimal input method.

[0110] The analysis unit can adjust the level of detail of the analysis based on the importance of the symptoms during the analysis. For example, in the case of a serious symptom, a detailed analysis is performed to provide a highly accurate result. In addition, in the case of a mild symptom, the analysis unit can also perform a simple analysis to provide a quick result. Furthermore, in the case of multiple symptoms, the analysis unit can also prioritize the analysis of symptoms with a high importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the symptoms. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input symptom importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0111] The identification unit can improve the accuracy of identifying the medical department by considering the interrelationships of the symptoms at the time of identification. For example, when multiple symptoms are related, the identification unit can identify the medical department by considering the interrelationships. The identification unit can also analyze the interrelationships of the symptoms and identify the most suitable medical department. Furthermore, the identification unit can improve the accuracy of identifying the medical department based on the interrelationships of the symptoms. In this way, the identification unit can improve the accuracy of identifying the medical department by considering the interrelationships of the symptoms. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input symptom interrelationship data to the generation AI and cause the generation AI to identify the medical department.

[0112] The providing unit can provide optimal medical institution information by referring to the user's past medical institution use history at the time of providing. For example, the providing unit provides optimal medical institution information based on medical institutions used by the user in the past. The providing unit can also provide highly reliable medical institutions preferentially based on the user's past medical institution use history. Furthermore, the providing unit can analyze the user's past medical institution use history and provide the most suitable medical institution information. As a result, the providing unit can provide optimal medical institution information for the user by referring to the past medical institution use history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past medical institution use history data to the generation AI and cause the generation AI to provide optimal medical institution information.

[0113] When giving advice, the advice unit can provide optimal advice by referring to the user's past health history. For example, the advice unit can provide optimal diet and sleep advice based on the user's past health history. The advice unit can also provide advice for a specific symptom based on the user's past health history. Furthermore, the advice unit can analyze the user's past health history and provide the most effective advice. In this way, the advice unit can provide optimal advice for the user by referring to the past health history. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's past health history data into the generation AI and cause the generation AI to provide optimal advice.

[0114] The reception unit can estimate the user's emotion and adjust the symptom input interface based on the estimated user's emotion. For example, when the user is stressed, a simple and intuitive interface is provided to minimize the input procedure. In addition, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to input symptoms quickly. In this way, the reception unit enables the user to input symptoms without stress. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0115] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. For example, when the user feels anxious, the analysis unit can quickly perform an analysis and provide a result. In addition, when the user is relaxed, the analysis unit can also perform a detailed analysis and provide a highly accurate result. Furthermore, when the user is in a hurry, the analysis unit can also perform an analysis by focusing on the main symptoms. This allows the analysis unit to adjust the analysis algorithm according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the emotion estimation.

[0116] The identification unit can estimate the user's emotion and adjust the identification criteria of the medical department based on the estimated user's emotion. For example, when the user feels anxious, the identification unit quickly identifies the medical department. In addition, when the user is relaxed, the identification unit can also identify the medical department based on the main symptoms when the user is in a hurry. This allows the identification unit to adjust the identification criteria of the medical department according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the emotion estimation.

[0117] The providing unit can estimate the user's emotion and adjust the method of providing medical institution information based on the estimated user's emotion. For example, when the user feels anxious, simple and easy-to-understand information is provided. In addition, the providing unit can also provide detailed information when the user is relaxed. Furthermore, when the user is in a hurry, the providing unit can quickly provide only the main information. This allows the providing unit to adjust the method of providing medical institution information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0118] The advice unit can estimate the user's emotion and adjust the content of the advice based on the estimated user's emotion. For example, if the user feels anxious, the advice unit provides advice that gives a sense of security. In addition, the advice unit can provide detailed advice when the user is relaxed. Furthermore, the advice unit can provide concise and to the point advice when the user is in a hurry. This allows the advice unit to adjust the content of the advice according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0119] The process flow of the second embodiment will be briefly described below.

[0120] Step 1: The reception unit provides an interface for the user to input their current symptoms and physical condition. For example, the reception unit can accept text input or voice input. The user can input specific symptoms such as "I have a headache," "I have a sore throat," and "I have a fever." Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The generation AI analyzes the entered symptoms based on past medical data and case data. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the entered symptoms and generate information to identify the appropriate medical department. Step 3: The identification unit uses the generation AI to identify a medical specialty based on the information analyzed by the analysis unit. The generation AI identifies the most appropriate medical specialty based on the analyzed information. For example, for the information "I have a headache," it would suggest a neurologist or an internal medicine specialist. Step 4: The providing unit searches for the nearest medical institution based on the user's current location information and provides the information. The providing unit identifies the user's current location using, for example, GPS data or an IP address, and searches for the nearest medical institution. For example, it provides information such as "The nearest hospital with a neurology department is X Hospital." Step 5: If the advice unit makes a simplified judgment that medical treatment is not necessary, it will provide specific advice on diet, sleep, etc. to help the patient recover. For example, the advice unit will provide advice such as "drink plenty of fluids," "try to eat a nutritionally balanced diet," and "get enough sleep."

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0123] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements including the above-mentioned reception unit, analysis unit, specification unit, provision unit, and advice unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and the user can input text or voice. The analysis unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The specification unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and specifies a medical department based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and searches for the nearest medical institution based on the user's current location information and provides the information. The advice unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and provides specific advice for recovery of physical condition when there is no need for medical treatment. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0132] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements including the above-mentioned reception unit, analysis unit, specification unit, provision unit, and advice unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and the user can input text or voice. The analysis unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The specification unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and specifies a medical department based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and searches for the nearest medical institution based on the user's current location information and provides the information. The advice unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and provides specific advice for recovery of physical condition when there is no need for medical treatment. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0148] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements including the above-mentioned reception unit, analysis unit, specification unit, provision unit, and advice unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and the user can input text or voice. The analysis unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The specification unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and specifies a medical department based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and searches for the nearest medical institution based on the user's current location information and provides the information. The advice unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and provides specific advice for recovery of physical condition when there is no need for medical treatment. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0165] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0168] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] Each of the multiple elements including the reception unit, analysis unit, specification unit, provision unit, and advice unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and the user can input text or voice. The analysis unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The specification unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and specifies a medical department based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the robot 414, and searches for the nearest medical institution based on the user's current location information and provides the information. The advice unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and provides specific advice for recovery of physical condition when there is no need for medical treatment. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0175] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0177] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0179] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0182] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0183] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0184] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0185] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0186] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0187] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0188] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0190] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0192] (Appendix 1) A reception section for inputting symptoms; an analysis unit that analyzes the information input by the reception unit; An identification unit that identifies a medical department based on the information analyzed by the analysis unit; a providing unit that provides information on nearby medical institutions having the medical department identified by the identifying unit; An advice unit that provides specific advice on how to recover from physical condition when medical treatment is not necessary. A system characterized by: (Appendix 2) The analysis unit is Analyzing symptoms based on specific past medical data and case data 2. The system of claim 1, (Appendix 3) The identification unit is Identify the appropriate medical department based on analyzed information 2. The system of claim 1, (Appendix 4) The providing unit is Search for the nearest medical institution based on the user's current location and provide that information 2. The system of claim 1, (Appendix 5) The advice unit is Providing specific dietary and sleep advice to help you recover 2. The system of claim 1, (Appendix 6) The reception unit is Estimating a user's emotion and adjusting a symptom input interface based on the estimated user's emotion 2. The system of claim 1, (Appendix 7) The reception unit is Analyzes the user's symptom input history and suggests the optimal input method 2. The system of claim 1, (Appendix 8) The reception unit is When entering symptoms, filter input based on the user's current activity and environment 2. The system of claim 1, (Appendix 9) The reception unit is Estimate the user's emotions and prioritize the symptoms to be input based on the estimated user's emotions. 2. The system of claim 1, (Appendix 10) The reception unit is When entering symptoms, the system takes into account the user's geographic location information and prioritizes the most relevant symptoms. 2. The system of claim 1, (Appendix 11) The reception unit is When a symptom is entered, the system analyzes the user's social media activity and enters related symptoms. 2. The system of claim 1, (Appendix 12) The analysis unit is Inferring user emotions and adjusting analysis algorithms based on the inferred user emotions 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, adjust the level of detail based on the severity of symptoms 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, different analysis algorithms are applied depending on the symptom category. 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, analysis prioritization will be based on when symptoms were submitted. 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, adjust the order of analysis based on symptom relevance 2. The system of claim 1, (Appendix 18) The identification unit is Inferring a user's emotion and adjusting a specific criterion for a medical department based on the estimated user's emotion 2. The system of claim 1, (Appendix 19) The identification unit is When identifying, improve the accuracy of identifying medical departments by considering the correlation of symptoms. 2. The system of claim 1, (Appendix 20) The identification unit is When identifying a patient, the medical department is identified taking into consideration the attribute information of the person submitting the symptoms. 2. The system of claim 1, (Appendix 21) The identification unit is Inferring user emotion and adjusting the order in which specific medical department results are displayed based on the estimated user emotion 2. The system of claim 1, (Appendix 22) The identification unit is During identification, the medical department is identified taking into account the geographical distribution of symptoms. 2. The system of claim 1, (Appendix 23) The identification unit is When identifying symptoms, refer to related literature to improve the accuracy of identifying medical departments. 2. The system of claim 1, (Appendix 24) The providing unit is To estimate a user's emotion and adjust a method of providing medical institution information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 25) The providing unit is When providing information, the system refers to the user's past medical institution usage history to provide the most suitable medical institution information. 2. The system of claim 1, (Appendix 26) The providing unit is At the time of delivery, customize medical information based on the user's current living situation 2. The system of claim 1, (Appendix 27) The providing unit is Estimating user emotions and prioritizing medical institution information based on the estimated user emotions 2. The system of claim 1, (Appendix 28) The providing unit is When providing information, the system provides optimal medical institution information by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 29) The providing unit is At the time of provision, the user's social media activity is analyzed to provide medical institution information 2. The system of claim 1, (Appendix 30) The advice unit, Estimate the user's emotions and adjust the advice content based on the estimated user emotions 2. The system of claim 1, (Appendix 31) The advice unit, When providing advice, the system refers to the user's past health history to provide optimal advice. 2. The system of claim 1, (Appendix 32) The advice unit, When giving advice, customize the advice vehicle based on the user's current life situation 2. The system of claim 1, (Appendix 33) The advice unit, Estimate the user's emotions and prioritize advice based on the estimated user emotions. 2. The system of claim 1, (Appendix 34) The advice unit, When providing advice, the system takes into account the user's geographic location to provide optimal advice. 2. The system of claim 1, (Appendix 35) The advice unit, When providing advice, the system analyzes the user's social media activity to suggest advice measures. 2. The system of claim 1, [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception section for inputting symptoms; an analysis unit that analyzes the information input by the reception unit; An identification unit that identifies a medical department based on the information analyzed by the analysis unit; a providing unit that provides information on nearby medical institutions having the medical department identified by the identifying unit; An advice unit that provides specific advice on how to recover from physical condition when medical treatment is not necessary. A system characterized by:

2. The analysis unit is Analyzing symptoms based on specific past medical data and case data 2. The system of claim 1 .

3. The providing unit is Search for the nearest medical institution based on the user's current location and provide that information 2. The system of claim 1 .

4. The advice unit is Providing specific dietary and sleep advice to help you recover 2. The system of claim 1 .

5. The reception unit is Estimating a user's emotion and adjusting a symptom input interface based on the estimated user's emotion 2. The system of claim 1 .

6. The reception unit is Analyzes the user's symptom input history and suggests the optimal input method 2. The system of claim 1 .

7. The reception unit is When entering symptoms, filter input based on the user's current activity and environment 2. The system of claim 1 .

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