System

Abstract

To provide a system according to an embodiment which help patients whose diseases are not serious enough to require them to see doctors from purchasing commercial medicines to treatment of symptoms.SOLUTION: The system according to an embodiment includes a reception unit, a selection unit, a provision unit, a recording unit, and a monitoring unit. The reception unit receives an input of symptom. The selection unit analyzes the symptom received by the reception unit and selects a commercial medicine. The provision unit provides a manner of usage or precautions of the commercial medicine selected by the selection unit. The recording unit records process of the symptom. The monitoring unit analyzes the data recorded by the recording unit and monitors the process of the treatment.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately select appropriate over-the-counter medications for symptoms that do not require a visit to the hospital, and does not provide consistent support for their use and progress in recovery.

[0005] The system of the embodiment aims to support people with symptoms that are not serious enough to require a visit to the hospital, from purchasing over-the-counter medications to curing their symptoms. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a selection unit, a provision unit, a recording unit, and a monitoring unit. The reception unit receives input of symptoms. The selection unit analyzes the symptoms received by the reception unit and selects an over-the-counter drug. The provision unit provides usage instructions and precautions for the over-the-counter drug selected by the selection unit. The recording unit records the progress of the symptoms. The monitoring unit analyzes the data recorded by the recording unit and monitors the progress of healing. Effect of the Invention

[0007] The system according to the embodiment can support people with symptoms that do not require a visit to the hospital from purchasing over-the-counter medications to curing the symptoms. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The support system according to the embodiment of the present invention is a system that supports people with mild symptoms that are not serious enough to go to the hospital, from purchasing over-the-counter medicines to curing the symptoms. In this support system, the user inputs their symptoms, and the AI ​​analyzes the symptoms and suggests an appropriate over-the-counter medicine. The user purchases the suggested over-the-counter medicine and receives usage instructions and precautions from the AI. In addition, the user records the progress of the symptoms, and the AI ​​analyzes the data to monitor the progress of the cure. If necessary, additional advice and suggestions for other over-the-counter medicines are provided. For example, the user inputs specific symptoms such as "sore throat" or "mild headache". This information is input into the AI. The AI ​​then analyzes the input symptoms and suggests an appropriate over-the-counter medicine. The AI ​​selects the most suitable over-the-counter medicine based on the symptoms and provides the information to the user. For example, for "sore throat", a lozenge or spray that relieves the sore throat is suggested. The user purchases the suggested over-the-counter medicine and receives usage instructions and precautions from the AI. The AI ​​provides detailed explanations of the correct usage and precautions of the over-the-counter medicine, supporting the user to use the medicine safely. For example, it explains how to use the lozenge, the number of times it can be used per day, and side effects to be aware of. In addition, users record the progress of their symptoms, and the AI ​​analyzes the data to monitor the progress of healing. Users record changes in symptoms and the effects of medication on a daily basis and input the data into the AI. The AI ​​analyzes the collected data and evaluates the progress of healing. For example, it evaluates how much the sore throat has improved and how much the headache has been alleviated. If necessary, it provides additional advice and suggests other over-the-counter medications. Depending on the progress of healing, the AI ​​provides additional advice and suggests other over-the-counter medications. For example, if the sore throat does not improve, it suggests a different type of lozenge or spray. In addition, if the symptoms worsen, it advises the user to see a doctor. This system provides consistent support for people with mild symptoms that do not require a trip to the hospital, from purchasing over-the-counter medications to healing the symptoms. Users can improve their symptoms by selecting over-the-counter medications that are appropriate for their symptoms and using them correctly. In addition, AI monitoring and advice allow users to understand the progress of healing and take appropriate measures.This allows the support system to provide consistent support from the user's input of symptoms to selecting over-the-counter medications, providing instructions on how to use them, recording symptoms, and monitoring the progress of recovery.

[0029] The support system according to the embodiment includes a reception unit, a selection unit, a provision unit, a recording unit, and a monitoring unit. The reception unit provides an interface for the user to input his / her own symptoms. For example, the user can input specific symptoms such as "sore throat" or "mild headache". The reception unit can also use AI to analyze the user's input and generate data for suggesting appropriate over-the-counter medicines. The selection unit analyzes the symptoms received by the reception unit and selects appropriate over-the-counter medicines. The selection unit uses AI to select the optimal over-the-counter medicine based on the symptoms and provides the information to the user. For example, the selection unit suggests a lozenge or spray that relieves the pain in the throat for "sore throat". The provision unit provides the user with instructions on how to use the over-the-counter medicine selected by the selection unit and points to note. The provision unit uses AI to explain in detail the correct way to use the over-the-counter medicine and points to note, and supports the user to use the medicine safely. For example, the provision unit explains how to use the lozenge, the number of times it can be used per day, and side effects to be aware of. The recording unit provides an interface for the user to record the progress of the symptoms. The recording unit can also use AI to record changes in the user's symptoms and the effects of the medicine on a daily basis and analyze the data. For example, the recording unit evaluates how much the sore throat has improved and how much the headache has been alleviated. The monitoring unit analyzes the data recorded by the recording unit and monitors the progress of healing. The monitoring unit analyzes the data collected using AI and evaluates the progress of healing. For example, the monitoring unit evaluates how much the sore throat has improved and how much the headache has been alleviated. As a result, the support system according to the embodiment can consistently support the user's input of symptoms, selection of over-the-counter medicine, provision of usage instructions, recording of symptoms, and monitoring of the progress of healing.

[0030] The reception unit provides an interface for users to input their symptoms. For example, users can input specific symptoms such as "sore throat" or "mild headache." The reception unit can also use AI to analyze the user's input and generate data to suggest appropriate over-the-counter medications. Specifically, the user uses a smartphone or computer to access a dedicated application or website and inputs symptoms. The interface is designed to make it easy for users to input symptoms, and supports not only text input, but also a format in which users can select from multiple options and voice input. The AI ​​uses natural language processing technology to analyze the user's input and determine the type and severity of the symptoms. For example, if "sore throat" is input, the AI ​​will determine that the symptom is likely to be an early symptom of a cold or flu, and generate data to suggest appropriate over-the-counter medications. Even if the user inputs multiple symptoms, the AI ​​will analyze each symptom individually and make a comprehensive judgment. This allows the reception unit to accurately grasp the user's symptoms and smoothly provide data to the selection unit, which is the next step. In addition, the reception unit can also take into account the user's past input history and information on health status. For example, if there is a history of using over-the-counter medication for the same symptoms in the past, more appropriate suggestions can be made based on that information. This enables the reception unit to respond flexibly to the individual circumstances of each user, thereby improving the accuracy and reliability of the entire system.

[0031] The selection unit analyzes the symptoms received by the reception unit and selects an appropriate over-the-counter drug. The selection unit uses AI to select the most suitable over-the-counter drug based on the symptoms and provides the information to the user. Specifically, the AI ​​refers to a large-scale pharmaceutical database and identifies the over-the-counter drug that is most suitable for the user's symptoms. For example, for "sore throat," it suggests a lozenge or spray that relieves the sore throat. The AI ​​comprehensively considers information such as the type and severity of the symptoms, the user's age, gender, and medical history, and selects the most suitable over-the-counter drug. The selection unit also considers information on the ingredients, effects, and side effects of the drug. For example, if the user has an allergy, it preferentially selects a drug that does not contain that ingredient. In addition, the selection unit compares multiple over-the-counter drugs and suggests the most effective and safe drug. When providing the user with information on the selected over-the-counter drug, the selection unit also provides a detailed explanation of the drug's effects, usage, and side effects. This allows the user to use it as a reference when choosing the over-the-counter drug that is best for them. The selection unit can also collect user feedback and continuously improve the accuracy of the selection algorithm. For example, the system collects information on the effects and side effects of a suggested drug after the user has taken it, and adjusts the selection algorithm based on that data. This allows the selection unit to always select highly accurate drugs based on the latest information, supporting the user's health management.

[0032] The provision unit provides the user with the usage method and precautions for the over-the-counter drug selected by the selection unit. The provision unit uses AI to explain in detail the correct usage method and precautions for the over-the-counter drug, supporting the user to use the drug safely. Specifically, the provision unit provides the usage method of the selected over-the-counter drug in the form of text, images, videos, etc. For example, it provides detailed explanations on how to use the lozenge, the number of times to use it per day, and side effects to be aware of. AI can customize the way information is provided according to the user's level of understanding and interest. For example, it provides explanations using videos and illustrations to users who prefer visual information, and explains in detailed text to users who prefer text information. The provision unit also provides general precautions regarding the use of the drug and additional advice for specific symptoms. For example, it introduces ways to improve lifestyle habits to relieve a sore throat and relaxation techniques to relieve headaches. In addition, the provision unit also has a feedback function to check whether the user is using the drug correctly. For example, when a user inputs a question about how to use the drug, AI immediately provides an answer. The provision unit can also collect information on the effects and side effects after the user uses the drug, and improve the content provided based on that data. This enables the provider to always provide the user with the latest and most accurate information, supporting the safe use of medicines.

[0033] The recording unit provides an interface for users to record the progress of symptoms. The recording unit can also use AI to record changes in the user's symptoms and the effects of medication on a daily basis and analyze the data. Specifically, the user uses a smartphone or computer to access a dedicated application or website and enter changes in symptoms and medication usage. The interface is designed to make it easy for users to enter data, and supports not only text input, but also multiple choice format and voice input. The AI ​​analyzes the data entered by the user and evaluates the improvement of symptoms and the effectiveness of medication. For example, it evaluates how much the sore throat has improved and how much the headache has been alleviated. The recording unit can also visually display changes in symptoms and the effects of medication in graphs and charts based on the data entered by the user. This allows the user to understand their health condition at a glance. In addition, the recording unit can also predict future symptoms and propose measures based on the user's past data. For example, it can predict the risk of symptoms occurring in a specific season or environmental conditions based on past data and propose preventive measures. This allows the recording unit to support the user's health management and contribute to early detection and prevention of symptoms.

[0034] The monitoring unit analyzes the data recorded by the recording unit and monitors the progress of healing. The monitoring unit analyzes the data collected using AI and evaluates the progress of healing. Specifically, AI analyzes data on changes in the user's symptoms and the effects of the medicine to evaluate the progress of healing in real time. For example, it evaluates how much the sore throat has improved and how much the headache has been alleviated. The monitoring unit can also visually display the progress of healing in graphs and charts based on the user's data. This allows the user to grasp their healing status at a glance. Furthermore, the monitoring unit can issue a warning to the user if there is abnormal data or if the progress of healing is delayed. For example, if symptoms do not improve or new symptoms occur, the monitoring unit notifies the user to see a doctor. This allows the monitoring unit to continuously monitor the user's health condition and detect abnormalities early. The monitoring unit can also provide advice and support according to the progress of healing based on the user's data. For example, if healing is progressing smoothly, it advises the user to continue using the medicine, and if healing is delayed, it suggests additional measures. This enables the monitoring unit to support the user's health management and promote the progress of healing.

[0035] The reception unit can provide options when the user inputs symptoms. The reception unit, for example, provides options when the user inputs symptoms. For example, the user can select specific symptoms such as "sore throat" or "mild headache." This makes it easier for the user to input symptoms. Some or all of the above-mentioned processing in the reception unit may be performed using AI or may be performed without using AI. For example, the reception unit can input the symptoms selected by the user to AI, and the AI ​​can analyze the information and suggest appropriate over-the-counter medicines.

[0036] The selection unit can select an over-the-counter drug based on past data and the user's health condition. The selection unit selects an over-the-counter drug based on, for example, past data and the user's health condition. For example, the selection unit proposes an optimal over-the-counter drug in consideration of the user's past symptom data and medication history. This makes it possible to propose an optimal over-the-counter drug according to the user's health condition. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input past data and the user's health condition into AI, and the AI ​​can analyze the information to propose an appropriate over-the-counter drug.

[0037] The recording unit can provide an interface for recording the progress of symptoms. The recording unit, for example, provides an interface for recording the progress of symptoms. For example, the recording unit allows the user to record changes in symptoms and the effects of medication on a daily basis. This allows the user to easily record the progress of symptoms. Some or all of the above-mentioned processing in the recording unit may be performed using AI or may be performed without using AI. For example, the recording unit can input data recorded by the user into an AI, and the AI ​​can analyze the information to evaluate the progress of healing.

[0038] The recording unit can provide a reminder function that periodically prompts the user to record symptoms. The recording unit, for example, provides a reminder function that periodically prompts the user to record symptoms. For example, the recording unit periodically transmits a notification to the user that prompts the user to record symptoms. This allows the user to record symptoms without forgetting. Some or all of the above-mentioned processing in the recording unit may be performed using AI, or may be performed without using AI. For example, the recording unit can input the reminder function to AI, and the AI ​​can analyze the information and send a notification at an appropriate time.

[0039] The monitoring unit can provide additional advice or suggest other over-the-counter drugs according to the progress of healing. The monitoring unit, for example, provides additional advice or suggests other over-the-counter drugs according to the progress of healing. For example, the monitoring unit suggests a different type of lozenge or spray if the sore throat does not improve. Also, if the symptoms worsen, the monitoring unit advises the user to see a doctor. This allows the user to receive appropriate advice and suggestions for over-the-counter drugs according to the progress of healing. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can input the progress of healing into AI, and the AI ​​can analyze the information to provide appropriate advice and suggestions for over-the-counter drugs.

[0040] The reception unit can analyze the user's past symptom input history and select the optimal input method. The reception unit, for example, analyzes the user's past symptom input history and selects the optimal input method. For example, the reception unit automatically displays symptoms that the user frequently inputs in the past as candidates. In addition, the reception unit preferentially suggests input methods (such as voice and text) that the user has used in the past. Furthermore, symptoms to be used in a specific time period are predicted and suggested from the user's past input history. This makes it possible to provide the optimal input method based on the user's past history. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the past symptom input history to AI, and the AI ​​can analyze the information and suggest the optimal input method.

[0041] The reception unit can customize the input field based on the user's current living situation and areas of interest when inputting symptoms. For example, the reception unit customizes the input field based on the user's current living situation and areas of interest when inputting symptoms. For example, when the user is at work, a field that can be input in a short time is provided. Also, when the user is highly interested in health, a detailed symptom input field is provided. Furthermore, when the user is traveling, a symptom input field related to the environment of the travel destination is provided. This makes it possible to provide an input field according to the user's living situation and areas of interest. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest into AI, and the AI ​​can analyze the information to provide an optimal input field.

[0042] The reception unit can, when inputting symptoms, preferentially input highly relevant symptoms in consideration of the geographical location information of the user. For example, when inputting symptoms, the reception unit can, when inputting symptoms, preferentially input highly relevant symptoms in consideration of the geographical location information of the user. For example, when the user is at high altitude, the reception unit can preferentially input symptoms related to altitude sickness. Also, when the user is in an urban area, the reception unit can preferentially input symptoms specific to the city (e.g., allergies). Furthermore, when the user is traveling, the reception unit can preferentially input symptoms related to the environment of the travel destination. This makes it possible to preferentially input highly relevant symptoms based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI, and the AI ​​can analyze the information and suggest optimal symptoms.

[0043] The reception unit can analyze the user's social media activity at the time of symptom input and input related symptoms. The reception unit can analyze the user's social media activity at the time of symptom input and input related symptoms. For example, when a user posts "tired" on social media, the reception unit can input symptoms related to fatigue. Furthermore, when a user posts "headache", the reception unit can input symptoms related to headache. Furthermore, when a user posts "cold", the reception unit can input symptoms related to cold. In this way, related symptoms can be input based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity to AI, and the AI ​​can analyze the information and suggest optimal symptoms.

[0044] The selection unit can adjust the level of detail of the proposal based on the importance of the symptoms when selecting an over-the-counter drug. For example, the selection unit adjusts the level of detail of the proposal based on the importance of the symptoms when selecting an over-the-counter drug. For example, the selection unit proposes a detailed over-the-counter drug for a symptom of high importance. Also, for a symptom of low importance, the selection unit proposes a concise over-the-counter drug. Furthermore, for a symptom of medium importance, the selection unit proposes an over-the-counter drug with a moderate level of detail. This makes it possible to propose a detailed over-the-counter drug according to the importance of the symptom. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the importance of the symptoms to AI, and the AI ​​can analyze the information to provide an appropriate level of detail for the proposal.

[0045] The selection unit can apply different selection algorithms depending on the symptom category when selecting an over-the-counter drug. For example, the selection unit applies different selection algorithms depending on the symptom category when selecting an over-the-counter drug. For example, the selection unit applies a selection algorithm specialized for the respiratory system to symptoms of the respiratory system. Also, the selection unit applies a selection algorithm specialized for the digestive system to symptoms of the digestive system. Furthermore, the selection unit applies a selection algorithm specialized for the nervous system to symptoms of the nervous system. This makes it possible to select an optimal over-the-counter drug according to the symptom category. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the symptom category to AI, and the AI ​​can analyze the information and apply an appropriate selection algorithm.

[0046] The selection unit can determine the priority of the proposal based on the time of onset of the symptoms when selecting an over-the-counter drug. For example, the selection unit determines the priority of the proposal based on the time of onset of the symptoms when selecting an over-the-counter drug. For example, the selection unit preferentially proposes over-the-counter drugs for symptoms that have recently occurred. In addition, for symptoms that have continued for a long time, the selection unit proposes detailed over-the-counter drugs. Furthermore, for seasonal symptoms, over-the-counter drugs according to the season are proposed. This makes it possible to propose the optimal over-the-counter drug according to the time of onset of the symptoms. A part or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the time of onset of the symptoms to AI, and the AI ​​can analyze the information to determine the appropriate priority of the proposal.

[0047] The selection unit can adjust the order of suggestions based on the relevance of symptoms when selecting over-the-counter drugs. The selection unit, for example, adjusts the order of suggestions based on the relevance of symptoms when selecting over-the-counter drugs. For example, the selection unit first suggests over-the-counter drugs for the most relevant symptoms. Also, for less relevant symptoms, the selection unit suggests over-the-counter drugs later. Furthermore, when multiple symptoms are related, the order of suggestions is adjusted based on the respective relevance. This makes it possible to provide an optimal order of suggestions of over-the-counter drugs according to the relevance of symptoms. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the relevance of symptoms to AI, and the AI ​​can analyze the information to determine an appropriate order of suggestions.

[0048] The providing unit can select the optimal providing method by referring to the user's past drug use history when providing the usage method and precautions. For example, the providing unit selects the optimal providing method by referring to the user's past drug use history when providing the usage method and precautions. For example, the providing unit selects the optimal providing method by referring to the usage method of the drug used by the user in the past. In addition, the providing unit provides the drug with emphasis on precautions from the user's past drug use history. Furthermore, the user's past drug use history is analyzed and the most effective providing method is selected. This makes it possible to provide the optimal providing method based on the user's past drug use history. Part or all of the above-mentioned processing in the providing unit may be performed using AI or may be performed without using AI. For example, the providing unit can input the past drug use history into AI, and the AI ​​can analyze the information to determine the appropriate providing method.

[0049] The providing unit can customize the content to be provided based on the user's current living situation when providing the usage method and precautions. For example, the providing unit customizes the content to be provided based on the user's current living situation when providing the usage method and precautions. For example, when the user is at work, the providing unit selects content to be provided that can be understood in a short time. Furthermore, when the user is at home, the providing unit selects detailed content to be provided. Furthermore, when the user is traveling, the providing unit selects content to be provided that is related to the environment of the travel destination. This makes it possible to provide optimal content according to the user's current living situation. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input data on the user's living situation into AI, and the AI ​​can analyze the information to determine appropriate content to be provided.

[0050] The providing unit can select the optimal providing method in consideration of the geographical location information of the user when providing the usage method and the cautions. For example, the providing unit selects the optimal providing method in consideration of the geographical location information of the user when providing the usage method and the cautions. For example, when the user is at high altitude, cautions related to altitude sickness are provided. Also, when the user is in an urban area, cautions specific to the city are provided. Furthermore, when the user is traveling, cautions related to the environment of the travel destination are provided. This makes it possible to provide the optimal providing method based on the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI, and the AI ​​can analyze the information to determine the appropriate providing method.

[0051] The providing unit can adjust the content to be provided by analyzing the user's social media activity when providing the usage method and precautions. For example, when providing the usage method and precautions, the providing unit adjusts the content to be provided by analyzing the user's social media activity. For example, if a user posts "I'm tired" on social media, precautions related to fatigue are provided. If a user posts "I have a headache," a usage method related to headaches is provided. If a user posts "I'm cold," precautions related to cold are provided. This makes it possible to provide optimal content to be provided based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity into AI, and the AI ​​can analyze the information to determine appropriate content to be provided.

[0052] The recording unit can select the optimal recording method by referring to the user's past symptom recording history when recording symptoms. For example, the recording unit selects the optimal recording method by referring to the user's past symptom recording history when recording symptoms. For example, the recording unit automatically displays symptoms that the user frequently recorded in the past as candidates. In addition, the recording unit preferentially suggests recording methods (audio, text, etc.) that the user used in the past. Furthermore, the recording unit predicts and suggests a recording method to be used in a specific time period from the user's past recording history. This makes it possible to provide an optimal recording method based on the user's past recording history. A part or all of the above-mentioned processing in the recording unit may be performed using AI, or may be performed without using AI. For example, the recording unit can input the past symptom recording history to AI, and the AI ​​can analyze the information and suggest an appropriate recording method.

[0053] The recording unit can customize the recording contents based on the user's current living situation when recording symptoms. For example, the recording unit customizes the recording contents based on the user's current living situation when recording symptoms. For example, when the user is at work, a field that can be recorded in a short time is provided. Also, when the user is at home, a detailed recording field is provided. Furthermore, when the user is traveling, a recording field related to the environment of the travel destination is provided. This makes it possible to provide optimal recording contents according to the user's current living situation. Some or all of the above-mentioned processing in the recording unit may be performed using AI, or may be performed without using AI. For example, the recording unit can input data on the user's living situation into AI, and the AI ​​can analyze the information to determine appropriate recording contents.

[0054] The recording unit can select the optimal recording method in consideration of the geographical location information of the user when recording symptoms. For example, the recording unit selects the optimal recording method in consideration of the geographical location information of the user when recording symptoms. For example, when the user is at high altitude, symptoms related to altitude sickness are preferentially recorded. Also, when the user is in an urban area, symptoms specific to the city (e.g., allergies) are preferentially recorded. Furthermore, when the user is traveling, symptoms related to the environment of the travel destination are preferentially recorded. This makes it possible to provide an optimal recording method based on the geographical location information of the user. A part or all of the above-mentioned processing in the recording unit may be performed using AI, or may be performed without using AI. For example, the recording unit can input the geographical location information of the user to AI, and the AI ​​can analyze the information to determine an appropriate recording method.

[0055] The recording unit can adjust the recorded content by analyzing the user's social media activity when recording symptoms. For example, the recording unit adjusts the recorded content by analyzing the user's social media activity when recording symptoms. For example, if a user posts "tired" on social media, the recording unit records symptoms related to fatigue. If a user posts "headache", the recording unit records symptoms related to headache. If a user posts "cold", the recording unit records symptoms related to cold. This makes it possible to provide optimal recorded content based on the user's social media activity. Some or all of the above-mentioned processing in the recording unit may be performed using AI, or may be performed without using AI. For example, the recording unit can input the user's social media activity into AI, and the AI ​​can analyze the information to determine appropriate recorded content.

[0056] The monitoring unit can predict the current progress status by referring to the past healing data when monitoring the progress status of healing. For example, the monitoring unit predicts the current progress status by referring to the past healing data when monitoring the progress status of healing. For example, the monitoring unit predicts the current progress status based on the user's past healing data. Also, the monitoring unit predicts the progress speed of healing from the past healing data. Furthermore, the past healing data is analyzed and the most effective healing method is proposed. This makes it possible to predict the current progress status based on the past healing data. A part or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can input the past healing data into AI, and the AI ​​can analyze the information to predict the current progress status.

[0057] The monitoring unit can apply different monitoring methods for each symptom category when monitoring the progress of healing. For example, the monitoring unit applies different monitoring methods for each symptom category when monitoring the progress of healing. For example, the monitoring unit applies a monitoring method specialized for the respiratory system to symptoms of the respiratory system. Also, the monitoring unit applies a monitoring method specialized for the digestive system to symptoms of the digestive system. Furthermore, the monitoring unit applies a monitoring method specialized for the nervous system to symptoms of the nervous system. This makes it possible to provide an optimal monitoring method according to the symptom category. A part or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can input the symptom category into AI, and the AI ​​can analyze the information and apply an appropriate monitoring method.

[0058] The monitoring unit can analyze changes in the progress of healing based on the time of onset of the symptoms when monitoring the progress of healing. For example, the monitoring unit analyzes changes in the progress of healing based on the time of onset of the symptoms when monitoring the progress of healing. For example, the monitoring unit quickly analyzes changes in the progress of a recently occurring symptom. Also, for a symptom that has continued for a long time, the monitoring unit analyzes changes in the progress of the symptom in detail. Furthermore, for a seasonal symptom, the monitoring unit analyzes changes in the progress of the symptom according to the season. This makes it possible to analyze optimal changes in the progress of healing based on the time of onset of the symptom. A part or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can input the time of onset of the symptom to AI, and the AI ​​can analyze the information to analyze changes in the progress of healing.

[0059] The monitoring unit can analyze the progress of the healing by referring to the symptom-related market data when monitoring the healing progress. For example, the monitoring unit analyzes the progress of the healing by referring to the symptom-related market data when monitoring the healing progress. For example, the monitoring unit analyzes the healing progress based on the market data. Also, the healing progress speed is predicted from the market data. Furthermore, the market data is analyzed to propose the most effective healing method. This makes it possible to analyze the optimal progress based on the symptom-related market data. A part or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can input the symptom-related market data to AI, and the AI ​​can analyze the information to analyze the progress.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The reception unit can analyze the user's past symptom input history and select the optimal input method. For example, the reception unit automatically displays symptoms that the user frequently input in the past as candidates. In addition, the reception unit preferentially suggests input methods (such as voice and text) that the user has used in the past. Furthermore, symptoms to be used in a specific time period are predicted and suggested from the user's past input history. This makes it possible to provide the optimal input method based on the user's past history. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the past symptom input history to AI, and the AI ​​can analyze the information and suggest the optimal input method.

[0062] The selection unit can adjust the level of detail of the proposal based on the importance of the symptoms when selecting an over-the-counter drug. For example, the selection unit proposes a detailed over-the-counter drug for a symptom of high importance. Also, the selection unit proposes a concise over-the-counter drug for a symptom of low importance. Furthermore, the selection unit proposes an over-the-counter drug with a moderate level of detail for a symptom of medium importance. This makes it possible to propose a detailed over-the-counter drug according to the importance of the symptom. Some or all of the above-mentioned processing in the selection unit may be performed using AI or may be performed without using AI. For example, the selection unit can input the importance of the symptom to AI, and the AI ​​can analyze the information to provide an appropriate level of detail for the proposal.

[0063] When providing the usage method and precautions, the providing unit can select the optimal providing method by referring to the user's past drug use history. For example, the providing unit selects the optimal providing method by referring to the usage method of the drug used by the user in the past. Also, the providing unit provides the drug with emphasis on precautions based on the user's past drug use history. Furthermore, the providing unit analyzes the user's past drug use history and selects the most effective providing method. This makes it possible to provide the optimal providing method based on the user's past drug use history. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the past drug use history into AI, and the AI ​​can analyze the information to determine the appropriate providing method.

[0064] When monitoring the progress of healing, the monitoring unit can predict the current progress by referring to past healing data. For example, the monitoring unit predicts the current progress based on the user's past healing data. Also, the monitoring unit predicts the progress speed of healing from the past healing data. Furthermore, the monitoring unit analyzes the past healing data and suggests the most effective healing method. This makes it possible to predict the current progress based on the past healing data. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can input past healing data into AI, and the AI ​​can analyze the information to predict the current progress.

[0065] When recording symptoms, the recording unit can select the optimal recording method by referring to the user's past symptom recording history. For example, the recording unit automatically displays symptoms that the user has frequently recorded in the past as candidates. In addition, the recording unit preferentially suggests recording methods (audio, text, etc.) that the user has used in the past. Furthermore, the recording unit predicts and suggests a recording method to be used in a specific time period from the user's past recording history. This makes it possible to provide an optimal recording method based on the user's past recording history. Some or all of the above-mentioned processing in the recording unit may be performed using AI, or may be performed without using AI. For example, the recording unit can input the past symptom recording history to AI, which can analyze the information and suggest an appropriate recording method.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The reception unit provides an interface for users to input their symptoms. For example, users can input specific symptoms such as "sore throat" or "mild headache." The reception unit can also use AI to analyze the user's input and generate data to suggest appropriate over-the-counter medications. Step 2: The selection unit analyzes the symptoms received by the reception unit and selects an appropriate over-the-counter medicine. The selection unit uses AI to select the most suitable over-the-counter medicine based on the symptoms and provides that information to the user. For example, the selection unit may suggest a lozenge or spray that relieves a sore throat for a "sore throat." Step 3: The provision unit provides the user with instructions on how to use the over-the-counter drugs selected by the selection unit and points to be aware of. The provision unit uses AI to provide detailed explanations of the correct way to use the over-the-counter drugs and points to be aware of, supporting the user in using the drugs safely. For example, the provision unit explains how to use the lozenges, the number of times to use them per day, and side effects to be aware of. Step 4: The recorder provides an interface for the user to record the progress of symptoms. The recorder can also use AI to record the changes in the user's symptoms and the effects of the medication on a daily basis and analyze the data. For example, the recorder can evaluate how much the sore throat has improved or how much the headache has been alleviated. Step 5: The monitoring unit analyzes the data recorded by the recording unit and monitors the progress of healing. The monitoring unit analyzes the data collected using AI and evaluates the progress of healing. For example, the monitoring unit evaluates how much the sore throat has improved and how much the headache has been alleviated.

[0068] (Example 2) The support system according to the embodiment of the present invention is a system that supports people with mild symptoms that are not serious enough to go to the hospital, from purchasing over-the-counter medicines to curing the symptoms. In this support system, the user inputs their symptoms, and the AI ​​analyzes the symptoms and suggests an appropriate over-the-counter medicine. The user purchases the suggested over-the-counter medicine and receives usage instructions and precautions from the AI. In addition, the user records the progress of the symptoms, and the AI ​​analyzes the data to monitor the progress of the cure. If necessary, additional advice and suggestions for other over-the-counter medicines are provided. For example, the user inputs specific symptoms such as "sore throat" or "mild headache". This information is input into the AI. The AI ​​then analyzes the input symptoms and suggests an appropriate over-the-counter medicine. The AI ​​selects the most suitable over-the-counter medicine based on the symptoms and provides the information to the user. For example, for "sore throat", a lozenge or spray that relieves the sore throat is suggested. The user purchases the suggested over-the-counter medicine and receives usage instructions and precautions from the AI. The AI ​​provides detailed explanations of the correct usage and precautions of the over-the-counter medicine, supporting the user to use the medicine safely. For example, it explains how to use the lozenge, the number of times it can be used per day, and side effects to be aware of. In addition, users record the progress of their symptoms, and the AI ​​analyzes the data to monitor the progress of healing. Users record changes in symptoms and the effects of medication on a daily basis and input the data into the AI. The AI ​​analyzes the collected data and evaluates the progress of healing. For example, it evaluates how much the sore throat has improved and how much the headache has been alleviated. If necessary, it provides additional advice and suggests other over-the-counter medications. Depending on the progress of healing, the AI ​​provides additional advice and suggests other over-the-counter medications. For example, if the sore throat does not improve, it suggests a different type of lozenge or spray. In addition, if the symptoms worsen, it advises the user to see a doctor. This system provides consistent support for people with mild symptoms that do not require a trip to the hospital, from purchasing over-the-counter medications to healing the symptoms. Users can improve their symptoms by selecting over-the-counter medications that are appropriate for their symptoms and using them correctly. In addition, AI monitoring and advice allow users to understand the progress of healing and take appropriate measures.This allows the support system to provide consistent support from the user's input of symptoms to selecting over-the-counter medications, providing instructions on how to use them, recording symptoms, and monitoring the progress of recovery.

[0069] The support system according to the embodiment includes a reception unit, a selection unit, a provision unit, a recording unit, and a monitoring unit. The reception unit provides an interface for the user to input his / her own symptoms. For example, the user can input specific symptoms such as "sore throat" or "mild headache". The reception unit can also use AI to analyze the user's input and generate data for suggesting appropriate over-the-counter medicines. The selection unit analyzes the symptoms received by the reception unit and selects appropriate over-the-counter medicines. The selection unit uses AI to select the optimal over-the-counter medicine based on the symptoms and provides the information to the user. For example, the selection unit suggests a lozenge or spray that relieves the pain in the throat for "sore throat". The provision unit provides the user with instructions on how to use the over-the-counter medicine selected by the selection unit and points to note. The provision unit uses AI to explain in detail the correct way to use the over-the-counter medicine and points to note, and supports the user to use the medicine safely. For example, the provision unit explains how to use the lozenge, the number of times it can be used per day, and side effects to be aware of. The recording unit provides an interface for the user to record the progress of the symptoms. The recording unit can also use AI to record changes in the user's symptoms and the effects of the medicine on a daily basis and analyze the data. For example, the recording unit evaluates how much the sore throat has improved and how much the headache has been alleviated. The monitoring unit analyzes the data recorded by the recording unit and monitors the progress of healing. The monitoring unit analyzes the data collected using AI and evaluates the progress of healing. For example, the monitoring unit evaluates how much the sore throat has improved and how much the headache has been alleviated. As a result, the support system according to the embodiment can consistently support the user's input of symptoms, selection of over-the-counter medicine, provision of usage instructions, recording of symptoms, and monitoring of the progress of healing.

[0070] The reception unit provides an interface for users to input their symptoms. For example, users can input specific symptoms such as "sore throat" or "mild headache." The reception unit can also use AI to analyze the user's input and generate data to suggest appropriate over-the-counter medications. Specifically, the user uses a smartphone or computer to access a dedicated application or website and inputs symptoms. The interface is designed to make it easy for users to input symptoms, and supports not only text input, but also a format in which users can select from multiple options and voice input. The AI ​​uses natural language processing technology to analyze the user's input and determine the type and severity of the symptoms. For example, if "sore throat" is input, the AI ​​will determine that the symptom is likely to be an early symptom of a cold or flu, and generate data to suggest appropriate over-the-counter medications. Even if the user inputs multiple symptoms, the AI ​​will analyze each symptom individually and make a comprehensive judgment. This allows the reception unit to accurately grasp the user's symptoms and smoothly provide data to the selection unit, which is the next step. In addition, the reception unit can also take into account the user's past input history and information on health status. For example, if there is a history of using over-the-counter medication for the same symptoms in the past, more appropriate suggestions can be made based on that information. This enables the reception unit to respond flexibly to the individual circumstances of each user, thereby improving the accuracy and reliability of the entire system.

[0071] The selection unit analyzes the symptoms received by the reception unit and selects an appropriate over-the-counter drug. The selection unit uses AI to select the most suitable over-the-counter drug based on the symptoms and provides the information to the user. Specifically, the AI ​​refers to a large-scale pharmaceutical database and identifies the over-the-counter drug that is most suitable for the user's symptoms. For example, for "sore throat," it suggests a lozenge or spray that relieves the sore throat. The AI ​​comprehensively considers information such as the type and severity of the symptoms, the user's age, gender, and medical history, and selects the most suitable over-the-counter drug. The selection unit also considers information on the ingredients, effects, and side effects of the drug. For example, if the user has an allergy, it preferentially selects a drug that does not contain that ingredient. In addition, the selection unit compares multiple over-the-counter drugs and suggests the most effective and safe drug. When providing the user with information on the selected over-the-counter drug, the selection unit also provides a detailed explanation of the drug's effects, usage, and side effects. This allows the user to use it as a reference when choosing the over-the-counter drug that is best for them. The selection unit can also collect user feedback and continuously improve the accuracy of the selection algorithm. For example, the system collects information on the effects and side effects of a suggested drug after the user has taken it, and adjusts the selection algorithm based on that data. This allows the selection unit to always select highly accurate drugs based on the latest information, supporting the user's health management.

[0072] The provision unit provides the user with the usage method and precautions for the over-the-counter drug selected by the selection unit. The provision unit uses AI to explain in detail the correct usage method and precautions for the over-the-counter drug, supporting the user to use the drug safely. Specifically, the provision unit provides the usage method of the selected over-the-counter drug in the form of text, images, videos, etc. For example, it provides detailed explanations on how to use the lozenge, the number of times to use it per day, and side effects to be aware of. AI can customize the way information is provided according to the user's level of understanding and interest. For example, it provides explanations using videos and illustrations to users who prefer visual information, and explains in detailed text to users who prefer text information. The provision unit also provides general precautions regarding the use of the drug and additional advice for specific symptoms. For example, it introduces ways to improve lifestyle habits to relieve a sore throat and relaxation techniques to relieve headaches. In addition, the provision unit also has a feedback function to check whether the user is using the drug correctly. For example, when a user inputs a question about how to use the drug, AI immediately provides an answer. The provision unit can also collect information on the effects and side effects after the user uses the drug, and improve the content provided based on that data. This enables the provider to always provide the user with the latest and most accurate information, supporting the safe use of medicines.

[0073] The recording unit provides an interface for users to record the progress of symptoms. The recording unit can also use AI to record changes in the user's symptoms and the effects of medication on a daily basis and analyze the data. Specifically, the user uses a smartphone or computer to access a dedicated application or website and enter changes in symptoms and medication usage. The interface is designed to make it easy for users to enter data, and supports not only text input, but also multiple choice format and voice input. The AI ​​analyzes the data entered by the user and evaluates the improvement of symptoms and the effectiveness of medication. For example, it evaluates how much the sore throat has improved and how much the headache has been alleviated. The recording unit can also visually display changes in symptoms and the effects of medication in graphs and charts based on the data entered by the user. This allows the user to understand their health condition at a glance. In addition, the recording unit can also predict future symptoms and propose measures based on the user's past data. For example, it can predict the risk of symptoms occurring in a specific season or environmental conditions based on past data and propose preventive measures. This allows the recording unit to support the user's health management and contribute to early detection and prevention of symptoms.

[0074] The monitoring unit analyzes the data recorded by the recording unit and monitors the progress of healing. The monitoring unit analyzes the data collected using AI and evaluates the progress of healing. Specifically, AI analyzes data on changes in the user's symptoms and the effects of the medicine to evaluate the progress of healing in real time. For example, it evaluates how much the sore throat has improved and how much the headache has been alleviated. The monitoring unit can also visually display the progress of healing in graphs and charts based on the user's data. This allows the user to grasp their healing status at a glance. Furthermore, the monitoring unit can issue a warning to the user if there is abnormal data or if the progress of healing is delayed. For example, if symptoms do not improve or new symptoms occur, the monitoring unit notifies the user to see a doctor. This allows the monitoring unit to continuously monitor the user's health condition and detect abnormalities early. The monitoring unit can also provide advice and support according to the progress of healing based on the user's data. For example, if healing is progressing smoothly, it advises the user to continue using the medicine, and if healing is delayed, it suggests additional measures. This enables the monitoring unit to support the user's health management and promote the progress of healing.

[0075] The reception unit can provide options when the user inputs symptoms. The reception unit, for example, provides options when the user inputs symptoms. For example, the user can select specific symptoms such as "sore throat" or "mild headache." This makes it easier for the user to input symptoms. Some or all of the above-mentioned processing in the reception unit may be performed using AI or may be performed without using AI. For example, the reception unit can input the symptoms selected by the user to AI, and the AI ​​can analyze the information and suggest appropriate over-the-counter medicines.

[0076] The selection unit can select an over-the-counter drug based on past data and the user's health condition. The selection unit selects an over-the-counter drug based on, for example, past data and the user's health condition. For example, the selection unit proposes an optimal over-the-counter drug in consideration of the user's past symptom data and medication history. This makes it possible to propose an optimal over-the-counter drug according to the user's health condition. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input past data and the user's health condition into AI, and the AI ​​can analyze the information to propose an appropriate over-the-counter drug.

[0077] The recording unit can provide an interface for recording the progress of symptoms. The recording unit, for example, provides an interface for recording the progress of symptoms. For example, the recording unit allows the user to record changes in symptoms and the effects of medication on a daily basis. This allows the user to easily record the progress of symptoms. Some or all of the above-mentioned processing in the recording unit may be performed using AI or may be performed without using AI. For example, the recording unit can input data recorded by the user into an AI, and the AI ​​can analyze the information to evaluate the progress of healing.

[0078] The recording unit can provide a reminder function that periodically prompts the user to record symptoms. The recording unit, for example, provides a reminder function that periodically prompts the user to record symptoms. For example, the recording unit periodically transmits a notification to the user that prompts the user to record symptoms. This allows the user to record symptoms without forgetting. Some or all of the above-mentioned processing in the recording unit may be performed using AI, or may be performed without using AI. For example, the recording unit can input the reminder function to AI, and the AI ​​can analyze the information and send a notification at an appropriate time.

[0079] The monitoring unit can provide additional advice or suggest other over-the-counter drugs according to the progress of healing. The monitoring unit, for example, provides additional advice or suggests other over-the-counter drugs according to the progress of healing. For example, the monitoring unit suggests a different type of lozenge or spray if the sore throat does not improve. Also, if the symptoms worsen, the monitoring unit advises the user to see a doctor. This allows the user to receive appropriate advice and suggestions for over-the-counter drugs according to the progress of healing. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can input the progress of healing into AI, and the AI ​​can analyze the information to provide appropriate advice and suggestions for over-the-counter drugs.

[0080] The reception unit can estimate the user's emotion and adjust the symptom input interface based on the emotion. The reception unit, for example, estimates the user's emotion and adjusts the symptom input interface based on the emotion. For example, when the user is stressed, a simple and intuitive interface is provided to minimize the input procedure. Also, when the user is relaxed, detailed input options are provided and a customizable input method is suggested. Furthermore, when the user is in a hurry, voice input is prioritized to enable the user to quickly input symptoms. This makes it possible to provide an optimal interface according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI, and the generation AI can analyze the information to provide an appropriate interface.

[0081] The reception unit can analyze the user's past symptom input history and select the optimal input method. The reception unit, for example, analyzes the user's past symptom input history and selects the optimal input method. For example, the reception unit automatically displays symptoms that the user frequently inputs in the past as candidates. In addition, the reception unit preferentially suggests input methods (such as voice and text) that the user has used in the past. Furthermore, symptoms to be used in a specific time period are predicted and suggested from the user's past input history. This makes it possible to provide the optimal input method based on the user's past history. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the past symptom input history to AI, and the AI ​​can analyze the information and suggest the optimal input method.

[0082] The reception unit can customize the input field based on the user's current living situation and areas of interest when inputting symptoms. For example, the reception unit customizes the input field based on the user's current living situation and areas of interest when inputting symptoms. For example, when the user is at work, a field that can be input in a short time is provided. Also, when the user is highly interested in health, a detailed symptom input field is provided. Furthermore, when the user is traveling, a symptom input field related to the environment of the travel destination is provided. This makes it possible to provide an input field according to the user's living situation and areas of interest. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest into AI, and the AI ​​can analyze the information to provide an optimal input field.

[0083] The reception unit can estimate the user's emotion and determine the priority of the symptoms to be input based on the emotion. The reception unit, for example, estimates the user's emotion and determines the priority of the symptoms to be input based on the emotion. For example, when the user feels anxious, the most anxious symptom is input preferentially. Also, when the user is relaxed, all symptoms are input evenly. Furthermore, when the user is in a hurry, the most important symptom is input preferentially. This makes it possible to determine the priority of the symptoms according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI, and the generation AI can analyze the information to determine an appropriate priority.

[0084] The reception unit can, when inputting symptoms, preferentially input highly relevant symptoms in consideration of the geographical location information of the user. For example, when inputting symptoms, the reception unit can, when inputting symptoms, preferentially input highly relevant symptoms in consideration of the geographical location information of the user. For example, when the user is at high altitude, the reception unit can preferentially input symptoms related to altitude sickness. Also, when the user is in an urban area, the reception unit can preferentially input symptoms specific to the city (e.g., allergies). Furthermore, when the user is traveling, the reception unit can preferentially input symptoms related to the environment of the travel destination. This makes it possible to preferentially input highly relevant symptoms based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI, and the AI ​​can analyze the information and suggest optimal symptoms.

[0085] The reception unit can analyze the user's social media activity at the time of symptom input and input related symptoms. The reception unit can analyze the user's social media activity at the time of symptom input and input related symptoms. For example, when a user posts "tired" on social media, the reception unit can input symptoms related to fatigue. Furthermore, when a user posts "headache", the reception unit can input symptoms related to headache. Furthermore, when a user posts "cold", the reception unit can input symptoms related to cold. In this way, related symptoms can be input based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity to AI, and the AI ​​can analyze the information and suggest optimal symptoms.

[0086] The selection unit can estimate the user's emotion and adjust the over-the-counter drug suggestion method based on the emotion. The selection unit, for example, estimates the user's emotion and adjusts the over-the-counter drug suggestion method based on the emotion. For example, if the user feels anxious, an over-the-counter drug suggestion method that gives a sense of security is used. Also, if the user is relaxed, an over-the-counter drug suggestion method that includes detailed information is used. Furthermore, if the user is in a hurry, a concise and quick over-the-counter drug suggestion method is used. This makes it possible to provide an optimal over-the-counter drug suggestion method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the selection unit may be performed using an AI, or may be performed without using an AI. For example, the selection unit can input the user's emotion data to the generation AI, and the generation AI can analyze the information to provide an appropriate suggestion method.

[0087] The selection unit can adjust the level of detail of the proposal based on the importance of the symptoms when selecting an over-the-counter drug. For example, the selection unit adjusts the level of detail of the proposal based on the importance of the symptoms when selecting an over-the-counter drug. For example, the selection unit proposes a detailed over-the-counter drug for a symptom of high importance. Also, for a symptom of low importance, the selection unit proposes a concise over-the-counter drug. Furthermore, for a symptom of medium importance, the selection unit proposes an over-the-counter drug with a moderate level of detail. This makes it possible to propose a detailed over-the-counter drug according to the importance of the symptom. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the importance of the symptoms to AI, and the AI ​​can analyze the information to provide an appropriate level of detail for the proposal.

[0088] The selection unit can apply different selection algorithms depending on the symptom category when selecting an over-the-counter drug. For example, the selection unit applies different selection algorithms depending on the symptom category when selecting an over-the-counter drug. For example, the selection unit applies a selection algorithm specialized for the respiratory system to symptoms of the respiratory system. Also, the selection unit applies a selection algorithm specialized for the digestive system to symptoms of the digestive system. Furthermore, the selection unit applies a selection algorithm specialized for the nervous system to symptoms of the nervous system. This makes it possible to select an optimal over-the-counter drug according to the symptom category. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the symptom category to AI, and the AI ​​can analyze the information and apply an appropriate selection algorithm.

[0089] The selection unit can estimate the user's emotion and adjust the length of the over-the-counter drug proposal based on the emotion. The selection unit, for example, estimates the user's emotion and adjusts the length of the over-the-counter drug proposal based on the emotion. For example, if the user feels anxious, a longer proposal including detailed explanations is made. Also, if the user is relaxed, a moderately long proposal is made. Furthermore, if the user is in a hurry, a concise and short proposal is made. This makes it possible to provide an optimal proposal length according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the user's emotion data to the generation AI, and the generation AI can analyze the information to provide an appropriate proposal length.

[0090] The selection unit can determine the priority of the proposal based on the time of onset of the symptoms when selecting an over-the-counter drug. For example, the selection unit determines the priority of the proposal based on the time of onset of the symptoms when selecting an over-the-counter drug. For example, the selection unit preferentially proposes over-the-counter drugs for symptoms that have recently occurred. In addition, for symptoms that have continued for a long time, the selection unit proposes detailed over-the-counter drugs. Furthermore, for seasonal symptoms, over-the-counter drugs according to the season are proposed. This makes it possible to propose the optimal over-the-counter drug according to the time of onset of the symptoms. A part or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the time of onset of the symptoms to AI, and the AI ​​can analyze the information to determine the appropriate priority of the proposal.

[0091] The selection unit can adjust the order of suggestions based on the relevance of symptoms when selecting over-the-counter drugs. The selection unit, for example, adjusts the order of suggestions based on the relevance of symptoms when selecting over-the-counter drugs. For example, the selection unit first suggests over-the-counter drugs for the most relevant symptoms. Also, for less relevant symptoms, the selection unit suggests over-the-counter drugs later. Furthermore, when multiple symptoms are related, the order of suggestions is adjusted based on the respective relevance. This makes it possible to provide an optimal order of suggestions of over-the-counter drugs according to the relevance of symptoms. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the relevance of symptoms to AI, and the AI ​​can analyze the information to determine an appropriate order of suggestions.

[0092] The providing unit can estimate the user's emotions and adjust the method of providing the usage method and the points of caution based on the emotions. The providing unit, for example, estimates the user's emotions and adjusts the method of providing the usage method and the points of caution based on the emotions. For example, if the user feels anxious, an explanation method that gives a sense of security is used. Also, if the user is relaxed, an explanation method including detailed information is used. Furthermore, if the user is in a hurry, a concise and quick explanation method is used. This makes it possible to provide the optimal usage method and points of caution according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI, and the generation AI can analyze the information to determine an appropriate method of providing.

[0093] The providing unit can select the optimal providing method by referring to the user's past drug use history when providing the usage method and precautions. For example, the providing unit selects the optimal providing method by referring to the user's past drug use history when providing the usage method and precautions. For example, the providing unit selects the optimal providing method by referring to the usage method of the drug used by the user in the past. In addition, the providing unit provides the drug with emphasis on precautions from the user's past drug use history. Furthermore, the user's past drug use history is analyzed and the most effective providing method is selected. This makes it possible to provide the optimal providing method based on the user's past drug use history. Part or all of the above-mentioned processing in the providing unit may be performed using AI or may be performed without using AI. For example, the providing unit can input the past drug use history into AI, and the AI ​​can analyze the information to determine the appropriate providing method.

[0094] The providing unit can customize the content to be provided based on the user's current living situation when providing the usage method and precautions. For example, the providing unit customizes the content to be provided based on the user's current living situation when providing the usage method and precautions. For example, when the user is at work, the providing unit selects content to be provided that can be understood in a short time. Furthermore, when the user is at home, the providing unit selects detailed content to be provided. Furthermore, when the user is traveling, the providing unit selects content to be provided that is related to the environment of the travel destination. This makes it possible to provide optimal content according to the user's current living situation. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input data on the user's living situation into AI, and the AI ​​can analyze the information to determine appropriate content to be provided.

[0095] The providing unit can estimate the user's emotion and determine the priority order of the usage method and the points of caution based on the emotion. The providing unit, for example, estimates the user's emotion and determines the priority order of the usage method and the points of caution based on the emotion. For example, when the user feels anxious, the most important points of caution are provided preferentially. Also, when the user is relaxed, all points of caution are provided equally. Furthermore, when the user is in a hurry, the most important usage method is provided preferentially. In this way, the usage method and the points of caution can be provided with the optimal priority order according to the user's emotion. The estimation of the emotion is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI, and the generation AI can analyze the information to determine an appropriate priority order.

[0096] The providing unit can select the optimal providing method in consideration of the geographical location information of the user when providing the usage method and the cautions. For example, the providing unit selects the optimal providing method in consideration of the geographical location information of the user when providing the usage method and the cautions. For example, when the user is at high altitude, cautions related to altitude sickness are provided. Also, when the user is in an urban area, cautions specific to the city are provided. Furthermore, when the user is traveling, cautions related to the environment of the travel destination are provided. This makes it possible to provide the optimal providing method based on the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI, and the AI ​​can analyze the information to determine the appropriate providing method.

[0097] The providing unit can adjust the content to be provided by analyzing the user's social media activity when providing the usage method and precautions. For example, when providing the usage method and precautions, the providing unit adjusts the content to be provided by analyzing the user's social media activity. For example, if a user posts "I'm tired" on social media, precautions related to fatigue are provided. If a user posts "I have a headache," a usage method related to headaches is provided. If a user posts "I'm cold," precautions related to cold are provided. This makes it possible to provide optimal content to be provided based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity into AI, and the AI ​​can analyze the information to determine appropriate content to be provided.

[0098] The recording unit can estimate the user's emotion and adjust the symptom recording interface based on the emotion. The recording unit, for example, estimates the user's emotion and adjusts the symptom recording interface based on the emotion. For example, when the user is stressed, a simple and intuitive interface is provided to minimize the recording procedure. Also, when the user is relaxed, detailed recording options are provided and a customizable recording method is suggested. Furthermore, when the user is in a hurry, voice input is prioritized to enable the user to quickly record symptoms. This makes it possible to provide an optimal interface according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recording unit may be performed using AI, or may be performed without using AI. For example, the recording unit can input the user's emotion data to the generation AI, and the generation AI can analyze the information to provide an appropriate interface.

[0099] The recording unit can select the optimal recording method by referring to the user's past symptom recording history when recording symptoms. For example, the recording unit selects the optimal recording method by referring to the user's past symptom recording history when recording symptoms. For example, the recording unit automatically displays symptoms that the user frequently recorded in the past as candidates. In addition, the recording unit preferentially suggests recording methods (audio, text, etc.) that the user used in the past. Furthermore, the recording unit predicts and suggests a recording method to be used in a specific time period from the user's past recording history. This makes it possible to provide an optimal recording method based on the user's past recording history. A part or all of the above-mentioned processing in the recording unit may be performed using AI, or may be performed without using AI. For example, the recording unit can input the past symptom recording history to AI, and the AI ​​can analyze the information and suggest an appropriate recording method.

[0100] The recording unit can customize the recording contents based on the user's current living situation when recording symptoms. For example, the recording unit customizes the recording contents based on the user's current living situation when recording symptoms. For example, when the user is at work, a field that can be recorded in a short time is provided. Also, when the user is at home, a detailed recording field is provided. Furthermore, when the user is traveling, a recording field related to the environment of the travel destination is provided. This makes it possible to provide optimal recording contents according to the user's current living situation. Some or all of the above-mentioned processing in the recording unit may be performed using AI, or may be performed without using AI. For example, the recording unit can input data on the user's living situation into AI, and the AI ​​can analyze the information to determine appropriate recording contents.

[0101] The recording unit can estimate the user's emotion and determine the priority of symptom recording based on the emotion. The recording unit, for example, estimates the user's emotion and determines the priority of symptom recording based on the emotion. For example, when the user feels anxious, the most anxious symptom is recorded preferentially. Also, when the user is relaxed, all symptoms are recorded evenly. Furthermore, when the user is in a hurry, the most important symptom is recorded preferentially. This allows symptoms to be recorded with optimal priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recording unit may be performed using AI, or may be performed without using AI. For example, the recording unit can input the user's emotion data to the generation AI, and the generation AI can analyze the information to determine an appropriate priority.

[0102] The recording unit can select the optimal recording method in consideration of the geographical location information of the user when recording symptoms. For example, the recording unit selects the optimal recording method in consideration of the geographical location information of the user when recording symptoms. For example, when the user is at high altitude, symptoms related to altitude sickness are preferentially recorded. Also, when the user is in an urban area, symptoms specific to the city (e.g., allergies) are preferentially recorded. Furthermore, when the user is traveling, symptoms related to the environment of the travel destination are preferentially recorded. This makes it possible to provide an optimal recording method based on the geographical location information of the user. A part or all of the above-mentioned processing in the recording unit may be performed using AI, or may be performed without using AI. For example, the recording unit can input the geographical location information of the user to AI, and the AI ​​can analyze the information to determine an appropriate recording method.

[0103] The recording unit can adjust the recorded content by analyzing the user's social media activity when recording symptoms. For example, the recording unit adjusts the recorded content by analyzing the user's social media activity when recording symptoms. For example, if a user posts "tired" on social media, the recording unit records symptoms related to fatigue. If a user posts "headache", the recording unit records symptoms related to headache. If a user posts "cold", the recording unit records symptoms related to cold. This makes it possible to provide optimal recorded content based on the user's social media activity. Some or all of the above-mentioned processing in the recording unit may be performed using AI, or may be performed without using AI. For example, the recording unit can input the user's social media activity into AI, and the AI ​​can analyze the information to determine appropriate recorded content.

[0104] The monitoring unit can estimate the user's emotion and adjust the display method of the healing progress status based on the emotion. The monitoring unit, for example, estimates the user's emotion and adjusts the display method of the healing progress status based on the emotion. For example, if the user feels anxious, a display method that gives a sense of security is used. If the user is relaxed, a display method including detailed information is used. Furthermore, if the user is in a hurry, a simple and quick display method is used. This makes it possible to provide the healing progress status in an optimal display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can input the user's emotion data to the generation AI, and the generation AI can analyze the information to determine an appropriate display method.

[0105] The monitoring unit can predict the current progress status by referring to the past healing data when monitoring the progress status of healing. For example, the monitoring unit predicts the current progress status by referring to the past healing data when monitoring the progress status of healing. For example, the monitoring unit predicts the current progress status based on the user's past healing data. Also, the monitoring unit predicts the progress speed of healing from the past healing data. Furthermore, the past healing data is analyzed and the most effective healing method is proposed. This makes it possible to predict the current progress status based on the past healing data. A part or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can input the past healing data into AI, and the AI ​​can analyze the information to predict the current progress status.

[0106] The monitoring unit can apply different monitoring methods for each symptom category when monitoring the progress of healing. For example, the monitoring unit applies different monitoring methods for each symptom category when monitoring the progress of healing. For example, the monitoring unit applies a monitoring method specialized for the respiratory system to symptoms of the respiratory system. Also, the monitoring unit applies a monitoring method specialized for the digestive system to symptoms of the digestive system. Furthermore, the monitoring unit applies a monitoring method specialized for the nervous system to symptoms of the nervous system. This makes it possible to provide an optimal monitoring method according to the symptom category. A part or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can input the symptom category into AI, and the AI ​​can analyze the information and apply an appropriate monitoring method.

[0107] The monitoring unit can estimate the user's emotion and adjust the importance of the healing progress status based on the emotion. The monitoring unit, for example, estimates the user's emotion and adjusts the importance of the healing progress status based on the emotion. For example, if the user feels anxious, the most important healing progress status is displayed preferentially. Also, if the user is relaxed, all healing progress statuses are displayed evenly. Furthermore, if the user is in a hurry, the most important healing progress status is displayed briefly. This makes it possible to provide the healing progress status with an optimal importance according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotion data to the generation AI, and the generation AI can analyze the information to determine an appropriate importance.

[0108] The monitoring unit can analyze changes in the progress of healing based on the time of onset of the symptoms when monitoring the progress of healing. For example, the monitoring unit analyzes changes in the progress of healing based on the time of onset of the symptoms when monitoring the progress of healing. For example, the monitoring unit quickly analyzes changes in the progress of a recently occurring symptom. Also, for a symptom that has continued for a long time, the monitoring unit analyzes changes in the progress of the symptom in detail. Furthermore, for a seasonal symptom, the monitoring unit analyzes changes in the progress of the symptom according to the season. This makes it possible to analyze optimal changes in the progress of healing based on the time of onset of the symptom. A part or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can input the time of onset of the symptom to AI, and the AI ​​can analyze the information to analyze changes in the progress of healing.

[0109] The monitoring unit can analyze the progress of the healing by referring to the symptom-related market data when monitoring the healing progress. For example, the monitoring unit analyzes the progress of the healing by referring to the symptom-related market data when monitoring the healing progress. For example, the monitoring unit analyzes the healing progress based on the market data. Also, the healing progress speed is predicted from the market data. Furthermore, the market data is analyzed to propose the most effective healing method. This makes it possible to analyze the optimal progress based on the symptom-related market data. A part or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can input the symptom-related market data to AI, and the AI ​​can analyze the information to analyze the progress.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The reception unit can analyze the user's past symptom input history and select the optimal input method. For example, the reception unit automatically displays symptoms that the user frequently input in the past as candidates. In addition, the reception unit preferentially suggests input methods (such as voice and text) that the user has used in the past. Furthermore, symptoms to be used in a specific time period are predicted and suggested from the user's past input history. This makes it possible to provide the optimal input method based on the user's past history. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the past symptom input history to AI, and the AI ​​can analyze the information and suggest the optimal input method.

[0112] The selection unit can adjust the level of detail of the proposal based on the importance of the symptoms when selecting an over-the-counter drug. For example, the selection unit proposes a detailed over-the-counter drug for a symptom of high importance. Also, the selection unit proposes a concise over-the-counter drug for a symptom of low importance. Furthermore, the selection unit proposes an over-the-counter drug with a moderate level of detail for a symptom of medium importance. This makes it possible to propose a detailed over-the-counter drug according to the importance of the symptom. Some or all of the above-mentioned processing in the selection unit may be performed using AI or may be performed without using AI. For example, the selection unit can input the importance of the symptom to AI, and the AI ​​can analyze the information to provide an appropriate level of detail for the proposal.

[0113] When providing the usage method and precautions, the providing unit can select the optimal providing method by referring to the user's past drug use history. For example, the providing unit selects the optimal providing method by referring to the usage method of the drug used by the user in the past. Also, the providing unit provides the drug with emphasis on precautions based on the user's past drug use history. Furthermore, the providing unit analyzes the user's past drug use history and selects the most effective providing method. This makes it possible to provide the optimal providing method based on the user's past drug use history. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the past drug use history into AI, and the AI ​​can analyze the information to determine the appropriate providing method.

[0114] When monitoring the progress of healing, the monitoring unit can predict the current progress by referring to past healing data. For example, the monitoring unit predicts the current progress based on the user's past healing data. Also, the monitoring unit predicts the progress speed of healing from the past healing data. Furthermore, the monitoring unit analyzes the past healing data and suggests the most effective healing method. This makes it possible to predict the current progress based on the past healing data. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can input past healing data into AI, and the AI ​​can analyze the information to predict the current progress.

[0115] When recording symptoms, the recording unit can select the optimal recording method by referring to the user's past symptom recording history. For example, the recording unit automatically displays symptoms that the user has frequently recorded in the past as candidates. In addition, the recording unit preferentially suggests recording methods (audio, text, etc.) that the user has used in the past. Furthermore, the recording unit predicts and suggests a recording method to be used in a specific time period from the user's past recording history. This makes it possible to provide an optimal recording method based on the user's past recording history. Some or all of the above-mentioned processing in the recording unit may be performed using AI, or may be performed without using AI. For example, the recording unit can input the past symptom recording history to AI, which can analyze the information and suggest an appropriate recording method.

[0116] The reception unit can estimate the user's emotion and adjust the symptom input interface based on the emotion. For example, when the user is stressed, a simple and intuitive interface is provided to minimize the input procedure. Also, when the user is relaxed, detailed input options are provided and a customizable input method is suggested. Furthermore, when the user is in a hurry, voice input is prioritized to allow the user to quickly input symptoms. This makes it possible to provide an optimal interface according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using an AI or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI, which can analyze the information to provide an appropriate interface.

[0117] The selection unit can estimate the user's emotions and adjust the over-the-counter drug suggestion method based on the emotions. For example, if the user feels anxious, an over-the-counter drug suggestion method that gives a sense of security is used. If the user is relaxed, an over-the-counter drug suggestion method that includes detailed information is used. If the user is in a hurry, a concise and quick over-the-counter drug suggestion method is used. This makes it possible to provide an optimal over-the-counter drug suggestion method according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the user's emotion data to the generation AI, and the generation AI can analyze the information and provide an appropriate suggestion method.

[0118] The providing unit can estimate the user's emotions and adjust the method of use and the method of providing cautions based on the emotions. For example, if the user feels anxious, an explanation method that gives a sense of security is used. If the user is relaxed, an explanation method including detailed information is used. If the user is in a hurry, a concise and quick explanation method is used. This makes it possible to provide optimal usage methods and cautions according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI, and the generation AI can analyze the information to determine an appropriate provision method.

[0119] The recording unit can estimate the user's emotions and adjust the symptom recording interface based on the emotions. For example, when the user is stressed, a simple and intuitive interface is provided to minimize the recording procedure. Also, when the user is relaxed, detailed recording options are provided and a customizable recording method is suggested. Furthermore, when the user is in a hurry, voice input is prioritized to enable the user to quickly record symptoms. This makes it possible to provide an optimal interface according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recording unit may be performed using AI, or may be performed without using AI. For example, the recording unit can input the user's emotion data to the generation AI, which can then analyze the information to provide an appropriate interface.

[0120] The monitoring unit can estimate the user's emotions and adjust the display method of the healing progress based on the emotions. For example, if the user feels anxious, a display method that gives a sense of security is used. If the user is relaxed, a display method including detailed information is used. If the user is in a hurry, a simple and quick display method is used. This makes it possible to provide the healing progress in an optimal display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can input the user's emotion data to the generation AI, and the generation AI can analyze the information to determine an appropriate display method.

[0121] The process flow of the second embodiment will be briefly described below.

[0122] Step 1: The reception unit provides an interface for users to input their symptoms. For example, users can input specific symptoms such as "sore throat" or "mild headache." The reception unit can also use AI to analyze the user's input and generate data to suggest appropriate over-the-counter medications. Step 2: The selection unit analyzes the symptoms received by the reception unit and selects an appropriate over-the-counter medicine. The selection unit uses AI to select the most suitable over-the-counter medicine based on the symptoms and provides that information to the user. For example, the selection unit may suggest a lozenge or spray that relieves a sore throat for a "sore throat." Step 3: The provision unit provides the user with instructions on how to use the over-the-counter drugs selected by the selection unit and points to be aware of. The provision unit uses AI to provide detailed explanations of the correct way to use the over-the-counter drugs and points to be aware of, supporting the user in using the drugs safely. For example, the provision unit explains how to use the lozenges, the number of times to use them per day, and side effects to be aware of. Step 4: The recorder provides an interface for the user to record the progress of symptoms. The recorder can also use AI to record the changes in the user's symptoms and the effects of the medication on a daily basis and analyze the data. For example, the recorder can evaluate how much the sore throat has improved or how much the headache has been alleviated. Step 5: The monitoring unit analyzes the data recorded by the recording unit and monitors the progress of healing. The monitoring unit analyzes the data collected using AI and evaluates the progress of healing. For example, the monitoring unit evaluates how much the sore throat has improved and how much the headache has been alleviated.

[0123] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0125] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements including the above-mentioned reception unit, selection unit, provision unit, recording unit, and monitoring unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and provides an interface for the user to input his / her own symptoms. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the symptoms received by the reception unit and selects an appropriate over-the-counter drug. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the user with the usage method and precautions for the over-the-counter drug selected by the selection unit. The recording unit is realized, for example, by the control unit 46A of the smart device 14, and provides an interface for the user to record the progress of the symptoms. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the data recorded by the recording unit and monitors the progress of the healing. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0130] 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.

[0131] 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 the voice according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0133] 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 performed in a secure state.

[0134] Fig. 4 shows an example of 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.

[0135] 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.

[0136] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0139] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0140] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0141] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements including the reception unit, selection unit, provision unit, recording unit, and monitoring unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and provides an interface for the user to input his / her own symptoms. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the symptoms received by the reception unit and selects an appropriate over-the-counter drug. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the user with instructions on how to use and precautions for the over-the-counter drug selected by the selection unit. The recording unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides an interface for the user to record the progress of the symptoms. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the data recorded by the recording unit and monitors the progress of the healing. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0146] 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.

[0147] 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 the voice according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0149] 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 performed in a secure state.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] 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.

[0152] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0153] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0155] 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 voice indicating a user input for 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.

[0156] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0157] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements including the above-mentioned reception unit, selection unit, provision unit, recording unit, and monitoring unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and provides an interface for the user to input his / her own symptoms. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the symptoms received by the reception unit and selects an appropriate over-the-counter drug. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the user with the usage method and precautions for the over-the-counter drug selected by the selection unit. The recording unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides an interface for the user to record the progress of the symptoms. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the data recorded by the recording unit and monitors the progress of the healing. The correspondence relationship between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0162] 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.

[0163] 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 the voice according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0165] 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 performed in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0167] 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.

[0168] 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.

[0169] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0170] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0171] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0172] 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 a voice indicating a user input for 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.

[0173] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0174] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0175] Each of the multiple elements including the reception unit, selection unit, provision unit, recording unit, and monitoring unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and provides an interface for the user to input his / her own symptoms. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the symptoms received by the reception unit and selects an appropriate over-the-counter drug. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the user with the usage method and precautions for the over-the-counter drug selected by the selection unit. The recording unit is realized, for example, by the control unit 46A of the robot 414, and provides an interface for the user to record the progress of the symptoms. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the data recorded by the recording unit and monitors the progress of the healing. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0177] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0179] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0181] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0183] 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.

[0184] 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 Universal Serial Bus (USB) 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.

[0185] In addition, 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 upon request from the data processing device 12.

[0186] 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.

[0187] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0188] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0189] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0190] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0192] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0194] (Appendix 1) A reception unit that receives input of symptoms; A selection unit that analyzes the symptoms received by the reception unit and selects an over-the-counter drug; a providing unit that provides a method of use and precautions for the over-the-counter drug selected by the selecting unit; A record section for recording the progress of symptoms; A monitoring unit that analyzes the data recorded by the recording unit and monitors the progress of healing. A system characterized by: (Appendix 2) The reception unit is Give users options for entering symptoms 2. The system of claim 1, (Appendix 3) The selection unit is Selecting over-the-counter drugs based on past data and the user's health condition 2. The system of claim 1, (Appendix 4) The recording unit is Provide an interface for recording symptoms over time 2. The system of claim 1, (Appendix 5) The recording unit is Provide a reminder function to regularly record symptoms 2. The system of claim 1, (Appendix 6) The monitoring unit is Provide additional advice or suggest other over-the-counter medications as healing progresses 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the user's emotions and adjust the symptom input interface based on those emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past symptom input history and select the optimal input method 2. The system of claim 1, (Appendix 9) The reception unit is When entering symptoms, customize the input fields based on the user's current life situation and areas of interest 2. The system of claim 1, (Appendix 10) The reception unit is Infer the user's emotions and prioritize the symptoms to be entered based on those emotions 2. The system of claim 1, (Appendix 11) The reception unit is When entering symptoms, the system takes into account the user's geographic location information and prioritizes the most relevant symptoms. 2. The system of claim 1, (Appendix 12) The reception unit is When a user enters a symptom, the system analyzes the user's social media activity and prompts the user to enter related symptoms. 2. The system of claim 1, (Appendix 13) The selection unit is Inferring user emotions and tailoring over-the-counter drug suggestions based on those emotions 2. The system of claim 1, (Appendix 14) The selection unit is When selecting over-the-counter medications, the level of detail in the suggestions is adjusted based on the severity of symptoms. 2. The system of claim 1, (Appendix 15) The selection unit is Apply different selection algorithms for over-the-counter drugs depending on symptom category 2. The system of claim 1, (Appendix 16) The selection unit is Infer user sentiment and adjust the length of over-the-counter drug suggestions based on that sentiment 2. The system of claim 1, (Appendix 17) The selection unit is When selecting over-the-counter medications, prioritize suggestions based on the time of symptom onset 2. The system of claim 1, (Appendix 18) The selection unit is When selecting over-the-counter medications, adjust the order of suggestions based on symptom relevance 2. The system of claim 1, (Appendix 19) The providing unit is Infer user emotions and adjust usage and attention provision based on those emotions 2. The system of claim 1, (Appendix 20) The providing unit is When providing usage instructions and cautions, the system selects the most appropriate method by referring to the user's past drug use history. 2. The system of claim 1, (Appendix 21) The providing unit is When providing usage instructions and tips, tailor the content based on the user's current life situation 2. The system of claim 1, (Appendix 22) The providing unit is Infer user emotions and prioritize usage methods and points of attention based on those emotions 2. The system of claim 1, (Appendix 23) The providing unit is When providing usage instructions and cautions, the optimal method of providing them is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 24) The providing unit is Analyze users' social media activity to tailor content when providing usage instructions and advice 2. The system of claim 1, (Appendix 25) The recording unit is Inferring user emotions and adjusting the symptom recording interface based on those emotions 2. The system of claim 1, (Appendix 26) The recording unit is When recording symptoms, the optimal recording method is selected by referring to the user's past symptom recording history. 2. The system of claim 1, (Appendix 27) The recording unit is When logging symptoms, customize what you log based on your current life situation 2. The system of claim 1, (Appendix 28) The recording unit is Inferring user emotions and prioritizing symptom records based on those emotions 2. The system of claim 1, (Appendix 29) The recording unit is When recording symptoms, the optimal recording method is selected based on the user's geographic location. 2. The system of claim 1, (Appendix 30) The recording unit is Analyze users' social media activity to tailor symptom logging 2. The system of claim 1, (Appendix 31) The monitoring unit is Infer the user's emotions and adjust how healing progress is displayed based on those emotions 2. The system of claim 1, (Appendix 32) The monitoring unit is When monitoring healing progress, look at past healing data to predict current progress 2. The system of claim 1, (Appendix 33) The monitoring unit is When monitoring healing progress, use different monitoring techniques for different symptom categories 2. The system of claim 1, (Appendix 34) The monitoring unit is Infer the user's emotions and adjust the importance of healing progress based on those emotions 2. The system of claim 1, (Appendix 35) The monitoring unit is When monitoring healing progress, analyze changes in progress based on when symptoms occur 2. The system of claim 1, (Appendix 36) The monitoring unit is When monitoring the progress of healing, analyze the progress by referring to relevant market data for the condition. 2. The system of claim 1, [Explanation of symbols]

[0195] 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 reception unit that receives input of symptoms; A selection unit that analyzes the symptoms received by the reception unit and selects an over-the-counter drug; a providing unit that provides a method of use and precautions for the over-the-counter drug selected by the selecting unit; A record section for recording the progress of symptoms; A monitoring unit that analyzes the data recorded by the recording unit and monitors the progress of healing. A system characterized by:

2. The reception unit is Give users options for entering symptoms 2. The system of claim 1 .

3. The selection unit is Selecting over-the-counter drugs based on past data and the user's health condition 2. The system of claim 1 .

4. The recording unit is Provide an interface for recording symptoms over time 2. The system of claim 1 .

5. The recording unit is Provide a reminder function to regularly record symptoms 2. The system of claim 1 .

6. The monitoring unit is Provide additional advice or suggest other over-the-counter medications as healing progresses 2. The system of claim 1 .

7. The reception unit is Estimate the user's emotions and adjust the symptom input interface based on those emotions 2. The system of claim 1 .

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

Images