system

Abstract

The system according to this embodiment aims to help users visit an appropriate hospital based on their health information and to recommend an appropriate hospital based on the results of tests conducted after the visit. [Solution] The system according to the embodiment comprises a reception unit, an estimation unit, a display unit, an analysis unit, and a suggestion unit. The reception unit receives the user's health information. The estimation unit analyzes the health information received by the reception unit and estimates the name of the illness. The display unit displays the hospital to be visited based on the name of the illness estimated by the estimation unit. The analysis unit analyzes the test results after the visit. The suggestion unit suggests a hospital to be referred to based on the results analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method executed by at least one processor, the method comprising: receiving a user utterance; adding the user utterance to a prompt including an instruction associated with a description of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance responding to the user utterance.

Prior Art Literature

Patent Literature

[0003]

Patent Document 1

Summary of the Invention

Problem to be Solved by the Invention

[0004] In the conventional technology, there has been a problem that it is not sufficiently performed to have an appropriate hospital receive a patient based on the user's physical condition information, or to introduce an appropriate hospital based on the test results after medical consultation.

[0005] An object of the system according to an embodiment is to enable a patient to consult an appropriate hospital based on the user's physical condition information, and introduce an appropriate hospital based on the test results after the medical consultation.

Means for Solving the Problem

[0006] The system according to this embodiment comprises a reception unit, an estimation unit, a display unit, an analysis unit, and a suggestion unit. The reception unit receives the user's health information. The estimation unit analyzes the health information received by the reception unit and estimates the name of the illness. The display unit displays the hospital the user should visit based on the name of the illness estimated by the estimation unit. The analysis unit analyzes the test results after the visit. The suggestion unit suggests a hospital to refer the user to based on the results analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can determine the appropriate hospital for a user based on their health information and recommend an appropriate hospital based on the test results after the visit. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that analyzes a user's physical condition information and displays a suspected illness and a hospital to which the user should visit. This system starts when the user inputs information about their physical condition, such as body temperature, nausea, headache, and the location and severity of pain. Next, the system analyzes this information and displays a suspected illness and a list of hospitals to which the user should visit. Furthermore, after the user has visited a hospital, the system analyzes the results of blood tests, MRI, CRI, X-rays, and ultrasound examinations performed there and suggests a hospital to which the user should be referred. This system can reduce the number of people who are turned away from hospitals. For example, when a user inputs information about their physical condition, such as body temperature, nausea, headache, and the location and severity of pain, the system estimates an illness based on this information and displays a list of hospitals to which the user should visit. Also, after the user has visited a hospital, the system analyzes the results of blood tests, MRI, CRI, X-rays, and ultrasound examinations and suggests the most suitable hospital. This allows the user to select an appropriate hospital and visit one efficiently. In this way, the system can efficiently analyze the user's physical condition information and suggest an appropriate illness and hospital.

[0029] The system according to this embodiment comprises a reception unit, an estimation unit, a display unit, an analysis unit, and a suggestion unit. The reception unit receives the user's health information. The user's health information includes, but is not limited to, body temperature, blood pressure, heart rate, and details of symptoms. The reception unit records, for example, information such as body temperature, nausea, and headache entered by the user. The reception unit can also record the user's health information in detail. For example, the reception unit saves the health information entered by the user to a database for later analysis. The estimation unit analyzes the health information received by the reception unit to estimate the name of the disease. The estimation unit analyzes the health information using, for example, AI to estimate the name of the disease. The estimation unit can estimate the name of the disease by analyzing the health condition, such as body temperature, nausea, and headache, as well as the location and degree of pain. For example, if the body temperature is high, the estimation unit estimates a disease accompanied by fever, and if there is nausea, it estimates a disease of the digestive system. The display unit displays the hospital to be visited based on the name of the disease estimated by the estimation unit. The display unit displays a list of hospitals the user should visit based on the estimated disease name. The display unit can select a hospital based on the estimated disease name, taking into account factors such as the hospital's specialty, distance, and rating. The analysis unit analyzes the test results after the visit. The analysis unit analyzes the results of blood tests, MRI, CRI, X-rays, and ultrasound examinations, for example. The analysis unit can use AI to analyze the test results and estimate the disease name and treatment plan. For example, the analysis unit can estimate anemia or infection from the blood test results and brain or spinal cord abnormalities from the MRI results. The suggestion unit suggests hospitals that should be referred based on the results analyzed by the analysis unit. The suggestion unit identifies hospitals based on specialist data, for example. The suggestion unit can suggest hospitals the user should visit based on the results analyzed by the analysis unit. For example, the suggestion unit suggests an internal medicine specialist from the blood test results and a neurologist from the MRI results. As a result, the system according to this embodiment can efficiently analyze the user's health information and suggest appropriate disease names and hospitals.

[0030] The reception desk can record the user's health information. For example, the reception desk can store the health information entered by the user in a database. For example, the reception desk can store information such as body temperature, blood pressure, heart rate, and details of symptoms entered by the user in a database. The reception desk can also periodically record the user's health information. For example, the reception desk can record the health information entered by the user daily and track long-term changes in health. This allows for more accurate analysis by recording the user's health information in detail. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the health information entered by the user into the AI ​​and have the AI ​​record the health information.

[0031] The analysis unit can analyze the results of blood tests, MRI, CRI, X-rays, and ultrasound examinations. For example, the analysis unit can analyze the results of blood tests. For example, it can estimate anemia or infection from the blood test results. The analysis unit can also analyze the results of MRI. For example, it can estimate abnormalities in the brain or spinal cord from the MRI results. The analysis unit can also analyze the results of CRI. For example, it can estimate abnormalities in the heart or blood vessels from the CRI results. The analysis unit can also analyze the results of X-rays. For example, it can estimate fractures or lung abnormalities from the X-ray results. The analysis unit can also analyze the results of ultrasound examinations. For example, it can estimate abnormalities in internal organs from the ultrasound examination results. By analyzing the results of various tests, it becomes possible to make more accurate hospital recommendations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the results of blood tests into AI and have AI perform the analysis.

[0032] The proposal unit can identify hospitals based on specialist data. For example, the proposal unit can identify hospitals based on specialist data. For example, the proposal unit can identify hospitals based on specialist qualifications and treatment records. The proposal unit can also evaluate hospitals based on specialist data. For example, the proposal unit can evaluate hospitals based on specialist treatment records and patient evaluations. The proposal unit can also set hospital selection criteria based on specialist data. For example, the proposal unit can set hospital selection criteria based on specialist qualifications and treatment records. This makes it possible to propose appropriate hospitals by identifying hospitals based on specialist data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input specialist data into AI and have the AI ​​perform hospital identification.

[0033] The estimation unit can estimate a disease name by analyzing the patient's physical condition, including body temperature, nausea, and headache, as well as the location and severity of pain. For example, the estimation unit can analyze body temperature. For example, if the body temperature is high, the estimation unit estimates a disease accompanied by fever. The estimation unit can also analyze nausea. For example, if nausea is present, the estimation unit estimates a disease of the digestive system. The estimation unit can also analyze headaches. For example, if headaches are present, the estimation unit estimates a disease of the nervous system. The estimation unit can also analyze the location and severity of pain. For example, if the pain is concentrated in a specific area, the estimation unit estimates a disease related to that area. By analyzing the patient's physical condition and the location and severity of pain, a more accurate estimation of the disease name becomes possible. Some or all of the above processing in the estimation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the estimation unit can input physical condition information such as body temperature, nausea, and headache into a generation AI and have the generation AI perform the disease name estimation.

[0034] The display unit can display a list of hospitals that should be visited based on the estimated disease name. For example, the display unit can display a list of hospitals that should be visited based on the estimated disease name. For example, the display unit can select a hospital based on the estimated disease name, taking into account the hospital's specialty, distance, and rating. The display unit can also display the list of hospitals visually. For example, the display unit can display information such as the hospital's name, address, telephone number, and medical department. The display unit can also display hospital ratings and reviews. For example, the display unit can display hospital rating scores and patient reviews. This allows the user to select an appropriate hospital by displaying a list of hospitals that should be visited based on the estimated disease name. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input a list of hospitals that should be visited based on the estimated disease name into a generating AI and have the generating AI display the list of hospitals.

[0035] The reception desk can analyze the user's past health information and provide an appropriate input format. For example, the reception desk can analyze the user's past health information. For example, the reception desk can automatically display frequently entered items based on the health information the user has entered in the past. The reception desk can also prioritize displaying input items related to specific symptoms based on the user's past health information. For example, the reception desk can analyze the user's past health information and provide shortcuts to reduce the effort required for input. This reduces the effort required for input and enables efficient input by analyzing past health information. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past health information into AI and have AI provide the input format.

[0036] The reception desk can filter the input of health information based on the user's lifestyle and medical history. For example, the reception desk considers the user's lifestyle. For instance, it prioritizes inputting relevant health information based on the user's smoking and drinking habits. The reception desk can also consider the user's medical history. For example, it filters relevant health information based on the user's past medical history. Furthermore, the reception desk can customize input fields considering the user's lifestyle and medical history. For example, it automatically adjusts input fields based on the user's lifestyle and medical history. This allows for the input of more relevant health information by filtering based on lifestyle and medical history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's lifestyle and medical history into an AI and have the AI ​​perform the filtering.

[0037] The reception desk can prioritize inputting highly relevant information when users enter their health information, taking into account their geographical location. For example, the reception desk can consider the user's geographical location. For instance, based on the user's current location, the reception desk prioritizes inputting health information related to region-specific diseases and symptoms. The reception desk can also consider the user's geographical location and refer to their medical history at nearby medical institutions. For example, based on the user's geographical location, the reception desk inputs health information related to the region's climate and environment. This allows for the priority input of information related to region-specific diseases and symptoms by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into AI and have AI input highly relevant information.

[0038] The reception desk can analyze the user's social media activity and input relevant information when the user enters their health information. For example, the reception desk can analyze the user's social media activity. For example, the reception desk can extract and input information about the user's recent health from the user's social media posts. The reception desk can also analyze the user's social media activity and automatically input keywords related to their health. For example, the reception desk can supplement the health information based on the user's social media activity history. In this way, information related to health can be supplemented by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into AI and have the AI ​​input the relevant information.

[0039] The estimation unit can adjust the level of detail of its estimation based on the importance of health information when estimating a disease name. For example, the estimation unit evaluates the importance of health information based on the severity of symptoms or the frequency of onset. The estimation unit can also adjust the level of detail of its estimation based on the importance of health information. For example, the estimation unit can provide a detailed disease name estimation result based on important health information. The estimation unit can also provide a concise disease name estimation result based on minor health information. Furthermore, the estimation unit adjusts the level of detail of its estimation results according to the importance of health information. By adjusting the level of detail of the estimation according to the importance of health information, more accurate estimation results can be provided. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without using AI. For example, the estimation unit can input the importance of health information into AI and have AI perform the adjustment of the level of detail of the estimation.

[0040] The estimation unit can apply different estimation algorithms depending on the category of health information when estimating a disease name. For example, the estimation unit classifies the categories of health information. For example, the estimation unit classifies health information into categories such as internal medicine, surgery, and psychiatry. The estimation unit can also apply different estimation algorithms depending on the category of health information. For example, the estimation unit applies a standard estimation algorithm to common symptoms such as body temperature and nausea. The estimation unit can also apply a specialized estimation algorithm to pain in a specific area or its degree. Furthermore, the estimation unit selects the optimal estimation algorithm according to the category of health information. By applying the optimal estimation algorithm according to the category of health information, more accurate estimation results can be provided. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input the categories of health information into AI and have the AI ​​perform the application of the estimation algorithm.

[0041] The estimation unit can determine the priority of estimations based on the timing of submission of health information when estimating a disease name. For example, the estimation unit evaluates the timing of submission of health information. For example, the estimation unit evaluates the submission timing based on the date and time of submission and the frequency of submission of health information. The estimation unit can also determine the priority of estimations based on the timing of submission of health information. For example, the estimation unit prioritizes estimating disease names based on recently submitted health information. The estimation unit can also adjust the priority of estimations based on past health information. Furthermore, the estimation unit determines the priority of estimations according to the timing of submission of health information. This allows for faster estimation results by determining the priority of estimations according to the timing of submission of health information. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without using AI. For example, the estimation unit can input the timing of submission of health information into AI and have AI perform the determination of the priority of estimations.

[0042] The estimation unit can adjust the order of estimations based on the relevance of health information when estimating disease names. For example, the estimation unit evaluates the relevance of health information based on commonalities in symptoms or onset patterns. The estimation unit can also adjust the order of estimations based on the relevance of health information. For example, the estimation unit displays the most relevant disease name first based on the relevance of health information. The estimation unit can also adjust the order of estimation results according to the relevance of health information. Furthermore, the estimation unit analyzes the relevance of health information and optimizes the order of estimations. By adjusting the order of estimations based on the relevance of health information, more relevant information can be provided. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input the relevance of health information into AI and have AI perform the adjustment of the estimation order.

[0043] The display unit can adjust the level of detail displayed based on the importance of the disease name when displaying a list of hospitals. The display unit can, for example, evaluate the importance of the disease name. For example, the display unit can evaluate the importance of the disease name based on the severity of the symptoms or the frequency of occurrence. The display unit can also adjust the level of detail displayed based on the importance of the disease name. For example, the display unit can display detailed hospital information based on important disease names. The display unit can also display concise hospital information based on minor disease names. Furthermore, the display unit adjusts the level of detail displayed according to the importance of the disease name. This allows the display unit to provide the user with the information they need by adjusting the level of detail displayed according to the importance of the disease name. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can input the importance of the disease name into AI and have AI perform the adjustment of the level of detail displayed.

[0044] The display unit can apply different display algorithms depending on the disease name category when displaying a list of hospitals. For example, the display unit classifies disease names into categories such as internal medicine, surgery, and psychiatry. The display unit can also apply different display algorithms depending on the disease name category. For example, the display unit applies a standard display algorithm to common disease names. The display unit can also apply a specialized display algorithm to disease names related to specific medical fields. Furthermore, the display unit selects the optimal display algorithm according to the disease name category. This allows the display unit to provide information that is easy for the user to understand by applying the optimal display algorithm according to the disease name category. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input disease name categories into AI and have AI perform the application of display algorithms.

[0045] The display unit can determine the display priority based on the timing of disease name submission when displaying a list of hospitals. The display unit evaluates the timing of disease name submission, for example. For example, the display unit evaluates the submission timing based on the date and frequency of submission. The display unit can also determine the display priority based on the timing of disease name submission. For example, the display unit prioritizes displaying hospitals based on recently submitted disease names. The display unit can also adjust the display priority based on past disease names. Furthermore, the display unit determines the display priority according to the timing of disease name submission. This enables faster information provision by determining the display priority according to the timing of disease name submission. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the timing of disease name submission into AI and have AI perform the determination of the display priority.

[0046] The display unit can adjust the display order based on the relevance of disease names when displaying a list of hospitals. The display unit can evaluate the relevance of disease names, for example, by evaluating the relevance of disease names based on commonalities in symptoms or onset patterns. The display unit can also adjust the display order based on the relevance of disease names. For example, the display unit can display the most relevant hospital first based on the relevance of disease names. The display unit can also adjust the display order according to the relevance of disease names. Furthermore, the display unit analyzes the relevance of disease names and optimizes the display order. This allows for the provision of more relevant information by adjusting the display order based on the relevance of disease names. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the relevance of disease names into AI and have AI perform the adjustment of the display order.

[0047] The analysis unit can adjust the level of detail of the analysis based on the importance of the test results when analyzing the test results. For example, the analysis unit evaluates the importance of the test results. For example, the analysis unit evaluates the importance of the test results based on the range of abnormal values ​​or the importance of the test items. The analysis unit can also adjust the level of detail of the analysis based on the importance of the test results. For example, the analysis unit provides detailed analysis results based on important test results. The analysis unit can also provide concise analysis results based on minor test results. The analysis unit adjusts the level of detail of the analysis according to the importance of the test results. By adjusting the level of detail of the analysis according to the importance of the test results, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the importance of the test results into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0048] The analysis unit can apply different analysis algorithms depending on the category of the test results when analyzing the test results. For example, the analysis unit classifies the categories of the test results. For example, the analysis unit classifies the test results into categories such as blood tests, imaging diagnostics, and genetic tests. The analysis unit can also apply different analysis algorithms depending on the category of the test results. For example, the analysis unit can apply a standard analysis algorithm to blood test results. The analysis unit can also apply a specialized analysis algorithm to MRI results. Furthermore, the analysis unit can select the optimal analysis algorithm according to the category of the test results. By applying the optimal analysis algorithm according to the category of the test results, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the categories of the test results into the AI ​​and have the AI ​​execute the application of the analysis algorithm.

[0049] The analysis unit can determine the priority of analysis based on the submission timing of the test results when analyzing test results. For example, the analysis unit evaluates the submission timing of the test results. For example, the analysis unit evaluates the submission timing based on the submission date and frequency of the test results. The analysis unit can also determine the priority of analysis based on the submission timing of the test results. For example, the analysis unit prioritizes analysis based on recently submitted test results. The analysis unit can also adjust the priority of analysis based on past test results. The analysis unit also determines the priority of analysis according to the submission timing of the test results. This allows for faster analysis results to be provided by determining the priority of analysis according to the submission timing of the test results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the submission timing of the test results into the AI ​​and have the AI ​​perform the determination of the analysis priority.

[0050] The analysis unit can adjust the order of analysis based on the relevance of the test results when analyzing the test results. For example, the analysis unit evaluates the relevance of the test results. For example, the analysis unit evaluates the relevance of the test results based on commonalities of abnormal values ​​or the relevance of test items. The analysis unit can also adjust the order of analysis based on the relevance of the test results. For example, the analysis unit analyzes the most relevant results first based on the relevance of the test results. The analysis unit can also adjust the order of analysis according to the relevance of the test results. Furthermore, the analysis unit analyzes the relevance of the test results and optimizes the order of analysis. By adjusting the order of analysis based on the relevance of the test results, more relevant information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the relevance of the test results into AI and have AI perform the adjustment of the order of analysis.

[0051] The suggestion unit can adjust the level of detail in its hospital suggestions based on the importance of the test results. For example, the suggestion unit evaluates the importance of the test results. For example, the suggestion unit evaluates the importance of the test results based on the range of abnormal values ​​and the importance of the test items. The suggestion unit can also adjust the level of detail in its suggestions based on the importance of the test results. For example, the suggestion unit can make detailed hospital suggestions based on important test results. The suggestion unit can also make concise hospital suggestions based on minor test results. The suggestion unit adjusts the level of detail in its suggestions according to the importance of the test results. This allows for more accurate suggestions by adjusting the level of detail in the suggestions according to the importance of the test results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the importance of the test results into the AI ​​and have the AI ​​adjust the level of detail in the suggestions.

[0052] The suggestion unit can apply different suggestion algorithms depending on the category of the test results when making hospital suggestions. For example, the suggestion unit classifies the categories of test results. For example, the suggestion unit classifies test results into categories such as blood tests, imaging diagnostics, and genetic tests. The suggestion unit can also apply different suggestion algorithms depending on the category of the test results. For example, the suggestion unit can apply a standard suggestion algorithm to blood test results. The suggestion unit can also apply a specialized suggestion algorithm to MRI results. Furthermore, the suggestion unit selects the most appropriate suggestion algorithm depending on the category of the test results. This allows for more accurate suggestions by applying the most appropriate suggestion algorithm according to the category of the test results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the categories of the test results into AI and have AI perform the application of the suggestion algorithm.

[0053] The proposal department can determine the priority of hospital proposals based on the timing of test result submissions. The proposal department can evaluate the timing of test result submissions, for example. For example, the proposal department can evaluate the submission timing based on the date and frequency of submission. The proposal department can also determine the priority of proposals based on the timing of test result submissions. For example, the proposal department can prioritize hospital proposals based on recently submitted test results. The proposal department can also adjust the priority of proposals based on past test results. Furthermore, the proposal department determines the priority of proposals according to the timing of test result submissions. This allows for faster proposals by determining the priority of proposals according to the timing of test result submissions. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the timing of test result submissions into AI and have the AI ​​determine the priority of proposals.

[0054] The suggestion unit can adjust the order of suggestions based on the relevance of the test results when suggesting hospitals. For example, the suggestion unit evaluates the relevance of the test results. For example, the suggestion unit evaluates the relevance of the test results based on commonalities of abnormal values ​​or the relevance of test items. The suggestion unit can also adjust the order of suggestions based on the relevance of the test results. For example, the suggestion unit suggests the most relevant hospital first based on the relevance of the test results. The suggestion unit can also adjust the order of suggestions according to the relevance of the test results. Furthermore, the suggestion unit analyzes the relevance of the test results and optimizes the order of suggestions. This makes it possible to make more relevant suggestions by adjusting the order of suggestions based on the relevance of the test results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the relevance of the test results into AI and have AI perform the adjustment of the order of suggestions.

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

[0056] The reception desk can receive user health information and also allow users to input records of their lifestyle and diet. For example, the reception desk provides an interface for users to input their daily meals and exercise levels, and stores this information in a database. Furthermore, the reception desk can regularly record information about the user's lifestyle, which can be used for long-term health management. This allows for integrated management of the user's health information and lifestyle information, enabling a more accurate understanding of their health status.

[0057] The analysis unit can consider the user's genetic information when analyzing the user's health information. For example, the analysis unit can store the user's genetic test results in a database and perform analysis by combining health information and genetic information. Furthermore, the analysis unit can assess the risk of specific diseases based on genetic information and suggest preventive measures. This enables more accurate analysis that takes into account the user's genetic background.

[0058] The suggestion department can provide health management advice based on the results of analyzing the user's physical condition information. For example, the suggestion department can propose appropriate exercise and meal plans based on the user's physical condition information. Furthermore, the suggestion department can also provide advice on stress management and sleep improvement based on the user's physical condition information. This allows users to receive specific advice on maintaining their health in their daily lives.

[0059] The estimation unit can consider the influence of seasons and weather when analyzing the user's health information. For example, the estimation unit stores seasonal disease incidence trends and changes in health due to weather changes in a database and incorporates this information into the analysis. Furthermore, the estimation unit can provide health management advice tailored to the season and weather. This enables more accurate disease diagnosis estimation that takes into account the influence of seasons and weather.

[0060] The display unit can provide health-related educational content based on the analysis of the user's health information. For example, the display unit can show information and preventative measures for specific diseases based on the user's health information. The display unit can also provide health-related videos and articles, offering users opportunities to learn about health. This allows users to deepen their knowledge about their own health.

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

[0062] Step 1: The reception desk receives the user's health information. This information includes body temperature, blood pressure, heart rate, and details of symptoms. The reception desk can record the information entered by the user, such as body temperature, nausea, and headache, and save it in a database. Step 2: The estimation unit analyzes the health information received by the reception unit to estimate the name of the illness. The estimation unit uses AI to analyze the health information, including body temperature, nausea, headaches, and the location and severity of pain, to estimate the name of the illness. Step 3: The display unit shows the hospitals the user should visit based on the disease name estimated by the estimation unit. Based on the estimated disease name, the display unit selects a hospital considering factors such as the hospital's specialty, distance, and rating, and displays it to the user. Step 4: The analysis unit analyzes the test results after the consultation. The analysis unit analyzes the results of blood tests, MRI, CRI, X-rays, and ultrasound examinations, and uses AI to estimate the disease name and treatment plan. Step 5: The Proposal Department suggests hospitals to refer the user to based on the results analyzed by the Analysis Department. The Proposal Department identifies hospitals based on specialist data and suggests which hospitals the user should visit.

[0063] (Example of form 2) The system according to an embodiment of the present invention is a system that analyzes a user's physical condition information and displays a suspected illness and a hospital to which the user should visit. This system starts when the user inputs information about their physical condition, such as body temperature, nausea, headache, and the location and severity of pain. Next, the system analyzes this information and displays a suspected illness and a list of hospitals to which the user should visit. Furthermore, after the user has visited a hospital, the system analyzes the results of blood tests, MRI, CRI, X-rays, and ultrasound examinations performed there and suggests a hospital to which the user should be referred. This system can reduce the number of people who are turned away from hospitals. For example, when a user inputs information about their physical condition, such as body temperature, nausea, headache, and the location and severity of pain, the system estimates an illness based on this information and displays a list of hospitals to which the user should visit. Also, after the user has visited a hospital, the system analyzes the results of blood tests, MRI, CRI, X-rays, and ultrasound examinations and suggests the most suitable hospital. This allows the user to select an appropriate hospital and visit one efficiently. In this way, the system can efficiently analyze the user's physical condition information and suggest an appropriate illness and hospital.

[0064] The system according to this embodiment comprises a reception unit, an estimation unit, a display unit, an analysis unit, and a suggestion unit. The reception unit receives the user's health information. The user's health information includes, but is not limited to, body temperature, blood pressure, heart rate, and details of symptoms. The reception unit records, for example, information such as body temperature, nausea, and headache entered by the user. The reception unit can also record the user's health information in detail. For example, the reception unit saves the health information entered by the user to a database for later analysis. The estimation unit analyzes the health information received by the reception unit to estimate the name of the disease. The estimation unit analyzes the health information using, for example, AI to estimate the name of the disease. The estimation unit can estimate the name of the disease by analyzing the health condition, such as body temperature, nausea, and headache, as well as the location and degree of pain. For example, if the body temperature is high, the estimation unit estimates a disease accompanied by fever, and if there is nausea, it estimates a disease of the digestive system. The display unit displays the hospital to be visited based on the name of the disease estimated by the estimation unit. The display unit displays a list of hospitals the user should visit based on the estimated disease name. The display unit can select a hospital based on the estimated disease name, taking into account factors such as the hospital's specialty, distance, and rating. The analysis unit analyzes the test results after the visit. The analysis unit analyzes the results of blood tests, MRI, CRI, X-rays, and ultrasound examinations, for example. The analysis unit can use AI to analyze the test results and estimate the disease name and treatment plan. For example, the analysis unit can estimate anemia or infection from the blood test results and brain or spinal cord abnormalities from the MRI results. The suggestion unit suggests hospitals that should be referred based on the results analyzed by the analysis unit. The suggestion unit identifies hospitals based on specialist data, for example. The suggestion unit can suggest hospitals the user should visit based on the results analyzed by the analysis unit. For example, the suggestion unit suggests an internal medicine specialist from the blood test results and a neurologist from the MRI results. As a result, the system according to this embodiment can efficiently analyze the user's health information and suggest appropriate disease names and hospitals.

[0065] The reception desk can record the user's health information. For example, the reception desk can store the health information entered by the user in a database. For example, the reception desk can store information such as body temperature, blood pressure, heart rate, and details of symptoms entered by the user in a database. The reception desk can also periodically record the user's health information. For example, the reception desk can record the health information entered by the user daily and track long-term changes in health. This allows for more accurate analysis by recording the user's health information in detail. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the health information entered by the user into the AI ​​and have the AI ​​record the health information.

[0066] The analysis unit can analyze the results of blood tests, MRI, CRI, X-rays, and ultrasound examinations. For example, the analysis unit can analyze the results of blood tests. For example, it can estimate anemia or infection from the blood test results. The analysis unit can also analyze the results of MRI. For example, it can estimate abnormalities in the brain or spinal cord from the MRI results. The analysis unit can also analyze the results of CRI. For example, it can estimate abnormalities in the heart or blood vessels from the CRI results. The analysis unit can also analyze the results of X-rays. For example, it can estimate fractures or lung abnormalities from the X-ray results. The analysis unit can also analyze the results of ultrasound examinations. For example, it can estimate abnormalities in internal organs from the ultrasound examination results. By analyzing the results of various tests, it becomes possible to make more accurate hospital recommendations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the results of blood tests into AI and have AI perform the analysis.

[0067] The proposal unit can identify hospitals based on specialist data. For example, the proposal unit can identify hospitals based on specialist data. For example, the proposal unit can identify hospitals based on specialist qualifications and treatment records. The proposal unit can also evaluate hospitals based on specialist data. For example, the proposal unit can evaluate hospitals based on specialist treatment records and patient evaluations. The proposal unit can also set hospital selection criteria based on specialist data. For example, the proposal unit can set hospital selection criteria based on specialist qualifications and treatment records. This makes it possible to propose appropriate hospitals by identifying hospitals based on specialist data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input specialist data into AI and have the AI ​​perform hospital identification.

[0068] The estimation unit can estimate a disease name by analyzing the patient's physical condition, including body temperature, nausea, and headache, as well as the location and severity of pain. For example, the estimation unit can analyze body temperature. For example, if the body temperature is high, the estimation unit estimates a disease accompanied by fever. The estimation unit can also analyze nausea. For example, if nausea is present, the estimation unit estimates a disease of the digestive system. The estimation unit can also analyze headaches. For example, if headaches are present, the estimation unit estimates a disease of the nervous system. The estimation unit can also analyze the location and severity of pain. For example, if the pain is concentrated in a specific area, the estimation unit estimates a disease related to that area. By analyzing the patient's physical condition and the location and severity of pain, a more accurate estimation of the disease name becomes possible. Some or all of the above processing in the estimation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the estimation unit can input physical condition information such as body temperature, nausea, and headache into a generation AI and have the generation AI perform the disease name estimation.

[0069] The display unit can display a list of hospitals that should be visited based on the estimated disease name. For example, the display unit can display a list of hospitals that should be visited based on the estimated disease name. For example, the display unit can select a hospital based on the estimated disease name, taking into account the hospital's specialty, distance, and rating. The display unit can also display the list of hospitals visually. For example, the display unit can display information such as the hospital's name, address, telephone number, and medical department. The display unit can also display hospital ratings and reviews. For example, the display unit can display hospital rating scores and patient reviews. This allows the user to select an appropriate hospital by displaying a list of hospitals that should be visited based on the estimated disease name. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input a list of hospitals that should be visited based on the estimated disease name into a generating AI and have the generating AI display the list of hospitals.

[0070] The reception desk can estimate the user's emotions and change the method of inputting health information based on the estimated emotions. For example, the reception desk can estimate the user's emotions using facial recognition technology. The reception desk can also estimate the user's emotions using voice analysis technology. For example, the reception desk can analyze the tone and speed of the user's voice to estimate their emotions. The reception desk can also change the method of inputting health information based on the user's emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of health information. This allows for more appropriate input by adjusting the method of inputting health information according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0071] The reception desk can analyze the user's past health information and provide an appropriate input format. For example, the reception desk can analyze the user's past health information. For example, the reception desk can automatically display frequently entered items based on the health information the user has entered in the past. The reception desk can also prioritize displaying input items related to specific symptoms based on the user's past health information. For example, the reception desk can analyze the user's past health information and provide shortcuts to reduce the effort required for input. This reduces the effort required for input and enables efficient input by analyzing past health information. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past health information into AI and have AI provide the input format.

[0072] The reception desk can filter the input of health information based on the user's lifestyle and medical history. For example, the reception desk considers the user's lifestyle. For instance, it prioritizes inputting relevant health information based on the user's smoking and drinking habits. The reception desk can also consider the user's medical history. For example, it filters relevant health information based on the user's past medical history. Furthermore, the reception desk can customize input fields considering the user's lifestyle and medical history. For example, it automatically adjusts input fields based on the user's lifestyle and medical history. This allows for the input of more relevant health information by filtering based on lifestyle and medical history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's lifestyle and medical history into an AI and have the AI ​​perform the filtering.

[0073] The reception desk can estimate the user's emotions and determine the order in which to input health information based on the estimated emotions. For example, the reception desk can estimate the user's emotions using facial recognition technology. The reception desk can also estimate the user's emotions using voice analysis technology. For example, the reception desk can analyze the tone and speed of the user's voice to estimate their emotions. The reception desk can also determine the order in which to input health information based on the user's emotions. For example, if the user is feeling anxious, the reception desk may prompt them to prioritize inputting important health information. The reception desk may also suggest inputting detailed health information if the user is relaxed. The reception desk may also prompt the user to prioritize inputting the most important health information if the user is in a hurry. In this way, by prioritizing health information according to the user's emotions, important information can be prioritized. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0074] The reception desk can prioritize inputting highly relevant information when users enter their health information, taking into account their geographical location. For example, the reception desk can consider the user's geographical location. For instance, based on the user's current location, the reception desk prioritizes inputting health information related to region-specific diseases and symptoms. The reception desk can also consider the user's geographical location and refer to their medical history at nearby medical institutions. For example, based on the user's geographical location, the reception desk inputs health information related to the region's climate and environment. This allows for the priority input of information related to region-specific diseases and symptoms by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into AI and have AI input highly relevant information.

[0075] The reception desk can analyze the user's social media activity and input relevant information when the user enters their health information. For example, the reception desk can analyze the user's social media activity. For example, the reception desk can extract and input information about the user's recent health from the user's social media posts. The reception desk can also analyze the user's social media activity and automatically input keywords related to their health. For example, the reception desk can supplement the health information based on the user's social media activity history. In this way, information related to health can be supplemented by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into AI and have the AI ​​input the relevant information.

[0076] The estimation unit can estimate the user's emotions and modify the method of estimating the disease name based on the estimated user emotions. For example, the estimation unit can estimate the user's emotions using facial recognition technology. The estimation unit can also estimate the user's emotions using voice analysis technology. For example, the estimation unit can analyze the tone and speed of the user's voice to estimate emotions. Furthermore, the estimation unit can modify the method of estimating the disease name based on the user's emotions. For example, if the user is feeling anxious, the estimation unit can provide a disease name estimation result that includes a detailed explanation. If the user is relaxed, the estimation unit can also provide a concise disease name estimation result. Furthermore, if the user is in a hurry, the estimation unit can quickly estimate the disease name and display the result. In this way, by adjusting the method of estimating the disease name according to the user's emotions, more appropriate estimation results can be provided. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0077] The estimation unit can adjust the level of detail of its estimation based on the importance of health information when estimating a disease name. For example, the estimation unit evaluates the importance of health information based on the severity of symptoms or the frequency of onset. The estimation unit can also adjust the level of detail of its estimation based on the importance of health information. For example, the estimation unit can provide a detailed disease name estimation result based on important health information. The estimation unit can also provide a concise disease name estimation result based on minor health information. Furthermore, the estimation unit adjusts the level of detail of its estimation results according to the importance of health information. By adjusting the level of detail of the estimation according to the importance of health information, more accurate estimation results can be provided. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without using AI. For example, the estimation unit can input the importance of health information into AI and have AI perform the adjustment of the level of detail of the estimation.

[0078] The estimation unit can apply different estimation algorithms depending on the category of health information when estimating a disease name. For example, the estimation unit classifies the categories of health information. For example, the estimation unit classifies health information into categories such as internal medicine, surgery, and psychiatry. The estimation unit can also apply different estimation algorithms depending on the category of health information. For example, the estimation unit applies a standard estimation algorithm to common symptoms such as body temperature and nausea. The estimation unit can also apply a specialized estimation algorithm to pain in a specific area or its degree. Furthermore, the estimation unit selects the optimal estimation algorithm according to the category of health information. By applying the optimal estimation algorithm according to the category of health information, more accurate estimation results can be provided. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input the categories of health information into AI and have the AI ​​perform the application of the estimation algorithm.

[0079] The estimation unit can estimate the user's emotions and change the order in which it displays the estimated disease names based on the estimated user's emotions. For example, the estimation unit can estimate the user's emotions using facial recognition technology. The estimation unit can also estimate the user's emotions using voice analysis technology. For example, the estimation unit can analyze the tone and speed of the user's voice to estimate their emotions. The estimation unit can also change the order in which it displays the estimated disease names based on the user's emotions. For example, if the user is feeling anxious, the estimation unit will display the most likely disease name first. If the user is relaxed, the estimation unit can also adjust the order according to the likelihood of the disease name. If the user is in a hurry, the estimation unit will quickly display the estimated disease name. This allows for the provision of more appropriate information by adjusting the order in which the estimated disease name is displayed according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0080] The estimation unit can determine the priority of estimations based on the timing of submission of health information when estimating a disease name. For example, the estimation unit evaluates the timing of submission of health information. For example, the estimation unit evaluates the submission timing based on the date and time of submission and the frequency of submission of health information. The estimation unit can also determine the priority of estimations based on the timing of submission of health information. For example, the estimation unit prioritizes estimating disease names based on recently submitted health information. The estimation unit can also adjust the priority of estimations based on past health information. Furthermore, the estimation unit determines the priority of estimations according to the timing of submission of health information. This allows for faster estimation results by determining the priority of estimations according to the timing of submission of health information. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without using AI. For example, the estimation unit can input the timing of submission of health information into AI and have AI perform the determination of the priority of estimations.

[0081] The estimation unit can adjust the order of estimations based on the relevance of health information when estimating disease names. For example, the estimation unit evaluates the relevance of health information based on commonalities in symptoms or onset patterns. The estimation unit can also adjust the order of estimations based on the relevance of health information. For example, the estimation unit displays the most relevant disease name first based on the relevance of health information. The estimation unit can also adjust the order of estimation results according to the relevance of health information. Furthermore, the estimation unit analyzes the relevance of health information and optimizes the order of estimations. By adjusting the order of estimations based on the relevance of health information, more relevant information can be provided. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input the relevance of health information into AI and have AI perform the adjustment of the estimation order.

[0082] The display unit can estimate the user's emotions and change the way the hospital list is displayed based on the estimated emotions. For example, the display unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the display unit can change the way the hospital list is displayed based on the user's emotions. For example, if the user is feeling anxious, the display unit can provide a simple and highly visible display. If the user is relaxed, the display unit can also provide a display that includes detailed information. If the user is in a hurry, the display unit can provide a concise display. By adjusting the display method of the hospital list according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0083] The display unit can adjust the level of detail displayed based on the importance of the disease name when displaying a list of hospitals. The display unit can, for example, evaluate the importance of the disease name. For example, the display unit can evaluate the importance of the disease name based on the severity of the symptoms or the frequency of occurrence. The display unit can also adjust the level of detail displayed based on the importance of the disease name. For example, the display unit can display detailed hospital information based on important disease names. The display unit can also display concise hospital information based on minor disease names. Furthermore, the display unit adjusts the level of detail displayed according to the importance of the disease name. This allows the display unit to provide the user with the information they need by adjusting the level of detail displayed according to the importance of the disease name. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can input the importance of the disease name into AI and have AI perform the adjustment of the level of detail displayed.

[0084] The display unit can apply different display algorithms depending on the disease name category when displaying a list of hospitals. For example, the display unit classifies disease names into categories such as internal medicine, surgery, and psychiatry. The display unit can also apply different display algorithms depending on the disease name category. For example, the display unit applies a standard display algorithm to common disease names. The display unit can also apply a specialized display algorithm to disease names related to specific medical fields. Furthermore, the display unit selects the optimal display algorithm according to the disease name category. This allows the display unit to provide information that is easy for the user to understand by applying the optimal display algorithm according to the disease name category. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input disease name categories into AI and have AI perform the application of display algorithms.

[0085] The display unit can estimate the user's emotions and change the display order of the hospital list based on the estimated emotions. The display unit can estimate the user's emotions, for example, by using facial recognition technology. The display unit can also estimate the user's emotions using voice analysis technology, for example, by analyzing the tone and speed of the user's voice. The display unit can also change the display order of the hospital list based on the user's emotions. For example, if the user is feeling anxious, the display unit will display the most reliable hospital first. The display unit can also adjust the order based on hospital ratings if the user is relaxed. The display unit can also display hospitals where quick consultations are possible first if the user is in a hurry. By adjusting the display order of the hospital list according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0086] The display unit can determine the display priority based on the timing of disease name submission when displaying a list of hospitals. The display unit evaluates the timing of disease name submission, for example. For example, the display unit evaluates the submission timing based on the date and frequency of submission. The display unit can also determine the display priority based on the timing of disease name submission. For example, the display unit prioritizes displaying hospitals based on recently submitted disease names. The display unit can also adjust the display priority based on past disease names. Furthermore, the display unit determines the display priority according to the timing of disease name submission. This enables faster information provision by determining the display priority according to the timing of disease name submission. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the timing of disease name submission into AI and have AI perform the determination of the display priority.

[0087] The display unit can adjust the display order based on the relevance of disease names when displaying a list of hospitals. The display unit can evaluate the relevance of disease names, for example, by evaluating the relevance of disease names based on commonalities in symptoms or onset patterns. The display unit can also adjust the display order based on the relevance of disease names. For example, the display unit can display the most relevant hospital first based on the relevance of disease names. The display unit can also adjust the display order according to the relevance of disease names. Furthermore, the display unit analyzes the relevance of disease names and optimizes the display order. This allows for the provision of more relevant information by adjusting the display order based on the relevance of disease names. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the relevance of disease names into AI and have AI perform the adjustment of the display order.

[0088] The analysis unit can estimate the user's emotions and modify the analysis method of the test results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions using facial recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate emotions. Furthermore, the analysis unit can modify the analysis method of the test results based on the user's emotions. For example, if the user is feeling anxious, the analysis unit can provide analysis results that include a detailed explanation. The analysis unit can also provide concise analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results quickly. In this way, by adjusting the analysis method of the test results according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the test results when analyzing the test results. For example, the analysis unit evaluates the importance of the test results. For example, the analysis unit evaluates the importance of the test results based on the range of abnormal values ​​or the importance of the test items. The analysis unit can also adjust the level of detail of the analysis based on the importance of the test results. For example, the analysis unit provides detailed analysis results based on important test results. The analysis unit can also provide concise analysis results based on minor test results. The analysis unit adjusts the level of detail of the analysis according to the importance of the test results. By adjusting the level of detail of the analysis according to the importance of the test results, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the importance of the test results into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the category of the test results when analyzing the test results. For example, the analysis unit classifies the categories of the test results. For example, the analysis unit classifies the test results into categories such as blood tests, imaging diagnostics, and genetic tests. The analysis unit can also apply different analysis algorithms depending on the category of the test results. For example, the analysis unit can apply a standard analysis algorithm to blood test results. The analysis unit can also apply a specialized analysis algorithm to MRI results. Furthermore, the analysis unit can select the optimal analysis algorithm according to the category of the test results. By applying the optimal analysis algorithm according to the category of the test results, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the categories of the test results into the AI ​​and have the AI ​​execute the application of the analysis algorithm.

[0091] The analysis unit can estimate the user's emotions and change the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions using facial recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the analysis unit can change the display method of the analysis results based on the user's emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0092] The analysis unit can determine the priority of analysis based on the submission timing of the test results when analyzing test results. For example, the analysis unit evaluates the submission timing of the test results. For example, the analysis unit evaluates the submission timing based on the submission date and frequency of the test results. The analysis unit can also determine the priority of analysis based on the submission timing of the test results. For example, the analysis unit prioritizes analysis based on recently submitted test results. The analysis unit can also adjust the priority of analysis based on past test results. The analysis unit also determines the priority of analysis according to the submission timing of the test results. This allows for faster analysis results to be provided by determining the priority of analysis according to the submission timing of the test results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the submission timing of the test results into the AI ​​and have the AI ​​perform the determination of the analysis priority.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the test results when analyzing the test results. For example, the analysis unit evaluates the relevance of the test results. For example, the analysis unit evaluates the relevance of the test results based on commonalities of abnormal values ​​or the relevance of test items. The analysis unit can also adjust the order of analysis based on the relevance of the test results. For example, the analysis unit analyzes the most relevant results first based on the relevance of the test results. The analysis unit can also adjust the order of analysis according to the relevance of the test results. Furthermore, the analysis unit analyzes the relevance of the test results and optimizes the order of analysis. By adjusting the order of analysis based on the relevance of the test results, more relevant information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the relevance of the test results into AI and have AI perform the adjustment of the order of analysis.

[0094] The suggestion unit can estimate the user's emotions and modify its hospital recommendation method based on the estimated emotions. For example, the suggestion unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the suggestion unit can modify its hospital recommendation method based on the user's emotions. For example, if the user is feeling anxious, it will provide hospital recommendations that include detailed explanations. If the user is relaxed, it can provide concise hospital recommendations. If the user is in a hurry, it will provide hospital recommendations quickly. This allows for more appropriate recommendations by adjusting the hospital recommendation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0095] The suggestion unit can adjust the level of detail in its hospital suggestions based on the importance of the test results. For example, the suggestion unit evaluates the importance of the test results. For example, the suggestion unit evaluates the importance of the test results based on the range of abnormal values ​​and the importance of the test items. The suggestion unit can also adjust the level of detail in its suggestions based on the importance of the test results. For example, the suggestion unit can make detailed hospital suggestions based on important test results. The suggestion unit can also make concise hospital suggestions based on minor test results. The suggestion unit adjusts the level of detail in its suggestions according to the importance of the test results. This allows for more accurate suggestions by adjusting the level of detail in the suggestions according to the importance of the test results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the importance of the test results into the AI ​​and have the AI ​​adjust the level of detail in the suggestions.

[0096] The suggestion unit can apply different suggestion algorithms depending on the category of the test results when making hospital suggestions. For example, the suggestion unit classifies the categories of test results. For example, the suggestion unit classifies test results into categories such as blood tests, imaging diagnostics, and genetic tests. The suggestion unit can also apply different suggestion algorithms depending on the category of the test results. For example, the suggestion unit can apply a standard suggestion algorithm to blood test results. The suggestion unit can also apply a specialized suggestion algorithm to MRI results. Furthermore, the suggestion unit selects the most appropriate suggestion algorithm depending on the category of the test results. This allows for more accurate suggestions by applying the most appropriate suggestion algorithm according to the category of the test results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the categories of the test results into AI and have AI perform the application of the suggestion algorithm.

[0097] The suggestion unit can estimate the user's emotions and change the order of suggested hospitals based on the estimated emotions. For example, the suggestion unit can estimate the user's emotions using facial recognition technology. The suggestion unit can also estimate the user's emotions using voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the suggestion unit can change the order of suggested hospitals based on the user's emotions. For example, if the user is feeling anxious, the suggestion unit will suggest the most reliable hospital first. If the user is relaxed, the suggestion unit can also adjust the order based on hospital ratings. If the user is in a hurry, the suggestion unit will suggest hospitals where they can be seen quickly first. This allows for more appropriate suggestions by adjusting the order of suggested hospitals according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0098] The proposal department can determine the priority of hospital proposals based on the timing of test result submissions. The proposal department can evaluate the timing of test result submissions, for example. For example, the proposal department can evaluate the submission timing based on the date and frequency of submission. The proposal department can also determine the priority of proposals based on the timing of test result submissions. For example, the proposal department can prioritize hospital proposals based on recently submitted test results. The proposal department can also adjust the priority of proposals based on past test results. Furthermore, the proposal department determines the priority of proposals according to the timing of test result submissions. This allows for faster proposals by determining the priority of proposals according to the timing of test result submissions. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the timing of test result submissions into AI and have the AI ​​determine the priority of proposals.

[0099] The suggestion unit can adjust the order of suggestions based on the relevance of the test results when suggesting hospitals. For example, the suggestion unit evaluates the relevance of the test results. For example, the suggestion unit evaluates the relevance of the test results based on commonalities of abnormal values ​​or the relevance of test items. The suggestion unit can also adjust the order of suggestions based on the relevance of the test results. For example, the suggestion unit suggests the most relevant hospital first based on the relevance of the test results. The suggestion unit can also adjust the order of suggestions according to the relevance of the test results. Furthermore, the suggestion unit analyzes the relevance of the test results and optimizes the order of suggestions. This makes it possible to make more relevant suggestions by adjusting the order of suggestions based on the relevance of the test results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the relevance of the test results into AI and have AI perform the adjustment of the order of suggestions.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The reception desk can receive user health information and also allow users to input records of their lifestyle and diet. For example, the reception desk provides an interface for users to input their daily meals and exercise levels, and stores this information in a database. Furthermore, the reception desk can regularly record information about the user's lifestyle, which can be used for long-term health management. This allows for integrated management of the user's health information and lifestyle information, enabling a more accurate understanding of their health status.

[0102] The analysis unit can consider the user's genetic information when analyzing the user's health information. For example, the analysis unit can store the user's genetic test results in a database and perform analysis by combining health information and genetic information. Furthermore, the analysis unit can assess the risk of specific diseases based on genetic information and suggest preventive measures. This enables more accurate analysis that takes into account the user's genetic background.

[0103] The suggestion department can provide health management advice based on the results of analyzing the user's physical condition information. For example, the suggestion department can propose appropriate exercise and meal plans based on the user's physical condition information. Furthermore, the suggestion department can also provide advice on stress management and sleep improvement based on the user's physical condition information. This allows users to receive specific advice on maintaining their health in their daily lives.

[0104] The estimation unit can consider the influence of seasons and weather when analyzing the user's health information. For example, the estimation unit stores seasonal disease incidence trends and changes in health due to weather changes in a database and incorporates this information into the analysis. Furthermore, the estimation unit can provide health management advice tailored to the season and weather. This enables more accurate disease diagnosis estimation that takes into account the influence of seasons and weather.

[0105] The display unit can provide health-related educational content based on the analysis of the user's health information. For example, the display unit can show information and preventative measures for specific diseases based on the user's health information. The display unit can also provide health-related videos and articles, offering users opportunities to learn about health. This allows users to deepen their knowledge about their own health.

[0106] The reception desk can estimate the user's emotions and customize the input interface for health information based on those emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive interface to reduce the effort required for input. Conversely, if the user is relaxed, the reception desk can provide more detailed input options to allow for the input of more information. This allows for more appropriate input by adjusting the input interface according to the user's emotions.

[0107] The estimation unit can estimate the user's emotions and change how it displays the estimated diagnosis based on those emotions. For example, if the user is feeling anxious, the estimation unit can provide a detailed explanation of the diagnosis to reassure them. Conversely, if the user is relaxed, the estimation unit can provide a concise diagnosis. By adjusting how the diagnosis is displayed according to the user's emotions, more appropriate information can be provided.

[0108] The analysis unit can estimate the user's emotions and modify the analysis method of the test results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide analysis results that include detailed explanations to reassure them. Conversely, if the user is relaxed, the analysis unit can provide concise analysis results. By adjusting the analysis method of the test results according to the user's emotions, more appropriate analysis results can be provided.

[0109] The suggestion function can estimate the user's emotions and modify its hospital recommendation method based on those estimates. For example, if the user is feeling anxious, the suggestion function will provide hospital recommendations that include detailed explanations. Conversely, if the user is relaxed, the suggestion function can provide concise hospital recommendations. By adjusting the hospital recommendation method according to the user's emotions, more appropriate recommendations can be made.

[0110] The display unit can estimate the user's emotions and change how the hospital list is displayed based on those emotions. For example, if the user is feeling anxious, the display unit provides a simple and highly visible display. Conversely, if the user is relaxed, the display unit can provide a display that includes detailed information. By adjusting the display of the hospital list according to the user's emotions, more appropriate information can be provided.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk receives the user's health information. This information includes body temperature, blood pressure, heart rate, and details of symptoms. The reception desk can record the information entered by the user, such as body temperature, nausea, and headache, and save it in a database. Step 2: The estimation unit analyzes the health information received by the reception unit to estimate the name of the illness. The estimation unit uses AI to analyze the health information, including body temperature, nausea, headaches, and the location and severity of pain, to estimate the name of the illness. Step 3: The display unit shows the hospitals the user should visit based on the disease name estimated by the estimation unit. Based on the estimated disease name, the display unit selects a hospital considering factors such as the hospital's specialty, distance, and rating, and displays it to the user. Step 4: The analysis unit analyzes the test results after the consultation. The analysis unit analyzes the results of blood tests, MRI, CRI, X-rays, and ultrasound examinations, and uses AI to estimate the disease name and treatment plan. Step 5: The Proposal Department suggests hospitals to refer the user to based on the results analyzed by the Analysis Department. The Proposal Department identifies hospitals based on specialist data and suggests which hospitals the user should visit.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the reception unit, estimation unit, display unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and records information such as body temperature, nausea, and headache entered by the user. The estimation unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses AI to analyze the physical condition information and estimate the name of the disease. The display unit is implemented by the output device 40 of the smart device 14 and displays a list of hospitals to be visited based on the estimated name of the disease. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the results of blood tests, MRI, CRI, X-rays, and ultrasound examinations. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and suggests hospitals to be referred to based on the analyzed results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, estimation unit, display unit, analysis unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and records information such as body temperature, nausea, and headache entered by the user. The estimation unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses AI to analyze physical condition information and estimate the name of the disease. The display unit is implemented by the speaker 240 of the smart glasses 214 and displays a list of hospitals to be visited based on the estimated name of the disease. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the results of blood tests, MRI, CRI, X-rays, and ultrasound examinations. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and suggests hospitals to be referred to based on the analyzed results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, estimation unit, display unit, analysis unit, and suggestion unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and records information such as body temperature, nausea, and headache entered by the user. The estimation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses AI to analyze physical condition information and estimate the name of the disease. The display unit is implemented by, for example, the display 343 of the headset terminal 314 and displays a list of hospitals to be visited based on the estimated name of the disease. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the results of blood tests, MRI, CRI, X-rays, and ultrasound examinations. The suggestion unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and suggests hospitals to be referred to based on the analyzed results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, estimation unit, display unit, analysis unit, and suggestion unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and records information such as body temperature, nausea, and headache entered by the user. The estimation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses AI to analyze physical condition information and estimate the name of the disease. The display unit is implemented by, for example, the speaker 240 of the robot 414 and displays a list of hospitals to be visited based on the estimated name of the disease. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the results of blood tests, MRI, CRI, X-rays, and ultrasound examinations. The suggestion unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and suggests hospitals to be referred to based on the analyzed results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A reception desk that receives users' health information, An estimation unit that analyzes the health information received by the reception unit and estimates the name of the disease, A display unit that shows the hospital to which a patient should be treated based on the disease name estimated by the estimation unit, The analysis unit analyzes the test results after the examination, The system includes a proposal unit that suggests hospitals to be recommended based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Record the user's health information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the results of blood tests, MRI, CRI, X-rays, and ultrasound examinations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Identify hospitals based on specialist data The system described in Appendix 1, characterized by the features described herein. (Note 5) The estimation unit, The system analyzes body temperature, nausea, headache symptoms, and the location and severity of pain to estimate the diagnosis. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is Display a list of hospitals you should visit based on the estimated diagnosis. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and changes the method of inputting health information based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past health information and provide an appropriate input format. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter their health information, filtering is performed based on their lifestyle and medical history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and determines the order of input health information based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter health information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter their health information, the system analyzes their social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The estimation unit, The system estimates the user's emotions and modifies the method of estimating the disease name based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The estimation unit, When estimating a disease name, the level of detail of the estimation is adjusted based on the importance of the health information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The estimation unit, When estimating a disease name, different estimation algorithms are applied depending on the category of health information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The estimation unit, The system estimates the user's emotions and changes the order in which the estimated disease names are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The estimation unit, When estimating a disease, the priority of estimation is determined based on when the health information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The estimation unit, When estimating a disease name, the order of estimations is adjusted based on the relevance of health information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is The system estimates the user's emotions and changes how the hospital list is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is When displaying a list of hospitals, adjust the level of detail based on the importance of the disease name. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is When displaying a list of hospitals, different display algorithms are applied depending on the disease category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is The system estimates the user's emotions and changes the display order of the hospital list based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is When displaying a list of hospitals, the display priority is determined based on when the diagnosis was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is When displaying a list of hospitals, the display order will be adjusted based on the relevance of the disease names. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, The system estimates the user's emotions and modifies the analysis method of the test results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, When analyzing test results, adjust the level of detail of the analysis based on the importance of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit, When analyzing test results, different analysis algorithms are applied depending on the category of the test result. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit, It estimates the user's emotions and changes how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit, When analyzing test results, the priority of analysis is determined based on when the test results were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit, When analyzing test results, the order of analysis is adjusted based on the relevance of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, The system estimates the user's emotions and modifies how hospitals are recommended based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When proposing hospital plans, adjust the level of detail based on the importance of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When proposing hospitals, different proposal algorithms are applied depending on the category of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, The system estimates the user's emotions and changes the order of hospital suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When hospitals submit proposals, the priority of the proposals is determined based on the timing of the submission of test results. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When proposing hospitals, adjust the order of proposals based on the relevance of the test results. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives users' health information, An estimation unit that analyzes the health information received by the reception unit and estimates the name of the disease, A display unit that shows the hospital to which a patient should be treated based on the disease name estimated by the estimation unit, The analysis unit analyzes the test results after the examination, The system comprises a proposal unit that suggests hospitals to be introduced based on the results of the analysis performed by the aforementioned analysis unit, The aforementioned reception unit is The system estimates the user's emotions and changes the method of inputting health information based on the estimated emotions. A system characterized by the following features.

2. A reception desk that receives users' health information, An estimation unit that analyzes the health information received by the reception unit and estimates the name of the disease, A display unit that shows the hospital to which a patient should be treated based on the disease name estimated by the estimation unit, The analysis unit analyzes the test results after the examination, The system comprises a proposal unit that suggests hospitals to be introduced based on the results of the analysis performed by the aforementioned analysis unit, The aforementioned reception unit is The system estimates the user's emotions and determines the order of input health information based on the estimated emotions. A system characterized by the following features.

3. The aforementioned reception unit is Record the user's health information. The system according to claim 1 or 2, characterized in that it is the same as described in claim 1 or 2.

4. The aforementioned proposal section is, Identify hospitals based on specialist data The system according to claim 1 or 2, characterized in that it is the same as described in claim 1 or 2.

5. The aforementioned display unit is Display a list of hospitals you should visit based on the estimated diagnosis. The system according to claim 1 or 2, characterized in that it is the same as described in claim 1 or 2.

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