System

Abstract

An object of the system according to the embodiment is to propose a medical institution suitable for a symptom or a desire of a user and to provide a coping method or related information.SOLUTION: A system includes a user input section, an information analysis section, a medical institution proposal section, and a handling method providing section. The user input unit inputs a desire or a symptom of a user. The information analysis unit analyzes the information input by the user input unit. The medical institution proposal unit proposes a candidate for a medical institution on the basis of a result analyzed by the information analysis unit. The handling method providing section provides a handling method suitable for the symptom and related information based on the result analyzed by the information analyzing section.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 for users to find a medical institution that suits their symptoms and needs, and there has been a lack of information and strategies to alleviate anxiety.

[0005] The system according to the embodiment aims to suggest medical institutions that match the symptoms and wishes of the user, and to provide treatment methods and related information. [Means for solving the problem]

[0006] The system according to the embodiment includes a user input unit, an information analysis unit, a medical institution suggestion unit, and a solution provision unit. The user input unit inputs the user's wishes or symptoms. The information analysis unit analyzes the information input by the user input unit. The medical institution suggestion unit suggests candidate medical institutions based on the results of the analysis by the information analysis unit. The solution provision unit provides solutions and related information tailored to the symptoms based on the results of the analysis by the information analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest medical institutions that match the symptoms and wishes of the user, and can provide treatment methods and related information. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The medical institution recommendation system according to the embodiment of the present invention is a system that recommends the most suitable medical institution based on the user's wishes and symptoms, and provides treatment methods and related information tailored to the symptoms. As a result, the medical institution recommendation system reduces the user's anxiety and enables the user to select the most suitable medical institution.

[0029] The medical institution suggestion system according to the embodiment includes a user input unit, an information analysis unit, a medical institution suggestion unit, and a remedy provision unit. The user input unit inputs a user's wishes and symptoms. For example, the user can input "I have a severe headache." The user input unit can also input "I'm looking for a nearby internal medicine doctor." The user input unit can also input "I have a skin rash." The information analysis unit analyzes the information input by the user input unit. For example, the information analysis unit analyzes the user's input using text analysis technology. The information analysis unit can also analyze the user's input using data mining technology. The information analysis unit can also analyze the user's input using a machine learning algorithm. The medical institution suggestion unit proposes candidate medical institutions based on the results of the analysis by the information analysis unit. For example, the medical institution suggestion unit proposes candidate medical institutions based on distance. The medical institution suggestion unit can also propose candidate medical institutions based on specialty fields. The medical institution suggestion unit can also propose candidate medical institutions based on evaluations. The remedy provision unit provides remedy measures and related information tailored to the symptoms based on the results of the analysis by the information analysis unit. For example, the remedy providing unit provides information on treatment methods. The remedy providing unit can also provide information on preventive measures. The remedy providing unit can also provide information on reference materials. As a result, the medical institution recommendation system according to the embodiment can reduce the user's anxiety by recommending the most appropriate medical institution based on the user's wishes and symptoms and providing remedy methods and related information tailored to the symptoms.

[0030] The user input unit can ask questions in real time in response to the user's input to extract more detailed information. For example, if the user inputs "I have a severe headache," the generation AI will ask follow-up questions in real time, such as "How often do you have headaches?" and "How severe is the pain?" If the user inputs "I'm looking for a nearby internal medicine doctor," the generation AI will ask questions to extract more detailed information, such as "Where exactly do you live?" and "What are your preferred consultation hours?" If the user inputs "I have a skin rash," the generation AI will ask follow-up questions in real time, such as "How widespread is the rash?" and "Do you have any itching or pain?" to collect more detailed information. By asking questions in real time in response to the user's input, more detailed information can be collected, enabling more accurate medical institution recommendations.

[0031] The information analysis unit analyzes the user's input and can provide more accurate information based on the user's past medical history and family medical history. For example, if the user inputs "I have a severe headache," the generation AI will refer to the user's past medical history to determine whether the user has previously experienced similar symptoms. For example, if the user has been diagnosed with migraines, the generation AI will take that information into account when providing advice. If the user inputs "I'm looking for a nearby internal medicine doctor," the generation AI will refer to the user's family medical history and consider genetic factors to suggest an appropriate medical institution. For example, if there is a family history of diabetes, the generation AI will suggest an internal medicine specialist based on that information. If the user inputs "I have a skin rash," the generation AI will consider the user's past medical history and family medical history to evaluate the possibility of allergies or skin diseases. For example, if the user has had an allergic reaction in the past, the generation AI will provide advice based on that information. This allows for more accurate medical institution recommendations by taking into account the user's past medical history and family medical history.

[0032] The user input unit can use voice or image input to allow the user to describe their symptoms more specifically. For example, when a user voice-inputs, "I have a severe headache," the generation AI uses voice recognition technology to convert the user's speech into text and collect more detailed information. For example, it asks a follow-up question, such as, "How often do you have headaches?" When a user voice-inputs, "I have a skin rash," the generation AI uses image recognition technology to analyze the condition of the rash and provide appropriate advice. For example, it asks a follow-up question, such as, "How widespread is the rash?" When a user voice-inputs, "I'm looking for a nearby internal medicine doctor," the generation AI uses voice recognition technology to convert the user's speech into text and collect more detailed information. For example, it asks a follow-up question, such as, "Where exactly do you live?" By using voice or image input, the user can describe their symptoms more specifically, enabling more accurate medical institution recommendations.

[0033] The information analysis unit collects data on the user's lifestyle and daily activities and can recommend medical institutions based on that information. For example, if a user inputs "I have a severe headache," the generation AI collects the user's lifestyle data (e.g., sleep patterns and eating habits) and suggests an appropriate medical institution based on that data. For example, if lack of sleep is thought to be the cause, the generation AI suggests a medical institution specializing in sleep. If a user inputs "I'm looking for a nearby internal medicine doctor," the generation AI collects the user's daily activity data (e.g., exercise habits and stress level) and suggests an appropriate medical institution based on that data. For example, if stress is high, the generation AI suggests an internal medicine doctor specializing in stress management. If a user inputs "I have a skin rash," the generation AI collects the user's lifestyle data (e.g., the cosmetics and detergents they use) and suggests an appropriate medical institution based on that data. For example, it suggests a dermatologist specializing in allergies. This allows the generation AI to suggest more appropriate medical institutions by taking into account the user's lifestyle and daily activity data.

[0034] The information analysis unit can introduce a scoring system to evaluate the reliability of information when analyzing reviews or expert information. For example, the generation AI collects information from online review sites and introduces a scoring system to evaluate the reliability of each review. For example, the information analysis unit calculates a score based on the reliability of the review poster and the specificity of the posted content. The information analysis unit also collects evaluation data from medical experts and introduces a scoring system to evaluate the reliability of each expert. For example, the score is calculated based on the expert's qualifications, years of experience, and past evaluations. To evaluate the reliability of reviews and expert information, the generation AI analyzes the source of the information, the posting date and time, and the consistency of the content, and calculates an overall reliability score. For example, information from multiple reliable sources is prioritized. In this way, by introducing a scoring system to evaluate the reliability of reviews and expert information, it is possible to provide users with highly reliable information.

[0035] The information analysis unit analyzes reviews or expert information in multiple languages ​​and can provide evaluations from an international perspective. For example, the generation AI collects information from a multilingual review site and analyzes reviews in each language. For example, it analyzes reviews in English, French, Chinese, etc. and provides evaluations from an international perspective. The information analysis unit also collects evaluation data from medical experts in multiple languages ​​and analyzes the evaluations in each language. For example, it analyzes evaluations in English, Spanish, Japanese, etc. and provides evaluations from an international perspective. The generation AI also analyzes multilingual reviews and expert information and provides optimal information according to the user's language settings. For example, if the user selects English, English reviews and evaluations are displayed preferentially. This makes it possible to provide evaluations from an international perspective by performing analysis in multiple languages.

[0036] The information analysis unit can visualize reviews or expert information to enable users to intuitively understand. For example, the generation AI collects information from review sites and visualizes the content of reviews. For example, it displays the number of stars in ratings and positive comments in graphs and charts. The information analysis unit also visualizes evaluation data from medical experts to enable users to intuitively understand. For example, it displays expert evaluations in radar charts and heat maps. The information analysis unit also analyzes reviews and expert information using the generation AI and visualizes it to enable users to intuitively understand. For example, it graphically displays evaluation trends and major keywords. In this way, visualizing the information makes it easier for users to intuitively understand.

[0037] The medical institution suggestion unit can suggest the most suitable medical institution based on past patient treatment results and satisfaction. For example, the generation AI collects past patient treatment result data and suggests the most suitable medical institution by taking into consideration the treatment success rate and patient satisfaction. For example, it prioritizes suggesting medical institutions with a high treatment success rate. The medical institution suggestion unit also analyzes patient satisfaction data and suggests medical institutions with high satisfaction. For example, it lists medical institutions with high satisfaction based on patient reviews and survey results. The generation AI also comprehensively evaluates past patient treatment results and satisfaction data and suggests the most suitable medical institution. For example, it prioritizes suggesting medical institutions with both a high treatment success rate and high satisfaction. This makes it possible to suggest more appropriate medical institutions by taking past patient treatment results and satisfaction into consideration.

[0038] The medical institution suggestion unit can automatically collect detailed information about the proposed medical institutions (for example, the doctor's specialty or treatment record) and provide it to the user. For example, the generation AI automatically collects detailed information about the proposed medical institutions and provides it to the user. For example, it lists the doctor's specialty and treatment record. The medical institution suggestion unit also collects profiles and past treatment records of the doctors at the proposed medical institutions and provides them to the user. For example, it displays the doctor's career and specialty in detail. The medical institution suggestion unit also automatically collects detailed information about the proposed medical institutions and provides it to the user. For example, it provides information about the medical institution's facilities and treatment methods. In this way, by automatically collecting detailed information about the proposed medical institutions, reference information can be provided to the user when selecting a medical institution.

[0039] The medical institution suggestion unit can propose an optimal route based on the transportation means and required time based on the location information of the proposed medical institution. For example, the medical institution suggestion unit proposes a route that takes into account the optimal transportation means and required time based on the location information of the medical institution proposed by the generation AI. For example, it displays how to use public transportation and the required time. The medical institution suggestion unit also proposes an optimal route from the user's current location based on the location information of the proposed medical institution. For example, it provides information on travel time by car and parking. The medical institution suggestion unit also proposes an optimal route that takes into account the transportation means and required time based on the location information of the medical institution proposed by the generation AI. For example, it displays travel time by walking or cycling. In this way, user convenience is improved by proposing an optimal route that takes into account the transportation means and required time.

[0040] The medical institution suggestion unit can visualize past patient reviews of the proposed medical institution, allowing the user to intuitively understand the ratings. For example, the medical institution suggestion unit collects and visualizes past patient reviews of the medical institution proposed by the generation AI. For example, it displays the number of stars in the rating and positive comments in graphs and charts. The medical institution suggestion unit also visualizes patient reviews of the proposed medical institution, allowing the user to intuitively understand the ratings. For example, it graphically displays rating trends and major keywords. The medical institution suggestion unit also visualizes past patient reviews of the medical institution proposed by the generation AI, allowing the user to intuitively understand the ratings. For example, it displays the distribution of ratings and positive comments in a heat map. In this way, visualizing past patient reviews makes it easier for the user to intuitively understand the ratings.

[0041] The solution provider can provide optimal solutions to symptoms based on the latest medical research and guidelines. For example, the AI ​​generator collects the latest medical research and guidelines and provides optimal solutions to the user's symptoms. For example, it provides specific advice such as, "If you have a severe headache, it's important to get plenty of rest first." The solution provider also provides optimal solutions to the user's symptoms based on the latest medical research and guidelines. For example, it provides specific advice such as, "If you have a skin rash, it's recommended that you avoid certain foods and cosmetics as they may be an allergy." The solution provider also provides optimal solutions to the user's symptoms based on the latest medical research and guidelines. For example, it provides specific advice such as, "If you're looking for a nearby internal medicine doctor, it's recommended that you first have an online initial consultation." This allows the system to provide users with reliable information by providing solutions based on the latest medical research and guidelines.

[0042] The solution provision unit can provide solutions to the user's symptoms as videos or interactive content, thereby deepening understanding. In the solution provision unit, for example, the generation AI provides solutions to the user's symptoms as videos. For example, a video explaining "relaxation methods for when you have a severe headache." In addition, the solution provision unit can provide solutions to the user's symptoms as interactive content. For example, a video explaining "what to do if you have a skin rash." In addition, the solution provision unit can provide solutions to the user's symptoms as videos or interactive content, thereby deepening understanding. For example, a video explaining "the process for an initial consultation when looking for a nearby internal medicine doctor." In this way, the use of videos and interactive content can deepen the user's understanding.

[0043] The remedy provision unit can provide remedies for symptoms from different cultural or regional perspectives, presenting the user with a variety of options. For example, the generating AI in the remedy provision unit provides remedies for symptoms from different cultural or regional perspectives. For example, it may introduce "Oriental medicine remedies for severe headaches." The generating AI may also provide remedies for symptoms from different cultural or regional perspectives, presenting the user with a variety of options. For example, it may introduce "traditional African medicine remedies for skin rashes." The generating AI may also provide remedies for symptoms from different cultural or regional perspectives, presenting the user with a variety of options. For example, it may introduce "European medical systems when looking for a nearby internal medicine doctor." This allows the user to be presented with a variety of options by providing remedies from different cultural or regional perspectives.

[0044] The solution provision unit can link information about solutions with the user's lifestyle and daily activity data to individually optimize the solutions. For example, the solution provision unit uses a generation AI to collect the user's lifestyle data (e.g., sleep patterns and eating habits) and individually optimize solutions to symptoms based on that data. For example, it provides specific advice such as, "If you have severe headaches, it is recommended that you get more sleep." The solution provision unit also uses a generation AI to collect the user's daily activity data (e.g., exercise habits and stress levels) and individually optimize solutions to symptoms based on that data. For example, it provides specific advice such as, "If you have a skin rash, it is recommended that you avoid certain exercises." The solution provision unit also uses a generation AI to link the user's lifestyle and daily activity data to individually optimize solutions to symptoms. For example, it provides specific advice such as, "If you are looking for a nearby internal medicine doctor, we will suggest the best appointment times based on your daily activity data." By linking the solution provision unit with the user's lifestyle and daily activity data, it is possible to provide individually optimized solutions.

[0045] The remedy providing unit allows the generating AI to provide individually optimized medical information based on the user's past search history and medical history. For example, the generating AI analyzes the user's past search history and provides individually optimized medical information based on that. For example, it provides the latest information related to symptoms and medical institutions searched for in the past. The remedy providing unit also analyzes the user's medical history and provides individually optimized medical information based on that. For example, it suggests appropriate medical institutions and remedy methods based on past diagnosis results and treatment history. The remedy providing unit also provides individually optimized medical information based on the user's past search history and medical history. For example, it provides the latest research results and treatment methods related to medical institutions and symptoms searched for in the past. In this way, by providing individually optimized medical information based on the user's past search history and medical history, it is possible to provide optimal information for the user.

[0046] The remedy provision unit allows the generation AI to provide preventive medical care and health management advice based on the user's health condition and lifestyle habits. For example, the remedy provision unit allows the generation AI to analyze the user's health condition and lifestyle habits and provide preventive medical care and health management advice based on the analysis. For example, specific advice such as "It is recommended that you continue to exercise regularly" is provided. The remedy provision unit also allows the generation AI to consider the user's health condition and lifestyle habits and provide preventive medical care and health management advice based on the analysis. For example, specific advice such as "Try to eat a balanced diet" is provided. The remedy provision unit also allows the generation AI to consider the user's health condition and lifestyle habits and provide preventive medical care and health management advice based on the analysis. For example, specific advice such as "It is recommended that you undergo regular health checkups" is provided. In this way, preventive medical care and health management advice can be provided by taking the user's health condition and lifestyle habits into consideration.

[0047] The remedy providing unit can seamlessly provide individually optimized medical information across different devices (smartphones, tablets, smartwatches). For example, the remedy providing unit may provide individually optimized medical information via a smartphone, allowing the user to access it anytime, anywhere. For example, the remedy providing unit may provide medical information via a smartphone app. The remedy providing unit may also provide individually optimized medical information via a tablet, allowing the user to view detailed information on a large screen. For example, the remedy providing unit may provide medical information via a tablet app. The remedy providing unit may also provide individually optimized medical information via a smartwatch, allowing the user to view health information in real time. For example, the remedy providing unit may provide medical information via the notification function of the smartwatch. This allows medical information to be seamlessly provided across different devices, allowing the user to access it anytime, anywhere.

[0048] The solution provision unit allows the generation AI to continuously improve the way medical information is provided based on user feedback. For example, the solution provision unit allows the generation AI to collect user feedback and improve the way medical information is provided based on that feedback. For example, the display format of information is changed to reflect user opinions. The solution provision unit also allows the generation AI to continuously improve the way medical information is provided based on user feedback. For example, new functions are added in response to user requests. The solution provision unit also allows the generation AI to analyze user feedback and improve the way medical information is provided based on that. For example, the accuracy and reliability of information is improved based on user evaluations. In this way, by improving the way medical information is provided based on user feedback, a system that is easier for users to use can be provided.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The medical institution recommendation system can further include a sensor unit that monitors the user's health condition. The sensor unit, for example, measures the user's heart rate and blood pressure in real time and transmits this data to the information analysis unit. The information analysis unit analyzes this data and can recommend the most appropriate medical institution based on the user's health condition. For example, if the heart rate is abnormally high, a medical institution specializing in cardiac care can be recommended. Also, if the blood pressure is high, a medical institution specializing in hypertension treatment can be recommended. This makes it possible to monitor the user's health condition in real time and recommend more appropriate medical institutions.

[0051] The information analysis unit can also collect and analyze data on the user's living environment. For example, if a user inputs "I have a severe headache," the generation AI can collect data on the user's living environment (such as noise levels and air quality) and suggest appropriate medical institutions based on that information. For example, if the noise level is high, it can suggest medical institutions that are taking measures to combat noise pollution. Also, if the air quality is poor, it can provide advice on improving the air quality. This makes it possible to suggest medical institutions that take the user's living environment into consideration.

[0052] The user input unit can further include a function to accept gesture input from the user. For example, when a user inputs "I have a severe headache," the gesture input function can analyze the user's hand movements and collect detailed information. For example, if the user makes a gesture of holding their head, the location and intensity of the pain can be estimated. Also, when a user inputs "I'm looking for a nearby internal medicine doctor," the gesture input function can analyze the user's pointing movements and identify a specific area. This allows the user to communicate their symptoms more specifically, making it possible to recommend medical institutions with high accuracy.

[0053] The information analysis unit can collect the user's dietary data and suggest medical institutions based on that. For example, if the user inputs "I have a severe headache," the generation AI can collect the user's dietary data and suggest appropriate medical institutions from the perspective of nutritional balance. For example, if a nutritional deficiency is thought to be the cause, the generation AI can suggest medical institutions that provide nutritional guidance. Also, if the user inputs "I'm looking for a nearby internal medicine doctor," the generation AI can collect the user's dietary data and suggest appropriate medical institutions based on their eating habits. This makes it possible to suggest medical institutions that take the user's dietary data into consideration.

[0054] The information analysis unit can collect the user's exercise data and suggest medical institutions based on that. For example, if the user inputs "I have a severe headache," the generation AI will collect the user's exercise data and, if it thinks that a lack of exercise is the cause, it can suggest medical institutions that provide exercise instruction. Also, if the user inputs "I'm looking for a nearby internal medicine doctor," the generation AI will collect the user's exercise data and suggest appropriate medical institutions based on the user's exercise habits. This makes it possible to suggest medical institutions that take the user's exercise data into consideration.

[0055] The information analysis unit can collect the user's sleep data and suggest medical institutions based on that. For example, if a user inputs "I have a severe headache," the generation AI will collect the user's sleep data and, if it thinks that lack of sleep is the cause, it can suggest a medical institution specializing in sleep. Also, if a user inputs "I'm looking for a nearby internal medicine doctor," the generation AI will collect the user's sleep data and suggest an appropriate medical institution based on their sleeping habits. This makes it possible to suggest medical institutions that take the user's sleep data into consideration.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The user input section inputs the user's wishes and symptoms. For example, the user can input "I have a bad headache." The user can also input "I'm looking for a nearby internal medicine doctor." The user can also input "I have a skin rash." Step 2: The information analysis unit analyzes the information input by the user input unit. For example, the information analysis unit analyzes the user's input using text analysis technology, data mining technology, or machine learning algorithms. Step 3: The medical institution suggestion unit suggests medical institution candidates based on the results of the analysis by the information analysis unit, for example, based on distance, specialty, and rating. Step 4: The remedy provider provides symptom-specific remedies and related information based on the results of the analysis by the information analyzer, such as information on treatments, preventive measures, and reference materials.

[0058] (Example 2) The medical institution recommendation system according to the embodiment of the present invention is a system that recommends the most suitable medical institution based on the user's wishes and symptoms, and provides treatment methods and related information tailored to the symptoms. As a result, the medical institution recommendation system reduces the user's anxiety and enables the user to select the most suitable medical institution.

[0059] The medical institution suggestion system according to the embodiment includes a user input unit, an information analysis unit, a medical institution suggestion unit, and a remedy provision unit. The user input unit inputs a user's wishes and symptoms. For example, the user can input "I have a severe headache." The user input unit can also input "I'm looking for a nearby internal medicine doctor." The user input unit can also input "I have a skin rash." The information analysis unit analyzes the information input by the user input unit. For example, the information analysis unit analyzes the user's input using text analysis technology. The information analysis unit can also analyze the user's input using data mining technology. The information analysis unit can also analyze the user's input using a machine learning algorithm. The medical institution suggestion unit proposes candidate medical institutions based on the results of the analysis by the information analysis unit. For example, the medical institution suggestion unit proposes candidate medical institutions based on distance. The medical institution suggestion unit can also propose candidate medical institutions based on specialty fields. The medical institution suggestion unit can also propose candidate medical institutions based on evaluations. The remedy provision unit provides remedy measures and related information tailored to the symptoms based on the results of the analysis by the information analysis unit. For example, the remedy providing unit provides information on treatment methods. The remedy providing unit can also provide information on preventive measures. The remedy providing unit can also provide information on reference materials. As a result, the medical institution recommendation system according to the embodiment can reduce the user's anxiety by recommending the most appropriate medical institution based on the user's wishes and symptoms and providing remedy methods and related information tailored to the symptoms.

[0060] The user input unit can ask questions in real time in response to the user's input to extract more detailed information. For example, if the user inputs "I have a severe headache," the generation AI will ask follow-up questions in real time, such as "How often do you have headaches?" and "How severe is the pain?" If the user inputs "I'm looking for a nearby internal medicine doctor," the generation AI will ask questions to extract more detailed information, such as "Where exactly do you live?" and "What are your preferred consultation hours?" If the user inputs "I have a skin rash," the generation AI will ask follow-up questions in real time, such as "How widespread is the rash?" and "Do you have any itching or pain?" to collect more detailed information. By asking questions in real time in response to the user's input, more detailed information can be collected, enabling more accurate medical institution recommendations.

[0061] The information analysis unit analyzes the user's input and can provide more accurate information based on the user's past medical history and family medical history. For example, if the user inputs "I have a severe headache," the generation AI will refer to the user's past medical history to determine whether the user has previously experienced similar symptoms. For example, if the user has been diagnosed with migraines, the generation AI will take that information into account when providing advice. If the user inputs "I'm looking for a nearby internal medicine doctor," the generation AI will refer to the user's family medical history and consider genetic factors to suggest an appropriate medical institution. For example, if there is a family history of diabetes, the generation AI will suggest an internal medicine specialist based on that information. If the user inputs "I have a skin rash," the generation AI will consider the user's past medical history and family medical history to evaluate the possibility of allergies or skin diseases. For example, if the user has had an allergic reaction in the past, the generation AI will provide advice based on that information. This allows for more accurate medical institution recommendations by taking into account the user's past medical history and family medical history.

[0062] The information analysis unit can use the emotion estimation function to analyze the user's emotion when inputting information and provide advice to reduce stress and anxiety. For example, when a user inputs "I have a severe headache," the emotion estimation function analyzes the user's stress level and provides advice to help them relax. For example, a message such as "Take a deep breath and relax" is displayed. When a user inputs "I'm looking for a nearby internal medicine doctor," the emotion estimation function detects the user's anxiety and provides advice to give them a sense of security. For example, a message such as "There is a nearby internal medicine doctor with a good reputation, so don't worry." When a user inputs "I have a skin rash," the emotion estimation function analyzes the user's anxiety and provides advice to give them a sense of security. For example, a message such as "The rash is often caused by a mild allergic reaction, so don't worry" is displayed. In this way, the user's emotion is analyzed and advice to reduce stress and anxiety is provided, thereby increasing the user's sense of security.

[0063] The user input unit can use voice or image input to allow the user to describe their symptoms more specifically. For example, when a user voice-inputs, "I have a severe headache," the generation AI uses voice recognition technology to convert the user's speech into text and collect more detailed information. For example, it asks a follow-up question, such as, "How often do you have headaches?" When a user voice-inputs, "I have a skin rash," the generation AI uses image recognition technology to analyze the condition of the rash and provide appropriate advice. For example, it asks a follow-up question, such as, "How widespread is the rash?" When a user voice-inputs, "I'm looking for a nearby internal medicine doctor," the generation AI uses voice recognition technology to convert the user's speech into text and collect more detailed information. For example, it asks a follow-up question, such as, "Where exactly do you live?" By using voice or image input, the user can describe their symptoms more specifically, enabling more accurate medical institution recommendations.

[0064] The information analysis unit collects data on the user's lifestyle and daily activities and can recommend medical institutions based on that information. For example, if a user inputs "I have a severe headache," the generation AI collects the user's lifestyle data (e.g., sleep patterns and eating habits) and suggests an appropriate medical institution based on that data. For example, if lack of sleep is thought to be the cause, the generation AI suggests a medical institution specializing in sleep. If a user inputs "I'm looking for a nearby internal medicine doctor," the generation AI collects the user's daily activity data (e.g., exercise habits and stress level) and suggests an appropriate medical institution based on that data. For example, if stress is high, the generation AI suggests an internal medicine doctor specializing in stress management. If a user inputs "I have a skin rash," the generation AI collects the user's lifestyle data (e.g., the cosmetics and detergents they use) and suggests an appropriate medical institution based on that data. For example, it suggests a dermatologist specializing in allergies. This allows the generation AI to suggest more appropriate medical institutions by taking into account the user's lifestyle and daily activity data.

[0065] The information analysis unit can use the emotion estimation function to analyze the user's emotions in real time when inputting information and provide an interface for eliciting positive emotions. For example, when a user inputs "I have a severe headache," the emotion estimation function analyzes the user's stress level in real time and provides an interface for relaxation. For example, calm music or images with a relaxing effect are displayed. Furthermore, when a user inputs "I'm looking for a nearby internal medicine doctor," the emotion estimation function analyzes the user's anxiety in real time and provides an interface for providing a sense of security. For example, encouraging messages or success stories are displayed. Furthermore, when a user inputs "I have a skin rash," the information analysis unit analyzes the user's anxiety in real time and provides an interface for providing a sense of security. For example, information indicating that the cause of the rash is mild is displayed. In this way, the user's emotions are analyzed in real time and an interface for eliciting positive emotions is provided, thereby increasing the user's sense of security.

[0066] The information analysis unit can introduce a scoring system to evaluate the reliability of information when analyzing reviews or expert information. For example, the generation AI collects information from online review sites and introduces a scoring system to evaluate the reliability of each review. For example, the information analysis unit calculates a score based on the reliability of the review poster and the specificity of the posted content. The information analysis unit also collects evaluation data from medical experts and introduces a scoring system to evaluate the reliability of each expert. For example, the score is calculated based on the expert's qualifications, years of experience, and past evaluations. To evaluate the reliability of reviews and expert information, the generation AI analyzes the source of the information, the posting date and time, and the consistency of the content, and calculates an overall reliability score. For example, information from multiple reliable sources is prioritized. In this way, by introducing a scoring system to evaluate the reliability of reviews and expert information, it is possible to provide users with highly reliable information.

[0067] The information analysis unit can use the emotion estimation function to analyze the emotional tone of reviews and prioritize displaying positive reviews. For example, the information analysis unit uses the generation AI to collect information from review sites and analyze the emotional tone of the reviews using the emotion estimation function. For example, reviews with positive emotions ("satisfied" and "gratitude") are prioritized for display. The information analysis unit also analyzes evaluation data from medical experts and analyzes the emotional tone of the evaluations using the emotion estimation function. For example, positive evaluations ("trustworthy" and "excellent technology") are prioritized for display. The information analysis unit also uses the generation AI to analyze reviews and expert information and use the emotion estimation function to prioritize displaying information with positive emotions. For example, positive evaluations such as "friendly service" and "effective treatment" are highlighted. This prioritizes displaying positive evaluations, giving users a sense of security.

[0068] The information analysis unit analyzes reviews or expert information in multiple languages ​​and can provide evaluations from an international perspective. For example, the generation AI collects information from a multilingual review site and analyzes reviews in each language. For example, it analyzes reviews in English, French, Chinese, etc. and provides evaluations from an international perspective. The information analysis unit also collects evaluation data from medical experts in multiple languages ​​and analyzes the evaluations in each language. For example, it analyzes evaluations in English, Spanish, Japanese, etc. and provides evaluations from an international perspective. The generation AI also analyzes multilingual reviews and expert information and provides optimal information according to the user's language settings. For example, if the user selects English, English reviews and evaluations are displayed preferentially. This makes it possible to provide evaluations from an international perspective by performing analysis in multiple languages.

[0069] The information analysis unit can visualize reviews or expert information to enable users to intuitively understand. For example, the generation AI collects information from review sites and visualizes the content of reviews. For example, it displays the number of stars in ratings and positive comments in graphs and charts. The information analysis unit also visualizes evaluation data from medical experts to enable users to intuitively understand. For example, it displays expert evaluations in radar charts and heat maps. The information analysis unit also analyzes reviews and expert information using the generation AI and visualizes it to enable users to intuitively understand. For example, it graphically displays evaluation trends and major keywords. In this way, visualizing the information makes it easier for users to intuitively understand.

[0070] The information analysis unit uses the emotion estimation function to analyze the emotional responses of reviews or expert information, and can provide information that users can most empathize with. For example, the information analysis unit uses the generation AI to collect information from review sites and analyze the emotional responses of reviews using the emotion estimation function. For example, it prioritizes displaying reviews with a high degree of empathy. The information analysis unit also analyzes evaluation data from medical experts and uses the emotion estimation function to analyze the emotional responses of the evaluations. For example, it prioritizes displaying evaluations with a high degree of empathy. The information analysis unit also uses the generation AI to analyze reviews and expert information and uses the emotion estimation function to provide information that users can most empathize with. For example, it highlights information with a high degree of empathy, such as "kind service" and "effective treatment." This provides users with information they can empathize with, thereby increasing their trust.

[0071] The medical institution suggestion unit can suggest the most suitable medical institution based on past patient treatment results and satisfaction. For example, the generation AI collects past patient treatment result data and suggests the most suitable medical institution by taking into consideration the treatment success rate and patient satisfaction. For example, it prioritizes suggesting medical institutions with a high treatment success rate. The medical institution suggestion unit also analyzes patient satisfaction data and suggests medical institutions with high satisfaction. For example, it lists medical institutions with high satisfaction based on patient reviews and survey results. The generation AI also comprehensively evaluates past patient treatment results and satisfaction data and suggests the most suitable medical institution. For example, it prioritizes suggesting medical institutions with both a high treatment success rate and high satisfaction. This makes it possible to suggest more appropriate medical institutions by taking past patient treatment results and satisfaction into consideration.

[0072] The medical institution suggestion unit can automatically collect detailed information about the proposed medical institutions (for example, the doctor's specialty or treatment record) and provide it to the user. For example, the generation AI automatically collects detailed information about the proposed medical institutions and provides it to the user. For example, it lists the doctor's specialty and treatment record. The medical institution suggestion unit also collects profiles and past treatment records of the doctors at the proposed medical institutions and provides them to the user. For example, it displays the doctor's career and specialty in detail. The medical institution suggestion unit also automatically collects detailed information about the proposed medical institutions and provides it to the user. For example, it provides information about the medical institution's facilities and treatment methods. In this way, by automatically collecting detailed information about the proposed medical institutions, reference information can be provided to the user when selecting a medical institution.

[0073] The medical institution suggestion unit can use the emotion estimation function to analyze the user's emotions toward the proposed medical institution and prioritize suggesting medical institutions that give the user the most sense of security. For example, the medical institution suggestion unit analyzes the user's emotions toward the proposed medical institution in real time using a generation AI and prioritizes suggesting medical institutions that give the user a sense of security. For example, it provides information to reduce the user's anxiety. The medical institution suggestion unit also uses the emotion estimation function to analyze the user's emotions toward the proposed medical institution and lists medical institutions that give the user the most sense of security. For example, it prioritizes suggesting medical institutions that elicit positive emotions. The medical institution suggestion unit also analyzes the user's emotions toward the proposed medical institution using a generation AI and prioritizes suggesting medical institutions that give the user a sense of security. For example, it selects medical institutions that give the user a sense of security based on the user's emotion data. In this way, the user's anxiety is reduced by analyzing the user's emotions and preferentially suggesting medical institutions that give the user a sense of security.

[0074] The medical institution suggestion unit can propose an optimal route based on the transportation means and required time based on the location information of the proposed medical institution. For example, the medical institution suggestion unit proposes a route that takes into account the optimal transportation means and required time based on the location information of the medical institution proposed by the generation AI. For example, it displays how to use public transportation and the required time. The medical institution suggestion unit also proposes an optimal route from the user's current location based on the location information of the proposed medical institution. For example, it provides information on travel time by car and parking. The medical institution suggestion unit also proposes an optimal route that takes into account the transportation means and required time based on the location information of the medical institution proposed by the generation AI. For example, it displays travel time by walking or cycling. In this way, user convenience is improved by proposing an optimal route that takes into account the transportation means and required time.

[0075] The medical institution suggestion unit can visualize past patient reviews of the proposed medical institution, allowing the user to intuitively understand the ratings. For example, the medical institution suggestion unit collects and visualizes past patient reviews of the medical institution proposed by the generation AI. For example, it displays the number of stars in the rating and positive comments in graphs and charts. The medical institution suggestion unit also visualizes patient reviews of the proposed medical institution, allowing the user to intuitively understand the ratings. For example, it graphically displays rating trends and major keywords. The medical institution suggestion unit also visualizes past patient reviews of the medical institution proposed by the generation AI, allowing the user to intuitively understand the ratings. For example, it displays the distribution of ratings and positive comments in a heat map. In this way, visualizing past patient reviews makes it easier for the user to intuitively understand the ratings.

[0076] The solution provider can provide optimal solutions to symptoms based on the latest medical research and guidelines. For example, the AI ​​generator collects the latest medical research and guidelines and provides optimal solutions to the user's symptoms. For example, it provides specific advice such as, "If you have a severe headache, it's important to get plenty of rest first." The solution provider also provides optimal solutions to the user's symptoms based on the latest medical research and guidelines. For example, it provides specific advice such as, "If you have a skin rash, it's recommended that you avoid certain foods and cosmetics as they may be an allergy." The solution provider also provides optimal solutions to the user's symptoms based on the latest medical research and guidelines. For example, it provides specific advice such as, "If you're looking for a nearby internal medicine doctor, it's recommended that you first have an online initial consultation." This allows the system to provide users with reliable information by providing solutions based on the latest medical research and guidelines.

[0077] The solution provision unit can provide solutions to the user's symptoms as videos or interactive content, thereby deepening understanding. In the solution provision unit, for example, the generation AI provides solutions to the user's symptoms as videos. For example, a video explaining "relaxation methods for when you have a severe headache." In addition, the solution provision unit can provide solutions to the user's symptoms as interactive content. For example, a video explaining "what to do if you have a skin rash." In addition, the solution provision unit can provide solutions to the user's symptoms as videos or interactive content, thereby deepening understanding. For example, a video explaining "the process for an initial consultation when looking for a nearby internal medicine doctor." In this way, the use of videos and interactive content can deepen the user's understanding.

[0078] The coping method providing unit can use the emotion estimation function to provide information on relaxation methods or mental care to reduce the user's anxiety and stress. For example, the generation AI uses the emotion estimation function to provide relaxation methods to reduce the user's anxiety and stress. For example, the coping method providing unit displays a message such as "Take a deep breath and relax." The coping method providing unit also uses the emotion estimation function to provide information on mental care to reduce the user's anxiety and stress. For example, the coping method providing unit provides specific advice such as "Try meditation or yoga." The generation AI also uses the emotion estimation function to provide information on relaxation methods or mental care to reduce the user's anxiety and stress. For example, the coping method providing unit provides specific advice such as "Try listening to music that has a relaxing effect." In this way, providing information on relaxation methods or mental care to reduce the user's anxiety and stress increases the user's sense of security.

[0079] The remedy provision unit can provide remedies for symptoms from different cultural or regional perspectives, presenting the user with a variety of options. For example, the generating AI in the remedy provision unit provides remedies for symptoms from different cultural or regional perspectives. For example, it may introduce "Oriental medicine remedies for severe headaches." The generating AI may also provide remedies for symptoms from different cultural or regional perspectives, presenting the user with a variety of options. For example, it may introduce "traditional African medicine remedies for skin rashes." The generating AI may also provide remedies for symptoms from different cultural or regional perspectives, presenting the user with a variety of options. For example, it may introduce "European medical systems when looking for a nearby internal medicine doctor." This allows the user to be presented with a variety of options by providing remedies from different cultural or regional perspectives.

[0080] The solution provision unit can link information about solutions with the user's lifestyle and daily activity data to individually optimize the solutions. For example, the solution provision unit uses a generation AI to collect the user's lifestyle data (e.g., sleep patterns and eating habits) and individually optimize solutions to symptoms based on that data. For example, it provides specific advice such as, "If you have severe headaches, it is recommended that you get more sleep." The solution provision unit also uses a generation AI to collect the user's daily activity data (e.g., exercise habits and stress levels) and individually optimize solutions to symptoms based on that data. For example, it provides specific advice such as, "If you have a skin rash, it is recommended that you avoid certain exercises." The solution provision unit also uses a generation AI to link the user's lifestyle and daily activity data to individually optimize solutions to symptoms. For example, it provides specific advice such as, "If you are looking for a nearby internal medicine doctor, we will suggest the best appointment times based on your daily activity data." By linking the solution provision unit with the user's lifestyle and daily activity data, it is possible to provide individually optimized solutions.

[0081] The coping method providing unit can use the emotion estimation function to monitor the user's emotions in real time when practicing a coping method and provide positive feedback. For example, the generation AI in the coping method providing unit uses the emotion estimation function to monitor the user's emotions in real time when practicing a coping method and provide positive feedback. For example, it displays a message such as, "You seem relaxed, keep it up." The coping method providing unit also uses the emotion estimation function to monitor the user's emotions in real time when practicing a coping method and provide positive feedback. For example, it displays a message such as, "You seem to be less stressed, keep it up." The generation AI also uses the emotion estimation function to monitor the user's emotions in real time when practicing a coping method and provide positive feedback. For example, it displays a message such as, "You're making good progress, keep it up." In this way, the generation AI monitors the user's emotions in real time when practicing a coping method and provides positive feedback, thereby increasing the user's sense of security.

[0082] The remedy providing unit allows the generating AI to provide individually optimized medical information based on the user's past search history and medical history. For example, the generating AI analyzes the user's past search history and provides individually optimized medical information based on that. For example, it provides the latest information related to symptoms and medical institutions searched for in the past. The remedy providing unit also analyzes the user's medical history and provides individually optimized medical information based on that. For example, it suggests appropriate medical institutions and remedy methods based on past diagnosis results and treatment history. The remedy providing unit also provides individually optimized medical information based on the user's past search history and medical history. For example, it provides the latest research results and treatment methods related to medical institutions and symptoms searched for in the past. In this way, by providing individually optimized medical information based on the user's past search history and medical history, it is possible to provide optimal information for the user.

[0083] The remedy provision unit allows the generation AI to provide preventive medical care and health management advice based on the user's health condition and lifestyle habits. For example, the remedy provision unit allows the generation AI to analyze the user's health condition and lifestyle habits and provide preventive medical care and health management advice based on the analysis. For example, specific advice such as "It is recommended that you continue to exercise regularly" is provided. The remedy provision unit also allows the generation AI to consider the user's health condition and lifestyle habits and provide preventive medical care and health management advice based on the analysis. For example, specific advice such as "Try to eat a balanced diet" is provided. The remedy provision unit also allows the generation AI to consider the user's health condition and lifestyle habits and provide preventive medical care and health management advice based on the analysis. For example, specific advice such as "It is recommended that you undergo regular health checkups" is provided. In this way, preventive medical care and health management advice can be provided by taking the user's health condition and lifestyle habits into consideration.

[0084] The remedy providing unit can seamlessly provide individually optimized medical information across different devices (smartphones, tablets, smartwatches). For example, the remedy providing unit may provide individually optimized medical information via a smartphone, allowing the user to access it anytime, anywhere. For example, the remedy providing unit may provide medical information via a smartphone app. The remedy providing unit may also provide individually optimized medical information via a tablet, allowing the user to view detailed information on a large screen. For example, the remedy providing unit may provide medical information via a tablet app. The remedy providing unit may also provide individually optimized medical information via a smartwatch, allowing the user to view health information in real time. For example, the remedy providing unit may provide medical information via the notification function of the smartwatch. This allows medical information to be seamlessly provided across different devices, allowing the user to access it anytime, anywhere.

[0085] The solution provision unit allows the generation AI to continuously improve the way medical information is provided based on user feedback. For example, the solution provision unit allows the generation AI to collect user feedback and improve the way medical information is provided based on that feedback. For example, the display format of information is changed to reflect user opinions. The solution provision unit also allows the generation AI to continuously improve the way medical information is provided based on user feedback. For example, new functions are added in response to user requests. The solution provision unit also allows the generation AI to analyze user feedback and improve the way medical information is provided based on that. For example, the accuracy and reliability of information is improved based on user evaluations. In this way, by improving the way medical information is provided based on user feedback, a system that is easier for users to use can be provided.

[0086] The remedy provision unit uses the emotion estimation function to provide medical information according to the user's emotional state in real time, thereby promoting a positive medical experience. For example, the generation AI in the remedy provision unit uses the emotion estimation function to provide medical information according to the user's emotional state in real time. For example, if the user is feeling anxious, information that gives a sense of security is provided. The remedy provision unit also uses the emotion estimation function to provide medical information according to the user's emotional state in real time, thereby promoting a positive medical experience. For example, if the user is feeling stressed, advice on how to relax is provided. The remedy provision unit also uses the emotion estimation function to provide medical information according to the user's emotional state in real time, thereby promoting a positive medical experience. For example, if the user is feeling anxious, positive messages and success stories are provided. In this way, by providing medical information according to the user's emotional state in real time, a positive medical experience can be promoted.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The medical institution recommendation system can further include a sensor unit that monitors the user's health condition. The sensor unit, for example, measures the user's heart rate and blood pressure in real time and transmits this data to the information analysis unit. The information analysis unit analyzes this data and can recommend the most appropriate medical institution based on the user's health condition. For example, if the heart rate is abnormally high, a medical institution specializing in cardiac care can be recommended. Also, if the blood pressure is high, a medical institution specializing in hypertension treatment can be recommended. This makes it possible to monitor the user's health condition in real time and recommend more appropriate medical institutions.

[0089] The user input unit can further include a function for analyzing the user's voice tone. For example, when a user inputs "I have a severe headache," the voice tone analysis function can analyze the user's voice tone and determine the level of stress or anxiety. This makes it possible to ask questions that take the user's emotional state into consideration. For example, if it is determined that the user is highly stressed, it can ask a question such as "Have there been any changes in your living environment recently?". Furthermore, when a user inputs "I'm looking for a nearby internal medicine doctor," the voice tone analysis function can analyze the voice tone and ask questions that correspond to the user's emotional state. This makes it possible to collect detailed information that takes the user's emotional state into consideration.

[0090] The information analysis unit can also collect and analyze data on the user's living environment. For example, if a user inputs "I have a severe headache," the generation AI can collect data on the user's living environment (such as noise levels and air quality) and suggest appropriate medical institutions based on that information. For example, if the noise level is high, it can suggest medical institutions that are taking measures to combat noise pollution. Also, if the air quality is poor, it can provide advice on improving the air quality. This makes it possible to suggest medical institutions that take the user's living environment into consideration.

[0091] The information analysis unit can use the emotion estimation function to analyze the emotion of the user at the time of input and suggest medical institutions according to the user's emotion. For example, when a user inputs "I have a severe headache," the emotion estimation function can analyze the user's anxiety level and suggest medical institutions that can help alleviate the anxiety. For example, it can suggest medical institutions that provide a relaxing environment. Also, when a user inputs "I'm looking for a nearby internal medicine doctor," the emotion estimation function can analyze the user's stress level and suggest medical institutions that can help alleviate stress. This makes it possible to suggest medical institutions that take the user's emotional state into consideration.

[0092] The user input unit can further include a function to accept gesture input from the user. For example, when a user inputs "I have a severe headache," the gesture input function can analyze the user's hand movements and collect detailed information. For example, if the user makes a gesture of holding their head, the location and intensity of the pain can be estimated. Also, when a user inputs "I'm looking for a nearby internal medicine doctor," the gesture input function can analyze the user's pointing movements and identify a specific area. This allows the user to communicate their symptoms more specifically, making it possible to recommend medical institutions with high accuracy.

[0093] The information analysis unit can collect the user's dietary data and suggest medical institutions based on that. For example, if the user inputs "I have a severe headache," the generation AI can collect the user's dietary data and suggest appropriate medical institutions from the perspective of nutritional balance. For example, if a nutritional deficiency is thought to be the cause, the generation AI can suggest medical institutions that provide nutritional guidance. Also, if the user inputs "I'm looking for a nearby internal medicine doctor," the generation AI can collect the user's dietary data and suggest appropriate medical institutions based on their eating habits. This makes it possible to suggest medical institutions that take the user's dietary data into consideration.

[0094] The information analysis unit can use the emotion estimation function to analyze the emotion of the user when inputting information in real time and provide an interface that corresponds to the user's emotion. For example, when a user inputs "I have a bad headache," the emotion estimation function can analyze the user's stress level in real time and provide an interface for relaxation, for example, by displaying calming music or images with a relaxing effect. Also, when a user inputs "I'm looking for a nearby internal medicine doctor," the emotion estimation function can analyze the user's anxiety in real time and provide an interface that gives a sense of security. In this way, the user's emotional state is analyzed in real time and an interface that elicits positive emotions is provided, thereby increasing the user's sense of security.

[0095] The information analysis unit can collect the user's exercise data and suggest medical institutions based on that. For example, if the user inputs "I have a severe headache," the generation AI will collect the user's exercise data and, if it thinks that a lack of exercise is the cause, it can suggest medical institutions that provide exercise instruction. Also, if the user inputs "I'm looking for a nearby internal medicine doctor," the generation AI will collect the user's exercise data and suggest appropriate medical institutions based on the user's exercise habits. This makes it possible to suggest medical institutions that take the user's exercise data into consideration.

[0096] The information analysis unit uses the emotion estimation function to analyze the emotional tone of reviews and prioritize displaying positive reviews. For example, the generation AI collects information from review sites and uses the emotion estimation function to analyze the emotional tone of the reviews. For example, reviews with positive emotions ("satisfied" and "gratitude") are prioritized for display. The information analysis unit also analyzes evaluation data from medical experts and uses the emotion estimation function to analyze the emotional tone of the evaluations. For example, positive evaluations ("trustworthy" and "excellent technology") are prioritized for display. The information analysis unit also analyzes reviews and expert information using the emotion estimation function to prioritize displaying information with positive emotions. For example, positive evaluations such as "friendly service" and "effective treatment" are highlighted. This prioritizes displaying positive evaluations, giving users a sense of security.

[0097] The information analysis unit can collect the user's sleep data and suggest medical institutions based on that. For example, if a user inputs "I have a severe headache," the generation AI will collect the user's sleep data and, if it thinks that lack of sleep is the cause, it can suggest a medical institution specializing in sleep. Also, if a user inputs "I'm looking for a nearby internal medicine doctor," the generation AI will collect the user's sleep data and suggest an appropriate medical institution based on their sleeping habits. This makes it possible to suggest medical institutions that take the user's sleep data into consideration.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The user input section inputs the user's wishes and symptoms. For example, the user can input "I have a bad headache." The user can also input "I'm looking for a nearby internal medicine doctor." The user can also input "I have a skin rash." Step 2: The information analysis unit analyzes the information input by the user input unit. For example, the information analysis unit analyzes the user's input using text analysis technology, data mining technology, or machine learning algorithms. Step 3: The medical institution suggestion unit suggests medical institution candidates based on the results of the analysis by the information analysis unit, for example, based on distance, specialty, and rating. Step 4: The remedy provider provides symptom-specific remedies and related information based on the results of the analysis by the information analyzer, such as information on treatments, preventive measures, and reference materials.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] 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 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] 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 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] 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.

[0146] 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.

[0147] 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 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] 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. [Explanation of symbols]

[0167] 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 user input section for inputting a user's wishes or symptoms; an information analysis unit that analyzes the information input by the user input unit; a medical institution suggestion unit that suggests medical institution candidates based on the results of the analysis by the information analysis unit; a remedy providing unit that provides remedy and related information tailored to the symptoms based on the results of the analysis by the information analyzing unit. A system characterized by:

2. The user input unit Using voice or image input, the user can more specifically describe the symptoms.

2. The system of claim 1.

3. The information analysis unit When analyzing reviews or expert information, a scoring system is implemented to assess the reliability of said information.

2. The system of claim 1.

4. The medical institution suggestion unit Recommend the most suitable medical institution based on past patient treatment results and satisfaction 2. The system of claim 1.

5. The solution providing unit Providing optimal treatment for the condition based on the latest medical research and guidelines 2. The system of claim 1.

6. The information analysis unit Analyzes user emotions as they type and provides advice to reduce stress and anxiety 2. The system of claim 1.

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