system

Abstract

The system according to the embodiment aims to efficiently match patients with the most suitable doctor based on their symptoms. [Solution] A system according to an embodiment includes a data upload unit, a data search unit, and a recommendation unit. The data upload unit uploads detailed data on a patient's symptoms. The data search unit searches the data uploaded by the data upload unit. The recommendation unit recommends a medical examination based on the data searched by the data search unit.

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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that the process of finding the doctor best suited to a patient's symptoms is complicated, making it difficult to achieve efficient matching.

[0005] The system according to the embodiment aims to efficiently match patients with the most suitable doctor based on their symptoms. [Means for solving the problem]

[0006] The system according to the embodiment includes a data upload unit, a data search unit, and a recommendation unit. The data upload unit uploads detailed data on a patient's symptoms. The data search unit searches the data uploaded by the data upload unit. The recommendation unit recommends a medical examination based on the data searched by the data search unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently match the most suitable doctor based on the patient's symptoms. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A matching system according to an embodiment of the present invention accumulates data on the "detailed symptoms of illness" experienced by patients and identifies doctors who can treat them. In this system, patients upload details of their symptoms to the system, doctors search the data, and recommend appropriate consultations. Patients can also search for detailed data on symptoms treated by doctors and contact them for consultations. Furthermore, doctors can search the careers and medical history of other doctors to gain knowledge. This system efficiently matches patients and doctors, enabling appropriate medical care to be provided. First, patients upload details of their symptoms to the system. For example, they enter detailed descriptions of symptoms such as fever, cough, and body aches into the system. This data is stored in the system. Next, doctors search the data. The doctors search the patient's symptom data from the system's database and recommend appropriate consultations. For example, doctors with specialized knowledge of specific symptoms are displayed in the search results. Patients can also search for detailed data on symptoms treated by doctors. Patients can search for detailed data on symptoms treated by doctors from the system's database and contact doctors for consultations. For example, information about doctors who have treated a specific symptom in the past is displayed. Furthermore, doctors can search the careers and medical history of other doctors to absorb knowledge. Doctors can search the careers and medical history of other doctors from the system's database to improve their own knowledge. For example, they can learn what kind of treatment was given for a specific symptom. This system efficiently matches patients with doctors, enabling appropriate medical care to be provided. This allows the matching system to accumulate detailed data on the patient's symptoms and identify the appropriate doctor.

[0029] The matching system according to the embodiment includes a data upload unit, a data search unit, and a recommendation unit. The data upload unit allows a patient to upload detailed data about their symptoms. The detailed data about the patient's symptoms includes, but is not limited to, fever, cough, and body aches. The data upload unit provides, for example, an interface through which the patient logs in to the system and inputs detailed symptoms. The data upload unit also allows the patient to upload detailed symptoms via voice input or image data. For example, the patient can take a photo of their symptoms using a smartphone and upload it to the system. The data search unit searches the data uploaded by the data upload unit. The data search unit provides, for example, an interface through which a doctor logs in to the system and searches for patient symptom data. The data search unit has keyword search and filtering functions, allowing the doctor to quickly search for data related to a specific symptom. For example, when a doctor enters "fever," symptom data related to the patient's fever is displayed. The recommendation unit recommends a medical consultation based on the data searched by the data search unit. The recommendation unit has, for example, an algorithm for recommending an appropriate doctor based on the search results. The recommendation unit compares the patient's symptom data with the doctor's specialty and recommends the most suitable doctor. For example, if symptom data related to fever is searched, a doctor with specialized knowledge about fever is recommended. This allows the matching system according to the embodiment to accumulate detailed data on the patient's symptoms and identify the appropriate doctor.

[0030] The data search unit may include a contact unit that allows patients to search for detailed data on symptoms treated by a doctor. The contact unit, for example, provides an interface for patients to contact a doctor to see a doctor. The contact unit has functions such as email transmission, chat function, and telephone call, allowing patients to quickly contact a doctor. For example, a patient can view a doctor's profile within the system and inquire about a consultation by email. The contact unit may also provide a function that allows patients to directly chat with doctors with questions. For example, a patient can use the chat function within the system to ask a doctor questions about their symptoms in real time. The contact unit may also provide a function that allows patients to contact a doctor by telephone. For example, a patient can obtain a doctor's phone number within the system and make an inquiry directly by telephone. This allows patients to search for detailed data on symptoms treated by a doctor and contact a doctor to see a doctor.

[0031] The data search unit may include a knowledge absorption unit that enables doctors to search the careers and medical history of other doctors. The knowledge absorption unit, for example, provides an interface for doctors to search the careers and medical history of other doctors. The knowledge absorption unit has functions such as database search, literature review, and expert interviews, allowing doctors to efficiently absorb the knowledge of other doctors. For example, a doctor can search the careers and medical history of other doctors within the system to learn treatment methods for specific symptoms. The knowledge absorption unit also enables doctors to search the medical history of other doctors to learn what treatments were performed. For example, a doctor can search the medical history of specific symptoms within the system to learn successful and unsuccessful cases of treatment. Furthermore, the knowledge absorption unit may provide a function that enables doctors to directly interview other doctors. For example, a doctor can request an interview with another doctor within the system and directly absorb specialized knowledge. This allows doctors to search the careers and medical history of other doctors and absorb knowledge.

[0032] The data upload unit allows a patient to upload details of their symptoms to the system. Examples of details of a patient's symptoms include, but are not limited to, fever, cough, and body aches. The data upload unit, for example, provides an interface through which a patient logs in to the system and inputs details of their symptoms. The data upload unit also allows a patient to upload details of their symptoms by voice input or as image data. For example, a patient can take a photo of their symptoms using a smartphone and upload it to the system. This allows a patient to upload details of their symptoms to the system.

[0033] The recommendation unit can recommend an appropriate doctor based on the search results. The recommendation unit is equipped with, for example, an algorithm that recommends an appropriate doctor based on the search results. The recommendation unit compares the patient's symptom data with the doctor's specialty and recommends the most appropriate doctor. For example, if symptom data related to fever is searched, a doctor with specialized knowledge of fevers is recommended. The recommendation unit can also determine the priority of consultations based on the severity of the patient's symptoms and the patient's doctor visit history. For example, a doctor who can respond quickly is recommended for a patient with serious symptoms. Furthermore, the recommendation unit can make customized recommendations based on the patient's wishes and past consultation history. For example, the recommendation unit recommends the most appropriate doctor taking into account the evaluations and feedback of doctors the patient has visited in the past. This makes it possible to make appropriate consultation recommendations based on the search results.

[0034] The contact unit allows patients to contact doctors to see a doctor. For example, the contact unit provides an interface for patients to contact doctors to see a doctor. The contact unit has functions such as email transmission, chat function, and telephone call, allowing patients to quickly contact doctors. For example, a patient can view a doctor's profile within the system and inquire about seeing a doctor by email. The contact unit can also provide a function for patients to directly ask questions to doctors via chat. For example, a patient can use the chat function within the system to ask a doctor questions about their symptoms in real time. Furthermore, the contact unit can also provide a function for patients to contact doctors by telephone. For example, a patient can obtain a doctor's phone number within the system and make an inquiry directly by telephone. This allows patients to contact doctors to see a doctor.

[0035] The knowledge absorption unit allows doctors to search the careers and medical records of other doctors and absorb knowledge. For example, the knowledge absorption unit provides an interface for doctors to search the careers and medical records of other doctors. The knowledge absorption unit has functions such as database search, literature review, and expert interviews, allowing doctors to efficiently absorb knowledge from other doctors. For example, a doctor can search the careers of other doctors within the system to learn treatment methods for specific symptoms. The knowledge absorption unit also allows doctors to search the medical records of other doctors and learn what treatments were performed. For example, a doctor can search the medical records for specific symptoms within the system to learn about successful and unsuccessful treatments. Furthermore, the knowledge absorption unit can provide a function for doctors to directly interview other doctors. For example, a doctor can request an interview with another doctor within the system and directly absorb specialized knowledge. This allows doctors to search the careers and medical records of other doctors and absorb knowledge.

[0036] The data upload unit can analyze the patient's past symptom data and propose an optimal data input format. The data upload unit, for example, automatically proposes an optimal input format based on symptom data previously entered by the patient. The data upload unit can also prioritize displaying frequently entered items from the patient's past symptom data. For example, the data upload unit automatically proposes an optimal input format based on symptom data previously entered by the patient. The data upload unit can also analyze the patient's past symptom data and propose an optimal input format for a specific symptom. For example, the data upload unit can analyze the patient's past symptom data and propose an optimal input format for a specific symptom. This makes it possible to analyze the patient's past symptom data and propose an optimal data input format. Some or all of the above-described processing in the data upload unit may be performed using, for example, AI, or may be performed without using AI. For example, the data upload unit can input the patient's past symptom data to a generation AI and cause the generation AI to propose an optimal data input format.

[0037] The data upload unit can automatically customize input items based on the patient's current health condition when uploading symptom data. For example, when the patient inputs their current health condition, the data upload unit automatically customizes the necessary input items based on that information. The data upload unit can also monitor the patient's current health condition in real time and dynamically adjust the input items. For example, when the patient inputs their current health condition, the data upload unit automatically customizes the necessary input items based on that information. The data upload unit can also automatically change the priority of input items based on the patient's health condition. For example, the data upload unit automatically changes the priority of input items based on the patient's health condition. This makes it possible to automatically customize the input items based on the patient's current health condition. Some or all of the above-described processing in the data upload unit may be performed using, or without, AI. For example, the data upload unit can input the patient's health condition data to a generation AI and cause the generation AI to customize the input items.

[0038] When uploading symptom data, the data upload unit can prioritize input of highly relevant data taking into account the patient's geographical location information. For example, the data upload unit prioritizes input of data related to symptoms and illnesses specific to a region based on the patient's current location. The data upload unit can also automatically supplement information on local medical institutions taking into account the patient's geographical location information. For example, the data upload unit prioritizes input of data related to symptoms and illnesses specific to a region based on the patient's current location. The data upload unit can also prioritize input of data related to the local climate and environment based on the patient's geographical location information. For example, the data upload unit prioritizes input of data related to the local climate and environment based on the patient's geographical location information. This makes it possible to prioritize input of highly relevant data taking into account the patient's geographical location information. Some or all of the above-described processing in the data upload unit may be performed using, or without, AI. For example, the data upload unit can input the patient's geographical location information to the generation AI and cause the generation AI to prioritize input of highly relevant data.

[0039] The data upload unit can analyze the patient's social media activities when uploading symptom data and automatically complete the associated data. For example, the data upload unit analyzes the patient's social media posts and automatically completes information related to the symptoms. The data upload unit can also extract data related to health conditions from the patient's social media activities and complete input items. For example, the data upload unit analyzes the patient's social media posts and automatically completes information related to the symptoms. The data upload unit can also automatically complete trend information related to symptoms based on the patient's social media activities. For example, the data upload unit automatically completes trend information related to symptoms based on the patient's social media activities. This makes it possible to analyze the patient's social media activities and automatically complete associated data. Some or all of the above-described processing in the data upload unit may be performed using AI, for example, or may be performed without using AI. For example, the data upload unit can input the patient's social media data to a generation AI and cause the generation AI to complete the associated data.

[0040] During a search, the data search unit can adjust the priority of search results based on the importance of the symptom data. For example, the data search unit prioritizes display of symptom data with high importance. The data search unit can also postpone display of symptom data with low importance. For example, the data search unit prioritizes display of symptom data with high importance. The data search unit can also dynamically adjust the display order of search results based on the importance. For example, the data search unit dynamically adjusts the display order of search results based on the importance. This makes it possible to adjust the priority of search results based on the importance of the symptom data. Some or all of the above-described processing in the data search unit may be performed using AI, for example, or may be performed without using AI. For example, the data search unit can input the importance of symptom data to the generation AI and cause the generation AI to adjust the priority of the search results.

[0041] During a search, the data search unit can apply different search algorithms depending on the symptom category. For example, the data search unit applies a specialized search algorithm to respiratory system symptoms. Furthermore, the data search unit can apply a different search algorithm to digestive system symptoms. For example, the data search unit applies a specialized search algorithm to respiratory system symptoms. Furthermore, the data search unit can also apply yet another search algorithm to nervous system symptoms. For example, the data search unit applies yet another search algorithm to nervous system symptoms. This makes it possible to apply different search algorithms depending on the symptom category. Some or all of the above-described processing in the data search unit may be performed using AI, for example, or may be performed without using AI. For example, the data search unit can input the symptom category to the generation AI and cause the generation AI to apply the search algorithm.

[0042] During a search, the data search unit can provide optimal search results by referring to the patient's past search history. The data search unit, for example, prioritizes displaying highly relevant search results based on the patient's past search history. The data search unit can also prioritize displaying frequently searched items from the patient's past search history. For example, the data search unit prioritizes displaying highly relevant search results based on the patient's past search history. The data search unit can also analyze the patient's past search history to provide optimal search results. For example, the data search unit analyzes the patient's past search history and provides optimal search results. This makes it possible to provide optimal search results by referring to the patient's past search history. Some or all of the above-described processing in the data search unit may be performed using, for example, AI, or may be performed without using AI. For example, the data search unit can input the patient's past search history into the generation AI and cause the generation AI to provide optimal search results.

[0043] During a search, the data search unit can prioritize displaying highly relevant search results by taking into account the patient's geographical location information. For example, the data search unit can prioritize displaying search results related to symptoms or illnesses specific to a region based on the patient's current location. The data search unit can also prioritize displaying information about local medical institutions by taking into account the patient's geographical location information. For example, the data search unit can prioritize displaying search results related to symptoms or illnesses specific to a region based on the patient's current location. The data search unit can also prioritize displaying search results related to the local climate or environment based on the patient's geographical location information. For example, the data search unit can prioritize displaying search results related to the local climate or environment based on the patient's geographical location information. This makes it possible to prioritize displaying highly relevant search results by taking into account the patient's geographical location information. Some or all of the above-described processing in the data search unit may be performed using, or without, AI. For example, the data search unit can input the patient's geographical location information to the generation AI and cause the generation AI to prioritize displaying highly relevant search results.

[0044] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the symptom when making a recommendation. For example, the recommendation unit makes a detailed recommendation for a symptom with a high importance. Furthermore, the recommendation unit can make a concise recommendation for a symptom with a low importance. For example, the recommendation unit makes a detailed recommendation for a symptom with a high importance. Furthermore, the recommendation unit can dynamically adjust the level of detail of the recommendation based on the importance. For example, the recommendation unit dynamically adjusts the level of detail of the recommendation based on the importance. This makes it possible to adjust the level of detail of the recommendation based on the importance of the symptom. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the importance of the symptom to the generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0045] When making a recommendation, the recommendation unit can apply different recommendation algorithms depending on the symptom category. For example, the recommendation unit can apply a specialized recommendation algorithm to respiratory system symptoms. Furthermore, the recommendation unit can apply a different recommendation algorithm to digestive system symptoms. For example, the recommendation unit can apply a specialized recommendation algorithm to respiratory system symptoms. Furthermore, the recommendation unit can also apply yet another recommendation algorithm to nervous system symptoms. For example, the recommendation unit can apply yet another recommendation algorithm to nervous system symptoms. This makes it possible to apply different recommendation algorithms depending on the symptom category. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input the symptom category to the generation AI and cause the generation AI to apply the recommendation algorithm.

[0046] When making a recommendation, the recommendation unit can provide the optimal recommendation by referring to the patient's past medical history. The recommendation unit, for example, prioritizes displaying highly relevant recommendations based on the patient's past medical history. The recommendation unit can also prioritize displaying frequently visited items based on the patient's past medical history. For example, the recommendation unit prioritizes displaying highly relevant recommendations based on the patient's past medical history. The recommendation unit can also analyze the patient's past medical history to provide the optimal recommendation. For example, the recommendation unit analyzes the patient's past medical history and provides the optimal recommendation. This makes it possible to provide the optimal recommendation by referring to the patient's past medical history. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the patient's past medical history into the generation AI and cause the generation AI to provide the optimal recommendation.

[0047] When making recommendations, the recommendation unit can prioritize displaying highly relevant recommendations taking into account the patient's geographical location information. The recommendation unit, for example, prioritizes displaying recommendations related to symptoms or illnesses specific to a region based on the patient's current location. The recommendation unit can also prioritize displaying information about local medical institutions taking into account the patient's geographical location information. For example, the recommendation unit prioritizes displaying recommendations related to symptoms or illnesses specific to a region based on the patient's current location. The recommendation unit can also prioritize displaying recommendations related to the local climate or environment based on the patient's geographical location information. For example, the recommendation unit prioritizes displaying recommendations related to the local climate or environment based on the patient's geographical location information. This makes it possible to prioritize displaying highly relevant recommendations taking into account the patient's geographical location information. Some or all of the above-described processing in the recommendation unit may be performed, for example, using AI or without using AI. For example, the recommendation unit can input the patient's geographic location information into the generation AI and cause the generation AI to prioritize the display of highly relevant recommendations.

[0048] When making contact, the contact unit can suggest the optimal contact method by referring to the patient's past medical history. For example, the contact unit automatically suggests the optimal contact method based on the patient's past medical history. The contact unit can also prioritize suggesting frequently used contact methods based on the patient's past medical history. For example, the contact unit automatically suggests the optimal contact method based on the patient's past medical history. The contact unit can also analyze the patient's past medical history and suggest the most effective contact method. For example, the contact unit analyzes the patient's past medical history and suggests the most effective contact method. This makes it possible to suggest the optimal contact method by referring to the patient's past medical history. Some or all of the above-mentioned processing in the contact unit may be performed using, for example, AI, or may be performed without using AI. For example, the contact unit can input the patient's past medical history into a generation AI and have the generation AI suggest the optimal contact method.

[0049] The contact unit can propose an optimal contact method when making contact, taking into account the patient's geographical location information. The contact unit can propose a region-specific contact method, for example, based on the patient's current location. The contact unit can also automatically supplement information on local medical institutions, taking into account the patient's geographical location information. For example, the contact unit can propose a region-specific contact method based on the patient's current location. The contact unit can also propose an optimal contact method based on the patient's geographical location information. For example, the contact unit proposes an optimal contact method based on the patient's geographical location information. This makes it possible to propose an optimal contact method taking into account the patient's geographical location information. Some or all of the above-described processing in the contact unit may be performed using AI, for example, or may be performed without using AI. For example, the contact unit can input the patient's geographical location information to the generation AI and cause the generation AI to propose an optimal contact method.

[0050] During knowledge absorption, the knowledge absorption unit can suggest an optimal knowledge absorption method by referring to the doctor's past learning history. The knowledge absorption unit, for example, automatically suggests an optimal knowledge absorption method based on the doctor's past learning history. The knowledge absorption unit can also preferentially suggest frequently used knowledge absorption methods based on the doctor's past learning history. For example, the knowledge absorption unit automatically suggests an optimal knowledge absorption method based on the doctor's past learning history. The knowledge absorption unit can also analyze the doctor's past learning history and suggest the most effective knowledge absorption method. For example, the knowledge absorption unit analyzes the doctor's past learning history and suggests the most effective knowledge absorption method. This makes it possible to suggest an optimal knowledge absorption method by referring to the doctor's past learning history. Some or all of the above-described processing in the knowledge absorption unit may be performed using, for example, AI, or may be performed without using AI. For example, the knowledge absorption unit can input the doctor's past learning history into a generation AI and cause the generation AI to suggest an optimal knowledge absorption method.

[0051] The knowledge absorption unit can apply different knowledge absorption algorithms based on the doctor's specialty when absorbing knowledge. For example, the knowledge absorption unit applies a specialized knowledge absorption algorithm to a respiratory system specialist. Furthermore, the knowledge absorption unit can apply a different knowledge absorption algorithm to a gastroenterological system specialist. For example, the knowledge absorption unit applies a specialized knowledge absorption algorithm to a respiratory system specialist. Furthermore, the knowledge absorption unit can apply yet another knowledge absorption algorithm to a neurological system specialist. For example, the knowledge absorption unit applies yet another knowledge absorption algorithm to a neurological system specialist. This makes it possible to apply different knowledge absorption algorithms based on the doctor's specialty. Some or all of the above-described processing in the knowledge absorption unit may be performed using, or without, AI, for example. For example, the knowledge absorption unit can input the doctor's specialty to the generation AI and cause the generation AI to apply the knowledge absorption algorithm.

[0052] The knowledge absorption unit can propose an optimal knowledge absorption method by taking into account the doctor's geographical location information when absorbing knowledge. The knowledge absorption unit, for example, proposes a region-specific knowledge absorption method based on the doctor's current location. The knowledge absorption unit can also automatically supplement information on local medical institutions by taking into account the doctor's geographical location information. For example, the knowledge absorption unit proposes a region-specific knowledge absorption method based on the doctor's current location. The knowledge absorption unit can also propose an optimal knowledge absorption method based on the doctor's geographical location information. For example, the knowledge absorption unit proposes an optimal knowledge absorption method based on the doctor's geographical location information. This makes it possible to propose an optimal knowledge absorption method by taking into account the doctor's geographical location information. Some or all of the above-described processing in the knowledge absorption unit may be performed using AI, for example, or may be performed without using AI. For example, the knowledge absorption unit can input the doctor's geographical location information to the generation AI and cause the generation AI to execute the proposal of an optimal knowledge absorption method.

[0053] The knowledge absorption unit can analyze the doctor's social media activities and provide related knowledge during knowledge absorption. For example, the knowledge absorption unit can analyze the doctor's social media posts and automatically provide related knowledge. The knowledge absorption unit can also extract information related to the doctor's field of expertise from the doctor's social media activities to complement the knowledge absorption. For example, the knowledge absorption unit can analyze the doctor's social media posts and automatically provide related knowledge. The knowledge absorption unit can also provide the latest medical trend information based on the doctor's social media activities. For example, the knowledge absorption unit can provide the latest medical trend information based on the doctor's social media activities. This makes it possible to analyze the doctor's social media activities and provide related knowledge. Some or all of the above-described processing in the knowledge absorption unit may be performed using, for example, AI, or may be performed without using AI. For example, the knowledge absorption unit can input the doctor's social media data into the generation AI and cause the generation AI to provide related knowledge.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The data search unit can provide optimal search results by referring to the patient's past search history. For example, it can prioritize displaying highly relevant search results based on the patient's past search history. It can also prioritize displaying frequently searched items from the patient's past search history. It can also analyze the patient's past search history and provide optimal search results. This makes it possible to provide optimal search results by referring to the patient's past search history.

[0056] The data upload unit can analyze the patient's past symptom data and propose the optimal data input format. For example, it can automatically propose the optimal input format based on symptom data previously entered by the patient. It can also prioritize and display frequently entered items from the patient's past symptom data. It can also propose the optimal input format for specific symptoms. This makes it possible to analyze the patient's past symptom data and propose the optimal data input format.

[0057] During a search, the data search unit can apply different search algorithms depending on the symptom category. For example, a specialized search algorithm can be applied to respiratory symptoms. A different search algorithm can be applied to digestive symptoms. Furthermore, a different search algorithm can be applied to nervous system symptoms. In this way, different search algorithms can be applied depending on the symptom category.

[0058] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the symptom when making a recommendation. For example, a detailed recommendation can be made for a symptom with a high level of importance. Also, a brief recommendation can be made for a symptom with a low level of importance. Furthermore, the level of detail of the recommendation can be dynamically adjusted based on the importance. This makes it possible to adjust the level of detail of the recommendation based on the importance of the symptom.

[0059] The knowledge absorption unit can suggest the most appropriate knowledge absorption method by referring to the doctor's past learning history when absorbing knowledge. For example, the most appropriate knowledge absorption method can be automatically suggested based on the doctor's past learning history. Also, it can preferentially suggest frequently used knowledge absorption methods based on the doctor's past learning history. Furthermore, it can analyze the doctor's past learning history and suggest the most effective knowledge absorption method. In this way, it is possible to suggest the most appropriate knowledge absorption method by referring to the doctor's past learning history.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: In the data upload section, patients upload detailed data of their symptoms. Patients can log in to the system and use the interface to input details of their symptoms. They can also upload details of their symptoms by voice input or as image data. For example, patients can take photos of their symptoms using their smartphones and upload them to the system. Step 2: The data search unit searches the data uploaded by the data upload unit. It provides an interface for doctors to log in to the system and search for patient symptom data. The data search unit has keyword search and filtering functions, allowing doctors to quickly search for data related to specific symptoms. For example, if a doctor enters "fever," patient symptom data related to fever will be displayed. Step 3: The recommendation unit recommends a medical examination based on the data retrieved by the data search unit. The recommendation unit is equipped with an algorithm that recommends an appropriate doctor based on the search results. It matches the patient's symptom data with the doctor's specialty and recommends the most suitable doctor. For example, if symptom data related to fever is searched, a doctor with specialized knowledge of fevers will be recommended.

[0062] (Example 2) A matching system according to an embodiment of the present invention accumulates data on the "detailed symptoms of illness" experienced by patients and identifies doctors who can treat them. In this system, patients upload details of their symptoms to the system, doctors search the data, and recommend appropriate consultations. Patients can also search for detailed data on symptoms treated by doctors and contact them for consultations. Furthermore, doctors can search the careers and medical history of other doctors to gain knowledge. This system efficiently matches patients and doctors, enabling appropriate medical care to be provided. First, patients upload details of their symptoms to the system. For example, they enter detailed descriptions of symptoms such as fever, cough, and body aches into the system. This data is stored in the system. Next, doctors search the data. The doctors search the patient's symptom data from the system's database and recommend appropriate consultations. For example, doctors with specialized knowledge of specific symptoms are displayed in the search results. Patients can also search for detailed data on symptoms treated by doctors. Patients can search for detailed data on symptoms treated by doctors from the system's database and contact doctors for consultations. For example, information about doctors who have treated a specific symptom in the past is displayed. Furthermore, doctors can search the careers and medical history of other doctors to absorb knowledge. Doctors can search the careers and medical history of other doctors from the system's database to improve their own knowledge. For example, they can learn what kind of treatment was given for a specific symptom. This system efficiently matches patients with doctors, enabling appropriate medical care to be provided. This allows the matching system to accumulate detailed data on the patient's symptoms and identify the appropriate doctor.

[0063] The matching system according to the embodiment includes a data upload unit, a data search unit, and a recommendation unit. The data upload unit allows a patient to upload detailed data about their symptoms. The detailed data about the patient's symptoms includes, but is not limited to, fever, cough, and body aches. The data upload unit provides, for example, an interface through which the patient logs in to the system and inputs detailed symptoms. The data upload unit also allows the patient to upload detailed symptoms via voice input or image data. For example, the patient can take a photo of their symptoms using a smartphone and upload it to the system. The data search unit searches the data uploaded by the data upload unit. The data search unit provides, for example, an interface through which a doctor logs in to the system and searches for patient symptom data. The data search unit has keyword search and filtering functions, allowing the doctor to quickly search for data related to a specific symptom. For example, when a doctor enters "fever," symptom data related to the patient's fever is displayed. The recommendation unit recommends a medical consultation based on the data searched by the data search unit. The recommendation unit has, for example, an algorithm for recommending an appropriate doctor based on the search results. The recommendation unit compares the patient's symptom data with the doctor's specialty and recommends the most suitable doctor. For example, if symptom data related to fever is searched, a doctor with specialized knowledge about fever is recommended. This allows the matching system according to the embodiment to accumulate detailed data on the patient's symptoms and identify the appropriate doctor.

[0064] The data search unit may include a contact unit that allows patients to search for detailed data on symptoms treated by a doctor. The contact unit, for example, provides an interface for patients to contact a doctor to see a doctor. The contact unit has functions such as email transmission, chat function, and telephone call, allowing patients to quickly contact a doctor. For example, a patient can view a doctor's profile within the system and inquire about a consultation by email. The contact unit may also provide a function that allows patients to directly chat with doctors with questions. For example, a patient can use the chat function within the system to ask a doctor questions about their symptoms in real time. The contact unit may also provide a function that allows patients to contact a doctor by telephone. For example, a patient can obtain a doctor's phone number within the system and make an inquiry directly by telephone. This allows patients to search for detailed data on symptoms treated by a doctor and contact a doctor to see a doctor.

[0065] The data search unit may include a knowledge absorption unit that enables doctors to search the careers and medical history of other doctors. The knowledge absorption unit, for example, provides an interface for doctors to search the careers and medical history of other doctors. The knowledge absorption unit has functions such as database search, literature review, and expert interviews, allowing doctors to efficiently absorb the knowledge of other doctors. For example, a doctor can search the careers and medical history of other doctors within the system to learn treatment methods for specific symptoms. The knowledge absorption unit also enables doctors to search the medical history of other doctors to learn what treatments were performed. For example, a doctor can search the medical history of specific symptoms within the system to learn successful and unsuccessful cases of treatment. Furthermore, the knowledge absorption unit may provide a function that enables doctors to directly interview other doctors. For example, a doctor can request an interview with another doctor within the system and directly absorb specialized knowledge. This allows doctors to search the careers and medical history of other doctors and absorb knowledge.

[0066] The data upload unit allows a patient to upload details of their symptoms to the system. Examples of details of a patient's symptoms include, but are not limited to, fever, cough, and body aches. The data upload unit, for example, provides an interface through which a patient logs in to the system and inputs details of their symptoms. The data upload unit also allows a patient to upload details of their symptoms by voice input or as image data. For example, a patient can take a photo of their symptoms using a smartphone and upload it to the system. This allows a patient to upload details of their symptoms to the system.

[0067] The recommendation unit can recommend an appropriate doctor based on the search results. The recommendation unit is equipped with, for example, an algorithm that recommends an appropriate doctor based on the search results. The recommendation unit compares the patient's symptom data with the doctor's specialty and recommends the most appropriate doctor. For example, if symptom data related to fever is searched, a doctor with specialized knowledge of fevers is recommended. The recommendation unit can also determine the priority of consultations based on the severity of the patient's symptoms and the patient's doctor visit history. For example, a doctor who can respond quickly is recommended for a patient with serious symptoms. Furthermore, the recommendation unit can make customized recommendations based on the patient's wishes and past consultation history. For example, the recommendation unit recommends the most appropriate doctor taking into account the evaluations and feedback of doctors the patient has visited in the past. This makes it possible to make appropriate consultation recommendations based on the search results.

[0068] The contact unit allows patients to contact doctors to see a doctor. For example, the contact unit provides an interface for patients to contact doctors to see a doctor. The contact unit has functions such as email transmission, chat function, and telephone call, allowing patients to quickly contact doctors. For example, a patient can view a doctor's profile within the system and inquire about seeing a doctor by email. The contact unit can also provide a function for patients to directly ask questions to doctors via chat. For example, a patient can use the chat function within the system to ask a doctor questions about their symptoms in real time. Furthermore, the contact unit can also provide a function for patients to contact doctors by telephone. For example, a patient can obtain a doctor's phone number within the system and make an inquiry directly by telephone. This allows patients to contact doctors to see a doctor.

[0069] The knowledge absorption unit allows doctors to search the careers and medical records of other doctors and absorb knowledge. For example, the knowledge absorption unit provides an interface for doctors to search the careers and medical records of other doctors. The knowledge absorption unit has functions such as database search, literature review, and expert interviews, allowing doctors to efficiently absorb knowledge from other doctors. For example, a doctor can search the careers of other doctors within the system to learn treatment methods for specific symptoms. The knowledge absorption unit also allows doctors to search the medical records of other doctors and learn what treatments were performed. For example, a doctor can search the medical records for specific symptoms within the system to learn about successful and unsuccessful treatments. Furthermore, the knowledge absorption unit can provide a function for doctors to directly interview other doctors. For example, a doctor can request an interview with another doctor within the system and directly absorb specialized knowledge. This allows doctors to search the careers and medical records of other doctors and absorb knowledge.

[0070] The data upload unit can estimate the patient's emotions and adjust the input method for detailed symptom data based on the estimated patient emotions. For example, if the patient is feeling anxious, the data upload unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the patient is relaxed, the data upload unit can provide detailed input options and suggest a customizable input method. For example, if the patient is relaxed, the data upload unit can provide detailed input options and suggest a customizable input method. Furthermore, if the patient is in a hurry, the data upload unit can prioritize voice input to enable quick input of detailed symptom data. For example, if the patient is in a hurry, the data upload unit can prioritize voice input to enable quick input of detailed symptom data. This allows the input method for detailed symptom data to be adjusted according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data upload unit can be performed using, for example, AI, or without AI. For example, the data upload unit can input the patient's emotional data into the generation AI and cause the generation AI to adjust the input method based on the emotion.

[0071] The data upload unit can analyze the patient's past symptom data and propose an optimal data input format. The data upload unit, for example, automatically proposes an optimal input format based on symptom data previously entered by the patient. The data upload unit can also prioritize displaying frequently entered items from the patient's past symptom data. For example, the data upload unit automatically proposes an optimal input format based on symptom data previously entered by the patient. The data upload unit can also analyze the patient's past symptom data and propose an optimal input format for a specific symptom. For example, the data upload unit can analyze the patient's past symptom data and propose an optimal input format for a specific symptom. This makes it possible to analyze the patient's past symptom data and propose an optimal data input format. Some or all of the above-described processing in the data upload unit may be performed using, for example, AI, or may be performed without using AI. For example, the data upload unit can input the patient's past symptom data to a generation AI and cause the generation AI to propose an optimal data input format.

[0072] The data upload unit can automatically customize input items based on the patient's current health condition when uploading symptom data. For example, when the patient inputs their current health condition, the data upload unit automatically customizes the necessary input items based on that information. The data upload unit can also monitor the patient's current health condition in real time and dynamically adjust the input items. For example, when the patient inputs their current health condition, the data upload unit automatically customizes the necessary input items based on that information. The data upload unit can also automatically change the priority of input items based on the patient's health condition. For example, the data upload unit automatically changes the priority of input items based on the patient's health condition. This makes it possible to automatically customize the input items based on the patient's current health condition. Some or all of the above-described processing in the data upload unit may be performed using, or without, AI. For example, the data upload unit can input the patient's health condition data to a generation AI and cause the generation AI to customize the input items.

[0073] The data upload unit can estimate the patient's emotions and adjust the timing of data upload based on the estimated patient's emotions. For example, if the patient is feeling stressed, the data upload unit prompts the patient to upload data when they are relaxed. Furthermore, if the patient is relaxed, the data upload unit can prompt the patient to upload data immediately. For example, if the patient is feeling stressed, the data upload unit prompts the patient to upload data when they are relaxed. Furthermore, if the patient is in a hurry, the data upload unit can set a reminder to upload data later. For example, if the patient is in a hurry, the data upload unit sets a reminder to upload data later. This allows the timing of data upload to be adjusted according to the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data upload unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data upload unit can input the patient's emotion data into the generation AI and cause the generation AI to adjust the timing of data upload.

[0074] When uploading symptom data, the data upload unit can prioritize input of highly relevant data taking into account the patient's geographical location information. For example, the data upload unit prioritizes input of data related to symptoms and illnesses specific to a region based on the patient's current location. The data upload unit can also automatically supplement information on local medical institutions taking into account the patient's geographical location information. For example, the data upload unit prioritizes input of data related to symptoms and illnesses specific to a region based on the patient's current location. The data upload unit can also prioritize input of data related to the local climate and environment based on the patient's geographical location information. For example, the data upload unit prioritizes input of data related to the local climate and environment based on the patient's geographical location information. This makes it possible to prioritize input of highly relevant data taking into account the patient's geographical location information. Some or all of the above-described processing in the data upload unit may be performed using, or without, AI. For example, the data upload unit can input the patient's geographical location information to the generation AI and cause the generation AI to prioritize input of highly relevant data.

[0075] The data upload unit can analyze the patient's social media activities when uploading symptom data and automatically complete the associated data. For example, the data upload unit analyzes the patient's social media posts and automatically completes information related to the symptoms. The data upload unit can also extract data related to health conditions from the patient's social media activities and complete input items. For example, the data upload unit analyzes the patient's social media posts and automatically completes information related to the symptoms. The data upload unit can also automatically complete trend information related to symptoms based on the patient's social media activities. For example, the data upload unit automatically completes trend information related to symptoms based on the patient's social media activities. This makes it possible to analyze the patient's social media activities and automatically complete associated data. Some or all of the above-described processing in the data upload unit may be performed using AI, for example, or may be performed without using AI. For example, the data upload unit can input the patient's social media data to a generation AI and cause the generation AI to complete the associated data.

[0076] The data search unit can estimate the patient's emotions and adjust the display method of search results based on the estimated patient's emotions. For example, if the patient is feeling anxious, the data search unit displays simple, highly visible search results. Furthermore, if the patient is relaxed, the data search unit can display search results including detailed information. For example, if the patient is feeling anxious, the data search unit displays simple, highly visible search results. Furthermore, if the patient is in a hurry, the data search unit can display search results that focus on the main points. This allows the display method of search results to be adjusted according to the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data search unit can input patient emotion data into the generation AI and cause the generation AI to adjust the display method of the search results.

[0077] During a search, the data search unit can adjust the priority of search results based on the importance of the symptom data. For example, the data search unit prioritizes display of symptom data with high importance. The data search unit can also postpone display of symptom data with low importance. For example, the data search unit prioritizes display of symptom data with high importance. The data search unit can also dynamically adjust the display order of search results based on the importance. For example, the data search unit dynamically adjusts the display order of search results based on the importance. This makes it possible to adjust the priority of search results based on the importance of the symptom data. Some or all of the above-described processing in the data search unit may be performed using AI, for example, or may be performed without using AI. For example, the data search unit can input the importance of symptom data to the generation AI and cause the generation AI to adjust the priority of the search results.

[0078] During a search, the data search unit can apply different search algorithms depending on the symptom category. For example, the data search unit applies a specialized search algorithm to respiratory system symptoms. Furthermore, the data search unit can apply a different search algorithm to digestive system symptoms. For example, the data search unit applies a specialized search algorithm to respiratory system symptoms. Furthermore, the data search unit can also apply yet another search algorithm to nervous system symptoms. For example, the data search unit applies yet another search algorithm to nervous system symptoms. This makes it possible to apply different search algorithms depending on the symptom category. Some or all of the above-described processing in the data search unit may be performed using AI, for example, or may be performed without using AI. For example, the data search unit can input the symptom category to the generation AI and cause the generation AI to apply the search algorithm.

[0079] The data search unit can estimate the patient's emotions and filter search results based on the estimated patient's emotions. For example, if the patient is feeling anxious, the data search unit can prioritize displaying search results that provide a sense of security. Furthermore, if the patient is relaxed, the data search unit can display search results that include detailed information. For example, if the patient is feeling anxious, the data search unit can prioritize displaying search results that provide a sense of security. Furthermore, if the patient is in a hurry, the data search unit can display search results that focus on the main points. This allows filtering of search results according to the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data search unit can input patient emotion data into the generation AI and have the generation AI filter the search results.

[0080] During a search, the data search unit can provide optimal search results by referring to the patient's past search history. The data search unit, for example, prioritizes displaying highly relevant search results based on the patient's past search history. The data search unit can also prioritize displaying frequently searched items from the patient's past search history. For example, the data search unit prioritizes displaying highly relevant search results based on the patient's past search history. The data search unit can also analyze the patient's past search history to provide optimal search results. For example, the data search unit analyzes the patient's past search history and provides optimal search results. This makes it possible to provide optimal search results by referring to the patient's past search history. Some or all of the above-described processing in the data search unit may be performed using, for example, AI, or may be performed without using AI. For example, the data search unit can input the patient's past search history into the generation AI and cause the generation AI to provide optimal search results.

[0081] During a search, the data search unit can prioritize displaying highly relevant search results by taking into account the patient's geographical location information. For example, the data search unit can prioritize displaying search results related to symptoms or illnesses specific to a region based on the patient's current location. The data search unit can also prioritize displaying information about local medical institutions by taking into account the patient's geographical location information. For example, the data search unit can prioritize displaying search results related to symptoms or illnesses specific to a region based on the patient's current location. The data search unit can also prioritize displaying search results related to the local climate or environment based on the patient's geographical location information. For example, the data search unit can prioritize displaying search results related to the local climate or environment based on the patient's geographical location information. This makes it possible to prioritize displaying highly relevant search results by taking into account the patient's geographical location information. Some or all of the above-described processing in the data search unit may be performed using, or without, AI. For example, the data search unit can input the patient's geographical location information to the generation AI and cause the generation AI to prioritize displaying highly relevant search results.

[0082] The recommendation unit can estimate the patient's emotions and adjust the way the recommendation is expressed based on the estimated patient's emotions. For example, if the patient is feeling anxious, the recommendation unit can make a recommendation using an expression that gives a sense of security. Furthermore, if the patient is relaxed, the recommendation unit can make a recommendation using an expression that includes detailed information. For example, if the patient is feeling anxious, the recommendation unit can make a recommendation using an expression that gives a sense of security. Furthermore, if the patient is in a hurry, the recommendation unit can make a recommendation using an expression that focuses on the main points. For example, if the patient is in a hurry, the recommendation unit can make a recommendation using an expression that focuses on the main points. This makes it possible to adjust the way the recommendation is expressed depending on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or without AI. For example, the recommendation unit can input patient emotional data into the generation AI and have the generation AI adjust the way the recommendation is expressed.

[0083] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the symptom when making a recommendation. For example, the recommendation unit makes a detailed recommendation for a symptom with a high importance. Furthermore, the recommendation unit can make a concise recommendation for a symptom with a low importance. For example, the recommendation unit makes a detailed recommendation for a symptom with a high importance. Furthermore, the recommendation unit can dynamically adjust the level of detail of the recommendation based on the importance. For example, the recommendation unit dynamically adjusts the level of detail of the recommendation based on the importance. This makes it possible to adjust the level of detail of the recommendation based on the importance of the symptom. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the importance of the symptom to the generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0084] When making a recommendation, the recommendation unit can apply different recommendation algorithms depending on the symptom category. For example, the recommendation unit can apply a specialized recommendation algorithm to respiratory system symptoms. Furthermore, the recommendation unit can apply a different recommendation algorithm to digestive system symptoms. For example, the recommendation unit can apply a specialized recommendation algorithm to respiratory system symptoms. Furthermore, the recommendation unit can also apply yet another recommendation algorithm to nervous system symptoms. For example, the recommendation unit can apply yet another recommendation algorithm to nervous system symptoms. This makes it possible to apply different recommendation algorithms depending on the symptom category. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input the symptom category to the generation AI and cause the generation AI to apply the recommendation algorithm.

[0085] The recommendation unit can estimate the patient's emotions and determine the priority of recommendations based on the estimated patient's emotions. For example, if the patient is feeling anxious, the recommendation unit can prioritize displaying recommendations that provide a sense of security. Furthermore, if the patient is relaxed, the recommendation unit can prioritize displaying recommendations that include detailed information. For example, if the patient is feeling anxious, the recommendation unit can prioritize displaying recommendations that provide a sense of security. Furthermore, if the patient is in a hurry, the recommendation unit can prioritize displaying recommendations that focus on the main points. For example, if the patient is in a hurry, the recommendation unit can prioritize displaying recommendations that focus on the main points. This makes it possible to determine the priority of recommendations according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit may input patient emotion data into the generation AI and have the generation AI determine the priority of recommendations.

[0086] When making a recommendation, the recommendation unit can provide the optimal recommendation by referring to the patient's past medical history. The recommendation unit, for example, prioritizes displaying highly relevant recommendations based on the patient's past medical history. The recommendation unit can also prioritize displaying frequently visited items based on the patient's past medical history. For example, the recommendation unit prioritizes displaying highly relevant recommendations based on the patient's past medical history. The recommendation unit can also analyze the patient's past medical history to provide the optimal recommendation. For example, the recommendation unit analyzes the patient's past medical history and provides the optimal recommendation. This makes it possible to provide the optimal recommendation by referring to the patient's past medical history. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the patient's past medical history into the generation AI and cause the generation AI to provide the optimal recommendation.

[0087] When making recommendations, the recommendation unit can prioritize displaying highly relevant recommendations taking into account the patient's geographical location information. The recommendation unit, for example, prioritizes displaying recommendations related to symptoms or illnesses specific to a region based on the patient's current location. The recommendation unit can also prioritize displaying information about local medical institutions taking into account the patient's geographical location information. For example, the recommendation unit prioritizes displaying recommendations related to symptoms or illnesses specific to a region based on the patient's current location. The recommendation unit can also prioritize displaying recommendations related to the local climate or environment based on the patient's geographical location information. For example, the recommendation unit prioritizes displaying recommendations related to the local climate or environment based on the patient's geographical location information. This makes it possible to prioritize displaying highly relevant recommendations taking into account the patient's geographical location information. Some or all of the above-described processing in the recommendation unit may be performed, for example, using AI or without using AI. For example, the recommendation unit can input the patient's geographic location information into the generation AI and cause the generation AI to prioritize the display of highly relevant recommendations.

[0088] The contact unit can estimate the patient's emotions and adjust the contact method based on the estimated patient's emotions. For example, if the patient is feeling anxious, the contact unit can suggest a contact method that provides a sense of security. Furthermore, if the patient is relaxed, the contact unit can suggest a contact method that includes detailed information. For example, if the patient is feeling anxious, the contact unit can suggest a contact method that provides a sense of security. Furthermore, if the patient is in a hurry, the contact unit can suggest a quick and concise contact method. For example, if the patient is in a hurry, the contact unit can suggest a quick and concise contact method. This allows the contact method to be adjusted according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the contact unit may be performed using AI, for example, or without AI. For example, the contact unit can input patient emotion data into the generation AI and have the generation AI adjust the contact method.

[0089] When making contact, the contact unit can suggest the optimal contact method by referring to the patient's past medical history. For example, the contact unit automatically suggests the optimal contact method based on the patient's past medical history. The contact unit can also prioritize suggesting frequently used contact methods based on the patient's past medical history. For example, the contact unit automatically suggests the optimal contact method based on the patient's past medical history. The contact unit can also analyze the patient's past medical history and suggest the most effective contact method. For example, the contact unit analyzes the patient's past medical history and suggests the most effective contact method. This makes it possible to suggest the optimal contact method by referring to the patient's past medical history. Some or all of the above-mentioned processing in the contact unit may be performed using, for example, AI, or may be performed without using AI. For example, the contact unit can input the patient's past medical history into a generation AI and have the generation AI suggest the optimal contact method.

[0090] The contact unit can estimate the patient's emotions and determine the priority of contact based on the estimated patient's emotions. For example, if the patient is feeling anxious, the contact unit can prioritize contact that provides a sense of security. Furthermore, if the patient is relaxed, the contact unit can prioritize contact that includes detailed information. For example, if the patient is feeling anxious, the contact unit can prioritize contact that provides a sense of security. Furthermore, if the patient is in a hurry, the contact unit can prioritize quick and concise contact. For example, if the patient is in a hurry, the contact unit can prioritize quick and concise contact. This allows the priority of contact to be determined according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the contact unit can be performed using, for example, AI, or without AI. For example, the contact unit can input patient emotion data into the generation AI and have the generation AI determine the priority of contact.

[0091] The contact unit can propose an optimal contact method when making contact, taking into account the patient's geographical location information. The contact unit can propose a region-specific contact method, for example, based on the patient's current location. The contact unit can also automatically supplement information on local medical institutions, taking into account the patient's geographical location information. For example, the contact unit can propose a region-specific contact method based on the patient's current location. The contact unit can also propose an optimal contact method based on the patient's geographical location information. For example, the contact unit proposes an optimal contact method based on the patient's geographical location information. This makes it possible to propose an optimal contact method taking into account the patient's geographical location information. Some or all of the above-described processing in the contact unit may be performed using AI, for example, or may be performed without using AI. For example, the contact unit can input the patient's geographical location information to the generation AI and cause the generation AI to propose an optimal contact method.

[0092] The knowledge absorption unit can estimate the doctor's emotions and adjust the knowledge absorption method based on the estimated doctor's emotions. For example, if the doctor is tired, the knowledge absorption unit provides a concise and to-the-point knowledge absorption method. Furthermore, if the doctor is relaxed, the knowledge absorption unit can provide a knowledge absorption method that includes detailed information. For example, if the doctor is tired, the knowledge absorption unit provides a concise and to-the-point knowledge absorption method. Furthermore, if the doctor is excited, the knowledge absorption unit can provide a knowledge absorption method that adds visually stimulating effects. For example, if the doctor is excited, the knowledge absorption unit provides a knowledge absorption method that adds visually stimulating effects. This allows the knowledge absorption method to be adjusted according to the doctor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the knowledge absorption unit can be performed, for example, using AI, or without AI. For example, the knowledge absorption unit can input the doctor's emotional data into the generation AI and have the generation AI adjust the knowledge absorption method.

[0093] During knowledge absorption, the knowledge absorption unit can suggest an optimal knowledge absorption method by referring to the doctor's past learning history. The knowledge absorption unit, for example, automatically suggests an optimal knowledge absorption method based on the doctor's past learning history. The knowledge absorption unit can also preferentially suggest frequently used knowledge absorption methods based on the doctor's past learning history. For example, the knowledge absorption unit automatically suggests an optimal knowledge absorption method based on the doctor's past learning history. The knowledge absorption unit can also analyze the doctor's past learning history and suggest the most effective knowledge absorption method. For example, the knowledge absorption unit analyzes the doctor's past learning history and suggests the most effective knowledge absorption method. This makes it possible to suggest an optimal knowledge absorption method by referring to the doctor's past learning history. Some or all of the above-described processing in the knowledge absorption unit may be performed using, for example, AI, or may be performed without using AI. For example, the knowledge absorption unit can input the doctor's past learning history into a generation AI and cause the generation AI to suggest an optimal knowledge absorption method.

[0094] The knowledge absorption unit can apply different knowledge absorption algorithms based on the doctor's specialty when absorbing knowledge. For example, the knowledge absorption unit applies a specialized knowledge absorption algorithm to a respiratory system specialist. Furthermore, the knowledge absorption unit can apply a different knowledge absorption algorithm to a gastroenterological system specialist. For example, the knowledge absorption unit applies a specialized knowledge absorption algorithm to a respiratory system specialist. Furthermore, the knowledge absorption unit can apply yet another knowledge absorption algorithm to a neurological system specialist. For example, the knowledge absorption unit applies yet another knowledge absorption algorithm to a neurological system specialist. This makes it possible to apply different knowledge absorption algorithms based on the doctor's specialty. Some or all of the above-described processing in the knowledge absorption unit may be performed using, or without, AI, for example. For example, the knowledge absorption unit can input the doctor's specialty to the generation AI and cause the generation AI to apply the knowledge absorption algorithm.

[0095] The knowledge absorption unit can estimate the doctor's emotions and determine the priority of knowledge absorption based on the estimated doctor's emotions. For example, if the doctor is tired, the knowledge absorption unit can prioritize knowledge absorption that is concise and to the point. Furthermore, if the doctor is relaxed, the knowledge absorption unit can prioritize knowledge absorption that includes detailed information. For example, if the doctor is tired, the knowledge absorption unit can prioritize knowledge absorption that is concise and to the point. Furthermore, if the doctor is excited, the knowledge absorption unit can prioritize knowledge absorption that adds visually stimulating effects. For example, if the doctor is excited, the knowledge absorption unit can prioritize knowledge absorption that adds visually stimulating effects. This makes it possible to determine the priority of knowledge absorption according to the doctor's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the knowledge absorption unit can be performed, for example, using AI or without AI. For example, the knowledge absorption unit can input the doctor's emotional data into the generation AI and have the generation AI determine the priority of knowledge absorption.

[0096] The knowledge absorption unit can propose an optimal knowledge absorption method by taking into account the doctor's geographical location information when absorbing knowledge. The knowledge absorption unit, for example, proposes a region-specific knowledge absorption method based on the doctor's current location. The knowledge absorption unit can also automatically supplement information on local medical institutions by taking into account the doctor's geographical location information. For example, the knowledge absorption unit proposes a region-specific knowledge absorption method based on the doctor's current location. The knowledge absorption unit can also propose an optimal knowledge absorption method based on the doctor's geographical location information. For example, the knowledge absorption unit proposes an optimal knowledge absorption method based on the doctor's geographical location information. This makes it possible to propose an optimal knowledge absorption method by taking into account the doctor's geographical location information. Some or all of the above-described processing in the knowledge absorption unit may be performed using AI, for example, or may be performed without using AI. For example, the knowledge absorption unit can input the doctor's geographical location information to the generation AI and cause the generation AI to execute the proposal of an optimal knowledge absorption method.

[0097] The knowledge absorption unit can analyze the doctor's social media activities and provide related knowledge during knowledge absorption. For example, the knowledge absorption unit can analyze the doctor's social media posts and automatically provide related knowledge. The knowledge absorption unit can also extract information related to the doctor's field of expertise from the doctor's social media activities to complement the knowledge absorption. For example, the knowledge absorption unit can analyze the doctor's social media posts and automatically provide related knowledge. The knowledge absorption unit can also provide the latest medical trend information based on the doctor's social media activities. For example, the knowledge absorption unit can provide the latest medical trend information based on the doctor's social media activities. This makes it possible to analyze the doctor's social media activities and provide related knowledge. Some or all of the above-described processing in the knowledge absorption unit may be performed using, for example, AI, or may be performed without using AI. For example, the knowledge absorption unit can input the doctor's social media data into the generation AI and cause the generation AI to provide related knowledge. === Hard Collateral 1-1 === Each of the multiple elements including the data upload unit, data search unit, and recommendation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data upload unit provides an interface for a patient to input details of their symptoms using the reception device 38 of the smart device 14. The data search unit is realized by the specific processing unit 290 of the data processing device 12 and provides a function for a doctor to search for symptom data of the patient. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and includes an algorithm for recommending an appropriate doctor based on the symptom data of the patient. === Hard Collateral 1-2 === Each of the multiple elements including the data upload unit, data search unit, and recommendation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the data upload unit provides an interface for a patient to input details of their symptoms by voice using the microphone 238 of the smart glasses 214. The data search unit is realized by the specific processing unit 290 of the data processing device 12 and provides a function for a doctor to search for symptom data of the patient. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and includes an algorithm for recommending an appropriate doctor based on the symptom data of the patient. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned data upload unit, data search unit, and recommendation unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the data upload unit provides an interface that allows a patient to take a photo of their symptoms using the camera 42 of the headset terminal 314 and upload the photo to the system. The data search unit is realized by the specific processing unit 290 of the data processing device 12 and provides a function for a doctor to search for symptom data of the patient. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and includes an algorithm that recommends an appropriate doctor based on the symptom data of the patient. === Hard Collateral 1-4 === Each of the multiple elements including the data upload unit, data search unit, and recommendation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data upload unit provides an interface for a patient to input details of their symptoms by voice using the microphone 238 of the robot 414. The data search unit is realized by the specific processing unit 290 of the data processing device 12 and provides a function for a doctor to search for symptom data of the patient. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and includes an algorithm for recommending an appropriate doctor based on the symptom data of the patient.

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

[0099] The data upload unit can estimate the patient's emotions and adjust the symptom detail data input method based on the estimated patient's emotions. For example, if the patient is feeling anxious, a simple and intuitive interface can be provided to minimize input steps. Alternatively, if the patient is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the patient is in a hurry, voice input can be prioritized to allow the patient to quickly input symptom detail data. This makes it possible to adjust the symptom detail data input method according to the patient's emotions.

[0100] The data search unit can provide optimal search results by referring to the patient's past search history. For example, it can prioritize displaying highly relevant search results based on the patient's past search history. It can also prioritize displaying frequently searched items from the patient's past search history. It can also analyze the patient's past search history and provide optimal search results. This makes it possible to provide optimal search results by referring to the patient's past search history.

[0101] The recommendation unit can estimate the patient's emotions and adjust the way recommendations are expressed based on the estimated patient's emotions. For example, if the patient is feeling anxious, the recommendation can be made using an expression that gives a sense of security. If the patient is relaxed, the recommendation can be made using an expression that includes detailed information. Furthermore, if the patient is in a hurry, the recommendation can be made using an expression that focuses on the main points. This makes it possible to adjust the way recommendations are expressed depending on the patient's emotions.

[0102] The data upload unit can analyze the patient's past symptom data and propose the optimal data input format. For example, it can automatically propose the optimal input format based on symptom data previously entered by the patient. It can also prioritize and display frequently entered items from the patient's past symptom data. It can also propose the optimal input format for specific symptoms. This makes it possible to analyze the patient's past symptom data and propose the optimal data input format.

[0103] The data search unit can estimate the patient's emotions and adjust the display method of search results based on the estimated patient's emotions. For example, if the patient is feeling anxious, simple and highly visible search results can be displayed. If the patient is relaxed, search results including detailed information can be displayed. Furthermore, if the patient is in a hurry, search results that focus on the main points can be displayed. In this way, the display method of search results can be adjusted according to the patient's emotions.

[0104] During a search, the data search unit can apply different search algorithms depending on the symptom category. For example, a specialized search algorithm can be applied to respiratory symptoms. A different search algorithm can be applied to digestive symptoms. Furthermore, a different search algorithm can be applied to nervous system symptoms. In this way, different search algorithms can be applied depending on the symptom category.

[0105] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the symptom when making a recommendation. For example, a detailed recommendation can be made for a symptom with a high level of importance. Also, a brief recommendation can be made for a symptom with a low level of importance. Furthermore, the level of detail of the recommendation can be dynamically adjusted based on the importance. This makes it possible to adjust the level of detail of the recommendation based on the importance of the symptom.

[0106] The contact unit can estimate the patient's emotions and adjust the contact method based on the estimated patient's emotions. For example, if the patient is feeling anxious, it can suggest a contact method that gives the patient a sense of security. If the patient is relaxed, it can suggest a contact method that includes detailed information. Furthermore, if the patient is in a hurry, it can suggest a quick and concise contact method. In this way, it is possible to adjust the contact method according to the patient's emotions.

[0107] The knowledge absorption unit can estimate the doctor's emotions and adjust the knowledge absorption method based on the estimated doctor's emotions. For example, if the doctor is tired, a concise knowledge absorption method that focuses on the main points can be provided. If the doctor is relaxed, a knowledge absorption method that includes detailed information can be provided. Furthermore, if the doctor is excited, a knowledge absorption method that adds visually stimulating effects can be provided. In this way, the knowledge absorption method can be adjusted according to the doctor's emotions.

[0108] The knowledge absorption unit can suggest the most appropriate knowledge absorption method by referring to the doctor's past learning history when absorbing knowledge. For example, the most appropriate knowledge absorption method can be automatically suggested based on the doctor's past learning history. Also, it can preferentially suggest frequently used knowledge absorption methods based on the doctor's past learning history. Furthermore, it can analyze the doctor's past learning history and suggest the most effective knowledge absorption method. In this way, it is possible to suggest the most appropriate knowledge absorption method by referring to the doctor's past learning history.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: In the data upload section, patients upload detailed data of their symptoms. Patients can log in to the system and use the interface to input details of their symptoms. They can also upload details of their symptoms by voice input or as image data. For example, patients can take photos of their symptoms using their smartphones and upload them to the system. Step 2: The data search unit searches the data uploaded by the data upload unit. It provides an interface for doctors to log in to the system and search for patient symptom data. The data search unit has keyword search and filtering functions, allowing doctors to quickly search for data related to specific symptoms. For example, if a doctor enters "fever," patient symptom data related to fever will be displayed. Step 3: The recommendation unit recommends a medical examination based on the data retrieved by the data search unit. The recommendation unit is equipped with an algorithm that recommends an appropriate doctor based on the search results. It matches the patient's symptom data with the doctor's specialty and recommends the most suitable doctor. For example, if symptom data related to fever is searched, a doctor with specialized knowledge of fevers will be recommended.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0112] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0113] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0116] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 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, RAM 30, and 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.

[0118] 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.

[0119] 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 audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] 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 carried out in a secure state.

[0122] Fig. 4 shows an example of the 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.

[0123] 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.

[0124] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0128] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 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, RAM 30, and 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.

[0134] 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.

[0135] 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 audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] 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 carried out in a secure state.

[0138] 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.

[0139] 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.

[0140] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0142] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0144] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0148] 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.

[0149] 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, RAM 30, and 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.

[0150] 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.

[0151] 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 audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] 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 carried out in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0155] 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.

[0156] 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.

[0157] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0159] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding 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.

[0161] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0167] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0171] 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.

[0172] 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 USB (Universal Serial Bus) 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.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] 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.

[0175] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0176] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0177] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0178] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0180] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0182] [Explanation of symbols]

[0183] 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 data upload unit for uploading detailed data of a patient's symptoms; a data search unit that searches for data uploaded by the data upload unit; a recommendation unit that recommends medical examinations based on the data searched by the data search unit. A system characterized by:

2. The aforementioned data retrieval unit, Equipped with a contact section that allows patients to search for detailed data on symptoms treated by doctors The system of claim 1 .

3. The data search unit Equipped with a knowledge absorbing section that allows doctors to search the careers and medical records of other doctors The system of claim 1 .

4. The aforementioned data upload unit, Patients upload details of their symptoms into the system The system of claim 1 .

5. The recommendation unit is, Recommend appropriate medical appointments based on search results The system of claim 1 .

6. The contact portion is The patient contacts the doctor to schedule an appointment 3. The system of claim 2.

7. The aforementioned knowledge absorption unit is Doctors can search other doctors' careers and medical histories to absorb knowledge.

4. The system of claim 3.

8. The data upload unit Inferring patient emotions and adjusting symptom details entry based on the inferred emotions The system of claim 1 .

9. The data upload unit Analyzes patient history and suggests optimal data entry format The system of claim 1 .

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