System

Abstract

An object of the system according to the embodiment is to provide a quick and appropriate coping method for a symptom having a possibility of poisoning.SOLUTION: A system includes a symptom input section, an analysis section, a handling method provision section, and an emergency contact provision section. The symptom input unit inputs a symptom that the user may be addicted to. The analysis unit analyzes the symptom input by the symptom input unit. The coping method providing part provides a coping method based on the poisoning cause specified by the analysis part. The emergency contact provider provides an emergency contact on the basis of the handling method provided by the handling method provider.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to provide a quick and appropriate response to symptoms of possible poisoning.

[0005] The system of the embodiment aims to provide a quick and appropriate response to symptoms of possible poisoning. [Means for solving the problem]

[0006] The system according to the embodiment includes a symptom input unit, an analysis unit, a remedy provision unit, and an emergency contact provision unit. The symptom input unit allows a user to input symptoms that may be poisoning. The analysis unit analyzes the symptoms input by the symptom input unit. The remedy provision unit provides a remedy based on the cause of poisoning identified by the analysis unit. The emergency contact provision unit provides an emergency contact based on the remedy provided by the remedy provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a quick and appropriate response to possible symptoms of poisoning. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The acute poisoning symptom determination system according to an embodiment of the present invention is a system in which a user inputs symptoms that may indicate poisoning, a generation AI analyzes the symptoms, identifies the potential cause of poisoning, and provides countermeasures and emergency contact information. This allows the acute poisoning symptom determination system to quickly determine whether the user has symptoms that may indicate poisoning and provide appropriate countermeasures and emergency contact information.

[0029] An acute poisoning symptom determination system according to an embodiment includes a symptom input unit, an analysis unit, a remedy provision unit, and an emergency contact provision unit. The symptom input unit allows a user to input symptoms of possible poisoning. For example, the user inputs specific symptoms such as "feeling nauseous," "feeling dizzy," or "feeling confused." The analysis unit allows a generation AI to analyze the symptoms input by the symptom input unit. For example, the generation AI may analyze the symptoms using a text generation AI (e.g., LLM) to identify possible causes of poisoning. The generation AI may also analyze combinations of symptoms using a multimodal generation AI. The generation AI may also recognize symptom patterns and match them with a database to identify the cause of poisoning. The remedy provision unit provides remedy measures based on the cause of poisoning identified by the analysis unit. For example, the generation AI may suggest specific remedy measures such as "drink water and rest," "get some fresh air," or "consult a doctor immediately." The generation AI may also provide first aid measures based on the user's symptoms. The generation AI may also customize remedy measures based on past success stories. The emergency contact information providing unit provides emergency contact information based on the response provided by the response information providing unit. For example, the generation AI provides contact information for the nearest hospital or emergency service. The generation AI can also provide information on the nearest medical institution or pharmacy taking into account the user's current location. The generation AI can also send the user's symptom data to experts in advance to enable a prompt response. As a result, the acute poisoning symptom determination system according to the embodiment can quickly determine whether the user has symptoms that may be poisoning and provide appropriate responses and emergency contact information. For example, if the user experiences nausea or dizziness, the generation AI can immediately identify the possibility of food poisoning and provide appropriate responses. Furthermore, if the user has impaired consciousness, the generation AI can identify the possibility of carbon monoxide poisoning and quickly connect the user to an emergency medical expert. This allows the user to take prompt and appropriate action, minimizing the damage caused by poisoning.

[0030] The analysis unit can improve the accuracy of analysis by comparing symptoms with a database of past cases and identifying similar cases. For example, the analysis unit uses the generation AI to compare symptoms entered by the user, such as "nausea," "dizziness," or "loss of consciousness," with the database of past cases to identify similar cases. For example, the analysis unit can refer to the data of patients who have previously complained of similar symptoms to find commonalities. The analysis unit can also evaluate the degree of similarity of symptoms and the proximity of the time of onset based on the database of past cases to identify similar cases. The analysis unit can also improve the accuracy of analysis by taking into account the update frequency and data format of the database of past cases. In this way, the accuracy of analysis is improved by referring to the database of past cases.

[0031] The symptom input unit automatically recognizes symptoms using voice input or image analysis, eliminating the need for text input. For example, when a user vocally inputs symptoms such as "I feel nauseous" or "I feel dizzy," the generation AI automatically converts the input into text using voice recognition technology and analyzes it. For example, the voice input is converted into text in real time and used for analysis. In addition, when a user inputs symptoms using an image, the generation AI automatically recognizes the symptoms using image analysis technology and converts them into text. For example, when a user uploads an image of a rash, the generation AI analyzes the image and identifies the type of rash. The symptom input unit can also improve the accuracy of symptom input using voice recognition algorithms or image recognition algorithms. This eliminates the need for text input by using voice input or image analysis.

[0032] The symptom input unit can automatically acquire symptoms from wearable devices such as smartwatches and fitness trackers. For example, when a user wears a smartwatch or fitness tracker, the generative AI automatically acquires data such as heart rate, body temperature, and activity level from the device and uses it to analyze symptoms. For example, it can detect abnormal heart rate and body temperature fluctuations. The symptom input unit can also evaluate the user's health status based on the data acquired from the wearable device and reflect this in symptom analysis. The symptom input unit can also utilize the functions of the smartwatch or fitness tracker to monitor the user's exercise level and sleep patterns and use them to analyze symptoms. This automatically acquiring symptoms from the wearable device reduces the user's effort in inputting information.

[0033] The analysis unit can refer to the user's lifestyle habits and dietary history to perform a more detailed analysis. For example, when a user inputs symptoms, the generation AI refers to the user's lifestyle habits data (e.g., sleep time and amount of exercise) and uses this to analyze the symptoms. For example, it considers the possibility that dizziness is caused by lack of sleep. The analysis unit can also refer to the user's dietary history to evaluate the possibility that a specific food is related to poisoning symptoms. The analysis unit can also identify the cause of symptoms based on the user's lifestyle habits and dietary history, improving the accuracy of the analysis. This makes it possible to perform a more detailed analysis by referring to lifestyle habits and dietary history.

[0034] The analysis unit can identify the cause of poisoning by taking into account geographical information and environmental data. For example, the analysis unit identifies the cause of poisoning by taking into account geographical information (e.g., the temperature and humidity of the current location) in addition to the symptoms input by the generation AI. For example, it considers the possibility of heatstroke in a hot and humid environment. The analysis unit can also refer to the user's environmental data (e.g., air quality and water quality) to evaluate the possibility that specific environmental factors are related to the poisoning symptoms. The analysis unit can also identify region-specific causes of poisoning based on geographical information and environmental data, improving the accuracy of the analysis. In this way, by taking into account geographical information and environmental data, the accuracy of identifying the cause of poisoning is improved.

[0035] The analysis unit can analyze the frequency and time periods of symptom occurrence and identify the cause of poisoning that occurs during a specific time period. For example, the generation AI can analyze the frequency and time periods of symptom occurrence entered by the user and identify the cause of poisoning that occurs during a specific time period. For example, if many symptoms occur at night, the analysis unit can consider the possibility of carbon monoxide poisoning during sleep. The analysis unit can also evaluate the number of occurrences and incidence rate within a specific period based on the frequency of symptom occurrence and identify the cause of poisoning. The analysis unit can also evaluate the cause of poisoning associated with a specific time period based on the time period of symptom occurrence, improving the accuracy of the analysis. In this way, by analyzing the frequency and time periods of occurrence, the accuracy of identifying the cause of poisoning is improved.

[0036] The analysis unit can compare the case data of other users and identify common causes of poisoning. For example, the analysis unit compares the symptoms entered by the generation AI with the case data of other users to identify common causes of poisoning. For example, if there are many users in the same area who complain of similar symptoms, a common cause of poisoning can be identified. The analysis unit can also evaluate the degree of similarity of symptoms and the proximity of the onset times based on the case data of other users to identify common causes of poisoning. The analysis unit can also refer to the case data of other users to evaluate whether a specific cause of poisoning has a widespread impact. This makes it possible to identify common causes of poisoning by comparing with the case data of other users.

[0037] The analysis unit can refer to the user's past health checkup data and medical history to more accurately identify the cause of poisoning. For example, the generation AI can refer to the user's past health checkup data to identify the cause of poisoning. For example, if the user has had a specific allergy in the past, it can consider the possibility that the allergy is related to the poisoning symptoms. The analysis unit can also refer to the user's medical history to identify the cause of poisoning based on past diagnosis results and prescription drug history. The analysis unit can also evaluate whether a specific health condition is affecting the poisoning symptoms based on the user's health checkup data and medical history. This allows the cause of poisoning to be identified more accurately by referring to past health checkup data and medical history.

[0038] The solution providing unit can customize the solution to the user's symptoms based on past success cases. For example, the solution providing unit uses a generation AI to customize the solution to the user's symptoms based on past success cases. For example, the solution providing unit can refer to what solutions have been used by patients with similar symptoms in the past and suggest the optimal solution. The solution providing unit can also provide first aid methods according to the user's symptoms based on past success cases. The solution providing unit can also continuously improve the solution to the user's symptoms based on past success cases. In this way, by customizing the solution based on past success cases, more effective solutions can be provided.

[0039] The emergency contact providing unit can provide information on the nearest medical institutions and pharmacies taking into account the user's current location. For example, the generation AI provides information on the nearest medical institutions and pharmacies based on the user's current location. For example, the generation AI obtains the user's location information and displays the contact information for the nearest hospitals and pharmacies. The emergency contact providing unit can also provide emergency response guidelines for each region based on the user's current location. The emergency contact providing unit can also provide the contact information for the nearest emergency service based on the user's current location. In this way, information on the nearest medical institutions and pharmacies can be provided by taking into account the current location.

[0040] The solution providing unit can visually provide solutions to the user's symptoms using videos and images. For example, the solution providing unit uses a generation AI to visually provide solutions to the user's symptoms using videos and images. For example, first aid methods can be shown in videos so that the user can understand them visually. The solution providing unit can also visually provide solutions to the user's symptoms using images. The solution providing unit can also provide solutions in a format that is easy for the user to understand, taking into account the resolution and length of the videos and images. In this way, providing solutions visually using videos and images makes it easier for the user to understand the solutions.

[0041] The solution providing unit can improve the solution based on feedback from other users. For example, the solution providing unit uses a generation AI to improve the solution to a user's symptoms based on feedback from other users. For example, the solution providing unit can refer to feedback from users who have had similar symptoms in the past and suggest the optimal solution. The solution providing unit can also evaluate and improve the effectiveness of the solution based on feedback from other users. The solution providing unit can also collect user feedback and reflect it in improving the solution. In this way, more effective solutions can be provided by improving the solution based on feedback from other users.

[0042] The emergency contact providing unit can automatically select and connect the most appropriate specialist depending on the user's symptoms. For example, the generation AI automatically selects and connects the most appropriate specialist depending on the user's symptoms. For example, if the user complains of loss of consciousness, a neurology specialist will be selected. The emergency contact providing unit can also collect opinions from multiple specialists depending on the user's symptoms and provide the most appropriate course of action. The emergency contact providing unit can also send the user's symptom data to specialists in advance, enabling a prompt response. This allows the most appropriate specialist to be automatically selected and connected, enabling a prompt response.

[0043] The emergency contact providing unit can send the user's symptom data to experts in advance, enabling a prompt response. For example, the generation AI can send the user's symptom data to experts in advance, enabling a prompt response. For example, if the user complains of loss of consciousness, the data can be sent to a neurology specialist. The emergency contact providing unit can also prepare experts to respond promptly based on the user's symptom data. The emergency contact providing unit can also continuously monitor the user's symptom data and provide information to experts in real time. In this way, sending symptom data in advance enables prompt response by experts.

[0044] The emergency contact providing unit can collect opinions from multiple experts depending on the user's symptoms and provide the optimal solution. For example, the generation AI can collect opinions from multiple experts depending on the user's symptoms and provide the optimal solution. For example, if the user complains of loss of consciousness, it can collect opinions from neurology and emergency medical experts. The emergency contact providing unit can also provide the optimal solution for the user's symptoms based on the opinions from multiple experts. The emergency contact providing unit can also continuously collect expert opinions and reflect them in improving the solution. In this way, the optimal solution can be provided by collecting opinions from multiple experts.

[0045] The emergency response guideline providing unit can customize emergency response guidelines according to the user's symptoms based on past success cases. For example, the emergency response guideline providing unit customizes emergency response guidelines according to the user's symptoms based on past success cases using a generation AI. For example, the emergency response guideline providing unit provides optimal guidelines by referring to the responses given to patients with similar symptoms in the past. The emergency response guideline providing unit can also provide first aid procedures according to the user's symptoms based on past success cases. The emergency response guideline providing unit can also continuously improve the emergency response guidelines for the user's symptoms based on past success cases. In this way, customizing emergency response guidelines based on past success cases enables more effective responses.

[0046] The emergency response guideline providing unit can provide emergency response guidelines for each region, taking into account the user's current location. For example, the generation AI provides emergency response guidelines for each region based on the user's current location. For example, the generation AI obtains the user's location information and displays contact information for medical institutions and emergency services in that region. The emergency response guideline providing unit can also provide region-specific emergency response guidelines based on the user's current location. The emergency response guideline providing unit can also continuously update the emergency response guidelines for each region, based on the user's current location. This makes it possible to provide emergency response guidelines for each region by taking into account the current location.

[0047] The emergency response guideline providing unit can visually provide emergency response guidelines according to the user's symptoms using videos or images. For example, the emergency response guideline providing unit visually provides emergency response guidelines according to the user's symptoms using videos or images by using a generation AI. For example, first aid methods are shown in videos so that the user can visually understand. The emergency response guideline providing unit can also visually provide emergency response guidelines according to the user's symptoms using images. The emergency response guideline providing unit can also provide emergency response guidelines in a format that is easy for the user to understand, taking into account the resolution and length of the videos or images. In this way, visual provision using videos or images makes it easier for the user to understand the emergency response guidelines.

[0048] The emergency response guideline providing unit can improve the emergency response guidelines based on feedback from other users. For example, the emergency response guideline providing unit improves emergency response guidelines that the generation AI creates based on the user's symptoms based on feedback from other users. For example, the emergency response guideline providing unit refers to feedback from users who have had similar symptoms in the past and provides optimal guidelines. The emergency response guideline providing unit can also evaluate and improve the effectiveness of the emergency response guidelines based on feedback from other users. The emergency response guideline providing unit can also collect user feedback and reflect it in improving the emergency response guidelines. In this way, by improving the emergency response guidelines based on feedback from other users, more effective responses are possible.

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

[0050] When a user inputs their symptoms into the acute poisoning symptom assessment system, the symptom input unit automatically recognizes them using voice input and image analysis, eliminating the need for text input. For example, if a user inputs symptoms such as "I feel nauseous" or "I feel dizzy," the generation AI automatically converts them into text using voice recognition technology and analyzes them. In addition, if a user uploads an image of a rash, the generation AI can perform image analysis to identify the type of rash. This eliminates the need for text input by using voice input and image analysis.

[0051] The acute poisoning symptom determination system allows the analysis unit to refer to the user's lifestyle and dietary history to perform a more detailed analysis. For example, when a user inputs their symptoms, the generation AI refers to the user's lifestyle data (such as sleep time and amount of exercise) and uses this to analyze the symptoms. It can take into account the possibility that dizziness is caused by lack of sleep. It can also refer to the user's dietary history to evaluate the possibility that specific foods are related to poisoning symptoms. This allows for a more detailed analysis by referring to lifestyle and dietary history.

[0052] The acute poisoning symptom determination system's symptom input unit can automatically acquire symptoms from wearable devices such as smartwatches and fitness trackers. For example, if a user is wearing a smartwatch or fitness tracker, the generation AI automatically acquires data such as heart rate, body temperature, and activity level from these devices and uses this data to analyze symptoms. Abnormal heart rate and body temperature fluctuations can be detected. This allows symptoms to be acquired automatically from the wearable device, eliminating the need for users to input information.

[0053] The acute poisoning symptom assessment system's analysis unit can identify the cause of poisoning by taking into account geographical information and environmental data. For example, the generation AI can identify the cause of poisoning by taking into account geographical information (e.g., the temperature and humidity of the current location) in addition to the symptoms entered by the user. It can also take into account the possibility of heatstroke in hot and humid environments. It can also refer to the user's environmental data (e.g., air quality and water quality) to evaluate the possibility that specific environmental factors are related to poisoning symptoms. In this way, by taking into account geographical information and environmental data, the accuracy of identifying the cause of poisoning is improved.

[0054] In the acute poisoning symptom determination system, the analysis unit analyzes the frequency and time of symptom occurrence, and can identify the cause of poisoning that occurs during a specific time period. For example, the generation AI analyzes the frequency and time of symptom occurrence entered by the user and identifies the cause of poisoning that occurs during a specific time period. If many symptoms occur at night, the possibility of carbon monoxide poisoning during sleep can be considered. In addition, based on the frequency of symptom occurrence, the number of occurrences and incidence rate within a specific period can be evaluated to identify the cause of poisoning. This improves the accuracy of identifying the cause of poisoning by analyzing the frequency and time period of occurrence.

[0055] The acute poisoning symptom determination system's analysis unit can compare the case data of other users to identify common causes of poisoning. For example, the generation AI compares the symptoms entered by the user with the case data of other users to identify common causes of poisoning. If there are many users in the same area who complain of similar symptoms, it can identify common causes of poisoning. It can also identify common causes of poisoning by evaluating the degree of similarity of symptoms and the proximity of the onset times based on the case data of other users. This makes it possible to identify common causes of poisoning by comparing with the case data of other users.

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

[0057] Step 1: The symptom input section allows the user to input symptoms that may be indicative of poisoning. For example, the user inputs specific symptoms such as "feeling nauseous," "feeling dizzy," or "feeling confused." Step 2: In the analysis unit, the generation AI analyzes the symptoms input by the symptom input unit. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the symptoms and identify possible causes of poisoning. The generation AI may also use a multimodal generation AI to analyze combinations of symptoms. The generation AI may also recognize patterns in symptoms and match them with a database to identify the cause of poisoning. Step 3: The solution provider provides solutions based on the cause of poisoning identified by the analysis unit. For example, the generator AI suggests specific solutions such as "drink water and rest," "get some fresh air," and "consult a doctor immediately." The generator AI can also provide first aid methods based on the user's symptoms. The generator AI can also customize solutions based on past success stories. Step 4: The emergency contact information provider provides emergency contact information based on the solutions provided by the solution provider. For example, the generation AI provides contact information for the nearest hospital or emergency service. The generation AI can also provide information on the nearest medical institution or pharmacy, taking into account the user's current location. The generation AI can also send the user's symptom data to a specialist in advance, enabling a prompt response.

[0058] (Example 2) The acute poisoning symptom determination system according to an embodiment of the present invention is a system in which a user inputs symptoms that may indicate poisoning, a generation AI analyzes the symptoms, identifies the potential cause of poisoning, and provides countermeasures and emergency contact information. This allows the acute poisoning symptom determination system to quickly determine whether the user has symptoms that may indicate poisoning and provide appropriate countermeasures and emergency contact information.

[0059] An acute poisoning symptom determination system according to an embodiment includes a symptom input unit, an analysis unit, a remedy provision unit, and an emergency contact provision unit. The symptom input unit allows a user to input symptoms of possible poisoning. For example, the user inputs specific symptoms such as "feeling nauseous," "feeling dizzy," or "feeling confused." The analysis unit allows a generation AI to analyze the symptoms input by the symptom input unit. For example, the generation AI may analyze the symptoms using a text generation AI (e.g., LLM) to identify possible causes of poisoning. The generation AI may also analyze combinations of symptoms using a multimodal generation AI. The generation AI may also recognize symptom patterns and match them with a database to identify the cause of poisoning. The remedy provision unit provides remedy measures based on the cause of poisoning identified by the analysis unit. For example, the generation AI may suggest specific remedy measures such as "drink water and rest," "get some fresh air," or "consult a doctor immediately." The generation AI may also provide first aid measures based on the user's symptoms. The generation AI may also customize remedy measures based on past success stories. The emergency contact information providing unit provides emergency contact information based on the response provided by the response information providing unit. For example, the generation AI provides contact information for the nearest hospital or emergency service. The generation AI can also provide information on the nearest medical institution or pharmacy taking into account the user's current location. The generation AI can also send the user's symptom data to experts in advance to enable a prompt response. As a result, the acute poisoning symptom determination system according to the embodiment can quickly determine whether the user has symptoms that may be poisoning and provide appropriate responses and emergency contact information. For example, if the user experiences nausea or dizziness, the generation AI can immediately identify the possibility of food poisoning and provide appropriate responses. Furthermore, if the user has impaired consciousness, the generation AI can identify the possibility of carbon monoxide poisoning and quickly connect the user to an emergency medical expert. This allows the user to take prompt and appropriate action, minimizing the damage caused by poisoning.

[0060] The analysis unit can improve the accuracy of analysis by comparing symptoms with a database of past cases and identifying similar cases. For example, the analysis unit uses the generation AI to compare symptoms entered by the user, such as "nausea," "dizziness," or "loss of consciousness," with the database of past cases to identify similar cases. For example, the analysis unit can refer to the data of patients who have previously complained of similar symptoms to find commonalities. The analysis unit can also evaluate the degree of similarity of symptoms and the proximity of the time of onset based on the database of past cases to identify similar cases. The analysis unit can also improve the accuracy of analysis by taking into account the update frequency and data format of the database of past cases. In this way, the accuracy of analysis is improved by referring to the database of past cases.

[0061] The symptom input unit automatically recognizes symptoms using voice input or image analysis, eliminating the need for text input. For example, when a user vocally inputs symptoms such as "I feel nauseous" or "I feel dizzy," the generation AI automatically converts the input into text using voice recognition technology and analyzes it. For example, the voice input is converted into text in real time and used for analysis. In addition, when a user inputs symptoms using an image, the generation AI automatically recognizes the symptoms using image analysis technology and converts them into text. For example, when a user uploads an image of a rash, the generation AI analyzes the image and identifies the type of rash. The symptom input unit can also improve the accuracy of symptom input using voice recognition algorithms or image recognition algorithms. This eliminates the need for text input by using voice input or image analysis.

[0062] The analysis unit uses the emotion estimation function to analyze the user's emotional state at the time of input and can take into account the impact of stress and anxiety on symptoms. For example, when a user inputs symptoms, the analysis unit uses the emotion estimation function to analyze the user's emotional state using the generation AI and consider the impact of stress and anxiety on the symptoms. For example, the analysis unit analyzes facial expressions and tone of voice at the time of input and calculates an emotion score. The analysis unit can also use the emotion estimation function to evaluate the user's stress level and anxiety and reflect this in the symptom analysis. The analysis unit can also use the emotion estimation function to monitor the user's emotional state in real time and continuously evaluate the impact of stress and anxiety on the symptoms. In this way, the emotion estimation function can take into account the impact of stress and anxiety on the symptoms.

[0063] The symptom input unit can automatically acquire symptoms from wearable devices such as smartwatches and fitness trackers. For example, when a user wears a smartwatch or fitness tracker, the generative AI automatically acquires data such as heart rate, body temperature, and activity level from the device and uses it to analyze symptoms. For example, it can detect abnormal heart rate and body temperature fluctuations. The symptom input unit can also evaluate the user's health status based on the data acquired from the wearable device and reflect this in symptom analysis. The symptom input unit can also utilize the functions of the smartwatch or fitness tracker to monitor the user's exercise level and sleep patterns and use them to analyze symptoms. This automatically acquiring symptoms from the wearable device reduces the user's effort in inputting information.

[0064] The analysis unit can refer to the user's lifestyle habits and dietary history to perform a more detailed analysis. For example, when a user inputs symptoms, the generation AI refers to the user's lifestyle habits data (e.g., sleep time and amount of exercise) and uses this to analyze the symptoms. For example, it considers the possibility that dizziness is caused by lack of sleep. The analysis unit can also refer to the user's dietary history to evaluate the possibility that a specific food is related to poisoning symptoms. The analysis unit can also identify the cause of symptoms based on the user's lifestyle habits and dietary history, improving the accuracy of the analysis. This makes it possible to perform a more detailed analysis by referring to lifestyle habits and dietary history.

[0065] The analysis unit can use the emotion estimation function to analyze the user's emotions in real time when they input their symptoms and provide positive feedback. For example, when a user inputs their symptoms, the analysis unit uses the emotion estimation function to have the generation AI analyze the user's emotional state in real time and provide positive feedback. For example, the analysis unit can analyze facial expressions and tone of voice when inputting and display an encouraging message. The analysis unit can also use the emotion estimation function to evaluate the user's emotional state and suggest ways to relax if stress or anxiety is high. The analysis unit can also use the emotion estimation function to monitor the user's emotional state and provide continuous positive feedback. In this way, the emotion estimation function can provide positive feedback to the user.

[0066] The analysis unit can identify the cause of poisoning by taking into account geographical information and environmental data. For example, the analysis unit identifies the cause of poisoning by taking into account geographical information (e.g., the temperature and humidity of the current location) in addition to the symptoms input by the generation AI. For example, it considers the possibility of heatstroke in a hot and humid environment. The analysis unit can also refer to the user's environmental data (e.g., air quality and water quality) to evaluate the possibility that specific environmental factors are related to the poisoning symptoms. The analysis unit can also identify region-specific causes of poisoning based on geographical information and environmental data, improving the accuracy of the analysis. In this way, by taking into account geographical information and environmental data, the accuracy of identifying the cause of poisoning is improved.

[0067] The analysis unit can analyze the frequency and time periods of symptom occurrence and identify the cause of poisoning that occurs during a specific time period. For example, the generation AI can analyze the frequency and time periods of symptom occurrence entered by the user and identify the cause of poisoning that occurs during a specific time period. For example, if many symptoms occur at night, the analysis unit can consider the possibility of carbon monoxide poisoning during sleep. The analysis unit can also evaluate the number of occurrences and incidence rate within a specific period based on the frequency of symptom occurrence and identify the cause of poisoning. The analysis unit can also evaluate the cause of poisoning associated with a specific time period based on the time period of symptom occurrence, improving the accuracy of the analysis. In this way, by analyzing the frequency and time periods of occurrence, the accuracy of identifying the cause of poisoning is improved.

[0068] The analysis unit can compare the case data of other users and identify common causes of poisoning. For example, the analysis unit compares the symptoms entered by the generation AI with the case data of other users to identify common causes of poisoning. For example, if there are many users in the same area who complain of similar symptoms, a common cause of poisoning can be identified. The analysis unit can also evaluate the degree of similarity of symptoms and the proximity of the onset times based on the case data of other users to identify common causes of poisoning. The analysis unit can also refer to the case data of other users to evaluate whether a specific cause of poisoning has a widespread impact. This makes it possible to identify common causes of poisoning by comparing with the case data of other users.

[0069] The analysis unit can refer to the user's past health checkup data and medical history to more accurately identify the cause of poisoning. For example, the generation AI can refer to the user's past health checkup data to identify the cause of poisoning. For example, if the user has had a specific allergy in the past, it can consider the possibility that the allergy is related to the poisoning symptoms. The analysis unit can also refer to the user's medical history to identify the cause of poisoning based on past diagnosis results and prescription drug history. The analysis unit can also evaluate whether a specific health condition is affecting the poisoning symptoms based on the user's health checkup data and medical history. This allows the cause of poisoning to be identified more accurately by referring to past health checkup data and medical history.

[0070] The analysis unit can use the emotion estimation function to analyze the user's emotional state and identify emotionally related causes of addiction. For example, the analysis unit uses the emotion estimation function to analyze the user's emotional state and identify emotionally related causes of addiction. For example, if stress or anxiety is high, it considers the possibility that psychological factors are affecting the addiction symptoms. The analysis unit can also use the emotion estimation function to evaluate the user's emotional state and identify emotionally related causes of addiction. The analysis unit can also use the emotion estimation function to monitor the user's emotional state and continuously evaluate emotional factors. In this way, the emotion estimation function can be used to identify emotionally related causes of addiction.

[0071] The solution providing unit can customize the solution to the user's symptoms based on past success cases. For example, the solution providing unit uses a generation AI to customize the solution to the user's symptoms based on past success cases. For example, the solution providing unit can refer to what solutions have been used by patients with similar symptoms in the past and suggest the optimal solution. The solution providing unit can also provide first aid methods according to the user's symptoms based on past success cases. The solution providing unit can also continuously improve the solution to the user's symptoms based on past success cases. In this way, by customizing the solution based on past success cases, more effective solutions can be provided.

[0072] The emergency contact providing unit can provide information on the nearest medical institutions and pharmacies taking into account the user's current location. For example, the generation AI provides information on the nearest medical institutions and pharmacies based on the user's current location. For example, the generation AI obtains the user's location information and displays the contact information for the nearest hospitals and pharmacies. The emergency contact providing unit can also provide emergency response guidelines for each region based on the user's current location. The emergency contact providing unit can also provide the contact information for the nearest emergency service based on the user's current location. In this way, information on the nearest medical institutions and pharmacies can be provided by taking into account the current location.

[0073] The coping method providing unit can use the emotion estimation function to provide coping methods according to the user's emotional state and give a sense of security. For example, the generation AI uses the emotion estimation function to provide coping methods according to the user's emotional state. For example, if stress or anxiety is high, the coping method providing unit can display relaxation tips or messages that give a sense of security. The coping method providing unit can also use the emotion estimation function to evaluate the user's emotional state and provide coping methods that give a sense of security. The coping method providing unit can also use the emotion estimation function to monitor the user's emotional state and continuously provide coping methods that give a sense of security. In this way, by using the emotion estimation function, coping methods that give a sense of security can be provided to the user.

[0074] The solution providing unit can visually provide solutions to the user's symptoms using videos and images. For example, the solution providing unit uses a generation AI to visually provide solutions to the user's symptoms using videos and images. For example, first aid methods can be shown in videos so that the user can understand them visually. The solution providing unit can also visually provide solutions to the user's symptoms using images. The solution providing unit can also provide solutions in a format that is easy for the user to understand, taking into account the resolution and length of the videos and images. In this way, providing solutions visually using videos and images makes it easier for the user to understand the solutions.

[0075] The solution providing unit can improve the solution based on feedback from other users. For example, the solution providing unit uses a generation AI to improve the solution to a user's symptoms based on feedback from other users. For example, the solution providing unit can refer to feedback from users who have had similar symptoms in the past and suggest the optimal solution. The solution providing unit can also evaluate and improve the effectiveness of the solution based on feedback from other users. The solution providing unit can also collect user feedback and reflect it in improving the solution. In this way, more effective solutions can be provided by improving the solution based on feedback from other users.

[0076] The coping method providing unit can use the emotion estimation function to analyze the user's emotional state and provide coping methods that provide emotional relief. For example, the generation AI uses the emotion estimation function to analyze the user's emotional state and provide coping methods that provide emotional relief. For example, if stress or anxiety is high, the coping method providing unit can display relaxation techniques or messages that provide a sense of security. The coping method providing unit can also use the emotion estimation function to evaluate the user's emotional state and provide coping methods that provide a sense of security. The coping method providing unit can also use the emotion estimation function to monitor the user's emotional state and continuously provide coping methods that provide a sense of security. In this way, by using the emotion estimation function, coping methods that provide a sense of security can be provided to the user.

[0077] The emergency contact providing unit can automatically select and connect the most appropriate specialist depending on the user's symptoms. For example, the generation AI automatically selects and connects the most appropriate specialist depending on the user's symptoms. For example, if the user complains of loss of consciousness, a neurology specialist will be selected. The emergency contact providing unit can also collect opinions from multiple specialists depending on the user's symptoms and provide the most appropriate course of action. The emergency contact providing unit can also send the user's symptom data to specialists in advance, enabling a prompt response. This allows the most appropriate specialist to be automatically selected and connected, enabling a prompt response.

[0078] The emergency contact providing unit can send the user's symptom data to experts in advance, enabling a prompt response. For example, the generation AI can send the user's symptom data to experts in advance, enabling a prompt response. For example, if the user complains of loss of consciousness, the data can be sent to a neurology specialist. The emergency contact providing unit can also prepare experts to respond promptly based on the user's symptom data. The emergency contact providing unit can also continuously monitor the user's symptom data and provide information to experts in real time. In this way, sending symptom data in advance enables prompt response by experts.

[0079] The emergency contact providing unit can collect opinions from multiple experts depending on the user's symptoms and provide the optimal solution. For example, the generation AI can collect opinions from multiple experts depending on the user's symptoms and provide the optimal solution. For example, if the user complains of loss of consciousness, it can collect opinions from neurology and emergency medical experts. The emergency contact providing unit can also provide the optimal solution for the user's symptoms based on the opinions from multiple experts. The emergency contact providing unit can also continuously collect expert opinions and reflect them in improving the solution. In this way, the optimal solution can be provided by collecting opinions from multiple experts.

[0080] The emergency contact providing unit can use the emotion estimation function to analyze the user's emotional state and select an expert who can provide emotional comfort. For example, the generation AI in the emergency contact providing unit uses the emotion estimation function to analyze the user's emotional state and select an expert who can provide emotional comfort. For example, if the user is highly stressed or anxious, an expert who can respond in a relaxed manner is selected. The emergency contact providing unit can also use the emotion estimation function to evaluate the user's emotional state and select an expert who can provide comfort. The emergency contact providing unit can also use the emotion estimation function to monitor the user's emotional state and continuously select an expert who can provide comfort. In this way, the emotion estimation function can be used to select an expert who can provide emotional comfort.

[0081] The emergency response guideline providing unit can customize emergency response guidelines according to the user's symptoms based on past success cases. For example, the emergency response guideline providing unit customizes emergency response guidelines according to the user's symptoms based on past success cases using a generation AI. For example, the emergency response guideline providing unit provides optimal guidelines by referring to the responses given to patients with similar symptoms in the past. The emergency response guideline providing unit can also provide first aid procedures according to the user's symptoms based on past success cases. The emergency response guideline providing unit can also continuously improve the emergency response guidelines for the user's symptoms based on past success cases. In this way, customizing emergency response guidelines based on past success cases enables more effective responses.

[0082] The emergency response guideline providing unit can provide emergency response guidelines for each region, taking into account the user's current location. For example, the generation AI provides emergency response guidelines for each region based on the user's current location. For example, the generation AI obtains the user's location information and displays contact information for medical institutions and emergency services in that region. The emergency response guideline providing unit can also provide region-specific emergency response guidelines based on the user's current location. The emergency response guideline providing unit can also continuously update the emergency response guidelines for each region, based on the user's current location. This makes it possible to provide emergency response guidelines for each region by taking into account the current location.

[0083] The emergency response guideline providing unit can provide emergency response guidelines according to the user's emotional state using the emotion estimation function, thereby giving a sense of security. For example, the generation AI can use the emotion estimation function to provide emergency response guidelines according to the user's emotional state. For example, if stress or anxiety is high, the emergency response guideline providing unit can display relaxation tips or a message that gives a sense of security. The emergency response guideline providing unit can also use the emotion estimation function to evaluate the user's emotional state and provide emergency response guidelines that give a sense of security. The emergency response guideline providing unit can also use the emotion estimation function to monitor the user's emotional state and continuously provide emergency response guidelines that give a sense of security. In this way, by using the emotion estimation function, emergency response guidelines that give a sense of security can be provided to the user.

[0084] The emergency response guideline providing unit can visually provide emergency response guidelines according to the user's symptoms using videos or images. For example, the emergency response guideline providing unit visually provides emergency response guidelines according to the user's symptoms using videos or images by using a generation AI. For example, first aid methods are shown in videos so that the user can visually understand. The emergency response guideline providing unit can also visually provide emergency response guidelines according to the user's symptoms using images. The emergency response guideline providing unit can also provide emergency response guidelines in a format that is easy for the user to understand, taking into account the resolution and length of the videos or images. In this way, visual provision using videos or images makes it easier for the user to understand the emergency response guidelines.

[0085] The emergency response guideline providing unit can improve the emergency response guidelines based on feedback from other users. For example, the emergency response guideline providing unit improves emergency response guidelines that the generation AI creates based on the user's symptoms based on feedback from other users. For example, the emergency response guideline providing unit refers to feedback from users who have had similar symptoms in the past and provides optimal guidelines. The emergency response guideline providing unit can also evaluate and improve the effectiveness of the emergency response guidelines based on feedback from other users. The emergency response guideline providing unit can also collect user feedback and reflect it in improving the emergency response guidelines. In this way, by improving the emergency response guidelines based on feedback from other users, more effective responses are possible.

[0086] The emergency response guideline providing unit can use the emotion estimation function to analyze the user's emotional state and provide emergency response guidelines that provide emotional relief. For example, the generation AI can use the emotion estimation function to analyze the user's emotional state and provide emergency response guidelines that provide emotional relief. For example, if stress or anxiety is high, the emergency response guideline providing unit can display relaxation techniques and messages that provide a sense of security. The emergency response guideline providing unit can also use the emotion estimation function to evaluate the user's emotional state and provide emergency response guidelines that provide a sense of security. The emergency response guideline providing unit can also use the emotion estimation function to monitor the user's emotional state and continuously provide emergency response guidelines that provide a sense of security. In this way, by using the emotion estimation function, emergency response guidelines that provide a sense of security can be provided.

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

[0088] When a user inputs their symptoms into the acute poisoning symptom assessment system, the symptom input unit automatically recognizes them using voice input and image analysis, eliminating the need for text input. For example, if a user inputs symptoms such as "I feel nauseous" or "I feel dizzy," the generation AI automatically converts them into text using voice recognition technology and analyzes them. In addition, if a user uploads an image of a rash, the generation AI can perform image analysis to identify the type of rash. This eliminates the need for text input by using voice input and image analysis.

[0089] The acute poisoning symptom determination system allows the analysis unit to refer to the user's lifestyle and dietary history to perform a more detailed analysis. For example, when a user inputs their symptoms, the generation AI refers to the user's lifestyle data (such as sleep time and amount of exercise) and uses this to analyze the symptoms. It can take into account the possibility that dizziness is caused by lack of sleep. It can also refer to the user's dietary history to evaluate the possibility that specific foods are related to poisoning symptoms. This allows for a more detailed analysis by referring to lifestyle and dietary history.

[0090] In the acute poisoning symptom determination system, the analysis unit uses an emotion estimation function to analyze the user's emotional state at the time of input and can consider the impact of stress and anxiety on symptoms. For example, the analysis unit analyzes facial expressions and voice tone at the time of input and calculates an emotion score. The system can also evaluate the user's stress level and anxiety level and reflect them in the symptom analysis. In this way, the emotion estimation function can take into account the impact of stress and anxiety on symptoms.

[0091] The acute poisoning symptom determination system's symptom input unit can automatically acquire symptoms from wearable devices such as smartwatches and fitness trackers. For example, if a user is wearing a smartwatch or fitness tracker, the generation AI automatically acquires data such as heart rate, body temperature, and activity level from these devices and uses this data to analyze symptoms. Abnormal heart rate and body temperature fluctuations can be detected. This allows symptoms to be acquired automatically from the wearable device, eliminating the need for users to input information.

[0092] The acute poisoning symptom assessment system uses the emotion estimation function to analyze the emotions of the user when they input their symptoms in real time, and can provide positive feedback. For example, it can analyze facial expressions and tone of voice when inputting and display encouraging messages. It can also evaluate the user's emotional state and suggest ways to relax if they are experiencing high levels of stress or anxiety. In this way, the emotion estimation function can provide positive feedback to the user.

[0093] The acute poisoning symptom assessment system's analysis unit can identify the cause of poisoning by taking into account geographical information and environmental data. For example, the generation AI can identify the cause of poisoning by taking into account geographical information (e.g., the temperature and humidity of the current location) in addition to the symptoms entered by the user. It can also take into account the possibility of heatstroke in hot and humid environments. It can also refer to the user's environmental data (e.g., air quality and water quality) to evaluate the possibility that specific environmental factors are related to poisoning symptoms. In this way, by taking into account geographical information and environmental data, the accuracy of identifying the cause of poisoning is improved.

[0094] In the acute poisoning symptom determination system, the analysis unit analyzes the frequency and time of symptom occurrence, and can identify the cause of poisoning that occurs during a specific time period. For example, the generation AI analyzes the frequency and time of symptom occurrence entered by the user and identifies the cause of poisoning that occurs during a specific time period. If many symptoms occur at night, the possibility of carbon monoxide poisoning during sleep can be considered. In addition, based on the frequency of symptom occurrence, the number of occurrences and incidence rate within a specific period can be evaluated to identify the cause of poisoning. This improves the accuracy of identifying the cause of poisoning by analyzing the frequency and time period of occurrence.

[0095] In the acute poisoning symptom determination system, the analysis unit uses the emotion estimation function to analyze the user's emotional state and identify emotionally related causes of poisoning. For example, the generation AI uses the emotion estimation function to analyze the user's emotional state and identify emotionally related causes of poisoning. If stress or anxiety is high, it can take into account the possibility that psychological factors are affecting poisoning symptoms. This makes it possible to identify emotionally related causes of poisoning using the emotion estimation function.

[0096] The acute poisoning symptom determination system's analysis unit can compare the case data of other users to identify common causes of poisoning. For example, the generation AI compares the symptoms entered by the user with the case data of other users to identify common causes of poisoning. If there are many users in the same area who complain of similar symptoms, it can identify common causes of poisoning. It can also identify common causes of poisoning by evaluating the degree of similarity of symptoms and the proximity of the onset times based on the case data of other users. This makes it possible to identify common causes of poisoning by comparing with the case data of other users.

[0097] In the acute poisoning symptom determination system, the remedy provision unit uses the emotion estimation function to provide a remedy that matches the user's emotional state, giving them a sense of security. For example, the generation AI uses the emotion estimation function to provide a remedy that matches the user's emotional state. If stress or anxiety is high, it can display a message that tells users how to relax or gives them a sense of security. In this way, the emotion estimation function can provide a remedy that gives the user a sense of security.

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

[0099] Step 1: The symptom input section allows the user to input symptoms that may be indicative of poisoning. For example, the user inputs specific symptoms such as "feeling nauseous," "feeling dizzy," or "feeling confused." Step 2: In the analysis unit, the generation AI analyzes the symptoms input by the symptom input unit. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the symptoms and identify possible causes of poisoning. The generation AI may also use a multimodal generation AI to analyze combinations of symptoms. The generation AI may also recognize patterns in symptoms and match them with a database to identify the cause of poisoning. Step 3: The solution provider provides solutions based on the cause of poisoning identified by the analysis unit. For example, the generator AI suggests specific solutions such as "drink water and rest," "get some fresh air," and "consult a doctor immediately." The generator AI can also provide first aid methods based on the user's symptoms. The generator AI can also customize solutions based on past success stories. Step 4: The emergency contact information provider provides emergency contact information based on the solutions provided by the solution provider. For example, the generation AI provides contact information for the nearest hospital or emergency service. The generation AI can also provide information on the nearest medical institution or pharmacy, taking into account the user's current location. The generation AI can also send the user's symptom data to a specialist in advance, enabling a prompt response.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0121] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

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

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

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0141] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0159] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

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

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a symptom input section in which a user inputs symptoms of possible poisoning; an analysis unit that analyzes the symptoms input by the symptom input unit; a countermeasure providing unit that provides a countermeasure based on the cause of poisoning identified by the analysis unit; an emergency contact point providing unit that provides an emergency contact point based on the solution provided by the solution providing unit; A system characterized by:

2. The symptom input unit The symptoms are automatically recognized using voice input and image analysis, eliminating the need for text input.

2. The system of claim 1.

3. The analysis unit Conduct a more detailed analysis by looking at the user's lifestyle and dietary history 2. The system of claim 1.

4. The solution providing unit Customize how users address the symptoms based on past success stories 2. The system of claim 1.

5. The emergency contact information providing unit Automatically select and connect with the most suitable specialist based on the user's symptoms.

2. The system of claim 1.

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