system

Abstract

The system according to this embodiment aims to streamline the medical interview process and quickly estimate the name of the disease. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an estimation unit, and a provision unit. The collection unit collects disease condition information. The analysis unit analyzes the disease condition information collected by the collection unit. The estimation unit estimates the disease name based on the information analyzed by the analysis unit. The provision unit presents treatment methods based on the disease name estimated by the estimation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that inquiring about symptoms and estimating disease names impose a burden on medical staff and it is difficult to make an efficient diagnosis.

[0005] The system according to the embodiment aims to streamline the inquiry about symptoms and quickly estimate the disease name.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an estimation unit, and a provision unit. The collection unit collects symptom information. The analysis unit analyzes the symptom information collected by the collection unit. The estimation unit estimates a disease name based on the information analyzed by the analysis unit. The provision unit presents a coping method based on the disease name estimated by the estimation unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the medical interview process and quickly estimate the name of the disease. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The medical symptom questionnaire system according to an embodiment of the present invention is a system that uses an AI agent to conduct a medical symptom questionnaire and estimate the name of the disease. In this medical symptom questionnaire system, the user inputs their medical symptoms into the AI ​​agent, the AI ​​agent analyzes the input medical symptom information, estimates a relevant disease name, and presents a temporary course of action based on the estimated disease name. For example, if the user inputs information such as "I have a headache," "I have a fever," and "I have a cough," the AI ​​agent analyzes this information and estimates that it may be influenza. Furthermore, the AI ​​agent suggests a course of action such as "Drink plenty of fluids," "Rest," and "See a doctor." This mechanism allows people who do not know the name of their disease and want to know a temporary course of action to quickly obtain guidance even when they are feeling unwell. Users can easily receive a medical questionnaire through the AI ​​agent and receive an estimated disease name and a suggestion of a course of action. This reduces anxiety when the medical symptoms are unknown and enables appropriate action to be taken. Thus, the medical symptom questionnaire system can use an AI agent to conduct a medical symptom questionnaire, estimate the name of the disease, and suggest a course of action.

[0029] The medical condition interview system according to the embodiment comprises a collection unit, an analysis unit, an estimation unit, and a provision unit. The collection unit collects medical condition information. The collection unit collects medical condition information entered by the user as text input or voice input, for example. The collection unit can collect text information using keyboard input or touch input, for example. The collection unit can also collect voice input using speech recognition technology. For example, the collection unit collects the user's voice using a microphone and converts it into text data using speech recognition technology. The analysis unit analyzes the medical condition information collected by the collection unit. The analysis unit learns medical interview knowledge equivalent to that of a doctor and medical condition information, and estimates the name of the disease based on the input information. The analysis unit can analyze the medical condition information using AI, for example, and estimate the name of the disease. The estimation unit estimates the name of the disease based on the information analyzed by the analysis unit. The estimation unit can estimate the name of the disease using AI, for example. The estimation unit estimates the name of the disease using an AI model that takes medical condition information as input and outputs a name of the disease, for example. The providing unit presents treatment methods based on the disease name estimated by the estimation unit. For example, the providing unit presents a provisional treatment method based on the estimated disease name. The providing unit can present treatment methods using AI, for example. For example, the providing unit presents treatment methods using an AI model that takes a disease name as input and outputs treatment methods. As a result, the disease condition consultation system according to the embodiment can efficiently collect and analyze disease condition information, estimate disease names, and present treatment methods.

[0030] The data collection unit collects medical condition information. For example, the unit collects medical condition information entered by the user as text input or voice input. The unit can collect text information using, for example, keyboard input or touch input. Specifically, when a user enters symptoms using a keyboard, the unit acquires the text data in real time and stores it in the database. In the case of touch input, the user can select or input symptoms using the touchscreen of a smartphone or tablet. This allows users to operate intuitively and provide information quickly. Furthermore, the data collection unit can also collect voice input using speech recognition technology. For example, the unit collects the user's voice using a microphone and converts it into text data using speech recognition technology. Speech recognition technology incorporates natural language processing technology, allowing it to accurately analyze the user's speech and store it as text data. This allows users to report symptoms by voice without using their hands, which is particularly convenient for elderly and disabled users. The data collection unit integrates these diverse input methods to efficiently collect medical condition information from users. Furthermore, the data collection unit centrally manages the collected data, making it accessible to the analysis and estimation units. This allows the data collection unit to streamline the process of collecting medical condition information and improve the overall performance of the system.

[0031] The analysis unit analyzes the disease information collected by the collection unit. For example, the analysis unit learns medical history and disease information equivalent to that of a physician, and estimates the disease name based on the input information. The analysis unit can, for example, use AI to analyze disease information and estimate the disease name. Specifically, the AI ​​learns from a large amount of medical data and understands the relationship between disease symptoms and disease names. For example, it uses natural language processing technology to analyze collected text data and extract symptom patterns and frequencies. Furthermore, it can use image recognition technology to convert audio data into text data and analyze it similarly. The analysis unit utilizes these technologies to analyze the collected disease information in detail and extract the information necessary for estimating the disease name. The AI ​​can analyze the relationship between disease symptoms and disease names with high accuracy based on past diagnostic data and medical literature. This allows the analysis unit to quickly and accurately analyze the collected data and provide the information necessary for estimating the disease name. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term disease trend analysis and risk assessment. For example, by analyzing the frequency and seasonality of specific symptoms, it becomes possible to predict future disease conditions and formulate countermeasures. This allows the analysis unit to handle not only real-time disease condition analysis but also long-term health management and prevention, improving the overall reliability and usefulness of the system.

[0032] The estimation unit estimates the disease name based on the information analyzed by the analysis unit. The estimation unit can, for example, use AI to estimate the disease name. Specifically, it uses an AI model that takes disease symptom information as input and outputs a disease name. The AI ​​model is built using deep learning technology and achieves highly accurate disease name estimation by learning from vast amounts of medical data. For example, it uses a neural network to analyze the collected disease symptom information in a multi-layered network and estimate the most appropriate disease name. This allows the estimation unit to quickly and accurately estimate the disease name based on the collected disease symptom information. Furthermore, the estimation unit can present multiple disease name candidates and display their probability and confidence level. This allows users and healthcare professionals to evaluate the reliability of the estimated disease name and take appropriate measures. The estimation unit can also improve the accuracy of the estimation results by referring to past diagnostic data and medical literature. For example, it can correct the estimation results based on data from past patients with similar symptoms to present a more accurate disease name. This allows the estimation unit to always provide highly accurate disease name estimation that reflects the latest medical knowledge, supporting the user's health management.

[0033] The service provider presents treatment methods based on the disease name estimated by the estimation unit. For example, the service provider may present an initial treatment method based on the estimated disease name. The service provider can, for example, use AI to present treatment methods. Specifically, it uses an AI model that takes a disease name as input and outputs treatment methods to present treatment methods. The AI ​​model learns from medical databases and medical guidelines to provide the optimal treatment method corresponding to the disease name. For example, it can present recommended medications, treatments, and lifestyle improvements for a specific disease name. This allows the service provider to quickly learn appropriate treatment methods and prevent the worsening of the disease. Furthermore, the service provider provides detailed explanations and precautions for the treatment methods to help users understand and implement them accurately. For example, it provides specific explanations of how to use medications, their side effects, and the procedures and precautions for treatments. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the treatment methods. For example, it can monitor the user's condition and effects after implementing the treatment method and revise the treatment method as needed. This allows the service provider to provide users with quick and appropriate treatment methods and support the improvement of their disease condition.

[0034] The data collection unit can collect medical condition information entered by the user as text input or voice input. The data collection unit can collect text information using, for example, keyboard input or touch input. The data collection unit can also collect voice input using speech recognition technology. For example, the data collection unit can collect the user's voice using a microphone and convert it into text data using speech recognition technology. This allows the user to input medical condition information in text or voice. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the voice data collected using speech recognition technology into a generating AI and have the generating AI perform the generation of text data from the voice data.

[0035] The analysis unit learns medical interview knowledge equivalent to that of a physician and disease condition information, and can estimate disease names based on the input information. The analysis unit can, for example, use AI to analyze disease condition information and estimate disease names. The analysis unit estimates disease names using, for example, an AI model that has learned medical interview knowledge equivalent to that of a physician. This makes it possible to estimate disease names by utilizing medical interview knowledge equivalent to that of a physician. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can analyze disease condition information and estimate disease names using an AI model that has learned medical interview knowledge equivalent to that of a physician.

[0036] The service provider can present initial treatment methods based on the estimated disease name. For example, the service provider can present a temporary treatment method based on the estimated disease name. The service provider can present treatment methods using AI, for example. For example, the service provider can present treatment methods using an AI model that takes a disease name as input and outputs treatment methods. This allows the service provider to present treatment methods based on the estimated disease name. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the estimated disease name into a generating AI and have the generating AI perform the task of presenting treatment methods.

[0037] The medical interview system according to the embodiment further comprises a learning unit. The learning unit trains an AI agent using past interview data. The learning unit, for example, collects past interview data and trains the AI ​​agent. The learning unit, for example, collects data including the patient's medical history and diagnosis results and trains the AI ​​agent. The learning unit, for example, analyzes past interview data using AI and trains the AI ​​agent. This improves the accuracy of the AI ​​agent by utilizing past interview data. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past interview data into a generating AI and have the generating AI perform the training of the AI ​​agent.

[0038] The data collection unit can analyze the user's past medical condition information and select the optimal data collection method. For example, the data collection unit can select the optimal question format based on the medical condition information previously entered by the user. For example, the data collection unit can ask detailed questions about specific symptoms based on the user's past medical condition information. For example, the data collection unit can analyze the user's past medical condition information and prioritize asking relevant questions. This allows the data collection unit to select the optimal data collection method based on the user's past medical condition information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past medical condition information into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter the collected medical condition information based on the user's current living situation and environment. For example, if the user is at work, the data collection unit collects medical condition information with concise questions. If the user is at home, for example, the data collection unit collects medical condition information with detailed questions. If the user is out, for example, the data collection unit prioritizes voice input for collecting medical condition information. This allows the data collection unit to filter the medical condition information according to the user's living situation and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's living situation and environment data into a generating AI and have the generating AI perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting information on medical conditions. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information on diseases prevalent in that region. For example, if the user is traveling, the data collection unit will collect information on health risks at the travel destination. For example, if the user is at home, the data collection unit will collect information on local medical facilities. This allows the data collection unit to collect highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant information.

[0041] The data collection unit can analyze the user's social media activity and collect relevant information when collecting medical condition information. For example, the data collection unit can ask relevant questions based on health information shared by the user on social media. For example, the data collection unit can collect information about specific symptoms from the user's social media activity. For example, the data collection unit can ask detailed questions about medical conditions mentioned by the user on social media. This allows the data collection unit to collect relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the disease condition information during the analysis. For example, the analysis unit will analyze information about important symptoms in detail. For example, the analysis unit will analyze information about minor symptoms concisely. For example, the analysis unit will adjust the level of detail of the analysis according to the progression of the disease. This allows the level of detail of the analysis to be adjusted according to the importance of the disease condition information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input disease condition information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the disease during analysis. For example, the analysis unit uses an analysis algorithm specifically for infectious diseases for information related to infectious diseases. For example, the analysis unit uses an analysis algorithm specifically for chronic diseases for information related to chronic diseases. For example, the analysis unit uses an analysis algorithm specifically for mental illnesses for information related to mental symptoms. This allows the appropriate analysis algorithm to be applied according to the category of the disease. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input disease category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the timing of submission of disease condition information during the analysis. For example, the analysis unit may prioritize the analysis of recently submitted disease condition information. For example, the analysis unit may prioritize the analysis of disease condition information that is of high urgency. For example, the analysis unit may adjust the order of analysis based on the submission timing. This allows the analysis priority to be determined based on the timing of submission of disease condition information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input disease condition information submission timing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of disease condition information during the analysis. For example, the analysis unit prioritizes the analysis of disease condition information that is highly relevant. The analysis unit adjusts the order of analysis based on the relevance of disease condition information. For example, the analysis unit postpones the analysis of less relevant disease condition information. This allows the order of analysis to be adjusted based on the relevance of disease condition information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input disease condition information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The estimation unit can improve the accuracy of its estimation by considering the interrelationships of disease condition information during estimation. For example, the estimation unit analyzes the interrelationships of disease condition information to improve the accuracy of its estimation. For example, the estimation unit improves the accuracy of its estimation by considering the relationships between disease condition information. For example, the estimation unit improves the accuracy of its estimation based on the interrelationships of disease condition information. This improves the accuracy of its estimation by considering the interrelationships of disease condition information. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without using AI. For example, the estimation unit can input data on the interrelationships of disease condition information into a generating AI and have the generating AI perform the estimation accuracy improvement.

[0047] The estimation unit can perform estimations while considering the attribute information of the person submitting the medical condition information. For example, the estimation unit estimates the disease name by considering the submitter's age. For example, the estimation unit estimates the disease name by considering the submitter's gender. For example, the estimation unit estimates the disease name by considering the submitter's medical history. This allows the estimation of the disease name to take into account the submitter's attribute information. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without using AI. For example, the estimation unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the estimation of the disease name.

[0048] The estimation unit can perform estimations while considering the geographical distribution of disease information. For example, the estimation unit can estimate region-specific diseases by considering geographical distribution. For example, the estimation unit can estimate disease names based on geographical distribution. For example, the estimation unit can improve the accuracy of disease name estimation by considering geographical distribution. This makes it possible to estimate disease names while considering the geographical distribution of disease information. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without using AI. For example, the estimation unit can input geographical distribution data of disease information into a generating AI and have the generating AI perform disease name estimation.

[0049] The estimation unit can improve the accuracy of its estimation by referring to relevant literature on disease symptoms during the estimation process. For example, the estimation unit improves the accuracy of its disease name estimation by referring to relevant literature. For example, the estimation unit estimates the disease name by comparing disease symptom information with relevant literature. For example, the estimation unit improves the accuracy of its disease name estimation based on relevant literature. This improves the accuracy of the estimation by referring to relevant literature on disease symptoms. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input relevant literature data on disease symptoms into a generating AI and have the generating AI perform the estimation accuracy improvement.

[0050] The service provider can adjust the level of detail in the treatment methods based on the importance of the disease name when presenting the information. For example, the service provider will present detailed treatment methods for important disease names. For example, the service provider will present concise treatment methods for minor disease names. The service provider adjusts the level of detail in the treatment methods according to the importance of the disease name. This allows the level of detail in the treatment methods to be adjusted according to the importance of the disease name. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input disease name importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the treatment methods.

[0051] The service provider can apply different treatment methods depending on the disease category at the time of presentation. For example, for infectious diseases, the service provider will present treatment methods specifically for infectious diseases. For example, for chronic diseases, the service provider will present treatment methods specifically for chronic diseases. For example, for mental symptoms, the service provider will present treatment methods specifically for mental illnesses. This allows for the application of appropriate treatment methods according to the disease category. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input disease category data into a generating AI and have the generating AI execute the application of treatment methods.

[0052] The service provider can determine the priority of treatment methods based on the timing of disease name submission at the time of presentation. For example, the service provider will prioritize presenting treatment methods for recently estimated disease names. For example, the service provider will prioritize presenting treatment methods for disease names with high urgency. The service provider will adjust the priority of treatment methods based on the submission timing. This allows the service provider to determine the priority of treatment methods based on the timing of disease name submission. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input disease name submission timing data into a generating AI and have the generating AI perform the determination of the priority of treatment methods.

[0053] The service provider can adjust the order of treatment methods based on the relevance of disease names when presenting them. For example, the service provider will prioritize presenting treatment methods for highly relevant disease names. The service provider will adjust the order of treatment methods based on the relevance of disease names. For example, the service provider will postpone presenting treatment methods for less relevant disease names. This allows the order of treatment methods to be adjusted based on the relevance of disease names. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input disease name relevance data into a generating AI and have the generating AI perform the adjustment of the order of treatment methods.

[0054] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. For example, the learning unit analyzes past learning data and optimizes the learning algorithm. For example, the learning unit improves the accuracy of the learning algorithm by referring to past learning data. This allows the learning algorithm to be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0055] The learning unit can weight the training data based on the timing of the submission of disease condition information during training. For example, the learning unit may prioritize recently submitted disease condition information during training. For example, the learning unit may prioritize highly urgent disease condition information during training. For example, the learning unit may adjust the weighting of the training data based on the submission timing. This allows the training data to be weighted based on the timing of the submission of disease condition information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit may input disease condition information submission timing data into a generating AI and have the generating AI perform the weighting of the training data.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can collect more accurate information by referring to the user's past medical data when gathering user medical information. For example, the data collection unit can ask relevant questions based on the user's past diagnosis results and prescription history. It can also analyze the user's past medical information and ask detailed questions about specific symptoms. Furthermore, the data collection unit can optimize the method of collecting medical information based on the user's past medical data. This allows for the collection of more accurate medical information by utilizing the user's past medical data.

[0058] The analysis unit can analyze a user's medical condition information while considering the user's lifestyle and environmental factors. For example, the analysis unit can estimate a disease name based on information such as the user's diet, exercise habits, and stress level. Furthermore, the analysis unit can improve the accuracy of disease name estimation by considering information such as the user's living environment and work environment. In addition, the analysis unit can assess the risk of disease progression based on the user's lifestyle and environmental factors. This allows for more accurate disease name estimation by taking into account the user's lifestyle and environmental factors.

[0059] When the service provider suggests treatment options based on the estimated diagnosis, it can adjust the treatment options considering the user's access to medical resources. For example, the service provider can suggest the optimal treatment option considering the number and distance of medical facilities in the user's area. Furthermore, the service provider can suggest cost-effective treatment options considering the user's insurance status and financial situation. In addition, the service provider can prioritize suggesting treatment options with higher urgency based on the user's access to medical resources. This allows the service provider to suggest the optimal treatment option while considering the user's access to medical resources.

[0060] The data collection unit can take into account the user's family history and genetic information when collecting user medical information. For example, if the user's family has a history of a particular illness, the data collection unit can ask questions related to that history. Furthermore, based on the user's genetic information, the data collection unit can prioritize the collection of information on diseases with a high genetic risk. In addition, the data collection unit can optimize the method of collecting medical information based on the user's family history and genetic information. This allows for the collection of more accurate medical information by taking the user's family history and genetic information into consideration.

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

[0062] Step 1: The collection unit collects medical condition information. The collection unit collects medical condition information entered by the user as text input or voice input. For example, text information can be collected using keyboard input or touch input. The collection unit can also collect voice input using speech recognition technology. For example, it can collect the user's voice using a microphone and convert it into text data using speech recognition technology. Step 2: The analysis unit analyzes the disease information collected by the collection unit. The analysis unit learns from medical interview knowledge equivalent to that of a physician and disease information, and estimates the disease name based on the input information. For example, AI can be used to analyze disease information and estimate the disease name. Step 3: The estimation unit estimates the disease name based on the information analyzed by the analysis unit. The estimation unit uses an AI model that takes disease condition information as input and outputs a disease name to estimate the disease name. Step 4: The provider unit presents treatment methods based on the disease name estimated by the estimation unit. The provider unit presents initial treatment methods based on the estimated disease name. For example, it presents treatment methods using an AI model that takes a disease name as input and outputs treatment methods.

[0063] (Example of form 2) The medical symptom questionnaire system according to an embodiment of the present invention is a system that uses an AI agent to conduct a medical symptom questionnaire and estimate the name of the disease. In this medical symptom questionnaire system, the user inputs their medical symptoms into the AI ​​agent, the AI ​​agent analyzes the input medical symptom information, estimates a relevant disease name, and presents a temporary course of action based on the estimated disease name. For example, if the user inputs information such as "I have a headache," "I have a fever," and "I have a cough," the AI ​​agent analyzes this information and estimates that it may be influenza. Furthermore, the AI ​​agent suggests a course of action such as "Drink plenty of fluids," "Rest," and "See a doctor." This mechanism allows people who do not know the name of their disease and want to know a temporary course of action to quickly obtain guidance even when they are feeling unwell. Users can easily receive a medical questionnaire through the AI ​​agent and receive an estimated disease name and a suggestion of a course of action. This reduces anxiety when the medical symptoms are unknown and enables appropriate action to be taken. Thus, the medical symptom questionnaire system can use an AI agent to conduct a medical symptom questionnaire, estimate the name of the disease, and suggest a course of action.

[0064] The medical condition interview system according to the embodiment comprises a collection unit, an analysis unit, an estimation unit, and a provision unit. The collection unit collects medical condition information. The collection unit collects medical condition information entered by the user as text input or voice input, for example. The collection unit can collect text information using keyboard input or touch input, for example. The collection unit can also collect voice input using speech recognition technology. For example, the collection unit collects the user's voice using a microphone and converts it into text data using speech recognition technology. The analysis unit analyzes the medical condition information collected by the collection unit. The analysis unit learns medical interview knowledge equivalent to that of a doctor and medical condition information, and estimates the name of the disease based on the input information. The analysis unit can analyze the medical condition information using AI, for example, and estimate the name of the disease. The estimation unit estimates the name of the disease based on the information analyzed by the analysis unit. The estimation unit can estimate the name of the disease using AI, for example. The estimation unit estimates the name of the disease using an AI model that takes medical condition information as input and outputs a name of the disease, for example. The providing unit presents treatment methods based on the disease name estimated by the estimation unit. For example, the providing unit presents a provisional treatment method based on the estimated disease name. The providing unit can present treatment methods using AI, for example. For example, the providing unit presents treatment methods using an AI model that takes a disease name as input and outputs treatment methods. As a result, the disease condition consultation system according to the embodiment can efficiently collect and analyze disease condition information, estimate disease names, and present treatment methods.

[0065] The data collection unit collects medical condition information. For example, the unit collects medical condition information entered by the user as text input or voice input. The unit can collect text information using, for example, keyboard input or touch input. Specifically, when a user enters symptoms using a keyboard, the unit acquires the text data in real time and stores it in the database. In the case of touch input, the user can select or input symptoms using the touchscreen of a smartphone or tablet. This allows users to operate intuitively and provide information quickly. Furthermore, the data collection unit can also collect voice input using speech recognition technology. For example, the unit collects the user's voice using a microphone and converts it into text data using speech recognition technology. Speech recognition technology incorporates natural language processing technology, allowing it to accurately analyze the user's speech and store it as text data. This allows users to report symptoms by voice without using their hands, which is particularly convenient for elderly and disabled users. The data collection unit integrates these diverse input methods to efficiently collect medical condition information from users. Furthermore, the data collection unit centrally manages the collected data, making it accessible to the analysis and estimation units. This allows the data collection unit to streamline the process of collecting medical condition information and improve the overall performance of the system.

[0066] The analysis unit analyzes the disease information collected by the collection unit. For example, the analysis unit learns medical history and disease information equivalent to that of a physician, and estimates the disease name based on the input information. The analysis unit can, for example, use AI to analyze disease information and estimate the disease name. Specifically, the AI ​​learns from a large amount of medical data and understands the relationship between disease symptoms and disease names. For example, it uses natural language processing technology to analyze collected text data and extract symptom patterns and frequencies. Furthermore, it can use image recognition technology to convert audio data into text data and analyze it similarly. The analysis unit utilizes these technologies to analyze the collected disease information in detail and extract the information necessary for estimating the disease name. The AI ​​can analyze the relationship between disease symptoms and disease names with high accuracy based on past diagnostic data and medical literature. This allows the analysis unit to quickly and accurately analyze the collected data and provide the information necessary for estimating the disease name. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term disease trend analysis and risk assessment. For example, by analyzing the frequency and seasonality of specific symptoms, it becomes possible to predict future disease conditions and formulate countermeasures. This allows the analysis unit to handle not only real-time disease condition analysis but also long-term health management and prevention, improving the overall reliability and usefulness of the system.

[0067] The estimation unit estimates the disease name based on the information analyzed by the analysis unit. The estimation unit can, for example, use AI to estimate the disease name. Specifically, it uses an AI model that takes disease symptom information as input and outputs a disease name. The AI ​​model is built using deep learning technology and achieves highly accurate disease name estimation by learning from vast amounts of medical data. For example, it uses a neural network to analyze the collected disease symptom information in a multi-layered network and estimate the most appropriate disease name. This allows the estimation unit to quickly and accurately estimate the disease name based on the collected disease symptom information. Furthermore, the estimation unit can present multiple disease name candidates and display their probability and confidence level. This allows users and healthcare professionals to evaluate the reliability of the estimated disease name and take appropriate measures. The estimation unit can also improve the accuracy of the estimation results by referring to past diagnostic data and medical literature. For example, it can correct the estimation results based on data from past patients with similar symptoms to present a more accurate disease name. This allows the estimation unit to always provide highly accurate disease name estimation that reflects the latest medical knowledge, supporting the user's health management.

[0068] The service provider presents treatment methods based on the disease name estimated by the estimation unit. For example, the service provider may present an initial treatment method based on the estimated disease name. The service provider can, for example, use AI to present treatment methods. Specifically, it uses an AI model that takes a disease name as input and outputs treatment methods to present treatment methods. The AI ​​model learns from medical databases and medical guidelines to provide the optimal treatment method corresponding to the disease name. For example, it can present recommended medications, treatments, and lifestyle improvements for a specific disease name. This allows the service provider to quickly learn appropriate treatment methods and prevent the worsening of the disease. Furthermore, the service provider provides detailed explanations and precautions for the treatment methods to help users understand and implement them accurately. For example, it provides specific explanations of how to use medications, their side effects, and the procedures and precautions for treatments. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the treatment methods. For example, it can monitor the user's condition and effects after implementing the treatment method and revise the treatment method as needed. This allows the service provider to provide users with quick and appropriate treatment methods and support the improvement of their disease condition.

[0069] The data collection unit can collect medical condition information entered by the user as text input or voice input. The data collection unit can collect text information using, for example, keyboard input or touch input. The data collection unit can also collect voice input using speech recognition technology. For example, the data collection unit can collect the user's voice using a microphone and convert it into text data using speech recognition technology. This allows the user to input medical condition information in text or voice. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the voice data collected using speech recognition technology into a generating AI and have the generating AI perform the generation of text data from the voice data.

[0070] The analysis unit learns medical interview knowledge equivalent to that of a physician and disease condition information, and can estimate disease names based on the input information. The analysis unit can, for example, use AI to analyze disease condition information and estimate disease names. The analysis unit estimates disease names using, for example, an AI model that has learned medical interview knowledge equivalent to that of a physician. This makes it possible to estimate disease names by utilizing medical interview knowledge equivalent to that of a physician. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can analyze disease condition information and estimate disease names using an AI model that has learned medical interview knowledge equivalent to that of a physician.

[0071] The service provider can present initial treatment methods based on the estimated disease name. For example, the service provider can present a temporary treatment method based on the estimated disease name. The service provider can present treatment methods using AI, for example. For example, the service provider can present treatment methods using an AI model that takes a disease name as input and outputs treatment methods. This allows the service provider to present treatment methods based on the estimated disease name. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the estimated disease name into a generating AI and have the generating AI perform the task of presenting treatment methods.

[0072] The medical interview system according to the embodiment further comprises a learning unit. The learning unit trains an AI agent using past interview data. The learning unit, for example, collects past interview data and trains the AI ​​agent. The learning unit, for example, collects data including the patient's medical history and diagnosis results and trains the AI ​​agent. The learning unit, for example, analyzes past interview data using AI and trains the AI ​​agent. This improves the accuracy of the AI ​​agent by utilizing past interview data. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past interview data into a generating AI and have the generating AI perform the training of the AI ​​agent.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of collecting medical information based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect medical information at a time when the user can relax. For example, if the user is relaxed, the data collection unit will take more time to collect detailed medical information. For example, if the user is in a hurry, the data collection unit will quickly collect medical information with concise questions. This allows the timing of collecting medical information to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The data collection unit can analyze the user's past medical condition information and select the optimal data collection method. For example, the data collection unit can select the optimal question format based on the medical condition information previously entered by the user. For example, the data collection unit can ask detailed questions about specific symptoms based on the user's past medical condition information. For example, the data collection unit can analyze the user's past medical condition information and prioritize asking relevant questions. This allows the data collection unit to select the optimal data collection method based on the user's past medical condition information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past medical condition information into a generating AI and have the generating AI select the optimal data collection method.

[0075] The data collection unit can filter the collected medical condition information based on the user's current living situation and environment. For example, if the user is at work, the data collection unit collects medical condition information with concise questions. If the user is at home, for example, the data collection unit collects medical condition information with detailed questions. If the user is out, for example, the data collection unit prioritizes voice input for collecting medical condition information. This allows the data collection unit to filter the medical condition information according to the user's living situation and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's living situation and environment data into a generating AI and have the generating AI perform the filtering.

[0076] The data collection unit can estimate the user's emotions and determine the priority of medical information to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize questions about important symptoms. For example, if the user is relaxed, the data collection unit will take more time to collect detailed medical information. For example, if the user is in a hurry, the data collection unit will prioritize questions about major symptoms. This allows the system to prioritize medical information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting information on medical conditions. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information on diseases prevalent in that region. For example, if the user is traveling, the data collection unit will collect information on health risks at the travel destination. For example, if the user is at home, the data collection unit will collect information on local medical facilities. This allows the data collection unit to collect highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant information.

[0078] The data collection unit can analyze the user's social media activity and collect relevant information when collecting medical condition information. For example, the data collection unit can ask relevant questions based on health information shared by the user on social media. For example, the data collection unit can collect information about specific symptoms from the user's social media activity. For example, the data collection unit can ask detailed questions about medical conditions mentioned by the user on social media. This allows the data collection unit to collect relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will use concise and easy-to-understand language. If the user is relaxed, the analysis unit will provide detailed analysis results. If the user is in a hurry, the analysis unit will provide concise analysis results. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the disease condition information during the analysis. For example, the analysis unit will analyze information about important symptoms in detail. For example, the analysis unit will analyze information about minor symptoms concisely. For example, the analysis unit will adjust the level of detail of the analysis according to the progression of the disease. This allows the level of detail of the analysis to be adjusted according to the importance of the disease condition information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input disease condition information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the category of the disease during analysis. For example, the analysis unit uses an analysis algorithm specifically for infectious diseases for information related to infectious diseases. For example, the analysis unit uses an analysis algorithm specifically for chronic diseases for information related to chronic diseases. For example, the analysis unit uses an analysis algorithm specifically for mental illnesses for information related to mental symptoms. This allows the appropriate analysis algorithm to be applied according to the category of the disease. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input disease category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit provides a concise and to-the-point analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is in a hurry, the analysis unit provides an analysis result that can be understood in a short time. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The analysis unit can determine the priority of analysis based on the timing of submission of disease condition information during the analysis. For example, the analysis unit may prioritize the analysis of recently submitted disease condition information. For example, the analysis unit may prioritize the analysis of disease condition information that is of high urgency. For example, the analysis unit may adjust the order of analysis based on the submission timing. This allows the analysis priority to be determined based on the timing of submission of disease condition information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input disease condition information submission timing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0084] The analysis unit can adjust the order of analysis based on the relevance of disease condition information during the analysis. For example, the analysis unit prioritizes the analysis of disease condition information that is highly relevant. The analysis unit adjusts the order of analysis based on the relevance of disease condition information. For example, the analysis unit postpones the analysis of less relevant disease condition information. This allows the order of analysis to be adjusted based on the relevance of disease condition information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input disease condition information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0085] The estimation unit can estimate the user's emotions and adjust the method of estimating the disease name based on the estimated user emotions. For example, if the user is feeling anxious, the estimation unit uses a concise and easy-to-understand estimation method. For example, if the user is relaxed, the estimation unit uses a detailed estimation method. For example, if the user is in a hurry, the estimation unit uses a method to quickly estimate the disease name. This allows the method of estimating the disease name to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The estimation unit can improve the accuracy of its estimation by considering the interrelationships of disease condition information during estimation. For example, the estimation unit analyzes the interrelationships of disease condition information to improve the accuracy of its estimation. For example, the estimation unit improves the accuracy of its estimation by considering the relationships between disease condition information. For example, the estimation unit improves the accuracy of its estimation based on the interrelationships of disease condition information. This improves the accuracy of its estimation by considering the interrelationships of disease condition information. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without using AI. For example, the estimation unit can input data on the interrelationships of disease condition information into a generating AI and have the generating AI perform the estimation accuracy improvement.

[0087] The estimation unit can perform estimations while considering the attribute information of the person submitting the medical condition information. For example, the estimation unit estimates the disease name by considering the submitter's age. For example, the estimation unit estimates the disease name by considering the submitter's gender. For example, the estimation unit estimates the disease name by considering the submitter's medical history. This allows the estimation of the disease name to take into account the submitter's attribute information. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without using AI. For example, the estimation unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the estimation of the disease name.

[0088] The estimation unit can estimate the user's emotions and adjust the display method of the estimation results based on the estimated user's emotions. For example, if the user is feeling anxious, the estimation unit provides a concise and easy-to-understand display method. For example, if the user is relaxed, the estimation unit provides detailed estimation results. For example, if the user is in a hurry, the estimation unit provides a concise display method. This allows the display method of the estimation results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The estimation unit can perform estimations while considering the geographical distribution of disease information. For example, the estimation unit can estimate region-specific diseases by considering geographical distribution. For example, the estimation unit can estimate disease names based on geographical distribution. For example, the estimation unit can improve the accuracy of disease name estimation by considering geographical distribution. This makes it possible to estimate disease names while considering the geographical distribution of disease information. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without using AI. For example, the estimation unit can input geographical distribution data of disease information into a generating AI and have the generating AI perform disease name estimation.

[0090] The estimation unit can improve the accuracy of its estimation by referring to relevant literature on disease symptoms during the estimation process. For example, the estimation unit improves the accuracy of its disease name estimation by referring to relevant literature. For example, the estimation unit estimates the disease name by comparing disease symptom information with relevant literature. For example, the estimation unit improves the accuracy of its disease name estimation based on relevant literature. This improves the accuracy of the estimation by referring to relevant literature on disease symptoms. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input relevant literature data on disease symptoms into a generating AI and have the generating AI perform the estimation accuracy improvement.

[0091] The service provider can estimate the user's emotions and adjust the way it presents solutions based on the estimated emotions. For example, if the user is feeling anxious, the service provider will present concise and easy-to-understand solutions. If the user is relaxed, the service provider will present detailed solutions. If the user is in a hurry, the service provider will present concise solutions. This allows the service provider to adjust the way it presents solutions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The service provider can adjust the level of detail in the treatment methods based on the importance of the disease name when presenting the information. For example, the service provider will present detailed treatment methods for important disease names. For example, the service provider will present concise treatment methods for minor disease names. The service provider adjusts the level of detail in the treatment methods according to the importance of the disease name. This allows the level of detail in the treatment methods to be adjusted according to the importance of the disease name. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input disease name importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the treatment methods.

[0093] The service provider can apply different treatment methods depending on the disease category at the time of presentation. For example, for infectious diseases, the service provider will present treatment methods specifically for infectious diseases. For example, for chronic diseases, the service provider will present treatment methods specifically for chronic diseases. For example, for mental symptoms, the service provider will present treatment methods specifically for mental illnesses. This allows for the application of appropriate treatment methods according to the disease category. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input disease category data into a generating AI and have the generating AI execute the application of treatment methods.

[0094] The service provider can estimate the user's emotions and prioritize appropriate actions based on those emotions. For example, if the user is feeling anxious, the service provider will prioritize presenting important actions. If the user is relaxed, the service provider will prioritize presenting detailed actions. If the user is in a hurry, the service provider will prioritize presenting actions that can be taken quickly. This allows the service provider to prioritize actions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The service provider can determine the priority of treatment methods based on the timing of disease name submission at the time of presentation. For example, the service provider will prioritize presenting treatment methods for recently estimated disease names. For example, the service provider will prioritize presenting treatment methods for disease names with high urgency. The service provider will adjust the priority of treatment methods based on the submission timing. This allows the service provider to determine the priority of treatment methods based on the timing of disease name submission. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input disease name submission timing data into a generating AI and have the generating AI perform the determination of the priority of treatment methods.

[0096] The service provider can adjust the order of treatment methods based on the relevance of disease names when presenting them. For example, the service provider will prioritize presenting treatment methods for highly relevant disease names. The service provider will adjust the order of treatment methods based on the relevance of disease names. For example, the service provider will postpone presenting treatment methods for less relevant disease names. This allows the order of treatment methods to be adjusted based on the relevance of disease names. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input disease name relevance data into a generating AI and have the generating AI perform the adjustment of the order of treatment methods.

[0097] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is feeling anxious, the learning unit will select training data that provides a sense of security. For example, if the user is relaxed, the learning unit will select detailed training data. For example, if the user is in a hurry, the learning unit will select data that allows for rapid learning. This allows for the selection of training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. For example, the learning unit analyzes past learning data and optimizes the learning algorithm. For example, the learning unit improves the accuracy of the learning algorithm by referring to past learning data. This allows the learning algorithm to be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0099] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated emotions. For example, if the user is feeling anxious, the learning unit will learn frequently to provide reassurance. For example, if the user is relaxed, the learning unit will learn in detail. For example, if the user is in a hurry, the learning unit will learn quickly. This allows the learning frequency to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0100] The learning unit can weight the training data based on the timing of the submission of disease condition information during training. For example, the learning unit may prioritize recently submitted disease condition information during training. For example, the learning unit may prioritize highly urgent disease condition information during training. For example, the learning unit may adjust the weighting of the training data based on the submission timing. This allows the training data to be weighted based on the timing of the submission of disease condition information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit may input disease condition information submission timing data into a generating AI and have the generating AI perform the weighting of the training data.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The data collection unit can collect more accurate information by referring to the user's past medical data when gathering user medical information. For example, the data collection unit can ask relevant questions based on the user's past diagnosis results and prescription history. It can also analyze the user's past medical information and ask detailed questions about specific symptoms. Furthermore, the data collection unit can optimize the method of collecting medical information based on the user's past medical data. This allows for the collection of more accurate medical information by utilizing the user's past medical data.

[0103] The analysis unit can analyze a user's medical condition information while considering the user's lifestyle and environmental factors. For example, the analysis unit can estimate a disease name based on information such as the user's diet, exercise habits, and stress level. Furthermore, the analysis unit can improve the accuracy of disease name estimation by considering information such as the user's living environment and work environment. In addition, the analysis unit can assess the risk of disease progression based on the user's lifestyle and environmental factors. This allows for more accurate disease name estimation by taking into account the user's lifestyle and environmental factors.

[0104] When the service provider suggests treatment options based on the estimated diagnosis, it can adjust the treatment options considering the user's access to medical resources. For example, the service provider can suggest the optimal treatment option considering the number and distance of medical facilities in the user's area. Furthermore, the service provider can suggest cost-effective treatment options considering the user's insurance status and financial situation. In addition, the service provider can prioritize suggesting treatment options with higher urgency based on the user's access to medical resources. This allows the service provider to suggest the optimal treatment option while considering the user's access to medical resources.

[0105] The data collection unit can take into account the user's family history and genetic information when collecting user medical information. For example, if the user's family has a history of a particular illness, the data collection unit can ask questions related to that history. Furthermore, based on the user's genetic information, the data collection unit can prioritize the collection of information on diseases with a high genetic risk. In addition, the data collection unit can optimize the method of collecting medical information based on the user's family history and genetic information. This allows for the collection of more accurate medical information by taking the user's family history and genetic information into consideration.

[0106] The analysis unit can consider the user's psychological state when analyzing the user's medical condition information. For example, if the user is experiencing stress, the analysis unit can estimate the disease name by considering the impact of stress on the medical condition. Similarly, if the user is experiencing anxiety, the analysis unit can estimate the disease name by considering the impact of anxiety on the medical condition. Furthermore, the analysis unit can assess the risk of disease progression based on the user's psychological state. This allows for more accurate disease name estimation by taking the user's psychological state into account.

[0107] When the service provider presents coping strategies based on the estimated diagnosis, it can estimate the user's emotions and adjust the presentation of coping strategies based on those emotions. For example, if the user is feeling anxious, the service provider can present concise and easy-to-understand coping strategies. If the user is relaxed, the service provider can present detailed coping strategies. Furthermore, if the user is in a hurry, the service provider can present concise coping strategies. This allows the service provider to adjust the presentation of coping strategies according to the user's emotions.

[0108] The data collection unit can estimate the user's emotions when collecting user information about their medical condition and prioritize the information to collect based on those emotions. For example, if the user is feeling anxious, the data collection unit can prioritize questions about important symptoms. If the user is relaxed, the data collection unit can take more time to collect detailed information about their medical condition. Furthermore, if the user is in a hurry, the data collection unit can prioritize questions about major symptoms. This allows the system to prioritize medical information according to the user's emotions.

[0109] The analysis unit can estimate the user's emotions when analyzing the user's medical condition information and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can use concise and easy-to-understand language. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, the presentation of the analysis can be adjusted according to the user's emotions.

[0110] The estimation unit can estimate the user's emotions when estimating the user's medical condition information, and adjust the method of estimating the disease name based on the estimated emotions. For example, if the user is feeling anxious, the estimation unit can use a concise and easy-to-understand estimation method. If the user is relaxed, the estimation unit can use a detailed estimation method. Furthermore, if the user is in a hurry, the estimation unit can use a method to quickly estimate the disease name. In this way, the method of estimating the disease name can be adjusted according to the user's emotions.

[0111] When the service provider presents treatment options based on a suspected illness, it can estimate the user's emotions and prioritize treatment options based on those emotions. For example, if the user is feeling anxious, the service provider can prioritize presenting important treatment options. If the user is relaxed, the service provider can present more detailed treatment options. Furthermore, if the user is in a hurry, the service provider can prioritize presenting treatment options that can be implemented quickly. This allows the service provider to prioritize treatment options according to the user's emotions.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The collection unit collects medical condition information. The collection unit collects medical condition information entered by the user as text input or voice input. For example, text information can be collected using keyboard input or touch input. The collection unit can also collect voice input using speech recognition technology. For example, it can collect the user's voice using a microphone and convert it into text data using speech recognition technology. Step 2: The analysis unit analyzes the disease information collected by the collection unit. The analysis unit learns from medical interview knowledge equivalent to that of a physician and disease information, and estimates the disease name based on the input information. For example, AI can be used to analyze disease information and estimate the disease name. Step 3: The estimation unit estimates the disease name based on the information analyzed by the analysis unit. The estimation unit uses an AI model that takes disease condition information as input and outputs a disease name to estimate the disease name. Step 4: The provider unit presents treatment methods based on the disease name estimated by the estimation unit. The provider unit presents initial treatment methods based on the estimated disease name. For example, it presents treatment methods using an AI model that takes a disease name as input and outputs treatment methods.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the collection unit, analysis unit, estimation unit, provision unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects disease information using the microphone 38B and touch panel 38A of the smart device 14 and converts it into text data using the control unit 46A. The analysis unit analyzes the disease information using the identification processing unit 290 of the data processing unit 12 and estimates the disease name based on medical interview knowledge equivalent to that of a doctor. The estimation unit estimates the disease name using the identification processing unit 290 of the data processing unit 12. The provision unit presents treatment methods based on the disease name estimated by the identification processing unit 290 of the data processing unit 12. The learning unit analyzes past medical interview data using the identification processing unit 290 of the data processing unit 12 and trains the AI ​​agent. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the collection unit, analysis unit, estimation unit, provision unit, and learning unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects medical condition information using the microphone 238 of the smart glasses 214 and converts it into text data by the control unit 46A. The analysis unit analyzes the medical condition information using the identification processing unit 290 of the data processing unit 12 and estimates the disease name based on medical interview knowledge equivalent to that of a doctor. The estimation unit estimates the disease name using the identification processing unit 290 of the data processing unit 12. The provision unit presents treatment methods based on the disease name estimated by the identification processing unit 290 of the data processing unit 12. The learning unit analyzes past medical interview data using the identification processing unit 290 of the data processing unit 12 and trains the AI ​​agent. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, estimation unit, provision unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects medical condition information using the microphone 238 of the headset terminal 314 and converts it into text data using the control unit 46A. The analysis unit analyzes the medical condition information using the identification processing unit 290 of the data processing unit 12 and estimates the disease name based on medical interview knowledge equivalent to that of a physician. The estimation unit estimates the disease name using the identification processing unit 290 of the data processing unit 12. The provision unit presents treatment methods based on the disease name estimated by the identification processing unit 290 of the data processing unit 12. The learning unit analyzes past medical interview data using the identification processing unit 290 of the data processing unit 12 and trains the AI ​​agent. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the collection unit, analysis unit, estimation unit, provision unit, and learning unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects disease information using the microphone 238 of the robot 414 and converts it into text data by the control unit 46A. The analysis unit analyzes the disease information using the identification processing unit 290 of the data processing unit 12 and estimates the disease name based on medical interview knowledge equivalent to that of a doctor. The estimation unit estimates the disease name using the identification processing unit 290 of the data processing unit 12. The provision unit presents treatment methods based on the disease name estimated by the identification processing unit 290 of the data processing unit 12. The learning unit analyzes past medical interview data using the identification processing unit 290 of the data processing unit 12 and trains the AI ​​agent. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) A collection unit that collects medical condition information, An analysis unit analyzes the disease condition information collected by the aforementioned collection unit, An estimation unit that estimates the name of a disease based on the information analyzed by the aforementioned analysis unit, The system includes a provisioning unit that provides a treatment method based on the disease name estimated by the estimation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects medical information entered by the user as text or voice input. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, It learns medical interview knowledge and patient condition information equivalent to that of a doctor, and estimates the name of the disease based on the input information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Based on the suspected diagnosis, we will suggest initial treatment options. The system described in Appendix 1, characterized by the features described herein. (Note 5) The system further includes a learning unit that trains the AI ​​agent using past medical interview data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting medical information based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past medical history and select the appropriate data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting medical condition information, filtering is performed based on the user's current living situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and prioritizes the medical information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting medical information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting medical information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the patient's condition information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the disease condition. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on when the patient's condition information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of disease condition information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The estimation unit, The system estimates the user's emotions and adjusts the method of estimating the disease name based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The estimation unit, During estimation, the accuracy of the estimation is improved by considering the interrelationships of disease condition information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The estimation unit, During estimation, the attribute information of the person who submitted the medical condition information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The estimation unit, It estimates the user's emotions and adjusts how the estimation results are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The estimation unit, During estimation, the geographical distribution of disease condition information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The estimation unit, During estimation, we refer to relevant literature on medical conditions to improve the accuracy of the estimation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way it presents solutions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When presenting the information, adjust the level of detail in the treatment plan based on the importance of the disease name. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When presenting the diagnosis, different treatment methods will be applied depending on the category of the disease. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and prioritizes appropriate actions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When presenting the information, the priority of treatment methods will be determined based on when the diagnosis was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When presenting the information, adjust the order of treatment methods based on the relevance of the disease names. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned learning unit, During training, the training data is weighted based on when the medical condition information was submitted. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects medical condition information, An analysis unit analyzes the disease condition information collected by the aforementioned collection unit, An estimation unit that estimates the name of a disease based on the information analyzed by the aforementioned analysis unit, The system includes a provisioning unit that provides a treatment method based on the disease name estimated by the estimation unit. A system characterized by the following features.

2. The aforementioned collection unit is The system collects medical information entered by the user as text or voice input. The system according to feature 1.

3. The aforementioned analysis unit, It learns medical interview knowledge and patient condition information equivalent to that of a doctor, and estimates the name of the disease based on the input information. The system according to feature 1.

4. The aforementioned supply unit is, Based on the suspected diagnosis, we will suggest initial treatment options. The system according to feature 1.

5. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting medical information based on those emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze the user's past medical history and select the appropriate data collection method. The system according to feature 1.

7. The aforementioned collection unit is When collecting medical condition information, filtering is performed based on the user's current living situation and environment. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and prioritizes the medical information to collect based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is When collecting medical information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

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