system

Abstract

The system according to this embodiment aims to support the early detection and rapid response to acute poisoning. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an identification unit, a prediction unit, a proposal unit, and a provision unit. The reception unit receives symptoms entered by the user. The analysis unit analyzes the symptoms received by the reception unit using natural language processing technology. The identification unit identifies the cause of poisoning by comparing the symptoms analyzed by the analysis unit with a global database of poisoning cases. The prediction unit predicts the rate of progression and severity of symptoms based on the cause of poisoning identified by the identification unit. The proposal unit proposes countermeasures according to the urgency predicted by the prediction unit. The provision unit utilizes the user's location information based on the countermeasures proposed by the proposal unit to provide real-time availability information for nearby pharmacies and specialized medical institutions.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response 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, it is difficult to detect acute poisoning at an early stage and take prompt measures, and there is room for improvement.

[0005] The system according to the embodiment aims to assist in the early detection of acute poisoning and prompt response.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, an identification unit, a prediction unit, a proposal unit, and a provision unit. The reception unit receives symptoms entered by the user. The analysis unit analyzes the symptoms received by the reception unit using natural language processing technology. The identification unit identifies the cause of poisoning by comparing the symptoms analyzed by the analysis unit with a global database of poisoning cases. The prediction unit predicts the rate of progression and severity of symptoms based on the cause of poisoning identified by the identification unit. The proposal unit proposes countermeasures according to the urgency predicted by the prediction unit. The provision unit utilizes the user's location information based on the countermeasures proposed by the proposal unit to provide real-time availability information for nearby pharmacies and specialized medical institutions. [Effects of the Invention]

[0007] The system according to this embodiment can support the early detection and rapid response to acute poisoning. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The health management system according to an embodiment of the present invention is a system that utilizes the latest medical AI technology to support the early detection and rapid response to acute poisoning. This health management system analyzes symptoms entered by the user (nausea, dizziness, impaired consciousness, etc.) using advanced natural language processing technology and compares them with a global database of poisoning cases to identify potential causes of poisoning. For example, it also takes into account a history of contact with specific plants or chemicals, and combinations of drugs taken, to perform a more precise diagnosis. Furthermore, the AI ​​predicts the rate of progression and severity of symptoms and proposes countermeasures according to the urgency. In mild cases, it provides detailed instructions on first aid at home, and in severe cases, it immediately notifies the nearest emergency medical facility. It also utilizes the user's location information to provide real-time availability information for nearby pharmacies and specialized medical facilities. The health management system goes beyond being a mere symptom checker; it also links with the user's medical history and pre-existing medical conditions to provide personalized responses tailored to individual circumstances. Furthermore, it contributes to accident prevention by providing advice on daily life for poisoning prevention and guidelines for managing hazardous materials in the home. This system can be used in a variety of environments, including private homes, schools, and workplaces, contributing to improved poisoning risk management for society as a whole. This allows the health management system to support the early detection and rapid response to acute poisoning.

[0029] The health management system according to this embodiment comprises a reception unit, an analysis unit, an identification unit, a prediction unit, a proposal unit, and a provision unit. The reception unit receives symptoms entered by the user. Symptoms entered by the user include, but are not limited to, nausea, dizziness, and impaired consciousness. The reception unit can, for example, accept text input or voice input when the user enters symptoms into the app. The reception unit can also refer to the history of symptoms previously entered by the user and provide input assistance. The analysis unit analyzes the symptoms received by the reception unit using natural language processing technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis, but is not limited to these. The analysis unit analyzes the symptoms entered by the user and understands their content. The identification unit identifies the cause of poisoning by comparing the symptoms analyzed by the analysis unit with a global database of poisoning cases. The poisoning case database includes, for example, past poisoning cases, information on chemical substances and plants, but is not limited to these. The identification unit, for example, matches the symptoms entered by the user with information in the database to identify potential causes of poisoning. The prediction unit predicts the rate of symptom progression and severity based on the cause of poisoning identified by the identification unit. The prediction unit, for example, evaluates the rate of symptom progression on an hourly basis and quantifies the severity. The proposal unit proposes countermeasures according to the urgency predicted by the prediction unit. For example, the proposal unit provides detailed instructions on first aid at home in mild cases, and immediately notifies the nearest emergency medical facility in severe cases. The provision unit utilizes the user's location information based on the countermeasures proposed by the proposal unit to provide real-time information on the availability of nearby pharmacies and specialized medical facilities. For example, the provision unit obtains the user's location information from GPS data or Wi-Fi location information and provides real-time information on the availability of nearby medical facilities. As a result, the health management system according to the embodiment can efficiently analyze the user's symptoms, identify the cause of poisoning, and propose appropriate countermeasures.

[0030] The reception desk receives symptoms entered by the user. These symptoms may include, but are not limited to, nausea, dizziness, or impaired consciousness. The reception desk can accept text input or voice input when the user enters symptoms into the app. Specifically, when a user accesses the application using a smartphone or tablet and enters symptoms, they can use text input via a keyboard or voice input using speech recognition technology. In the case of voice input, the speech recognition engine analyzes what the user says and converts it into text data. Furthermore, the reception desk can refer to the user's past symptom history to assist with input. For example, if a user's previously entered symptoms are stored in a database, and a similar symptom is entered again, the system can refer to past data to complete the input. This allows users to enter symptoms quickly and accurately, improving the system's usability. The reception desk also has a function to analyze user input in real time and automatically correct input errors or unclear expressions. For example, if a user enters "dizziness," and different spellings such as "vertigo" or "dizziness" are used, the system can analyze them uniformly. This allows the reception desk to efficiently and accurately receive user input.

[0031] The analysis department analyzes the symptoms received by the reception department using natural language processing (NLP) technology. NLP includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. Specifically, morphological analysis is used to divide the user-entered text into individual words and analyze the part of speech and meaning of each word. Grammatical analysis analyzes the structure of the input sentence, clarifying relationships such as subject, predicate, and object. Semantic analysis understands the meaning of the entered symptoms and provides an appropriate interpretation based on the context. For example, if a user enters "I have a headache," the analysis department extracts the words "head" and "painful" and analyzes their meanings to understand the symptom of a headache. Furthermore, the analysis department not only analyzes and understands the symptoms entered by the user, but can also perform more accurate analysis by comparing them with past data and symptom data from other users. For example, it can refer to data from other users with the same symptoms to find common patterns and trends. This allows the analysis department to gain a deeper understanding of the user's symptoms and build a foundation for proposing appropriate countermeasures. Furthermore, the analysis department can improve the accuracy of symptom analysis by utilizing AI technology. For example, by using machine learning algorithms to learn from past symptom data, it can perform highly accurate analysis on new symptom data. This allows the analysis department to analyze users' symptoms quickly and accurately, improving the overall system performance.

[0032] The identification unit identifies the cause of poisoning by comparing the symptoms analyzed by the analysis unit with a global database of poisoning cases. This database includes, but is not limited to, past poisoning cases and information on chemicals and plants. Specifically, the database contains detailed records of the characteristics and toxicity of various chemicals, poisoning cases caused by plants and animals, and even region-specific poisoning cases. The identification unit compares these databases with the symptoms entered by the user to identify potential causes of poisoning. For example, if a user complains of nausea and dizziness, the identification unit compares these symptoms with the database to determine if they may be caused by poisoning from a specific chemical or plant. Furthermore, the identification unit can utilize AI technology to identify the relationship between symptoms and poisoning causes with high accuracy. For example, machine learning algorithms can be used to learn from past poisoning case data and perform highly accurate identification on new symptom data. This allows the identification unit to quickly and accurately identify the cause of poisoning for a user's symptoms and propose appropriate countermeasures. Additionally, the identification unit regularly updates the database information, adding new poisoning cases and chemical information to ensure that identification is always based on the latest information. This allows the specific unit to perform highly accurate identification of the user's symptoms based on the latest information at all times, thereby improving the reliability and safety of the entire system.

[0033] The prediction unit predicts the rate of symptom progression and severity based on the cause of poisoning identified by the identification unit. Specifically, it evaluates the rate of symptom progression on an hourly basis and quantifies the severity. For example, if the identified cause of poisoning is a specific chemical substance, it predicts how quickly the symptoms will progress based on the characteristics of that chemical substance and past cases. Furthermore, by quantifying the severity, it allows the user to intuitively understand how dangerous their current condition is. The prediction unit utilizes AI technology to predict the rate of symptom progression and severity with high accuracy based on past data. For example, it can use machine learning algorithms to learn from past poisoning case data and make highly accurate predictions even for new symptom data. This allows the prediction unit to quickly and accurately predict the rate of progression and severity of the user's symptoms and lay the foundation for proposing appropriate countermeasures. In addition, the prediction unit can continuously revise its prediction results based on data updated in real time to respond to the latest situation. For example, if the user's symptoms rapidly worsen, the prediction unit immediately incorporates new data and updates the prediction results. This allows the prediction unit to always provide highly accurate predictions based on the latest information and support quick and appropriate responses.

[0034] The suggestion unit proposes countermeasures based on the urgency predicted by the prediction unit. Specifically, for mild cases, it provides detailed instructions on first aid at home, and for severe cases, it immediately contacts the nearest emergency medical facility. For example, in the case of mild symptoms, the suggestion unit provides the user with specific first aid measures such as cooling, resting, and drinking fluids. This information is provided in various formats such as text, images, and videos to ensure that the user can easily understand and implement it. On the other hand, in the case of severe symptoms, the suggestion unit instructs the user to immediately contact an emergency medical facility and has a function to automatically contact the nearest emergency medical facility if necessary. Furthermore, the suggestion unit can also introduce appropriate medical facilities and specialists depending on the user's symptoms and urgency. For example, it can list doctors and medical facilities with specialized knowledge of specific causes of poisoning and present the user with the best option. This allows the suggestion unit to provide specific instructions for users to take quick and appropriate action, minimizing harm. The suggestion unit can also collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, based on the results of the user implementing the suggested first aid measures or the diagnosis from a medical institution, the suggested measures can be reviewed and more effective countermeasures can be provided. This allows the suggestion department to always provide users with the most suitable countermeasures, improving the reliability and effectiveness of the entire system.

[0035] The service provider utilizes the user's location information based on the proposed solutions from the proposal team to provide real-time information on the availability of nearby pharmacies and specialized medical institutions. Specifically, it obtains the user's location information from GPS data and Wi-Fi location information and provides real-time information on the availability of nearby medical institutions. For example, if a user requires urgent medical attention, the service provider displays a list of the nearest medical institutions and pharmacies based on the user's current location, and provides detailed information such as their availability, waiting times, and medical specialties. This allows the user to quickly select the appropriate medical institution and receive the necessary treatment. Furthermore, the service provider can strengthen its collaboration with medical institutions and update information in real time. For example, when a medical institution updates its availability, the service provider immediately reflects that information, providing the user with the latest information. The service provider also has a function to provide optimal route guidance based on the user's location information. For example, it displays the shortest route to the medical institution selected by the user in a navigation system, taking into account traffic conditions and public transportation information to help the user reach their destination quickly. In this way, the service provider can support users in receiving prompt and appropriate medical attention and maximize the effectiveness of the entire system. Furthermore, the service provider implements strict security measures regarding the handling of location information to protect user privacy. For example, encryption technology is used for acquiring, storing, and sharing location information to prevent unauthorized access and data leaks. This allows the service provider to support prompt and appropriate medical responses while protecting user privacy.

[0036] The service provider can link with the user's medical history and pre-existing medical conditions to provide personalized responses tailored to individual circumstances. For example, the service provider can refer to the user's medical history and propose countermeasures considering past diagnoses and treatment history. It can also refer to the user's pre-existing medical conditions and propose countermeasures considering chronic diseases and allergy information. This allows for the provision of more appropriate countermeasures based on the user's medical history and pre-existing medical conditions. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's medical history and pre-existing medical conditions into AI, which can then propose the optimal countermeasures.

[0037] The service provider can offer advice on daily life practices for poisoning prevention and guidelines for managing hazardous materials in the home. For example, it can provide precautions regarding the handling of specific plants or chemicals. It can also provide detailed instructions on how to store hazardous materials in the home. By providing advice and guidelines for poisoning prevention, it contributes to preventing accidents. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's lifestyle and home environment into the AI, which can then suggest optimal advice and guidelines.

[0038] The reception unit can analyze the user's past symptom input history and provide the optimal input interface. For example, the reception unit can automatically display symptoms that the user has frequently entered in the past as suggestions. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest symptoms that the user will enter at a specific time period based on their past input history. In this way, by analyzing past input history, the reception unit can provide the user with the optimal input interface. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's past input history data into a generating AI, which can then suggest the optimal input interface.

[0039] The reception unit can filter the input content based on the user's current health status and lifestyle when symptoms are entered. For example, the reception unit considers the user's current health status and prioritizes displaying highly relevant symptoms. The reception unit can also customize the input content based on the user's lifestyle (smoking, drinking, etc.). Furthermore, the reception unit can refer to the user's medical history and automatically suggest relevant symptoms. This allows the user to enter more relevant symptoms by filtering the input content based on their health status and lifestyle. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's health status data into a generating AI, which can then filter the input content to the optimal level.

[0040] The reception unit can prioritize the input of highly relevant symptoms by considering the user's geographical location when symptoms are entered. For example, the reception unit can prioritize displaying relevant symptoms based on the climate and environment of the user's current location. The reception unit can also suggest symptoms considering region-specific poisoning risks based on the user's geographical location. Furthermore, the reception unit can refer to the user's location information and prioritize displaying symptoms based on poisoning cases occurring in the vicinity. This makes it possible to input symptoms that take region-specific poisoning risks into account by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's location data into a generating AI, which can then suggest the most appropriate symptoms.

[0041] The reception desk can analyze the user's social media activity and input relevant symptoms when symptoms are entered. For example, the reception desk can analyze the user's social media posts and suggest relevant symptoms based on recent activity. The reception desk can also refer to posts from the user's friends on social media and suggest symptoms considering common addiction risks. Furthermore, the reception desk can prioritize displaying symptoms related to specific events or locations from the user's social media activity. This allows for the suggestion of relevant symptoms by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI, which can then suggest the most appropriate symptoms.

[0042] The analysis unit can adjust the level of detail in its analysis based on the severity of the symptoms. For example, it can perform a detailed analysis and provide relevant information for high-severity symptoms. It can also provide a concise analysis for low-severity symptoms. Furthermore, the analysis unit can prioritize analyses based on symptom severity and provide results quickly. This allows for efficient analysis by adjusting the level of detail based on symptom severity. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input symptom severity data into a generating AI, which can then propose the optimal analysis method.

[0043] The analysis unit can apply different analysis algorithms depending on the symptom category when analyzing symptoms. For example, the analysis unit can apply a specialized analysis algorithm to digestive system symptoms. It can also apply a specialized analysis algorithm to nervous system symptoms. Furthermore, it can apply a specialized analysis algorithm to respiratory system symptoms. By applying the appropriate analysis algorithm according to the symptom category, the accuracy of the analysis is improved. 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 symptom category data into a generating AI, which can then propose the optimal analysis algorithm.

[0044] The analysis unit can prioritize the analysis based on the timing of symptom onset when analyzing symptoms. For example, the analysis unit can prioritize the analysis of recently occurring symptoms and provide results quickly. Furthermore, for symptoms that have persisted for a long time, the analysis unit can perform a detailed analysis and provide relevant information. In addition, the analysis unit can adjust the analysis priority according to the timing of symptom onset to provide results efficiently. This enables efficient analysis by prioritizing analysis based on the timing of symptom onset. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom onset data into a generating AI, which can then propose the optimal analysis method.

[0045] The analysis unit can adjust the order of analysis based on the relevance of symptoms. For example, the analysis unit can prioritize the analysis of highly relevant symptoms and provide results quickly. It can also provide concise analysis results for less relevant symptoms. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of symptoms to provide results efficiently. This allows for efficient analysis by adjusting the order of analysis based on the relevance of symptoms. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom relevance data into a generating AI, which can then propose the optimal analysis method.

[0046] The identification unit can improve the accuracy of identifying the cause of poisoning by considering the interrelationships of symptoms. For example, if multiple symptoms occur simultaneously, the identification unit will identify the cause of poisoning by considering their interrelationships. The identification unit can also improve the accuracy of identification by considering the order in which symptoms occur. Furthermore, the identification unit can improve the accuracy of identification by considering the severity of symptoms. As a result, the accuracy of identifying the cause of poisoning is improved by considering the interrelationships of symptoms. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input symptom interrelationship data into a generating AI, which can then propose the optimal identification method.

[0047] The identification unit can identify the cause of poisoning by considering the attribute information of the person reporting the symptoms. For example, the identification unit can identify the cause of poisoning by considering the age and gender of the person reporting the symptoms. The identification unit can also identify the cause of poisoning by referring to the person's medical history. Furthermore, the identification unit can identify the cause of poisoning by considering the person's lifestyle (smoking, drinking, etc.). This allows for more accurate identification of the cause of poisoning by considering the attribute information of the person reporting the symptoms. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the person's attribute information data into a generating AI, which can then propose the optimal identification method.

[0048] The identification unit can improve the accuracy of identification by referring to relevant literature on symptoms when identifying the cause of poisoning. For example, the identification unit can identify the cause of poisoning by referring to relevant medical literature. The identification unit can also improve the accuracy of identification by referring to research papers related to symptoms. Furthermore, the identification unit can identify the cause of poisoning by referring to past cases related to symptoms. In this way, the accuracy of identifying the cause of poisoning is improved by referring to relevant literature. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input relevant literature data into a generating AI, and the generating AI can propose the optimal identification method.

[0049] The prediction unit can optimize its current predictions by referring to past prediction data when predicting the rate of symptom progression and severity. For example, the prediction unit predicts the current rate of symptom progression based on past prediction data. The prediction unit can also improve the accuracy of severity predictions by referring to past prediction data. Furthermore, the prediction unit can analyze past prediction data and apply the optimal prediction algorithm. This improves the accuracy of current predictions by referring to past prediction data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past prediction data into a generating AI, which can then propose the optimal prediction method.

[0050] The prediction unit can apply different prediction algorithms to each symptom category when predicting the rate of symptom progression and severity. For example, the prediction unit can apply a prediction algorithm specialized for the digestive system to digestive system symptoms. It can also apply a prediction algorithm specialized for the nervous system to nervous system symptoms, and a prediction algorithm specialized for the respiratory system to respiratory system symptoms. By applying the appropriate prediction algorithm according to the symptom category, prediction accuracy is improved. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input symptom category data into a generating AI, which can then propose the optimal prediction algorithm.

[0051] The prediction unit can determine the priority of predictions based on the timing of symptom onset when predicting the rate of symptom progression and severity. For example, the prediction unit can prioritize recently occurring symptoms and provide results quickly. The prediction unit can also provide detailed prediction results for symptoms that have persisted for a long time. Furthermore, the prediction unit can adjust the priority of predictions according to the timing of symptom onset to provide results efficiently. This enables efficient prediction by determining the priority of predictions based on the timing of symptom onset. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input symptom onset timing data into a generating AI, which can then propose the optimal prediction method.

[0052] The prediction unit can make predictions regarding the rate of symptom progression and severity by referring to relevant market data. For example, the prediction unit predicts the rate of symptom progression based on market data. The prediction unit can also improve the accuracy of severity predictions by referring to market data. Furthermore, the prediction unit can analyze market data and apply the optimal prediction algorithm. This improves prediction accuracy by referring to market data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input market data into a generating AI, which can then propose the optimal prediction method.

[0053] The proposal unit can adjust the level of detail in its suggestions based on the severity of the symptoms when proposing countermeasures. For example, it can propose detailed countermeasures for symptoms of high severity, and concise countermeasures for symptoms of low severity. Furthermore, the proposal unit can prioritize suggestions according to the severity of the symptoms and provide countermeasures quickly. This allows for the efficient provision of countermeasures by adjusting the level of detail in suggestions based on the severity of the symptoms. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input symptom severity data into a generating AI, which can then propose the optimal suggestion method.

[0054] The suggestion unit can apply different suggestion algorithms depending on the symptom category when suggesting countermeasures. For example, the suggestion unit can apply a suggestion algorithm specialized for the digestive system to digestive system symptoms. It can also apply a suggestion algorithm specialized for the nervous system to nervous system symptoms, and a suggestion algorithm specialized for the respiratory system to respiratory system symptoms. By applying the appropriate suggestion algorithm according to the symptom category, the accuracy of the suggestions is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input symptom category data into a generating AI, which can then suggest the optimal suggestion algorithm.

[0055] The proposal unit can prioritize countermeasures based on the timing of symptom onset when proposing solutions. For example, the proposal unit will prioritize solutions for recently occurring symptoms. Furthermore, the proposal unit can propose detailed solutions for symptoms that have persisted for a long period. In addition, the proposal unit can adjust the priority of solutions according to the timing of symptom onset to provide solutions efficiently. This enables the efficient provision of solutions by prioritizing solutions based on the timing of symptom onset. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input symptom onset data into a generating AI, which can then propose the optimal solution method.

[0056] The suggestion unit can adjust the order of suggested countermeasures based on the relevance of symptoms. For example, the suggestion unit will prioritize suggesting countermeasures for highly relevant symptoms. It can also suggest concise countermeasures for less relevant symptoms. Furthermore, the suggestion unit can adjust the order of suggestions according to the relevance of symptoms to provide countermeasures efficiently. This allows for the efficient provision of countermeasures by adjusting the order of suggestions based on the relevance of symptoms. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input symptom relevance data into a generating AI, which can then suggest the optimal suggestion method.

[0057] The service provider can select the optimal service provision method by referring to the user's past medical history when providing acceptance status. For example, the service provider can provide the optimal acceptance status based on the user's past medical history. The service provider can also refer to the user's medical history and provide relevant acceptance status. Furthermore, the service provider can analyze the user's past medical history and select the optimal service provision method. This allows the service provider to provide the optimal acceptance status by referring to the user's past medical history. 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 user's medical history data into a generating AI, which can then propose the optimal service provision method.

[0058] The service provider can customize the content of the service based on the user's current health condition when providing acceptance status. For example, the service provider can consider the user's current health condition and provide the optimal acceptance status. The service provider can also provide relevant acceptance status based on the user's current symptoms. Furthermore, the service provider can analyze the user's current health condition and customize the optimal content of the service. This allows for the provision of a more appropriate acceptance status by customizing the content of the service based on the user's current health condition. 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 user's health status data into a generating AI, which can then propose the optimal delivery method.

[0059] The service provider can select the optimal service provision method when providing information on available medical facilities, taking into account the user's geographical location. For example, the service provider can provide the optimal service provision based on the climate and environment of the user's current location. The service provider can also provide information on the availability of medical facilities specific to the region, based on the user's geographical location. Furthermore, the service provider can refer to the user's location information and prioritize providing information on the availability of nearby medical facilities. This allows the service provider to provide information on the availability of medical facilities specific to the region, taking into account the user's geographical location. 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 user's location data into a generating AI, which can then propose the optimal service provision method.

[0060] The service provider can analyze the user's social media activity to suggest services when providing admission status. For example, the service provider can analyze the user's social media posts and suggest the most suitable admission status based on recent activity. The service provider can also refer to posts from the user's friends on social media and provide admission status for common medical institutions. Furthermore, the service provider can prioritize providing admission status related to specific events or locations based on the user's social media activity. This allows the service provider to suggest relevant admission status by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's social media data into a generating AI, which can then suggest the most suitable admission status.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The reception desk can analyze the user's past symptom input history and provide the optimal input interface. For example, it can automatically display symptoms that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest symptoms that the user will enter at a specific time of day based on their past input history. In this way, by analyzing past input history, the reception desk can provide the user with the optimal input interface. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into a generating AI, which can then suggest the optimal input interface.

[0063] The reception unit can filter the input content based on the user's current health status and lifestyle when symptoms are entered. For example, it can prioritize displaying highly relevant symptoms, taking into account the user's current health status. It can also customize the input content based on the user's lifestyle (smoking, drinking, etc.). Furthermore, it can refer to the user's medical history and automatically suggest relevant symptoms. This allows users to enter more relevant symptoms by filtering the input content based on their health status and lifestyle. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's health status data into a generating AI, which can then filter the input content to the optimal level.

[0064] The reception unit can prioritize the input of highly relevant symptoms by considering the user's geographical location when symptoms are entered. For example, it can prioritize displaying relevant symptoms based on the climate and environment of the user's current location. It can also suggest symptoms considering region-specific poisoning risks based on the user's geographical location. Furthermore, it can refer to the user's location information and prioritize displaying symptoms based on poisoning cases occurring in the vicinity. This makes it possible to input symptoms that take region-specific poisoning risks into account by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's location data into a generating AI, which can then suggest the most appropriate symptoms.

[0065] The reception desk can analyze the user's social media activity and input relevant symptoms when symptoms are entered. For example, it can analyze the user's social media posts and suggest relevant symptoms based on recent activity. It can also refer to posts from the user's friends on social media and suggest symptoms considering common addiction risks. Furthermore, it can prioritize displaying symptoms related to specific events or locations based on the user's social media activity. This allows for the suggestion of relevant symptoms by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI, which can then suggest the most appropriate symptoms.

[0066] The analysis unit can adjust the level of detail in its analysis based on the importance of the symptoms. For example, it can perform a detailed analysis and provide relevant information for high-importance symptoms, while providing a concise analysis for low-importance symptoms. Furthermore, it can prioritize analyses based on symptom importance and provide results quickly. This allows for efficient analysis by adjusting the level of detail based on symptom importance. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom importance data into a generating AI, which can then propose the optimal analysis method.

[0067] The analysis unit can apply different analysis algorithms depending on the symptom category when analyzing symptoms. For example, digestive system symptoms can be analyzed using an analysis algorithm specialized for the digestive system. Similarly, nervous system symptoms can be analyzed using an analysis algorithm specialized for the nervous system. Furthermore, respiratory system symptoms can be analyzed using an analysis algorithm specialized for the respiratory system. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the symptom category. 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 symptom category data into a generating AI, which can then propose the optimal analysis algorithm.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk receives the symptoms entered by the user. Symptoms entered by the user may include, but are not limited to, nausea, dizziness, or impaired consciousness. The reception desk can, for example, accept text input or voice input when the user enters symptoms into the app. The reception desk can also refer to the user's past symptom history to assist with input. Step 2: The analysis unit analyzes the symptoms received by the reception unit using natural language processing techniques. Natural language processing techniques include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit analyzes the symptoms entered by the user and understands their content. Step 3: The identification unit identifies the cause of poisoning by comparing the symptoms analyzed by the analysis unit with a global database of poisoning cases. The poisoning case database includes, but is not limited to, past poisoning cases and information on chemicals and plants. For example, the identification unit may compare the symptoms entered by the user with information in the database to identify potential causes of poisoning. Step 4: The prediction unit predicts the rate of symptom progression and severity based on the cause of poisoning identified by the identification unit. For example, the prediction unit evaluates the rate of symptom progression on an hourly basis and quantifies the severity. Step 5: The proposal team proposes countermeasures corresponding to the urgency predicted by the prediction team. For example, in the case of a minor incident, the proposal team will provide detailed instructions on first aid procedures to be performed at home, and in the case of a serious incident, they will immediately contact the nearest emergency medical facility. Step 6: The service provider utilizes the user's location information based on the countermeasures proposed by the proposal provider to provide real-time information on the availability of nearby pharmacies and specialized medical institutions. For example, the service provider obtains the user's location information from GPS data or Wi-Fi location information and provides real-time information on the availability of nearby medical institutions.

[0070] (Example of form 2) The health management system according to an embodiment of the present invention is a system that utilizes the latest medical AI technology to support the early detection and rapid response to acute poisoning. This health management system analyzes symptoms entered by the user (nausea, dizziness, impaired consciousness, etc.) using advanced natural language processing technology and compares them with a global database of poisoning cases to identify potential causes of poisoning. For example, it also takes into account a history of contact with specific plants or chemicals, and combinations of drugs taken, to perform a more precise diagnosis. Furthermore, the AI ​​predicts the rate of progression and severity of symptoms and proposes countermeasures according to the urgency. In mild cases, it provides detailed instructions on first aid at home, and in severe cases, it immediately notifies the nearest emergency medical facility. It also utilizes the user's location information to provide real-time availability information for nearby pharmacies and specialized medical facilities. The health management system goes beyond being a mere symptom checker; it also links with the user's medical history and pre-existing medical conditions to provide personalized responses tailored to individual circumstances. Furthermore, it contributes to accident prevention by providing advice on daily life for poisoning prevention and guidelines for managing hazardous materials in the home. This system can be used in a variety of environments, including private homes, schools, and workplaces, contributing to improved poisoning risk management for society as a whole. This allows the health management system to support the early detection and rapid response to acute poisoning.

[0071] The health management system according to this embodiment comprises a reception unit, an analysis unit, an identification unit, a prediction unit, a proposal unit, and a provision unit. The reception unit receives symptoms entered by the user. Symptoms entered by the user include, but are not limited to, nausea, dizziness, and impaired consciousness. The reception unit can, for example, accept text input or voice input when the user enters symptoms into the app. The reception unit can also refer to the history of symptoms previously entered by the user and provide input assistance. The analysis unit analyzes the symptoms received by the reception unit using natural language processing technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis, but is not limited to these. The analysis unit analyzes the symptoms entered by the user and understands their content. The identification unit identifies the cause of poisoning by comparing the symptoms analyzed by the analysis unit with a global database of poisoning cases. The poisoning case database includes, for example, past poisoning cases, information on chemical substances and plants, but is not limited to these. The identification unit, for example, matches the symptoms entered by the user with information in the database to identify potential causes of poisoning. The prediction unit predicts the rate of symptom progression and severity based on the cause of poisoning identified by the identification unit. The prediction unit, for example, evaluates the rate of symptom progression on an hourly basis and quantifies the severity. The proposal unit proposes countermeasures according to the urgency predicted by the prediction unit. For example, the proposal unit provides detailed instructions on first aid at home in mild cases, and immediately notifies the nearest emergency medical facility in severe cases. The provision unit utilizes the user's location information based on the countermeasures proposed by the proposal unit to provide real-time information on the availability of nearby pharmacies and specialized medical facilities. For example, the provision unit obtains the user's location information from GPS data or Wi-Fi location information and provides real-time information on the availability of nearby medical facilities. As a result, the health management system according to the embodiment can efficiently analyze the user's symptoms, identify the cause of poisoning, and propose appropriate countermeasures.

[0072] The reception desk receives symptoms entered by the user. These symptoms may include, but are not limited to, nausea, dizziness, or impaired consciousness. The reception desk can accept text input or voice input when the user enters symptoms into the app. Specifically, when a user accesses the application using a smartphone or tablet and enters symptoms, they can use text input via a keyboard or voice input using speech recognition technology. In the case of voice input, the speech recognition engine analyzes what the user says and converts it into text data. Furthermore, the reception desk can refer to the user's past symptom history to assist with input. For example, if a user's previously entered symptoms are stored in a database, and a similar symptom is entered again, the system can refer to past data to complete the input. This allows users to enter symptoms quickly and accurately, improving the system's usability. The reception desk also has a function to analyze user input in real time and automatically correct input errors or unclear expressions. For example, if a user enters "dizziness," and different spellings such as "vertigo" or "dizziness" are used, the system can analyze them uniformly. This allows the reception desk to efficiently and accurately receive user input.

[0073] The analysis department analyzes the symptoms received by the reception department using natural language processing (NLP) technology. NLP includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. Specifically, morphological analysis is used to divide the user-entered text into individual words and analyze the part of speech and meaning of each word. Grammatical analysis analyzes the structure of the input sentence, clarifying relationships such as subject, predicate, and object. Semantic analysis understands the meaning of the entered symptoms and provides an appropriate interpretation based on the context. For example, if a user enters "I have a headache," the analysis department extracts the words "head" and "painful" and analyzes their meanings to understand the symptom of a headache. Furthermore, the analysis department not only analyzes and understands the symptoms entered by the user, but can also perform more accurate analysis by comparing them with past data and symptom data from other users. For example, it can refer to data from other users with the same symptoms to find common patterns and trends. This allows the analysis department to gain a deeper understanding of the user's symptoms and build a foundation for proposing appropriate countermeasures. Furthermore, the analysis department can improve the accuracy of symptom analysis by utilizing AI technology. For example, by using machine learning algorithms to learn from past symptom data, it can perform highly accurate analysis on new symptom data. This allows the analysis department to analyze users' symptoms quickly and accurately, improving the overall system performance.

[0074] The identification unit identifies the cause of poisoning by comparing the symptoms analyzed by the analysis unit with a global database of poisoning cases. This database includes, but is not limited to, past poisoning cases and information on chemicals and plants. Specifically, the database contains detailed records of the characteristics and toxicity of various chemicals, poisoning cases caused by plants and animals, and even region-specific poisoning cases. The identification unit compares these databases with the symptoms entered by the user to identify potential causes of poisoning. For example, if a user complains of nausea and dizziness, the identification unit compares these symptoms with the database to determine if they may be caused by poisoning from a specific chemical or plant. Furthermore, the identification unit can utilize AI technology to identify the relationship between symptoms and poisoning causes with high accuracy. For example, machine learning algorithms can be used to learn from past poisoning case data and perform highly accurate identification on new symptom data. This allows the identification unit to quickly and accurately identify the cause of poisoning for a user's symptoms and propose appropriate countermeasures. Additionally, the identification unit regularly updates the database information, adding new poisoning cases and chemical information to ensure that identification is always based on the latest information. This allows the specific unit to perform highly accurate identification of the user's symptoms based on the latest information at all times, thereby improving the reliability and safety of the entire system.

[0075] The prediction unit predicts the rate of symptom progression and severity based on the cause of poisoning identified by the identification unit. Specifically, it evaluates the rate of symptom progression on an hourly basis and quantifies the severity. For example, if the identified cause of poisoning is a specific chemical substance, it predicts how quickly the symptoms will progress based on the characteristics of that chemical substance and past cases. Furthermore, by quantifying the severity, it allows the user to intuitively understand how dangerous their current condition is. The prediction unit utilizes AI technology to predict the rate of symptom progression and severity with high accuracy based on past data. For example, it can use machine learning algorithms to learn from past poisoning case data and make highly accurate predictions even for new symptom data. This allows the prediction unit to quickly and accurately predict the rate of progression and severity of the user's symptoms and lay the foundation for proposing appropriate countermeasures. In addition, the prediction unit can continuously revise its prediction results based on data updated in real time to respond to the latest situation. For example, if the user's symptoms rapidly worsen, the prediction unit immediately incorporates new data and updates the prediction results. This allows the prediction unit to always provide highly accurate predictions based on the latest information and support quick and appropriate responses.

[0076] The suggestion unit proposes countermeasures based on the urgency predicted by the prediction unit. Specifically, for mild cases, it provides detailed instructions on first aid at home, and for severe cases, it immediately contacts the nearest emergency medical facility. For example, in the case of mild symptoms, the suggestion unit provides the user with specific first aid measures such as cooling, resting, and drinking fluids. This information is provided in various formats such as text, images, and videos to ensure that the user can easily understand and implement it. On the other hand, in the case of severe symptoms, the suggestion unit instructs the user to immediately contact an emergency medical facility and has a function to automatically contact the nearest emergency medical facility if necessary. Furthermore, the suggestion unit can also introduce appropriate medical facilities and specialists depending on the user's symptoms and urgency. For example, it can list doctors and medical facilities with specialized knowledge of specific causes of poisoning and present the user with the best option. This allows the suggestion unit to provide specific instructions for users to take quick and appropriate action, minimizing harm. The suggestion unit can also collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, based on the results of the user implementing the suggested first aid measures or the diagnosis from a medical institution, the suggested measures can be reviewed and more effective countermeasures can be provided. This allows the suggestion department to always provide users with the most suitable countermeasures, improving the reliability and effectiveness of the entire system.

[0077] The service provider utilizes the user's location information based on the proposed solutions from the proposal team to provide real-time information on the availability of nearby pharmacies and specialized medical institutions. Specifically, it obtains the user's location information from GPS data and Wi-Fi location information and provides real-time information on the availability of nearby medical institutions. For example, if a user requires urgent medical attention, the service provider displays a list of the nearest medical institutions and pharmacies based on the user's current location, and provides detailed information such as their availability, waiting times, and medical specialties. This allows the user to quickly select the appropriate medical institution and receive the necessary treatment. Furthermore, the service provider can strengthen its collaboration with medical institutions and update information in real time. For example, when a medical institution updates its availability, the service provider immediately reflects that information, providing the user with the latest information. The service provider also has a function to provide optimal route guidance based on the user's location information. For example, it displays the shortest route to the medical institution selected by the user in a navigation system, taking into account traffic conditions and public transportation information to help the user reach their destination quickly. In this way, the service provider can support users in receiving prompt and appropriate medical attention and maximize the effectiveness of the entire system. Furthermore, the service provider implements strict security measures regarding the handling of location information to protect user privacy. For example, encryption technology is used for acquiring, storing, and sharing location information to prevent unauthorized access and data leaks. This allows the service provider to support prompt and appropriate medical responses while protecting user privacy.

[0078] The service provider can link with the user's medical history and pre-existing medical conditions to provide personalized responses tailored to individual circumstances. For example, the service provider can refer to the user's medical history and propose countermeasures considering past diagnoses and treatment history. It can also refer to the user's pre-existing medical conditions and propose countermeasures considering chronic diseases and allergy information. This allows for the provision of more appropriate countermeasures based on the user's medical history and pre-existing medical conditions. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's medical history and pre-existing medical conditions into AI, which can then propose the optimal countermeasures.

[0079] The service provider can offer advice on daily life practices for poisoning prevention and guidelines for managing hazardous materials in the home. For example, it can provide precautions regarding the handling of specific plants or chemicals. It can also provide detailed instructions on how to store hazardous materials in the home. By providing advice and guidelines for poisoning prevention, it contributes to preventing accidents. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's lifestyle and home environment into the AI, which can then suggest optimal advice and guidelines.

[0080] The reception desk can estimate the user's emotions and adjust the symptom input method based on the estimated emotions. For example, if the user is feeling anxious, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick symptom input. This allows for more appropriate symptom input by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI, which can then estimate emotions.

[0081] The reception unit can analyze the user's past symptom input history and provide the optimal input interface. For example, the reception unit can automatically display symptoms that the user has frequently entered in the past as suggestions. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest symptoms that the user will enter at a specific time period based on their past input history. In this way, by analyzing past input history, the reception unit can provide the user with the optimal input interface. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's past input history data into a generating AI, which can then suggest the optimal input interface.

[0082] The reception unit can filter the input content based on the user's current health status and lifestyle when symptoms are entered. For example, the reception unit considers the user's current health status and prioritizes displaying highly relevant symptoms. The reception unit can also customize the input content based on the user's lifestyle (smoking, drinking, etc.). Furthermore, the reception unit can refer to the user's medical history and automatically suggest relevant symptoms. This allows the user to enter more relevant symptoms by filtering the input content based on their health status and lifestyle. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's health status data into a generating AI, which can then filter the input content to the optimal level.

[0083] The reception desk can estimate the user's emotions and prioritize the entered symptoms based on the estimated emotions. For example, if the user is feeling anxious, the reception desk will prioritize displaying the most urgent symptoms. If the user is relaxed, the reception desk can also provide a detailed list of symptoms for the user to select from. Furthermore, if the user is in a hurry, the reception desk can prioritize displaying the most common symptoms. This allows for priority processing of urgent symptoms by prioritizing symptoms according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI, which can then estimate emotions.

[0084] The reception unit can prioritize the input of highly relevant symptoms by considering the user's geographical location when symptoms are entered. For example, the reception unit can prioritize displaying relevant symptoms based on the climate and environment of the user's current location. The reception unit can also suggest symptoms considering region-specific poisoning risks based on the user's geographical location. Furthermore, the reception unit can refer to the user's location information and prioritize displaying symptoms based on poisoning cases occurring in the vicinity. This makes it possible to input symptoms that take region-specific poisoning risks into account by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's location data into a generating AI, which can then suggest the most appropriate symptoms.

[0085] The reception desk can analyze the user's social media activity and input relevant symptoms when symptoms are entered. For example, the reception desk can analyze the user's social media posts and suggest relevant symptoms based on recent activity. The reception desk can also refer to posts from the user's friends on social media and suggest symptoms considering common addiction risks. Furthermore, the reception desk can prioritize displaying symptoms related to specific events or locations from the user's social media activity. This allows for the suggestion of relevant symptoms by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI, which can then suggest the most appropriate symptoms.

[0086] The analysis unit can estimate the user's emotions and adjust the symptom analysis method based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide a quick and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result and explain it in a way that is easy for the user to understand. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying the most important information. This allows for more appropriate analysis results by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI, which can then estimate the emotions.

[0087] The analysis unit can adjust the level of detail in its analysis based on the severity of the symptoms. For example, it can perform a detailed analysis and provide relevant information for high-severity symptoms. It can also provide a concise analysis for low-severity symptoms. Furthermore, the analysis unit can prioritize analyses based on symptom severity and provide results quickly. This allows for efficient analysis by adjusting the level of detail based on symptom severity. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input symptom severity data into a generating AI, which can then propose the optimal analysis method.

[0088] The analysis unit can apply different analysis algorithms depending on the symptom category when analyzing symptoms. For example, the analysis unit can apply a specialized analysis algorithm to digestive system symptoms. It can also apply a specialized analysis algorithm to nervous system symptoms. Furthermore, it can apply a specialized analysis algorithm to respiratory system symptoms. By applying the appropriate analysis algorithm according to the symptom category, the accuracy of the analysis is improved. 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 symptom category data into a generating AI, which can then propose the optimal analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user facial expression data into a generative AI, which can then estimate emotions.

[0090] The analysis unit can prioritize the analysis based on the timing of symptom onset when analyzing symptoms. For example, the analysis unit can prioritize the analysis of recently occurring symptoms and provide results quickly. Furthermore, for symptoms that have persisted for a long time, the analysis unit can perform a detailed analysis and provide relevant information. In addition, the analysis unit can adjust the analysis priority according to the timing of symptom onset to provide results efficiently. This enables efficient analysis by prioritizing analysis based on the timing of symptom onset. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom onset data into a generating AI, which can then propose the optimal analysis method.

[0091] The analysis unit can adjust the order of analysis based on the relevance of symptoms. For example, the analysis unit can prioritize the analysis of highly relevant symptoms and provide results quickly. It can also provide concise analysis results for less relevant symptoms. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of symptoms to provide results efficiently. This allows for efficient analysis by adjusting the order of analysis based on the relevance of symptoms. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom relevance data into a generating AI, which can then propose the optimal analysis method.

[0092] The identification unit can estimate the user's emotions and adjust the method of identifying the cause of addiction based on the estimated user emotions. For example, if the user is feeling anxious, the identification unit can provide a quick and concise identification result. If the user is relaxed, the identification unit can also provide a detailed identification result and explain it in a way that is easy for the user to understand. Furthermore, if the user is in a hurry, the identification unit can prioritize displaying the most important information. This allows for more appropriate identification results by adjusting the identification method 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 identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input user facial expression data into the generative AI, which can then estimate emotions.

[0093] The identification unit can improve the accuracy of identifying the cause of poisoning by considering the interrelationships of symptoms. For example, if multiple symptoms occur simultaneously, the identification unit will identify the cause of poisoning by considering their interrelationships. The identification unit can also improve the accuracy of identification by considering the order in which symptoms occur. Furthermore, the identification unit can improve the accuracy of identification by considering the severity of symptoms. As a result, the accuracy of identifying the cause of poisoning is improved by considering the interrelationships of symptoms. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input symptom interrelationship data into a generating AI, which can then propose the optimal identification method.

[0094] The identification unit can identify the cause of poisoning by considering the attribute information of the person reporting the symptoms. For example, the identification unit can identify the cause of poisoning by considering the age and gender of the person reporting the symptoms. The identification unit can also identify the cause of poisoning by referring to the person's medical history. Furthermore, the identification unit can identify the cause of poisoning by considering the person's lifestyle (smoking, drinking, etc.). This allows for more accurate identification of the cause of poisoning by considering the attribute information of the person reporting the symptoms. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the person's attribute information data into a generating AI, which can then propose the optimal identification method.

[0095] The identification unit can improve the accuracy of identification by referring to relevant literature on symptoms when identifying the cause of poisoning. For example, the identification unit can identify the cause of poisoning by referring to relevant medical literature. The identification unit can also improve the accuracy of identification by referring to research papers related to symptoms. Furthermore, the identification unit can identify the cause of poisoning by referring to past cases related to symptoms. In this way, the accuracy of identifying the cause of poisoning is improved by referring to relevant literature. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input relevant literature data into a generating AI, and the generating AI can propose the optimal identification method.

[0096] The prediction unit can estimate the user's emotions and adjust the prediction method for the rate of symptom progression and severity based on the estimated emotions. For example, if the user is feeling anxious, the prediction unit can provide a quick and concise prediction result. If the user is relaxed, the prediction unit can also provide a detailed prediction result and explain it in a way that is easy for the user to understand. Furthermore, if the user is in a hurry, the prediction unit can prioritize displaying the most important information. This allows for more appropriate prediction results by adjusting the prediction method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 prediction unit may be performed using AI or not using AI. For example, the prediction unit can input user facial expression data into the generative AI, which can then estimate emotions.

[0097] The prediction unit can optimize its current predictions by referring to past prediction data when predicting the rate of symptom progression and severity. For example, the prediction unit predicts the current rate of symptom progression based on past prediction data. The prediction unit can also improve the accuracy of severity predictions by referring to past prediction data. Furthermore, the prediction unit can analyze past prediction data and apply the optimal prediction algorithm. This improves the accuracy of current predictions by referring to past prediction data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past prediction data into a generating AI, which can then propose the optimal prediction method.

[0098] The prediction unit can apply different prediction algorithms to each symptom category when predicting the rate of symptom progression and severity. For example, the prediction unit can apply a prediction algorithm specialized for the digestive system to digestive system symptoms. It can also apply a prediction algorithm specialized for the nervous system to nervous system symptoms, and a prediction algorithm specialized for the respiratory system to respiratory system symptoms. By applying the appropriate prediction algorithm according to the symptom category, prediction accuracy is improved. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input symptom category data into a generating AI, which can then propose the optimal prediction algorithm.

[0099] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is feeling anxious, the prediction unit can provide a simple and highly visible display method. If the user is relaxed, the prediction unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the prediction unit can provide a concise display method. By adjusting the display method according to the user's emotions, more appropriate prediction results can be provided. 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 prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user facial expression data into the generative AI, which can then estimate emotions.

[0100] The prediction unit can determine the priority of predictions based on the timing of symptom onset when predicting the rate of symptom progression and severity. For example, the prediction unit can prioritize recently occurring symptoms and provide results quickly. The prediction unit can also provide detailed prediction results for symptoms that have persisted for a long time. Furthermore, the prediction unit can adjust the priority of predictions according to the timing of symptom onset to provide results efficiently. This enables efficient prediction by determining the priority of predictions based on the timing of symptom onset. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input symptom onset timing data into a generating AI, which can then propose the optimal prediction method.

[0101] The prediction unit can make predictions regarding the rate of symptom progression and severity by referring to relevant market data. For example, the prediction unit predicts the rate of symptom progression based on market data. The prediction unit can also improve the accuracy of severity predictions by referring to market data. Furthermore, the prediction unit can analyze market data and apply the optimal prediction algorithm. This improves prediction accuracy by referring to market data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input market data into a generating AI, which can then propose the optimal prediction method.

[0102] The suggestion unit can estimate the user's emotions and adjust the method of suggesting countermeasures based on the estimated emotions. For example, if the user is feeling anxious, the suggestion unit can suggest quick and concise countermeasures. If the user is relaxed, the suggestion unit can also suggest detailed countermeasures and explain them in a way that is easy for the user to understand. Furthermore, if the user is in a hurry, the suggestion unit can prioritize displaying the most important information. In this way, by adjusting the suggestion method according to the user's emotions, more appropriate countermeasures can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input the user's facial expression data into a generative AI, which can then estimate emotions.

[0103] The proposal unit can adjust the level of detail in its suggestions based on the severity of the symptoms when proposing countermeasures. For example, it can propose detailed countermeasures for symptoms of high severity, and concise countermeasures for symptoms of low severity. Furthermore, the proposal unit can prioritize suggestions according to the severity of the symptoms and provide countermeasures quickly. This allows for the efficient provision of countermeasures by adjusting the level of detail in suggestions based on the severity of the symptoms. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input symptom severity data into a generating AI, which can then propose the optimal suggestion method.

[0104] The suggestion unit can apply different suggestion algorithms depending on the symptom category when suggesting countermeasures. For example, the suggestion unit can apply a suggestion algorithm specialized for the digestive system to digestive system symptoms. It can also apply a suggestion algorithm specialized for the nervous system to nervous system symptoms, and a suggestion algorithm specialized for the respiratory system to respiratory system symptoms. By applying the appropriate suggestion algorithm according to the symptom category, the accuracy of the suggestions is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input symptom category data into a generating AI, which can then suggest the optimal suggestion algorithm.

[0105] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is feeling anxious, the suggestion unit will prioritize suggesting urgent solutions. If the user is relaxed, the suggestion unit can also suggest detailed solutions and allow the user to choose. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggesting the most common solutions. This allows for the provision of urgent solutions preferentially by prioritizing suggestions 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI, which can then estimate emotions.

[0106] The proposal unit can prioritize countermeasures based on the timing of symptom onset when proposing solutions. For example, the proposal unit will prioritize solutions for recently occurring symptoms. Furthermore, the proposal unit can propose detailed solutions for symptoms that have persisted for a long period. In addition, the proposal unit can adjust the priority of solutions according to the timing of symptom onset to provide solutions efficiently. This enables the efficient provision of solutions by prioritizing solutions based on the timing of symptom onset. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input symptom onset data into a generating AI, which can then propose the optimal solution method.

[0107] The suggestion unit can adjust the order of suggested countermeasures based on the relevance of symptoms. For example, the suggestion unit will prioritize suggesting countermeasures for highly relevant symptoms. It can also suggest concise countermeasures for less relevant symptoms. Furthermore, the suggestion unit can adjust the order of suggestions according to the relevance of symptoms to provide countermeasures efficiently. This allows for the efficient provision of countermeasures by adjusting the order of suggestions based on the relevance of symptoms. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input symptom relevance data into a generating AI, which can then suggest the optimal suggestion method.

[0108] The service provider can estimate the user's emotions and adjust the way it provides acceptance status based on the estimated emotions. For example, if the user is feeling anxious, the service provider can provide a quick and concise acceptance status. If the user is relaxed, the service provider can also provide a detailed acceptance status and explain it in a way that is easy for the user to understand. Furthermore, if the user is in a hurry, the service provider can prioritize displaying the most important information. This allows for the provision of more appropriate acceptance status by adjusting the service provider's approach 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 service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI, which can then estimate emotions.

[0109] The service provider can select the optimal service provision method by referring to the user's past medical history when providing acceptance status. For example, the service provider can provide the optimal acceptance status based on the user's past medical history. The service provider can also refer to the user's medical history and provide relevant acceptance status. Furthermore, the service provider can analyze the user's past medical history and select the optimal service provision method. This allows the service provider to provide the optimal acceptance status by referring to the user's past medical history. 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 user's medical history data into a generating AI, which can then propose the optimal service provision method.

[0110] The service provider can customize the content of the service based on the user's current health condition when providing acceptance status. For example, the service provider can consider the user's current health condition and provide the optimal acceptance status. The service provider can also provide relevant acceptance status based on the user's current symptoms. Furthermore, the service provider can analyze the user's current health condition and customize the optimal content of the service. This allows for the provision of a more appropriate acceptance status by customizing the content of the service based on the user's current health condition. 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 user's health status data into a generating AI, which can then propose the optimal delivery method.

[0111] The service provider can estimate the user's emotions and prioritize acceptance options based on those emotions. For example, if the user is feeling anxious, the service provider will prioritize providing high-urgency acceptance options. If the user is relaxed, the service provider can also provide detailed acceptance options for the user to choose from. Furthermore, if the user is in a hurry, the service provider can prioritize providing the most common acceptance options. This allows for prioritizing high-urgency acceptance options 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 service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI, which can then estimate emotions.

[0112] The service provider can select the optimal service provision method when providing information on available medical facilities, taking into account the user's geographical location. For example, the service provider can provide the optimal service provision based on the climate and environment of the user's current location. The service provider can also provide information on the availability of medical facilities specific to the region, based on the user's geographical location. Furthermore, the service provider can refer to the user's location information and prioritize providing information on the availability of nearby medical facilities. This allows the service provider to provide information on the availability of medical facilities specific to the region, taking into account the user's geographical location. 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 user's location data into a generating AI, which can then propose the optimal service provision method.

[0113] The service provider can analyze the user's social media activity to suggest services when providing admission status. For example, the service provider can analyze the user's social media posts and suggest the most suitable admission status based on recent activity. The service provider can also refer to posts from the user's friends on social media and provide admission status for common medical institutions. Furthermore, the service provider can prioritize providing admission status related to specific events or locations based on the user's social media activity. This allows the service provider to suggest relevant admission status by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's social media data into a generating AI, which can then suggest the most suitable admission status.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The reception desk can estimate the user's emotions and adjust the symptom input method based on the estimated emotions. For example, if the user is feeling anxious, it can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick symptom input. This allows for more appropriate symptom input by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI, which can then estimate the emotions.

[0116] The reception desk can analyze the user's past symptom input history and provide the optimal input interface. For example, it can automatically display symptoms that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest symptoms that the user will enter at a specific time of day based on their past input history. In this way, by analyzing past input history, the reception desk can provide the user with the optimal input interface. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into a generating AI, which can then suggest the optimal input interface.

[0117] The reception unit can filter the input content based on the user's current health status and lifestyle when symptoms are entered. For example, it can prioritize displaying highly relevant symptoms, taking into account the user's current health status. It can also customize the input content based on the user's lifestyle (smoking, drinking, etc.). Furthermore, it can refer to the user's medical history and automatically suggest relevant symptoms. This allows users to enter more relevant symptoms by filtering the input content based on their health status and lifestyle. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's health status data into a generating AI, which can then filter the input content to the optimal level.

[0118] The reception desk can estimate the user's emotions and prioritize the entered symptoms based on the estimated emotions. For example, if the user is feeling anxious, it can prioritize displaying the most urgent symptoms. If the user is relaxed, it can also provide a detailed list of symptoms for the user to choose from. Furthermore, if the user is in a hurry, it can prioritize displaying the most common symptoms. This allows for priority processing of urgent symptoms by prioritizing symptoms 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 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 reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI, which can then estimate the emotions.

[0119] The reception unit can prioritize the input of highly relevant symptoms by considering the user's geographical location when symptoms are entered. For example, it can prioritize displaying relevant symptoms based on the climate and environment of the user's current location. It can also suggest symptoms considering region-specific poisoning risks based on the user's geographical location. Furthermore, it can refer to the user's location information and prioritize displaying symptoms based on poisoning cases occurring in the vicinity. This makes it possible to input symptoms that take region-specific poisoning risks into account by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's location data into a generating AI, which can then suggest the most appropriate symptoms.

[0120] The reception desk can analyze the user's social media activity and input relevant symptoms when symptoms are entered. For example, it can analyze the user's social media posts and suggest relevant symptoms based on recent activity. It can also refer to posts from the user's friends on social media and suggest symptoms considering common addiction risks. Furthermore, it can prioritize displaying symptoms related to specific events or locations based on the user's social media activity. This allows for the suggestion of relevant symptoms by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI, which can then suggest the most appropriate symptoms.

[0121] The analysis unit can estimate the user's emotions and adjust the symptom analysis method based on the estimated user emotions. For example, if the user is feeling anxious, it can provide a quick and concise analysis result. If the user is relaxed, it can provide a detailed analysis result and explain it in a way that is easy for the user to understand. Furthermore, if the user is in a hurry, it can prioritize displaying the most important information. In this way, by adjusting the analysis method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI, which can then estimate the emotions.

[0122] The analysis unit can adjust the level of detail in its analysis based on the importance of the symptoms. For example, it can perform a detailed analysis and provide relevant information for high-importance symptoms, while providing a concise analysis for low-importance symptoms. Furthermore, it can prioritize analyses based on symptom importance and provide results quickly. This allows for efficient analysis by adjusting the level of detail based on symptom importance. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom importance data into a generating AI, which can then propose the optimal analysis method.

[0123] The analysis unit can apply different analysis algorithms depending on the symptom category when analyzing symptoms. For example, digestive system symptoms can be analyzed using an analysis algorithm specialized for the digestive system. Similarly, nervous system symptoms can be analyzed using an analysis algorithm specialized for the nervous system. Furthermore, respiratory system symptoms can be analyzed using an analysis algorithm specialized for the respiratory system. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the symptom category. 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 symptom category data into a generating AI, which can then propose the optimal analysis algorithm.

[0124] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI, and the generative AI can estimate emotions.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The reception desk receives the symptoms entered by the user. Symptoms entered by the user may include, but are not limited to, nausea, dizziness, or impaired consciousness. The reception desk can, for example, accept text input or voice input when the user enters symptoms into the app. The reception desk can also refer to the user's past symptom history to assist with input. Step 2: The analysis unit analyzes the symptoms received by the reception unit using natural language processing techniques. Natural language processing techniques include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit analyzes the symptoms entered by the user and understands their content. Step 3: The identification unit identifies the cause of poisoning by comparing the symptoms analyzed by the analysis unit with a global database of poisoning cases. The poisoning case database includes, but is not limited to, past poisoning cases and information on chemicals and plants. For example, the identification unit may compare the symptoms entered by the user with information in the database to identify potential causes of poisoning. Step 4: The prediction unit predicts the rate of symptom progression and severity based on the cause of poisoning identified by the identification unit. For example, the prediction unit evaluates the rate of symptom progression on an hourly basis and quantifies the severity. Step 5: The proposal team proposes countermeasures corresponding to the urgency predicted by the prediction team. For example, in the case of a minor incident, the proposal team will provide detailed instructions on first aid procedures to be performed at home, and in the case of a serious incident, they will immediately contact the nearest emergency medical facility. Step 6: The service provider utilizes the user's location information based on the countermeasures proposed by the proposal provider to provide real-time information on the availability of nearby pharmacies and specialized medical institutions. For example, the service provider obtains the user's location information from GPS data or Wi-Fi location information and provides real-time information on the availability of nearby medical institutions.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, prediction unit, proposal unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives symptoms entered by the user. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the symptoms using natural language processing technology. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and identifies the cause of poisoning by comparing it with a global database of poisoning cases. The prediction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and predicts the rate of progression and severity of symptoms. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes countermeasures according to the urgency. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides real-time availability information of nearby pharmacies and specialized medical institutions using the user's location information. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, prediction unit, proposal unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives symptoms entered by the user. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the symptoms using natural language processing technology. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and identifies the cause of poisoning by comparing it with a global database of poisoning cases. The prediction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and predicts the rate of progression and severity of symptoms. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes countermeasures according to the urgency. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides real-time availability information of nearby pharmacies and specialized medical institutions using the user's location information. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, prediction unit, proposal unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives symptoms entered by the user. The analysis unit is implemented by, for example, the identification unit 290 of the data processing unit 12 and analyzes the symptoms using natural language processing technology. The identification unit is implemented by, for example, the identification unit 290 of the data processing unit 12 and identifies the cause of poisoning by comparing it with a global database of poisoning cases. The prediction unit is implemented by, for example, the identification unit 290 of the data processing unit 12 and predicts the rate of progression and severity of symptoms. The proposal unit is implemented by, for example, the identification unit 290 of the data processing unit 12 and proposes countermeasures according to the urgency. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides real-time availability information of nearby pharmacies and specialized medical institutions using the user's location information. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, prediction unit, proposal unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives symptoms entered by the user. The analysis unit is implemented by, for example, the identification unit 290 of the data processing unit 12 and analyzes the symptoms using natural language processing technology. The identification unit is implemented by, for example, the identification unit 290 of the data processing unit 12 and identifies the cause of poisoning by comparing it with a global database of poisoning cases. The prediction unit is implemented by, for example, the identification unit 290 of the data processing unit 12 and predicts the rate of progression and severity of symptoms. The proposal unit is implemented by, for example, the identification unit 290 of the data processing unit 12 and proposes countermeasures according to the urgency. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides real-time availability information of nearby pharmacies and specialized medical institutions using the user's location information. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] (Note 1) A reception desk that receives the symptoms entered by the user, An analysis unit analyzes the symptoms received by the reception unit using natural language processing technology, The identification unit identifies the cause of poisoning by comparing the symptoms analyzed by the aforementioned analysis unit with a global database of poisoning cases, A prediction unit that predicts the rate of progression and severity of symptoms based on the cause of poisoning identified by the aforementioned identification unit, A proposal unit that proposes countermeasures corresponding to the urgency predicted by the prediction unit, The system includes a provisioning unit that utilizes the user's location information based on the countermeasures proposed by the aforementioned proposal unit to provide real-time information on the availability of nearby pharmacies and specialized medical institutions. A system characterized by the following features. (Note 2) The aforementioned supply unit is, It integrates with the user's medical history and pre-existing medical conditions to provide personalized support tailored to each individual's situation. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We provide advice on daily life practices to prevent poisoning and guidelines for managing hazardous materials in the home. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is The system estimates the user's emotions and adjusts the symptom input method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It analyzes the user's past symptom input history and provides the optimal input interface. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users enter symptoms, the system filters the input based on their current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the entered symptoms based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering symptoms, the system prioritizes the input of symptoms that are most relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter their symptoms, the system analyzes their social media activity and inputs relevant symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is The system estimates the user's emotions and adjusts the symptom analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is When analyzing symptoms, adjust the level of detail based on the severity of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is When analyzing symptoms, different analysis algorithms are applied depending on the symptom category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When analyzing symptoms, prioritize the analysis based on when the symptoms first appeared. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is When analyzing symptoms, adjust the order of analysis based on the relevance of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 16) The specified part is, We estimate the user's emotions and adjust the method for identifying the cause of addiction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The specified part is, When identifying the cause of poisoning, consider the interrelationships between symptoms to improve the accuracy of the identification. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, When identifying the cause of poisoning, the attribute information of the person who reported the symptoms should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, When identifying the cause of poisoning, referencing relevant literature on symptoms improves the accuracy of the identification. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, The system estimates the user's emotions and adjusts the prediction method for the rate of symptom progression and severity based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When predicting the rate of symptom progression and severity, past prediction data is referenced to optimize the current prediction. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, When predicting the rate of symptom progression and severity, different prediction algorithms are applied for each symptom category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, When predicting the rate of symptom progression and severity, the prediction priority is determined based on the timing of symptom onset. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prediction unit, When predicting the rate of symptom progression and severity, market data related to the symptoms is used to make predictions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, It estimates the user's emotions and adjusts the method of suggesting countermeasures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When proposing countermeasures, adjust the level of detail in the suggestions based on the severity of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When proposing countermeasures, different suggestion algorithms are applied depending on the category of symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When proposing countermeasures, prioritize the suggestions based on when the symptoms started. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When proposing countermeasures, adjust the order of suggestions based on the relevance of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how it provides feedback based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing information on acceptance status, the system selects the optimal method of provision by referring to the user's past medical history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing acceptance status, the content provided will be customized based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, It estimates user sentiment and prioritizes acceptance based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing acceptance status, the optimal delivery method will be selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned supply unit is, When providing acceptance status, we analyze the user's social media activity and propose content to be offered. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives the symptoms entered by the user, An analysis unit analyzes the symptoms received by the reception unit using natural language processing technology, The identification unit identifies the cause of poisoning by comparing the symptoms analyzed by the aforementioned analysis unit with a global database of poisoning cases, A prediction unit that predicts the rate of progression and severity of symptoms based on the cause of poisoning identified by the aforementioned identification unit, A proposal unit that proposes countermeasures corresponding to the urgency predicted by the prediction unit, The system includes a provisioning unit that utilizes the user's location information based on the countermeasures proposed by the aforementioned proposal unit to provide real-time information on the availability of nearby pharmacies and specialized medical institutions. A system characterized by the following features.

2. The aforementioned supply unit is, It integrates with the user's medical history and pre-existing medical conditions to provide personalized support tailored to each individual's situation. The system according to feature 1.

3. The aforementioned supply unit is, We provide advice on daily life practices to prevent poisoning and guidelines for managing hazardous materials in the home. The system according to feature 1.

4. The aforementioned reception unit is The system estimates the user's emotions and adjusts the symptom input method based on the estimated emotions. The system according to feature 1.

5. The aforementioned reception unit is It analyzes the user's past symptom input history and provides the optimal input interface. The system according to feature 1.

6. The aforementioned reception unit is When users enter symptoms, the system filters the input based on their current health status and lifestyle. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the entered symptoms based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is When entering symptoms, the system prioritizes the input of symptoms that are most relevant to the user's geographical location. The system according to feature 1.

9. The aforementioned reception unit is When users enter their symptoms, the system analyzes their social media activity and inputs relevant symptoms. The system according to feature 1.

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