System

Abstract

An object of a system according to an embodiment is to quickly and accurately determine the necessity and urgency of a medical examination based on the symptoms of a disease and to support the optimal distribution of medical resources.SOLUTION: A system includes a collection part, an analysis part, a support part, and a cooperation part. The collection unit collects information on a symptom of a user. The analysis unit evaluates the severity of the symptom based on the information collected by the collection unit, and determines the necessity or urgency of medical examination. The support unit supports determination of medical examination or suppression of medical expenses based on a result obtained by the analysis unit. The cooperation unit cooperates the result obtained by the support unit with the hospital.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has made it difficult to quickly and accurately determine the need for medical examination and urgency based on the symptoms of a disease, making it difficult to optimally allocate medical resources.

[0005] The system according to the embodiment aims to quickly and accurately determine the necessity and urgency of medical examination based on the symptoms of a disease, and to support optimal allocation of medical resources. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a support unit, and a linking unit. The collection unit collects information on the user's symptoms. The analysis unit evaluates the severity of the symptoms based on the information collected by the collection unit and determines the necessity or urgency of medical examination. The support unit supports the decision to see a doctor or the reduction of medical expenses based on the results obtained by the analysis unit. The linking unit links the results obtained by the support unit with hospitals. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately determine the necessity and urgency of medical examination based on the symptoms of the disease, and can support optimal allocation of medical resources. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An online medical interview system according to an embodiment of the present invention determines the need for and urgency of a medical examination based on a user's symptoms, thereby supporting the decision to see a doctor and reducing medical costs. The online medical interview system begins with the user completing a simple online medical interview. This interview is conducted using a generation AI, which asks the user questions about their symptoms. The generation AI then analyzes the user's responses to determine the need for and urgency of a medical examination. For example, if the user has symptoms such as a fever or cough, the generation AI evaluates the severity of the symptoms and determines whether an emergency medical examination is necessary. Furthermore, the generation AI supports the decision to see a doctor and reduce medical costs based on the results of the interview. For example, if the symptoms are mild, the generation AI suggests home remedies and advises the user to refrain from visiting a medical institution. On the other hand, if the symptoms are severe, the generation AI recommends immediate medical examination. Furthermore, the results of the interview are shared with hospitals, enabling smooth medical treatment. For example, by sharing the results of the interview with a doctor before the user visits the hospital, medical preparations are completed and a prompt examination can be performed. This allows the online medical interview system to reduce the burden on medical institutions and reduce medical costs. This allows the online medical interview system to determine the need and urgency of a doctor's examination based on the user's symptoms, and to support the decision to see a doctor and the reduction of medical costs. For example, by allowing users to take a medical interview online, quick and accurate examinations can be performed, reducing the burden on medical institutions. In addition, users can learn appropriate treatment methods based on their symptoms, which also contributes to the reduction of medical costs.

[0029] The online medical interview system according to the embodiment includes a collection unit, an analysis unit, a support unit, and a linking unit. The collection unit collects information about a user's symptoms. The information about the user's symptoms includes, but is not limited to, fever, cough, and headache. The collection unit collects, for example, symptom information entered by the user online. The collection unit can also use a generation AI to present questions about the user's symptoms and collect information based on the user's answers. For example, the collection unit presents specific questions such as "Do you have a fever?" or "Do you have a cough?" The analysis unit uses the generation AI to evaluate the severity of the symptoms based on the information collected by the collection unit and determine the need for and urgency of a medical examination. For example, the analysis unit evaluates the severity of the symptoms based on the user's answers and determines the need for and urgency of a medical examination. The analysis unit can also use the generation AI to analyze the user's answers and evaluate the severity of the symptoms. The support unit supports the user in deciding whether to see a doctor and reducing medical expenses based on the results obtained by the analysis unit. For example, the support unit suggests home remedies for mild symptoms and recommends visiting a medical institution for severe symptoms. The support unit can also use a generation AI to support the decision to visit a medical institution and the reduction of medical expenses. The linking unit links the results obtained by the support unit with hospitals. For example, the linking unit can share the results of the medical interview with a doctor to ensure smooth medical treatment. The linking unit can also use a generation AI to share the results of the medical interview with a doctor. As a result, the online medical interview system according to the embodiment can determine the necessity and urgency of a medical examination based on the user's symptoms, and support the decision to visit a medical institution and the reduction of medical expenses. For example, by allowing a user to take a medical interview online, a quick and accurate examination can be performed, reducing the burden on medical institutions. Furthermore, the user can learn appropriate remedies according to their symptoms, which also contributes to the reduction of medical expenses.

[0030] The online medical interview system includes a questioning unit that presents specific questions about the user's symptoms. The questioning unit presents specific questions about the user's symptoms. Examples of specific questions include, but are not limited to, questions such as, "Do you have a fever?" and "Do you have a cough?" The questioning unit can also present questions about the user's symptoms using a generation AI. For example, the generation AI generates a prompt such as "Do you have a fever?" and presents it to the user. The questioning unit can also present questions in various formats, such as multiple-choice format or free-form format. For example, the questioning unit can present questions in a format in which the user selects an answer from multiple-choice options, or in a format in which the user freely writes an answer. In this way, by presenting specific questions about the user's symptoms, more accurate information can be collected.

[0031] The analysis unit can evaluate the severity of symptoms based on the user's answers and determine the need for and urgency of medical examination. The analysis unit uses generative AI to evaluate the severity of symptoms based on the user's answers and determine the need for and urgency of medical examination. For example, the analysis unit analyzes the user's answers and evaluates the severity of symptoms. The analysis unit can also use generative AI to evaluate the severity of symptoms based on the user's answers. For example, the analysis unit can take into account the temperature and duration of the fever to determine whether emergency medical examination is necessary. The analysis unit can also evaluate the frequency and intensity of coughing and determine the need for medical examination. Furthermore, the analysis unit can evaluate the severity and duration of headaches and determine the need for medical examination. This enables appropriate response by evaluating the severity of symptoms based on the user's answers and determining the need for medical examination and urgency.

[0032] The support unit can suggest home remedies for mild symptoms and recommend a medical visit for severe symptoms. The support unit uses generative AI to suggest home remedies for mild symptoms and recommend a medical visit for severe symptoms. For example, for a mild headache, the support unit can suggest adequate rest and hydration as home remedies. For a mild cough, the support unit can also suggest drinking hot drinks as home remedies. For a mild fever, the support unit can also suggest using cooling sheets as home remedies. On the other hand, for severe symptoms, the support unit can recommend immediate medical visit. For example, the support unit can recommend a medical visit if a high fever persists. The support unit can also recommend immediate medical visit if there is severe chest pain. The support unit can also recommend emergency medical visit if there is difficulty breathing. This allows for appropriate remedies to be suggested based on the severity of the symptoms, thereby reducing medical costs and providing prompt medical care.

[0033] The collaboration unit shares the medical interview results with the doctor, enabling smooth medical treatment. The collaboration unit uses generative AI to share the medical interview results with the doctor, enabling smooth medical treatment. For example, the collaboration unit can share the medical interview results with the doctor before the user visits the hospital, which allows preparations for medical treatment to be made and enables a prompt examination. The collaboration unit can also use generative AI to automatically send the medical interview results to the doctor. For example, the collaboration unit can send the medical interview results to the doctor by email. The collaboration unit can also automatically register the medical interview results in the hospital's electronic medical record system. Furthermore, the collaboration unit can enable the doctor to check the medical interview results in real time. For example, the collaboration unit can display the medical interview results on the doctor's tablet device. In this way, sharing the medical interview results with the doctor allows preparations for medical treatment to be made and enables a prompt examination.

[0034] The question unit can pose specific questions such as "Do you have a fever?" or "Do you have a cough?" The question unit uses a generation AI to pose specific questions such as "Do you have a fever?" or "Do you have a cough?" For example, the question unit generates a prompt such as "Do you have a fever?" and presents it to the user. The question unit can also generate a prompt such as "Do you have a cough?" and present it to the user. The question unit can also generate a prompt such as "Do you have a headache?" and present it to the user. The question unit can pose questions in various formats, such as multiple-choice format or free-form format. For example, the question unit can pose questions in a format where the user selects an answer from multiple-choice format, or in a format where the user freely writes an answer. In this way, by presenting specific questions, detailed information about the user's symptoms can be collected.

[0035] The collection unit can analyze the user's past symptom history and select the optimal collection method. The collection unit uses the generation AI to analyze the user's past symptom history and select the optimal collection method. For example, the collection unit prioritizes collecting symptoms that the user has frequently reported in the past. The collection unit can also find specific patterns from the user's past symptom history and adjust questions based on those patterns. Furthermore, if symptoms appear during a specific time period based on the user's past symptom history, the collection unit can also collect data during that time period. The collection unit can also analyze the user's past symptom history using the generation AI. For example, the collection unit uses the generation AI to analyze the user's past symptom data and select the optimal collection method. In this way, the optimal collection method can be selected by analyzing the past symptom history.

[0036] The collection unit can filter the symptom information based on the user's current living situation and environment when collecting the symptom information. The collection unit uses the generation AI to filter the symptom information based on the user's current living situation and environment when collecting the symptom information. For example, the collection unit prioritizes collecting concise questions when the user is out. The collection unit can also collect detailed symptom information when the user is at home. Furthermore, when the user is at work, the collection unit can collect information in a short time so as not to interfere with work. The collection unit can also use the generation AI to analyze the user's living situation and environment and perform filtering. For example, the collection unit uses the generation AI to analyze the user's location information and determine whether the user is out. The collection unit can also use the generation AI to analyze the user's schedule information and determine whether the user is at work. This enables more appropriate information collection by filtering information based on the user's living situation and environment.

[0037] The collection unit can select the optimal collection means depending on the user's input method when collecting symptom information. The collection unit uses the generation AI to select the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting symptom information. For example, if the user prefers voice input, the collection unit can collect symptom information by voice. Also, if the user prefers text input, the collection unit can collect symptom information by text. Furthermore, if the user can provide an image, the collection unit can collect symptom information using image analysis. The collection unit can also analyze the user's input method using the generation AI and select the optimal collection means. For example, the collection unit can have the generation AI analyze the user's voice data and select voice input. Also, the collection unit can have the generation AI analyze the user's text data and select text input. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method.

[0038] When collecting symptom information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. When collecting symptom information, the collection unit uses the generation AI to prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit collects symptom information related to diseases that are prevalent in that area. Also, when the user is traveling, the collection unit can collect symptom information related to health risks at the user's destination. Furthermore, when the user is at home, the collection unit can collect symptom information based on information about local medical institutions. The collection unit can also use the generation AI to analyze the user's geographical location information and prioritize collecting highly relevant information. For example, the collection unit uses the generation AI to analyze the user's GPS data to identify the user's current location. Also, the collection unit can use the generation AI to analyze the user's address information and collect local medical information. In this way, highly relevant information can be prioritized by taking into account the geographical location information.

[0039] The collection unit can analyze the user's social media activity and collect related information when collecting symptom information. The collection unit uses the generation AI to analyze the user's social media activity and collect related information when collecting symptom information. For example, the collection unit collects additional symptom information based on symptoms reported by the user on social media. The collection unit can also analyze the content of the user's social media posts and collect related symptom information. Furthermore, the collection unit can refer to the activity of the user's friends on social media to collect related symptom information. The collection unit can also analyze the user's social media activity and collect related information using the generation AI. For example, the collection unit causes the generation AI to analyze the content of the user's posts and collect symptom information. The collection unit can also cause the generation AI to analyze the number of the user's followers and evaluate their influence. In this way, related information can be collected by analyzing social media activity.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting symptom information. The collection unit uses the generation AI to customize the collection method by reflecting the user's past feedback when collecting symptom information. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also select a preferred collection means from the user's past feedback. The collection unit can also adjust the collection timing by referring to the user's past feedback. The collection unit can also customize the collection method by analyzing the user's past feedback using the generation AI. For example, the collection unit improves the collection method by having the generation AI analyze the user's survey results. The collection unit can also select a collection means by having the generation AI analyze the user's reviews. In this way, the collection method can be customized by reflecting past feedback.

[0041] The analysis unit can improve the accuracy of the severity assessment by taking into account the interrelationships between symptoms during analysis. The analysis unit uses the generation AI to improve the accuracy of the severity assessment by taking into account the interrelationships between symptoms during analysis. For example, the analysis unit assesses the severity by taking into account the combination of fever and cough. The analysis unit can also assess the severity by taking into account the interrelationships between headache and nausea. The analysis unit can also assess the severity by taking into account the combination of dyspnea and chest pain. The analysis unit can also analyze the interrelationships between symptoms by using the generation AI. For example, the analysis unit uses the generation AI to analyze the interrelationships between symptoms by using co-occurrence network analysis. The analysis unit can also use the generation AI to analyze the interrelationships between symptoms by using correlation analysis. In this way, the accuracy of the severity assessment is improved by taking into account the interrelationships between symptoms.

[0042] The analysis unit can evaluate the severity by taking into account the user's attribute information during analysis. The analysis unit uses the generation AI to evaluate the severity by taking into account the user's attribute information during analysis. For example, the analysis unit can evaluate the severity by taking into account the user's age. The analysis unit can also evaluate the severity by taking into account the user's gender. Furthermore, the analysis unit can evaluate the severity by taking into account the user's medical history. The analysis unit can also analyze the user's attribute information by using the generation AI. For example, the analysis unit can use the generation AI to analyze the user's age data and evaluate the severity. The analysis unit can also use the generation AI to analyze the user's gender data and evaluate the severity. Furthermore, the analysis unit can use the generation AI to analyze the user's medical history data and evaluate the severity. This enables a more appropriate severity evaluation by taking into account the user's attribute information.

[0043] The analysis unit can weight the severity assessment based on the frequency of symptom occurrence during analysis. The analysis unit uses the generation AI to weight the severity assessment based on the frequency of symptom occurrence during analysis. For example, the analysis unit may assign a higher severity rating to symptoms that occur frequently. The analysis unit may also assign a lower severity rating to symptoms that occur infrequently. Furthermore, the analysis unit may adjust the weighting of the severity assessment based on the frequency of symptom occurrence. The analysis unit can also analyze the frequency of symptom occurrence using the generation AI. For example, the analysis unit may have the generation AI analyze the number of times a symptom occurs and assess the severity. The analysis unit may also have the generation AI analyze the duration of symptom occurrence and assess the severity. In this way, weighting based on the frequency of occurrence improves the accuracy of the severity assessment.

[0044] The analysis unit can perform severity assessments taking into account the geographic distribution of symptoms during analysis. The analysis unit uses the generation AI to perform severity assessments taking into account the geographic distribution of symptoms during analysis. For example, the analysis unit may rate the severity of symptoms associated with a disease that is prevalent in a particular region as high. The analysis unit can also assess the severity of symptoms based on the geographic distribution. Furthermore, the analysis unit can assess severity taking into account the medical resources in each region. The analysis unit can also analyze the geographic distribution of symptoms using the generation AI. For example, the generation AI analyzes the incidence rate of symptoms by region and assesses the severity. The analysis unit can also use the generation AI to analyze map data and assess the geographic distribution of symptoms. This allows for more accurate severity assessments by taking geographic distribution into account.

[0045] The analysis unit can improve the accuracy of the severity assessment by referring to related literature during analysis. The analysis unit uses the generation AI to improve the accuracy of the severity assessment by referring to related literature during analysis. For example, the analysis unit evaluates the severity by referring to the latest medical literature. The analysis unit can also evaluate the severity based on past case reports. Furthermore, the analysis unit can improve the accuracy of the severity assessment based on data from related literature. The analysis unit can also analyze the related literature using the generation AI. For example, the analysis unit has the generation AI analyze medical literature to evaluate the severity. The analysis unit can also have the generation AI analyze guidelines to evaluate the severity. In this way, the accuracy of the severity assessment is improved by referring to related literature.

[0046] The analysis unit can evaluate the severity by taking into account the market value of the symptom during analysis. The analysis unit uses the generation AI to evaluate the severity by taking into account the market value of the symptom during analysis. For example, the analysis unit may evaluate the severity higher for symptoms that require expensive treatment. The analysis unit may also evaluate the severity lower for symptoms with low market value. Furthermore, the analysis unit may adjust the weighting of the severity evaluation based on the market value of the symptom. The analysis unit can also analyze the market value of the symptom using the generation AI. For example, the analysis unit may have the generation AI analyze medical expense data and evaluate the severity. The analysis unit may also have the generation AI analyze treatment cost data and evaluate the severity. In this way, by taking market value into account, the accuracy of the severity evaluation is improved.

[0047] The support unit can analyze the user's past medical history and select the optimal support method when providing support. The support unit uses the generation AI to analyze the user's past medical history and select the optimal support method when providing support. For example, the support unit suggests the optimal method of consultation based on the user's past medical history. The support unit can also suggest methods to reduce medical expenses from the user's past medical history. The support unit can also analyze the user's past medical history and select the optimal support method. The support unit can also analyze the user's past medical history using the generation AI. For example, the support unit uses the generation AI to analyze the user's medical records and select the optimal support method. The support unit can also use the generation AI to analyze the user's prescription history and suggest methods to reduce medical expenses. In this way, the optimal support method can be selected by analyzing the past medical history.

[0048] The support unit can customize the support means based on the user's current living situation when providing support. The support unit uses the generation AI to customize the support means based on the user's current living situation when providing support. For example, if the user is at work, the support unit adjusts the support method so as not to interfere with work. Furthermore, if the user is at home, the support unit can suggest home remedies. Furthermore, if the user is traveling, the support unit can provide information on medical institutions at the user's travel destination. The support unit can also analyze the user's living situation and customize the support means using the generation AI. For example, the generation AI analyzes the user's schedule information and adjusts the support method. Furthermore, the generation AI can analyze the user's location information and provide information on medical institutions at the user's travel destination. This enables more appropriate support by customizing the support means based on the user's current living situation.

[0049] The support unit can improve the support method by reflecting user feedback when providing support. The support unit uses the generation AI to improve the support method by reflecting user feedback when providing support. For example, the support unit improves the support method based on feedback provided by the user. The support unit can also select a preferred support method from the user feedback. Furthermore, the support unit can adjust the timing of support by referring to the user feedback. The support unit can also analyze the user feedback and improve the support method by using the generation AI. For example, the support unit improves the support method by having the generation AI analyze the results of a user survey. The support unit can also improve the support method by having the generation AI analyze user reviews. In this way, the support method can be improved by reflecting feedback.

[0050] The support unit can select the optimal support method by taking into account the user's geographical location information when providing support. The support unit uses the generation AI to select the optimal support method by taking into account the user's geographical location information when providing support. For example, if the user is in a specific area, the support unit provides information on medical institutions in that area. Also, if the user is traveling, the support unit can provide information on medical institutions at the user's travel destination. Furthermore, if the user is at home, the support unit can select the support method based on information on local medical institutions. The support unit can also use the generation AI to analyze the user's geographical location information and select the optimal support method. For example, the generation AI in the support unit can analyze the user's GPS data to identify the user's current location. Also, the generation AI in the support unit can analyze the user's address information and provide local medical information. In this way, the optimal support method can be selected by taking into account the geographical location information.

[0051] The support unit can analyze the user's social media activity and suggest support measures when providing support. The support unit uses the generation AI to analyze the user's social media activity and suggest support measures when providing support. For example, the support unit can suggest additional support measures based on symptoms reported by the user on social media. The support unit can also analyze the content of the user's social media posts and suggest related support measures. Furthermore, the support unit can refer to the activity of the user's friends on social media and suggest related support measures. The support unit can also analyze the user's social media activity and suggest support measures using the generation AI. For example, the generation AI can analyze the content of the user's posts and suggest support measures. The support unit can also analyze the number of users' followers and evaluate their influence. In this way, relevant support measures can be suggested by analyzing social media activity.

[0052] The support unit can customize the support method by reflecting the user's past feedback when providing support. The support unit uses the generation AI to customize the support method by reflecting the user's past feedback when providing support. For example, the support unit improves the support method based on feedback provided by the user in the past. The support unit can also select a preferred support method from the user's past feedback. The support unit can also adjust the timing of support by referring to the user's past feedback. The support unit can also analyze the user's feedback and customize the support method by using the generation AI. For example, the support unit improves the support method by having the generation AI analyze the user's survey results. The support unit can also select a support method by having the generation AI analyze the user's reviews. In this way, the support method can be customized by reflecting past feedback.

[0053] At the time of linking, the linking unit can analyze the user's past medical history and select the optimal linking method. At the time of linking, the linking unit uses the generation AI to analyze the user's past medical history and select the optimal linking method. For example, the linking unit suggests the optimal linking method based on the user's past medical history. The linking unit can also select the linking method with a medical institution from the user's past medical history. The linking unit can also analyze the user's past medical history and select the optimal linking method. The linking unit can also analyze the user's past medical history using the generation AI. For example, the linking unit uses the generation AI to analyze the user's medical records and select the optimal linking method. The linking unit can also use the generation AI to analyze the user's prescription history and suggest the linking method with a medical institution. In this way, the optimal linking method can be selected by analyzing the past medical history.

[0054] The collaboration unit can customize the collaboration method based on the user's current living situation at the time of collaboration. The collaboration unit uses the generation AI to customize the collaboration method based on the user's current living situation at the time of collaboration. For example, if the user is at work, the collaboration unit adjusts the collaboration method so as not to interfere with work. Furthermore, if the user is at home, the collaboration unit can suggest home remedies. Furthermore, if the user is traveling, the collaboration unit can provide information on medical institutions at the user's travel destination. The collaboration unit can also analyze the user's living situation and customize the collaboration method using the generation AI. For example, the collaboration unit adjusts the collaboration method by analyzing the user's schedule information. Furthermore, the collaboration unit adjusts the collaboration method by analyzing the user's location information. This enables more appropriate collaboration by customizing the collaboration method based on the user's current living situation.

[0055] The collaboration unit can improve the collaboration method by reflecting user feedback at the time of collaboration. The collaboration unit uses the generation AI to improve the collaboration method by reflecting user feedback at the time of collaboration. For example, the collaboration unit improves the collaboration method based on feedback provided by the user. The collaboration unit can also select a preferred collaboration method from the user feedback. The collaboration unit can also customize the collaboration method by referring to the user feedback. The collaboration unit can also analyze the user feedback and improve the collaboration method by using the generation AI. For example, the collaboration unit improves the collaboration method by analyzing the results of a user survey using the generation AI. The collaboration unit can also select a collaboration method by analyzing user reviews using the generation AI. In this way, the collaboration method can be improved by reflecting feedback.

[0056] The collaboration unit can select the optimal collaboration method by taking into account the user's geographical location information when collaborating. The collaboration unit uses the generation AI to select the optimal collaboration method by taking into account the user's geographical location information when collaborating. For example, if the user is in a specific area, the collaboration unit provides information on medical institutions in that area. Furthermore, if the user is traveling, the collaboration unit can also provide information on medical institutions at the user's travel destination. Furthermore, if the user is at home, the collaboration unit can select the collaboration method based on information on local medical institutions. The collaboration unit can also use the generation AI to analyze the user's geographical location information and select the optimal collaboration method. For example, the collaboration unit uses the generation AI to analyze the user's GPS data and identify the user's current location. Furthermore, the collaboration unit can also use the generation AI to analyze the user's address information and provide local medical information. In this way, the optimal collaboration method can be selected by taking into account the geographical location information.

[0057] The collaboration unit can analyze the user's social media activity and suggest collaboration methods at the time of collaboration. The collaboration unit uses the generation AI to analyze the user's social media activity and suggest collaboration methods at the time of collaboration. For example, the collaboration unit can suggest additional collaboration methods based on symptoms reported by the user on social media. The collaboration unit can also analyze the content of the user's social media posts and suggest related collaboration methods. The collaboration unit can also suggest related collaboration methods based on the activity of the user's friends on social media. The collaboration unit can also analyze the user's social media activity and suggest collaboration methods using the generation AI. For example, the collaboration unit can analyze the content of the user's posts and suggest collaboration methods. The collaboration unit can also analyze the number of the user's followers and evaluate their influence. In this way, relevant collaboration methods can be suggested by analyzing social media activity.

[0058] The collaboration unit can customize the collaboration method by reflecting the user's past feedback at the time of collaboration. The collaboration unit uses the generation AI to customize the collaboration method by reflecting the user's past feedback at the time of collaboration. For example, the collaboration unit improves the collaboration method based on feedback provided by the user in the past. The collaboration unit can also select a preferred collaboration method from the user's past feedback. The collaboration unit can also adjust the collaboration timing by referring to the user's past feedback. The collaboration unit can also analyze the user's feedback and customize the collaboration method by using the generation AI. For example, the collaboration unit improves the collaboration method by analyzing the user's survey results. The collaboration unit can also select a collaboration method by analyzing the user's reviews. In this way, the collaboration method can be customized by reflecting past feedback.

[0059] The question unit can present the most appropriate question by referring to the user's past answer history when asking a question. The question unit uses the generation AI to present the most appropriate question by referring to the user's past answer history when asking a question. For example, the question unit presents related questions based on the content of answers given by the user in the past. The question unit can also find specific patterns from the user's past answer history and adjust the questions based on those patterns. The question unit can also present the most appropriate question based on the user's past answer history. The question unit can also use the generation AI to analyze the user's past answer history and present the most appropriate question. For example, the question unit uses the generation AI to analyze the user's survey results and present related questions. The question unit can also use the generation AI to analyze the user's medical records and adjust the questions. In this way, the most appropriate question can be presented by referring to the past answer history.

[0060] The questioning unit can customize the content of the questions based on the user's current living situation and environment when asking a question. The questioning unit uses the generation AI to customize the content of the questions based on the user's current living situation and environment when asking a question. For example, if the user is at work, the questioning unit can ask brief questions so as not to interfere with work. The questioning unit can also ask detailed questions when the user is at home. Furthermore, if the user is traveling, the questioning unit can ask questions related to health risks at the travel destination. The questioning unit can also analyze the user's living situation and environment and customize the content of the questions using the generation AI. For example, the generation AI can analyze the user's schedule information and adjust the questions. The generation AI can also analyze the user's location information and ask questions related to health risks at the travel destination. This allows the questioning unit to customize the content of the questions based on the user's current living situation and environment, enabling more appropriate questions to be asked.

[0061] The questioning unit can present the most appropriate question by taking into account the user's geographical location information when asking a question. The questioning unit uses a generation AI to present the most appropriate question by taking into account the user's geographical location information when asking a question. For example, if the user is in a specific area, the questioning unit can ask questions related to diseases that are prevalent in that area. Also, if the user is traveling, the questioning unit can ask questions related to health risks at the user's destination. Furthermore, if the user is at home, the questioning unit can present questions based on information about local medical institutions. The questioning unit can also use a generation AI to analyze the user's geographical location information and present the most appropriate question. For example, the generation AI in the questioning unit can analyze the user's GPS data to identify the user's current location. Also, the generation AI in the questioning unit can analyze the user's address information and provide local medical information. This allows the most appropriate question to be presented by taking into account the geographical location information.

[0062] The question unit can analyze the user's social media activity and present related questions when a question is asked. The question unit uses a generation AI to analyze the user's social media activity and present related questions when a question is asked. For example, the question unit asks additional questions based on symptoms reported by the user on social media. The question unit can also analyze the content of the user's social media posts and present related questions. Furthermore, the question unit can also present related questions based on the activity of the user's friends on social media. The question unit can also analyze the user's social media activity and present related questions using a generation AI. For example, the question unit uses a generation AI to analyze the content of the user's posts and present questions. The question unit can also use a generation AI to analyze the number of users' followers and evaluate their influence. This allows the question unit to present related questions by analyzing social media activity.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] When collecting information about the user's symptoms, the collection unit can customize the questions by referring to the user's past medical history. For example, the collection unit can preferentially present related questions based on symptoms previously reported by the user. The collection unit can also add questions related to a specific medical history from the user's past medical history. Furthermore, the collection unit can ask questions to check the progression of symptoms based on the user's past medical history. This allows for more appropriate information collection by taking the user's past medical history into consideration.

[0065] The analysis unit can take into account the user's lifestyle information when evaluating the severity of symptoms based on the user's answers. For example, the analysis unit can evaluate the severity by taking into account the user's smoking habits and drinking habits. The analysis unit can also evaluate the severity by taking into account the user's exercise habits and dietary habits. Furthermore, the analysis unit can evaluate the severity by taking into account the user's stress level and sleep patterns. This allows for a more accurate severity evaluation by taking into account the user's lifestyle information.

[0066] When sharing the medical interview results with a doctor, the collaboration unit can adjust the scope of information sharing based on the user's privacy settings. For example, if the user places importance on privacy, the collaboration unit will share only the minimum amount of information necessary. Also, if the user is proactive in sharing information, the collaboration unit can share detailed medical interview results. Furthermore, if the user wants to keep specific information private, the collaboration unit can exclude that information from sharing. This allows for more appropriate information sharing by adjusting the scope of information sharing based on the user's privacy settings.

[0067] The analysis unit can take into account the influence of seasons and weather when evaluating the severity of symptoms based on the user's answers. For example, the analysis unit can evaluate the severity taking into account the prevalence of influenza in winter. The analysis unit can also evaluate the severity taking into account symptoms during hay fever season. Furthermore, the analysis unit can evaluate the severity taking into account poor physical condition due to changes in weather. This allows for a more accurate severity evaluation by taking into account the influence of seasons and weather.

[0068] When sharing the medical interview results with a doctor, the collaboration unit can select the most appropriate medical institution by taking into account the user's geographical location information. For example, if the user is in a specific area, the collaboration unit shares the medical interview results with medical institutions in that area. Also, if the user is traveling, the collaboration unit can share the medical interview results with medical institutions in the user's travel destination. Furthermore, if the user is at home, the collaboration unit can share the medical interview results with nearby medical institutions. In this way, the medical interview results can be shared with the most appropriate medical institution by taking into account the geographical location information.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The collection unit collects information about the user's symptoms. Information about the user's symptoms includes, for example, fever, cough, and headache. The collection unit collects symptom information entered by the user online. The collection unit can also use a generation AI to present questions about the user's symptoms and collect information by the user's answers. For example, specific questions such as "Do you have a fever?" or "Do you have a cough?" can be presented. Step 2: The analysis unit evaluates the severity of the symptoms based on the information collected by the collection unit and determines the need for medical examination and urgency. The analysis unit can also use the generation AI to analyze the user's answers and evaluate the severity of the symptoms. Step 3: The support unit uses the results obtained by the analysis unit to help decide whether to see a doctor and reduce medical costs. For example, if symptoms are mild, it will suggest ways to deal with them at home, and if symptoms are severe, it will recommend visiting a medical institution. The support unit can also use generative AI to help decide whether to see a doctor and reduce medical costs. Step 4: The collaboration unit collaborates with the hospital on the results obtained by the support unit. For example, the collaboration unit can share the results of the medical interview with the doctor to ensure smooth medical treatment. The collaboration unit can also use the generative AI to share the results of the medical interview with the doctor.

[0071] (Example 2) An online medical interview system according to an embodiment of the present invention determines the need for and urgency of a medical examination based on a user's symptoms, thereby supporting the decision to see a doctor and reducing medical costs. The online medical interview system begins with the user completing a simple online medical interview. This interview is conducted using a generation AI, which asks the user questions about their symptoms. The generation AI then analyzes the user's responses to determine the need for and urgency of a medical examination. For example, if the user has symptoms such as a fever or cough, the generation AI evaluates the severity of the symptoms and determines whether an emergency medical examination is necessary. Furthermore, the generation AI supports the decision to see a doctor and reduce medical costs based on the results of the interview. For example, if the symptoms are mild, the generation AI suggests home remedies and advises the user to refrain from visiting a medical institution. On the other hand, if the symptoms are severe, the generation AI recommends immediate medical examination. Furthermore, the results of the interview are shared with hospitals, enabling smooth medical treatment. For example, by sharing the results of the interview with a doctor before the user visits the hospital, medical preparations are completed and a prompt examination can be performed. This allows the online medical interview system to reduce the burden on medical institutions and reduce medical costs. This allows the online medical interview system to determine the need and urgency of a doctor's examination based on the user's symptoms, and to support the decision to see a doctor and the reduction of medical costs. For example, by allowing users to take a medical interview online, quick and accurate examinations can be performed, reducing the burden on medical institutions. In addition, users can learn appropriate treatment methods based on their symptoms, which also contributes to the reduction of medical costs.

[0072] The online medical interview system according to the embodiment includes a collection unit, an analysis unit, a support unit, and a linking unit. The collection unit collects information about a user's symptoms. The information about the user's symptoms includes, but is not limited to, fever, cough, and headache. The collection unit collects, for example, symptom information entered by the user online. The collection unit can also use a generation AI to present questions about the user's symptoms and collect information based on the user's answers. For example, the collection unit presents specific questions such as "Do you have a fever?" or "Do you have a cough?" The analysis unit uses the generation AI to evaluate the severity of the symptoms based on the information collected by the collection unit and determine the need for and urgency of a medical examination. For example, the analysis unit evaluates the severity of the symptoms based on the user's answers and determines the need for and urgency of a medical examination. The analysis unit can also use the generation AI to analyze the user's answers and evaluate the severity of the symptoms. The support unit supports the user in deciding whether to see a doctor and reducing medical expenses based on the results obtained by the analysis unit. For example, the support unit suggests home remedies for mild symptoms and recommends visiting a medical institution for severe symptoms. The support unit can also use a generation AI to support the decision to visit a medical institution and the reduction of medical expenses. The linking unit links the results obtained by the support unit with hospitals. For example, the linking unit can share the results of the medical interview with a doctor to ensure smooth medical treatment. The linking unit can also use a generation AI to share the results of the medical interview with a doctor. As a result, the online medical interview system according to the embodiment can determine the necessity and urgency of a medical examination based on the user's symptoms, and support the decision to visit a medical institution and the reduction of medical expenses. For example, by allowing a user to take a medical interview online, a quick and accurate examination can be performed, reducing the burden on medical institutions. Furthermore, the user can learn appropriate remedies according to their symptoms, which also contributes to the reduction of medical expenses.

[0073] The online medical interview system includes a questioning unit that presents specific questions about the user's symptoms. The questioning unit presents specific questions about the user's symptoms. Examples of specific questions include, but are not limited to, questions such as, "Do you have a fever?" and "Do you have a cough?" The questioning unit can also present questions about the user's symptoms using a generation AI. For example, the generation AI generates a prompt such as "Do you have a fever?" and presents it to the user. The questioning unit can also present questions in various formats, such as multiple-choice format or free-form format. For example, the questioning unit can present questions in a format in which the user selects an answer from multiple-choice options, or in a format in which the user freely writes an answer. In this way, by presenting specific questions about the user's symptoms, more accurate information can be collected.

[0074] The analysis unit can evaluate the severity of symptoms based on the user's answers and determine the need for and urgency of medical examination. The analysis unit uses generative AI to evaluate the severity of symptoms based on the user's answers and determine the need for and urgency of medical examination. For example, the analysis unit analyzes the user's answers and evaluates the severity of symptoms. The analysis unit can also use generative AI to evaluate the severity of symptoms based on the user's answers. For example, the analysis unit can take into account the temperature and duration of the fever to determine whether emergency medical examination is necessary. The analysis unit can also evaluate the frequency and intensity of coughing and determine the need for medical examination. Furthermore, the analysis unit can evaluate the severity and duration of headaches and determine the need for medical examination. This enables appropriate response by evaluating the severity of symptoms based on the user's answers and determining the need for medical examination and urgency.

[0075] The support unit can suggest home remedies for mild symptoms and recommend a medical visit for severe symptoms. The support unit uses generative AI to suggest home remedies for mild symptoms and recommend a medical visit for severe symptoms. For example, for a mild headache, the support unit can suggest adequate rest and hydration as home remedies. For a mild cough, the support unit can also suggest drinking hot drinks as home remedies. For a mild fever, the support unit can also suggest using cooling sheets as home remedies. On the other hand, for severe symptoms, the support unit can recommend immediate medical visit. For example, the support unit can recommend a medical visit if a high fever persists. The support unit can also recommend immediate medical visit if there is severe chest pain. The support unit can also recommend emergency medical visit if there is difficulty breathing. This allows for appropriate remedies to be suggested based on the severity of the symptoms, thereby reducing medical costs and providing prompt medical care.

[0076] The collaboration unit shares the medical interview results with the doctor, enabling smooth medical treatment. The collaboration unit uses generative AI to share the medical interview results with the doctor, enabling smooth medical treatment. For example, the collaboration unit can share the medical interview results with the doctor before the user visits the hospital, which allows preparations for medical treatment to be made and enables a prompt examination. The collaboration unit can also use generative AI to automatically send the medical interview results to the doctor. For example, the collaboration unit can send the medical interview results to the doctor by email. The collaboration unit can also automatically register the medical interview results in the hospital's electronic medical record system. Furthermore, the collaboration unit can enable the doctor to check the medical interview results in real time. For example, the collaboration unit can display the medical interview results on the doctor's tablet device. In this way, sharing the medical interview results with the doctor allows preparations for medical treatment to be made and enables a prompt examination.

[0077] The question unit can pose specific questions such as "Do you have a fever?" or "Do you have a cough?" The question unit uses a generation AI to pose specific questions such as "Do you have a fever?" or "Do you have a cough?" For example, the question unit generates a prompt such as "Do you have a fever?" and presents it to the user. The question unit can also generate a prompt such as "Do you have a cough?" and present it to the user. The question unit can also generate a prompt such as "Do you have a headache?" and present it to the user. The question unit can pose questions in various formats, such as multiple-choice format or free-form format. For example, the question unit can pose questions in a format where the user selects an answer from multiple-choice format, or in a format where the user freely writes an answer. In this way, by presenting specific questions, detailed information about the user's symptoms can be collected.

[0078] The collection unit can estimate the user's emotions and adjust the timing of symptom information collection based on the estimated user emotions. The collection unit uses the generation AI to estimate the user's emotions and adjust the timing of symptom information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects symptom information during a time when the user is able to relax. Also, if the user is relaxed, the collection unit can take time to collect detailed symptom information. Furthermore, if the user is in a hurry, the collection unit can prioritize simple questions to collect symptom information. The collection unit can also estimate the user's emotions using the generation AI. For example, the collection unit uses the generation AI to analyze the user's facial expressions and estimate the emotions. Also, the collection unit can use the generation AI to analyze the user's voice and estimate the emotions. Furthermore, the collection unit can use the generation AI to analyze the user's text input and estimate the emotions. This enables more appropriate information collection by adjusting the collection timing according to the user's emotions.

[0079] The collection unit can analyze the user's past symptom history and select the optimal collection method. The collection unit uses the generation AI to analyze the user's past symptom history and select the optimal collection method. For example, the collection unit prioritizes collecting symptoms that the user has frequently reported in the past. The collection unit can also find specific patterns from the user's past symptom history and adjust questions based on those patterns. Furthermore, if symptoms appear during a specific time period based on the user's past symptom history, the collection unit can also collect data during that time period. The collection unit can also analyze the user's past symptom history using the generation AI. For example, the collection unit uses the generation AI to analyze the user's past symptom data and select the optimal collection method. In this way, the optimal collection method can be selected by analyzing the past symptom history.

[0080] The collection unit can filter the symptom information based on the user's current living situation and environment when collecting the symptom information. The collection unit uses the generation AI to filter the symptom information based on the user's current living situation and environment when collecting the symptom information. For example, the collection unit prioritizes collecting concise questions when the user is out. The collection unit can also collect detailed symptom information when the user is at home. Furthermore, when the user is at work, the collection unit can collect information in a short time so as not to interfere with work. The collection unit can also use the generation AI to analyze the user's living situation and environment and perform filtering. For example, the collection unit uses the generation AI to analyze the user's location information and determine whether the user is out. The collection unit can also use the generation AI to analyze the user's schedule information and determine whether the user is at work. This enables more appropriate information collection by filtering information based on the user's living situation and environment.

[0081] The collection unit can select the optimal collection means depending on the user's input method when collecting symptom information. The collection unit uses the generation AI to select the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting symptom information. For example, if the user prefers voice input, the collection unit can collect symptom information by voice. Also, if the user prefers text input, the collection unit can collect symptom information by text. Furthermore, if the user can provide an image, the collection unit can collect symptom information using image analysis. The collection unit can also analyze the user's input method using the generation AI and select the optimal collection means. For example, the collection unit can have the generation AI analyze the user's voice data and select voice input. Also, the collection unit can have the generation AI analyze the user's text data and select text input. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method.

[0082] The collection unit can estimate the user's emotions and determine the priority of symptom information to be collected based on the estimated user's emotions. The collection unit uses the generation AI to estimate the user's emotions and determine the priority of symptom information to be collected based on the estimated user's emotions. For example, if the user is feeling anxious, the collection unit prioritizes collecting symptom information with high urgency. The collection unit can also collect detailed symptom information if the user is relaxed. Furthermore, the collection unit can prioritize collecting important symptom information if the user is in a hurry. The collection unit can also estimate the user's emotions using the generation AI. For example, the collection unit uses the generation AI to analyze the user's facial expressions and estimate the emotions. The collection unit can also use the generation AI to analyze the user's voice and estimate the emotions. Furthermore, the collection unit can use the generation AI to analyze the user's text input and estimate the emotions. In this way, by prioritizing information based on the user's emotions, important information can be preferentially collected.

[0083] When collecting symptom information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. When collecting symptom information, the collection unit uses the generation AI to prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit collects symptom information related to diseases that are prevalent in that area. Also, when the user is traveling, the collection unit can collect symptom information related to health risks at the user's destination. Furthermore, when the user is at home, the collection unit can collect symptom information based on information about local medical institutions. The collection unit can also use the generation AI to analyze the user's geographical location information and prioritize collecting highly relevant information. For example, the collection unit uses the generation AI to analyze the user's GPS data to identify the user's current location. Also, the collection unit can use the generation AI to analyze the user's address information and collect local medical information. In this way, highly relevant information can be prioritized by taking into account the geographical location information.

[0084] The collection unit can analyze the user's social media activity and collect related information when collecting symptom information. The collection unit uses the generation AI to analyze the user's social media activity and collect related information when collecting symptom information. For example, the collection unit collects additional symptom information based on symptoms reported by the user on social media. The collection unit can also analyze the content of the user's social media posts and collect related symptom information. Furthermore, the collection unit can refer to the activity of the user's friends on social media to collect related symptom information. The collection unit can also analyze the user's social media activity and collect related information using the generation AI. For example, the collection unit causes the generation AI to analyze the content of the user's posts and collect symptom information. The collection unit can also cause the generation AI to analyze the number of the user's followers and evaluate their influence. In this way, related information can be collected by analyzing social media activity.

[0085] The collection unit can customize the collection method by reflecting the user's past feedback when collecting symptom information. The collection unit uses the generation AI to customize the collection method by reflecting the user's past feedback when collecting symptom information. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also select a preferred collection means from the user's past feedback. The collection unit can also adjust the collection timing by referring to the user's past feedback. The collection unit can also customize the collection method by analyzing the user's past feedback using the generation AI. For example, the collection unit improves the collection method by having the generation AI analyze the user's survey results. The collection unit can also select a collection means by having the generation AI analyze the user's reviews. In this way, the collection method can be customized by reflecting past feedback.

[0086] The analysis unit can estimate the user's emotions and adjust the criteria for symptom severity evaluation based on the estimated user emotions. The analysis unit can use the generation AI to estimate the user's emotions and adjust the criteria for symptom severity evaluation based on the estimated user emotions. For example, the analysis unit can tighten the criteria for severity evaluation if the user is feeling anxious. The analysis unit can also relax the criteria for severity evaluation if the user is relaxed. Furthermore, the analysis unit can adjust the criteria to quickly evaluate the severity if the user is in a hurry. The analysis unit can also estimate the user's emotions using the generation AI. For example, the analysis unit can have the generation AI analyze the user's facial expressions to estimate the emotions. The analysis unit can also have the generation AI analyze the user's voice to estimate the emotions. Furthermore, the analysis unit can have the generation AI analyze the user's text input to estimate the emotions. This allows for more accurate severity evaluation by adjusting the evaluation criteria based on the user's emotions.

[0087] The analysis unit can improve the accuracy of the severity assessment by taking into account the interrelationships between symptoms during analysis. The analysis unit uses the generation AI to improve the accuracy of the severity assessment by taking into account the interrelationships between symptoms during analysis. For example, the analysis unit assesses the severity by taking into account the combination of fever and cough. The analysis unit can also assess the severity by taking into account the interrelationships between headache and nausea. The analysis unit can also assess the severity by taking into account the combination of dyspnea and chest pain. The analysis unit can also analyze the interrelationships between symptoms by using the generation AI. For example, the analysis unit uses the generation AI to analyze the interrelationships between symptoms by using co-occurrence network analysis. The analysis unit can also use the generation AI to analyze the interrelationships between symptoms by using correlation analysis. In this way, the accuracy of the severity assessment is improved by taking into account the interrelationships between symptoms.

[0088] The analysis unit can evaluate the severity by taking into account the user's attribute information during analysis. The analysis unit uses the generation AI to evaluate the severity by taking into account the user's attribute information during analysis. For example, the analysis unit can evaluate the severity by taking into account the user's age. The analysis unit can also evaluate the severity by taking into account the user's gender. Furthermore, the analysis unit can evaluate the severity by taking into account the user's medical history. The analysis unit can also analyze the user's attribute information by using the generation AI. For example, the analysis unit can use the generation AI to analyze the user's age data and evaluate the severity. The analysis unit can also use the generation AI to analyze the user's gender data and evaluate the severity. Furthermore, the analysis unit can use the generation AI to analyze the user's medical history data and evaluate the severity. This enables a more appropriate severity evaluation by taking into account the user's attribute information.

[0089] The analysis unit can weight the severity assessment based on the frequency of symptom occurrence during analysis. The analysis unit uses the generation AI to weight the severity assessment based on the frequency of symptom occurrence during analysis. For example, the analysis unit may assign a higher severity rating to symptoms that occur frequently. The analysis unit may also assign a lower severity rating to symptoms that occur infrequently. Furthermore, the analysis unit may adjust the weighting of the severity assessment based on the frequency of symptom occurrence. The analysis unit can also analyze the frequency of symptom occurrence using the generation AI. For example, the analysis unit may have the generation AI analyze the number of times a symptom occurs and assess the severity. The analysis unit may also have the generation AI analyze the duration of symptom occurrence and assess the severity. In this way, weighting based on the frequency of occurrence improves the accuracy of the severity assessment.

[0090] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can use the generation AI to estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. The analysis unit can also estimate the user's emotions using the generation AI. For example, the analysis unit can use the generation AI to analyze the user's facial expressions and estimate the emotions. The analysis unit can also use the generation AI to analyze the user's voice and estimate the emotions. The analysis unit can also use the generation AI to analyze the user's text input and estimate the emotions. This allows the display method to be adjusted based on the user's emotions, making it possible to provide more appropriate information.

[0091] The analysis unit can perform severity assessments taking into account the geographic distribution of symptoms during analysis. The analysis unit uses the generation AI to perform severity assessments taking into account the geographic distribution of symptoms during analysis. For example, the analysis unit may rate the severity of symptoms associated with a disease that is prevalent in a particular region as high. The analysis unit can also assess the severity of symptoms based on the geographic distribution. Furthermore, the analysis unit can assess severity taking into account the medical resources in each region. The analysis unit can also analyze the geographic distribution of symptoms using the generation AI. For example, the generation AI analyzes the incidence rate of symptoms by region and assesses the severity. The analysis unit can also use the generation AI to analyze map data and assess the geographic distribution of symptoms. This allows for more accurate severity assessments by taking geographic distribution into account.

[0092] The analysis unit can improve the accuracy of the severity assessment by referring to related literature during analysis. The analysis unit uses the generation AI to improve the accuracy of the severity assessment by referring to related literature during analysis. For example, the analysis unit evaluates the severity by referring to the latest medical literature. The analysis unit can also evaluate the severity based on past case reports. Furthermore, the analysis unit can improve the accuracy of the severity assessment based on data from related literature. The analysis unit can also analyze the related literature using the generation AI. For example, the analysis unit has the generation AI analyze medical literature to evaluate the severity. The analysis unit can also have the generation AI analyze guidelines to evaluate the severity. In this way, the accuracy of the severity assessment is improved by referring to related literature.

[0093] The analysis unit can evaluate the severity by taking into account the market value of the symptom during analysis. The analysis unit uses the generation AI to evaluate the severity by taking into account the market value of the symptom during analysis. For example, the analysis unit may evaluate the severity higher for symptoms that require expensive treatment. The analysis unit may also evaluate the severity lower for symptoms with low market value. Furthermore, the analysis unit may adjust the weighting of the severity evaluation based on the market value of the symptom. The analysis unit can also analyze the market value of the symptom using the generation AI. For example, the analysis unit may have the generation AI analyze medical expense data and evaluate the severity. The analysis unit may also have the generation AI analyze treatment cost data and evaluate the severity. In this way, by taking market value into account, the accuracy of the severity evaluation is improved.

[0094] The support unit can estimate the user's emotions and adjust the decision to see a doctor and the method of reducing medical costs based on the estimated user emotions. The support unit uses a generation AI to estimate the user's emotions and adjust the decision to see a doctor and the method of reducing medical costs based on the estimated user emotions. For example, if the user feels anxious, the support unit recommends seeing a doctor and provides a sense of security. If the user is relaxed, the support unit can also suggest home remedies. Furthermore, if the user is in a hurry, the support unit can quickly decide to see a doctor. The support unit can also estimate the user's emotions using a generation AI. For example, the support unit uses a generation AI to analyze the user's facial expressions and estimate the user's emotions. The support unit can also use a generation AI to analyze the user's voice and estimate the user's emotions. Furthermore, the support unit can use a generation AI to analyze the user's text input and estimate the user's emotions. This allows the support method to be adjusted based on the user's emotions, enabling more appropriate decision to see a doctor and reducing medical costs.

[0095] The support unit can analyze the user's past medical history and select the optimal support method when providing support. The support unit uses the generation AI to analyze the user's past medical history and select the optimal support method when providing support. For example, the support unit suggests the optimal method of consultation based on the user's past medical history. The support unit can also suggest methods to reduce medical expenses from the user's past medical history. The support unit can also analyze the user's past medical history and select the optimal support method. The support unit can also analyze the user's past medical history using the generation AI. For example, the support unit uses the generation AI to analyze the user's medical records and select the optimal support method. The support unit can also use the generation AI to analyze the user's prescription history and suggest methods to reduce medical expenses. In this way, the optimal support method can be selected by analyzing the past medical history.

[0096] The support unit can customize the support means based on the user's current living situation when providing support. The support unit uses the generation AI to customize the support means based on the user's current living situation when providing support. For example, if the user is at work, the support unit adjusts the support method so as not to interfere with work. Furthermore, if the user is at home, the support unit can suggest home remedies. Furthermore, if the user is traveling, the support unit can provide information on medical institutions at the user's travel destination. The support unit can also analyze the user's living situation and customize the support means using the generation AI. For example, the generation AI analyzes the user's schedule information and adjusts the support method. Furthermore, the generation AI can analyze the user's location information and provide information on medical institutions at the user's travel destination. This enables more appropriate support by customizing the support means based on the user's current living situation.

[0097] The support unit can improve the support method by reflecting user feedback when providing support. The support unit uses the generation AI to improve the support method by reflecting user feedback when providing support. For example, the support unit improves the support method based on feedback provided by the user. The support unit can also select a preferred support method from the user feedback. Furthermore, the support unit can adjust the timing of support by referring to the user feedback. The support unit can also analyze the user feedback and improve the support method by using the generation AI. For example, the support unit improves the support method by having the generation AI analyze the results of a user survey. The support unit can also improve the support method by having the generation AI analyze user reviews. In this way, the support method can be improved by reflecting feedback.

[0098] The support unit can estimate the user's emotions and determine the priority of support based on the estimated user emotions. The support unit can use the generation AI to estimate the user's emotions and determine the priority of support based on the estimated user emotions. For example, if the user is feeling anxious, the support unit prioritizes support with high urgency. The support unit can also provide detailed support methods if the user is relaxed. Furthermore, the support unit can provide support quickly if the user is in a hurry. The support unit can also estimate the user's emotions using the generation AI. For example, the support unit can use the generation AI to analyze the user's facial expressions and estimate the emotions. The support unit can also use the generation AI to analyze the user's voice and estimate the emotions. Furthermore, the support unit can use the generation AI to analyze the user's text input and estimate the emotions. In this way, by determining the priority of support based on the user's emotions, important support can be provided preferentially.

[0099] The support unit can select the optimal support method by taking into account the user's geographical location information when providing support. The support unit uses the generation AI to select the optimal support method by taking into account the user's geographical location information when providing support. For example, if the user is in a specific area, the support unit provides information on medical institutions in that area. Also, if the user is traveling, the support unit can provide information on medical institutions at the user's travel destination. Furthermore, if the user is at home, the support unit can select the support method based on information on local medical institutions. The support unit can also use the generation AI to analyze the user's geographical location information and select the optimal support method. For example, the generation AI in the support unit can analyze the user's GPS data to identify the user's current location. Also, the generation AI in the support unit can analyze the user's address information and provide local medical information. In this way, the optimal support method can be selected by taking into account the geographical location information.

[0100] The support unit can analyze the user's social media activity and suggest support measures when providing support. The support unit uses the generation AI to analyze the user's social media activity and suggest support measures when providing support. For example, the support unit can suggest additional support measures based on symptoms reported by the user on social media. The support unit can also analyze the content of the user's social media posts and suggest related support measures. Furthermore, the support unit can refer to the activity of the user's friends on social media and suggest related support measures. The support unit can also analyze the user's social media activity and suggest support measures using the generation AI. For example, the generation AI can analyze the content of the user's posts and suggest support measures. The support unit can also analyze the number of users' followers and evaluate their influence. In this way, relevant support measures can be suggested by analyzing social media activity.

[0101] The support unit can customize the support method by reflecting the user's past feedback when providing support. The support unit uses the generation AI to customize the support method by reflecting the user's past feedback when providing support. For example, the support unit improves the support method based on feedback provided by the user in the past. The support unit can also select a preferred support method from the user's past feedback. The support unit can also adjust the timing of support by referring to the user's past feedback. The support unit can also analyze the user's feedback and customize the support method by using the generation AI. For example, the support unit improves the support method by having the generation AI analyze the user's survey results. The support unit can also select a support method by having the generation AI analyze the user's reviews. In this way, the support method can be customized by reflecting past feedback.

[0102] The collaboration unit can estimate the user's emotions and adjust the collaboration method based on the estimated user's emotions. The collaboration unit uses the generation AI to estimate the user's emotions and adjust the collaboration method based on the estimated user's emotions. For example, if the user is feeling anxious, the collaboration unit quickly collaborates with a doctor. The collaboration unit can also provide detailed collaboration methods if the user is relaxed. Furthermore, the collaboration unit can quickly collaborate if the user is in a hurry. The collaboration unit can also estimate the user's emotions using the generation AI. For example, the collaboration unit uses the generation AI to analyze the user's facial expressions and estimate the emotions. The collaboration unit can also use the generation AI to analyze the user's voice and estimate the emotions. Furthermore, the collaboration unit can use the generation AI to analyze the user's text input and estimate the emotions. This enables more appropriate collaboration by adjusting the collaboration method based on the user's emotions.

[0103] At the time of linking, the linking unit can analyze the user's past medical history and select the optimal linking method. At the time of linking, the linking unit uses the generation AI to analyze the user's past medical history and select the optimal linking method. For example, the linking unit suggests the optimal linking method based on the user's past medical history. The linking unit can also select the linking method with a medical institution from the user's past medical history. The linking unit can also analyze the user's past medical history and select the optimal linking method. The linking unit can also analyze the user's past medical history using the generation AI. For example, the linking unit uses the generation AI to analyze the user's medical records and select the optimal linking method. The linking unit can also use the generation AI to analyze the user's prescription history and suggest the linking method with a medical institution. In this way, the optimal linking method can be selected by analyzing the past medical history.

[0104] The collaboration unit can customize the collaboration method based on the user's current living situation at the time of collaboration. The collaboration unit uses the generation AI to customize the collaboration method based on the user's current living situation at the time of collaboration. For example, if the user is at work, the collaboration unit adjusts the collaboration method so as not to interfere with work. Furthermore, if the user is at home, the collaboration unit can suggest home remedies. Furthermore, if the user is traveling, the collaboration unit can provide information on medical institutions at the user's travel destination. The collaboration unit can also analyze the user's living situation and customize the collaboration method using the generation AI. For example, the collaboration unit adjusts the collaboration method by analyzing the user's schedule information. Furthermore, the collaboration unit adjusts the collaboration method by analyzing the user's location information. This enables more appropriate collaboration by customizing the collaboration method based on the user's current living situation.

[0105] The collaboration unit can improve the collaboration method by reflecting user feedback at the time of collaboration. The collaboration unit uses the generation AI to improve the collaboration method by reflecting user feedback at the time of collaboration. For example, the collaboration unit improves the collaboration method based on feedback provided by the user. The collaboration unit can also select a preferred collaboration method from the user feedback. The collaboration unit can also customize the collaboration method by referring to the user feedback. The collaboration unit can also analyze the user feedback and improve the collaboration method by using the generation AI. For example, the collaboration unit improves the collaboration method by analyzing the results of a user survey using the generation AI. The collaboration unit can also select a collaboration method by analyzing user reviews using the generation AI. In this way, the collaboration method can be improved by reflecting feedback.

[0106] The collaboration unit can estimate the user's emotions and determine the priority of collaboration based on the estimated user's emotions. The collaboration unit can estimate the user's emotions using the generation AI and determine the priority of collaboration based on the estimated user's emotions. For example, if the user is feeling anxious, the collaboration unit prioritizes collaboration with high urgency. The collaboration unit can also provide detailed collaboration methods if the user is relaxed. Furthermore, the collaboration unit can perform collaboration quickly if the user is in a hurry. The collaboration unit can also estimate the user's emotions using the generation AI. For example, the collaboration unit can use the generation AI to analyze the user's facial expressions and estimate the emotions. The collaboration unit can also use the generation AI to analyze the user's voice and estimate the emotions. Furthermore, the collaboration unit can use the generation AI to analyze the user's text input and estimate the emotions. In this way, by determining the priority of collaboration based on the user's emotions, important collaborations can be performed preferentially.

[0107] The collaboration unit can select the optimal collaboration method by taking into account the user's geographical location information when collaborating. The collaboration unit uses the generation AI to select the optimal collaboration method by taking into account the user's geographical location information when collaborating. For example, if the user is in a specific area, the collaboration unit provides information on medical institutions in that area. Furthermore, if the user is traveling, the collaboration unit can also provide information on medical institutions at the user's travel destination. Furthermore, if the user is at home, the collaboration unit can select the collaboration method based on information on local medical institutions. The collaboration unit can also use the generation AI to analyze the user's geographical location information and select the optimal collaboration method. For example, the collaboration unit uses the generation AI to analyze the user's GPS data and identify the user's current location. Furthermore, the collaboration unit can also use the generation AI to analyze the user's address information and provide local medical information. In this way, the optimal collaboration method can be selected by taking into account the geographical location information.

[0108] The collaboration unit can analyze the user's social media activity and suggest collaboration methods at the time of collaboration. The collaboration unit uses the generation AI to analyze the user's social media activity and suggest collaboration methods at the time of collaboration. For example, the collaboration unit can suggest additional collaboration methods based on symptoms reported by the user on social media. The collaboration unit can also analyze the content of the user's social media posts and suggest related collaboration methods. The collaboration unit can also suggest related collaboration methods based on the activity of the user's friends on social media. The collaboration unit can also analyze the user's social media activity and suggest collaboration methods using the generation AI. For example, the collaboration unit can analyze the content of the user's posts and suggest collaboration methods. The collaboration unit can also analyze the number of the user's followers and evaluate their influence. In this way, relevant collaboration methods can be suggested by analyzing social media activity.

[0109] The collaboration unit can customize the collaboration method by reflecting the user's past feedback at the time of collaboration. The collaboration unit uses the generation AI to customize the collaboration method by reflecting the user's past feedback at the time of collaboration. For example, the collaboration unit improves the collaboration method based on feedback provided by the user in the past. The collaboration unit can also select a preferred collaboration method from the user's past feedback. The collaboration unit can also adjust the collaboration timing by referring to the user's past feedback. The collaboration unit can also analyze the user's feedback and customize the collaboration method by using the generation AI. For example, the collaboration unit improves the collaboration method by analyzing the user's survey results. The collaboration unit can also select a collaboration method by analyzing the user's reviews. In this way, the collaboration method can be customized by reflecting past feedback.

[0110] The questioning unit can estimate the user's emotions and adjust the way the question is phrased based on the estimated user's emotions. The questioning unit can use the generation AI to estimate the user's emotions and adjust the way the question is phrased based on the estimated user's emotions. For example, if the user is feeling anxious, the questioning unit can ask a question in gentle language. If the user is relaxed, the questioning unit can also ask a detailed question. If the user is in a hurry, the questioning unit can also ask a concise question. The questioning unit can also estimate the user's emotions using the generation AI. For example, the questioning unit can use the generation AI to analyze the user's facial expressions and estimate the emotions. The questioning unit can also use the generation AI to analyze the user's voice and estimate the emotions. The questioning unit can also use the generation AI to analyze the user's text input and estimate the emotions. This allows the questioning unit to ask more appropriate questions by adjusting the way the question is phrased based on the user's emotions.

[0111] The question unit can present the most appropriate question by referring to the user's past answer history when asking a question. The question unit uses the generation AI to present the most appropriate question by referring to the user's past answer history when asking a question. For example, the question unit presents related questions based on the content of answers given by the user in the past. The question unit can also find specific patterns from the user's past answer history and adjust the questions based on those patterns. The question unit can also present the most appropriate question based on the user's past answer history. The question unit can also use the generation AI to analyze the user's past answer history and present the most appropriate question. For example, the question unit uses the generation AI to analyze the user's survey results and present related questions. The question unit can also use the generation AI to analyze the user's medical records and adjust the questions. In this way, the most appropriate question can be presented by referring to the past answer history.

[0112] The questioning unit can customize the content of the questions based on the user's current living situation and environment when asking a question. The questioning unit uses the generation AI to customize the content of the questions based on the user's current living situation and environment when asking a question. For example, if the user is at work, the questioning unit can ask brief questions so as not to interfere with work. The questioning unit can also ask detailed questions when the user is at home. Furthermore, if the user is traveling, the questioning unit can ask questions related to health risks at the travel destination. The questioning unit can also analyze the user's living situation and environment and customize the content of the questions using the generation AI. For example, the generation AI can analyze the user's schedule information and adjust the questions. The generation AI can also analyze the user's location information and ask questions related to health risks at the travel destination. This allows the questioning unit to customize the content of the questions based on the user's current living situation and environment, enabling more appropriate questions to be asked.

[0113] The questioning unit can estimate the user's emotions and prioritize questions based on the estimated user's emotions. The questioning unit can use the generation AI to estimate the user's emotions and prioritize questions based on the estimated user's emotions. For example, if the user is feeling anxious, the questioning unit prioritizes questions with a high degree of urgency. Also, if the user is relaxed, the questioning unit can ask detailed questions. Furthermore, if the user is in a hurry, the questioning unit can prioritize important questions. The questioning unit can also estimate the user's emotions using the generation AI. For example, the questioning unit can use the generation AI to analyze the user's facial expressions and estimate emotions. Also, the questioning unit can use the generation AI to analyze the user's voice and estimate emotions. Furthermore, the questioning unit can use the generation AI to analyze the user's text input and estimate emotions. In this way, by prioritizing questions based on the user's emotions, important questions can be asked with priority.

[0114] The questioning unit can present the most appropriate question by taking into account the user's geographical location information when asking a question. The questioning unit uses a generation AI to present the most appropriate question by taking into account the user's geographical location information when asking a question. For example, if the user is in a specific area, the questioning unit can ask questions related to diseases that are prevalent in that area. Also, if the user is traveling, the questioning unit can ask questions related to health risks at the user's destination. Furthermore, if the user is at home, the questioning unit can present questions based on information about local medical institutions. The questioning unit can also use a generation AI to analyze the user's geographical location information and present the most appropriate question. For example, the generation AI in the questioning unit can analyze the user's GPS data to identify the user's current location. Also, the generation AI in the questioning unit can analyze the user's address information and provide local medical information. This allows the most appropriate question to be presented by taking into account the geographical location information.

[0115] The question unit can analyze the user's social media activity and present related questions when a question is asked. The question unit uses a generation AI to analyze the user's social media activity and present related questions when a question is asked. For example, the question unit asks additional questions based on symptoms reported by the user on social media. The question unit can also analyze the content of the user's social media posts and present related questions. Furthermore, the question unit can also present related questions based on the activity of the user's friends on social media. The question unit can also analyze the user's social media activity and present related questions using a generation AI. For example, the question unit uses a generation AI to analyze the content of the user's posts and present questions. The question unit can also use a generation AI to analyze the number of users' followers and evaluate their influence. This allows the question unit to present related questions by analyzing social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, support unit, linking unit, and questioning unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects information on the user's symptoms. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information and evaluates the severity of the symptoms. The support unit is realized by the specific processing unit 290 of the data processing device 12 and supports the user in deciding whether to consult a doctor and reducing medical expenses. The linking unit is realized by the specific processing unit 290 of the data processing device 12 and shares the results of the medical interview with a doctor. The questioning unit is realized by the control unit 46A of the smart device 14 and presents specific questions on the user's symptoms. The collection unit can also estimate the user's emotions using a generation AI and adjust the timing of symptom information collection based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, support unit, collaboration unit, and question unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects information on the user's symptoms. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information and evaluates the severity of the symptoms. The support unit is realized by the specific processing unit 290 of the data processing device 12 and supports the user in deciding whether to consult a doctor and reducing medical expenses. The collaboration unit is realized by the specific processing unit 290 of the data processing device 12 and shares the results of the medical interview with a doctor. The question unit is realized by the control unit 46A of the smart glasses 214 and presents specific questions on the user's symptoms. The collection unit can also estimate the user's emotions using a generation AI and adjust the timing of symptom information collection based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, support unit, linking unit, and question unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset-type terminal 314 and collects information on the user's symptoms. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information and evaluates the severity of the symptoms. The support unit is realized by the specific processing unit 290 of the data processing device 12 and supports the user in deciding whether to consult a doctor and reducing medical expenses. The linking unit is realized by the specific processing unit 290 of the data processing device 12 and shares the results of the medical interview with a doctor. The questioning unit is realized by the control unit 46A of the headset-type terminal 314 and presents specific questions on the user's symptoms. The collection unit can also estimate the user's emotions using a generation AI and adjust the timing of symptom information collection based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, support unit, collaboration unit, and question unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects information on the user's symptoms. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information and evaluates the severity of the symptoms. The support unit is realized by the specific processing unit 290 of the data processing device 12 and supports the user in deciding whether to consult a doctor and reducing medical expenses. The collaboration unit is realized by the specific processing unit 290 of the data processing device 12 and shares the results of the medical interview with a doctor. The question unit is realized by the control unit 46A of the robot 414 and presents specific questions on the user's symptoms. The collection unit can also estimate the user's emotions using a generation AI and adjust the timing of symptom information collection based on the estimated emotions.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] When collecting information about the user's symptoms, the collection unit can customize the questions by referring to the user's past medical history. For example, the collection unit can preferentially present related questions based on symptoms previously reported by the user. The collection unit can also add questions related to a specific medical history from the user's past medical history. Furthermore, the collection unit can ask questions to check the progression of symptoms based on the user's past medical history. This allows for more appropriate information collection by taking the user's past medical history into consideration.

[0118] The questioning unit can estimate the user's emotions and adjust the order of questions based on the estimated user's emotions. For example, if the user feels anxious, the questioning unit can ask urgent questions first. If the user feels relaxed, the questioning unit can also postpone detailed questions. Furthermore, if the user is in a hurry, the questioning unit can also prioritize important questions. This allows for more effective information collection by adjusting the order of questions according to the user's emotions.

[0119] The analysis unit can take into account the user's lifestyle information when evaluating the severity of symptoms based on the user's answers. For example, the analysis unit can evaluate the severity by taking into account the user's smoking habits and drinking habits. The analysis unit can also evaluate the severity by taking into account the user's exercise habits and dietary habits. Furthermore, the analysis unit can evaluate the severity by taking into account the user's stress level and sleep patterns. This allows for a more accurate severity evaluation by taking into account the user's lifestyle information.

[0120] The support unit can estimate the user's emotions and suggest the timing of a medical examination based on the estimated user's emotions. For example, if the user feels anxious, the support unit can recommend an early medical examination. If the user feels relaxed, the support unit can also suggest that the user monitor the progress of symptoms. Furthermore, if the user is in a hurry, the support unit can also recommend a prompt medical examination. This allows for more appropriate medical support by suggesting the timing of a medical examination based on the user's emotions.

[0121] When sharing the medical interview results with a doctor, the collaboration unit can adjust the scope of information sharing based on the user's privacy settings. For example, if the user places importance on privacy, the collaboration unit will share only the minimum amount of information necessary. Also, if the user is proactive in sharing information, the collaboration unit can share detailed medical interview results. Furthermore, if the user wants to keep specific information private, the collaboration unit can exclude that information from sharing. This allows for more appropriate information sharing by adjusting the scope of information sharing based on the user's privacy settings.

[0122] The collection unit can estimate the user's emotions and adjust the level of detail of the information to be collected based on the estimated user's emotions. For example, if the user is feeling anxious, the collection unit can prioritize collecting simple questions. Also, if the user is relaxed, the collection unit can collect detailed information. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting important information. In this way, by adjusting the level of detail of information based on the user's emotions, more appropriate information collection is possible.

[0123] The analysis unit can take into account the influence of seasons and weather when evaluating the severity of symptoms based on the user's answers. For example, the analysis unit can evaluate the severity taking into account the prevalence of influenza in winter. The analysis unit can also evaluate the severity taking into account symptoms during hay fever season. Furthermore, the analysis unit can evaluate the severity taking into account poor physical condition due to changes in weather. This allows for a more accurate severity evaluation by taking into account the influence of seasons and weather.

[0124] The support unit can estimate the user's emotions and support the selection of a medical institution based on the estimated user's emotions. For example, if the user feels anxious, the support unit can recommend a highly reliable medical institution. If the user feels relaxed, the support unit can also suggest a nearby medical institution. Furthermore, if the user is in a hurry, the support unit can also recommend a medical institution that can respond quickly. This allows for more appropriate medical support by supporting the selection of a medical institution based on the user's emotions.

[0125] When sharing the medical interview results with a doctor, the collaboration unit can select the most appropriate medical institution by taking into account the user's geographical location information. For example, if the user is in a specific area, the collaboration unit shares the medical interview results with medical institutions in that area. Also, if the user is traveling, the collaboration unit can share the medical interview results with medical institutions in the user's travel destination. Furthermore, if the user is at home, the collaboration unit can share the medical interview results with nearby medical institutions. In this way, the medical interview results can be shared with the most appropriate medical institution by taking into account the geographical location information.

[0126] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, if the user is feeling anxious, the collection unit prioritizes collection of information with high urgency. The collection unit can also collect detailed information if the user is relaxed. Furthermore, the collection unit can also prioritize collection of important information if the user is in a hurry. This enables more appropriate information collection by determining the priority of information based on the user's emotions.

[0127] The processing flow of the second embodiment will be briefly explained below.

[0128] Step 1: The collection unit collects information about the user's symptoms. Information about the user's symptoms includes, for example, fever, cough, and headache. The collection unit collects symptom information entered by the user online. The collection unit can also use a generation AI to present questions about the user's symptoms and collect information by the user's answers. For example, specific questions such as "Do you have a fever?" or "Do you have a cough?" can be presented. Step 2: The analysis unit evaluates the severity of the symptoms based on the information collected by the collection unit and determines the need for medical examination and urgency. The analysis unit can also use the generation AI to analyze the user's answers and evaluate the severity of the symptoms. Step 3: The support unit uses the results obtained by the analysis unit to help decide whether to see a doctor and reduce medical costs. For example, if symptoms are mild, it will suggest ways to deal with them at home, and if symptoms are severe, it will recommend visiting a medical institution. The support unit can also use generative AI to help decide whether to see a doctor and reduce medical costs. Step 4: The collaboration unit collaborates with the hospital on the results obtained by the support unit. For example, the collaboration unit can share the results of the medical interview with the doctor to ensure smooth medical treatment. The collaboration unit can also use the generative AI to share the results of the medical interview with the doctor.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 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.

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

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0143] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0152] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0169] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0172] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0173] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0174] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0175] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0176] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0177] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0179] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0181] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0182] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0184] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0185] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0186] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0190] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0191] 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.

[0192] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0193] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0194] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0195] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0197] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0198] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0199] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0200] [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information about a user's symptoms; an analysis unit that evaluates the severity of symptoms and determines the necessity or urgency of a medical examination based on the information collected by the collection unit; a support unit that supports the patient in making a decision about whether to receive medical treatment or in reducing medical expenses based on the results obtained by the analysis unit; a linking unit that links the results obtained by the support unit with a hospital; A system characterized by:

2. A question section is provided that presents specific questions about the user's symptoms.

2. The system of claim 1.

3. The analysis unit Based on the user's answers, the severity of the symptoms is assessed and the need for medical attention and urgency are determined.

2. The system of claim 1.

4. The support unit If the symptoms are mild, home remedies are suggested, and if the symptoms are severe, a medical consultation is recommended.

2. The system of claim 1.

5. The linking unit is Share the results of your medical interview with your doctor to ensure smooth medical treatment 2. The system of claim 1.

6. The interrogation unit Present specific questions such as do you have a fever or a cough? 3. The system of claim 2.

7. The collecting unit The system estimates the user's emotions and adjusts the timing of symptom information collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past symptom history and select the optimal collection method 2. The system of claim 1.

9. The collecting unit When collecting symptom information, filtering is performed based on the user's current living situation and environment.

2. The system of claim 1.

Images