System

Abstract

A system is provided.SOLUTION: A system comprising: a device for a user to input a symptom; a server for receiving and analyzing input symptom data; database reference means for predicting a type of a currently prevalent cold based on an analysis result; countermeasure selection means for selecting an effective countermeasure based on the predicted type of the cold; and display means for notifying the user of the selected countermeasure.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Currently, it is difficult for people to self-diagnose cold symptoms and quickly find optimal countermeasures. It is particularly difficult to determine which types of colds are prevalent and find effective countermeasures based on that information when time is limited or symptoms are unclear. Furthermore, information available on the Internet is general, and specific and reliable countermeasures for individual symptoms are needed. The objective of this invention is to provide a system that can suggest optimal countermeasures based on the latest epidemic data and on the symptoms entered by the user. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means. A system is provided that includes a device for a user to input symptoms, a server that receives and analyzes the input symptom data, a database reference means for predicting the type of cold currently prevalent based on the analysis results, a countermeasure selection means for selecting effective countermeasures based on the predicted type of cold, and a display means for notifying the user of the selected countermeasures. The symptom data input by the device is sent to the server, which analyzes the input data to identify the type of cold that matches the symptoms. Furthermore, by obtaining prediction results by referencing the latest prevalence data, the system notifies the user of the selected countermeasures, providing quick and reliable countermeasures. Furthermore, by including a means for prompting the user to input additional data if the user's input data is insufficient, and a keyword extraction means for the server to analyze the input data, more accurate analysis and predictions are achieved.

[0006] A "user" is an entity that uses this system to input their own symptoms and receive countermeasure information.

[0007] "Device" refers to the equipment used by the user to enter their symptoms, and may include a smartphone, tablet, or PC.

[0008] "Symptom data" refers to information that indicates the specific condition of one's illness that a user enters into the device, such as "cough," "sore throat," or "slight fever."

[0009] The "server" is a computer system that receives symptom data sent from devices, analyzes it, and manages and provides prediction results and countermeasure information.

[0010] The "database reference means" is a means by which the server searches a database containing the latest cold epidemic status and identifies the type of cold that matches the symptom data.

[0011] The "countermeasure selection means" is a means by which the server selects effective countermeasures based on the predicted type of cold.

[0012] "Display means" refers to a means for visually presenting selected countermeasure information to the user, and includes the device's screen display and notification functions.

[0013] The "keyword extraction means" is a means for extracting important keywords when the server analyzes symptom data and using them for database search.

[0014] The "means for prompting additional input" is a means for requesting the user to input additional information if the symptom data input by the user is insufficient.

[0015] "Analysis" refers to the process of performing calculations and data processing to identify the type of cold based on the symptom data received by the server. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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).

[0028] 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.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] 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.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention relates to a system that includes a device where a user inputs their symptoms, a server that receives and analyzes the input symptom data, and a display means. Based on the symptoms input by the user, the system predicts the type of cold while referring to the latest epidemic data, and suggests effective countermeasures against it.

[0038] First, the user uses a device (such as a smartphone or tablet) to input their symptoms. The user enters their specific symptoms, such as "cough, sore throat, slight fever," into the device's input screen. The device then sends this input data to the server.

[0039] The server analyzes the symptom data it receives. Specifically, the server extracts keywords from the symptom data and searches a symptom database based on keywords such as "cough," "sore throat," and "mild fever." The symptom database contains data on common colds, influenza, and other infectious diseases.

[0040] The server then refers to a trend database, which contains the latest cold epidemic information provided by medical institutions and health authorities. The server then combines and analyzes the symptom data and trend data to predict the type of cold currently circulating.

[0041] The server then selects the most effective countermeasures based on the predicted type of cold. For example, if influenza A is predicted, it will recommend the need for rest and the use of over-the-counter anti-influenza medication. If necessary, it can also recommend a medical visit.

[0042] The selected countermeasure information is sent from the server to the device. The device notifies the user of this information and displays specific countermeasures. For example, a message such as "Influenza A may be the current epidemic. Get plenty of rest and use over-the-counter anti-influenza medication" may be displayed.

[0043] To give a concrete example, the following cases can be considered:

[0044] 1. User A enters symptoms such as "sneezing, runny nose, slight fever" into the device.

[0045] 2. The terminal sends the entered data to the server.

[0046] 3. The server analyzes the data and, by referring to the symptom database and epidemic database, predicts the likelihood of a "common cold."

[0047] 4. The server selects countermeasures such as "sufficient hydration, warm clothing, and vitamin C supplementation" and sends the information to the device.

[0048] 5. User A checks the countermeasure information displayed on the device and takes action.

[0049] In this way, this system allows users to quickly and accurately find the best measures for their symptoms, which is expected to lead to early cold prevention measures, prevent the illness from becoming severe, and help curb the spread of colds.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] The user enters symptoms into the device, for example, specific symptoms such as "cough, sore throat, slight fever."

[0053] Step 2:

[0054] The device formats the symptom data entered and sends it to the server, including information such as the user ID, symptoms, and the date and time of entry.

[0055] Step 3:

[0056] The server analyzes the received symptom data. Specifically, the server extracts keywords from the symptom data, such as "cough," "sore throat," and "low-grade fever."

[0057] Step 4:

[0058] The server uses a combination of keywords to search a symptom database, which contains information about the common cold, flu, and other infectious diseases.

[0059] Step 5:

[0060] The server references an epidemic database, which records the latest cold epidemic information provided by medical institutions and health authorities.

[0061] Step 6:

[0062] The server compares the symptom data with the epidemic data and predicts the type of cold currently circulating. For example, it determines that the symptom matches influenza type A.

[0063] Step 7:

[0064] Based on the predicted type of cold, the server searches a database of countermeasures and selects the most effective countermeasures, such as "getting rest, taking anti-influenza medicine, and staying hydrated."

[0065] Step 8:

[0066] The server then sends the selected countermeasure information to the terminal, including the cold type prediction results and countermeasures.

[0067] Step 9:

[0068] The device displays the received countermeasure information to the user, visually providing specific countermeasures. For example, it may say, "The current epidemic may be influenza type A. Get plenty of rest and use over-the-counter anti-influenza medication."

[0069] Step 10:

[0070] Users can take action based on the displayed countermeasure information, and in some cases can ask additional questions or provide further details.

[0071] Through each of the above steps, users can quickly and accurately find the optimal measures, which can help prevent colds and improve their condition quickly.

[0072] Example 1

[0073] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0074] Conventional cold prevention systems often took a long time to analyze the symptom data entered by the user and predict the appropriate type of cold and countermeasures. Furthermore, they lacked a mechanism for quickly and accurately proposing effective countermeasures based on the prevalent type of cold, resulting in delays before users could take appropriate measures. Furthermore, if the symptom data entered by the user contained insufficient information, the system lacked a function to prompt for additional input to compensate, which created a risk of reducing the accuracy of the analysis results. Additionally, the accuracy of keyword extraction based on symptom data was incomplete, which sometimes led to an inability to make an appropriate diagnosis.

[0075] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0076] In this invention, the server includes a terminal device for users to input symptoms, a computer that receives and analyzes the input symptom data, information reference means for predicting the type of disease currently prevalent based on the analysis results, means for selecting effective countermeasures based on the predicted type of disease, and notification means for notifying the user of the selected countermeasures. This enables rapid and accurate analysis of symptom data, enabling accurate prediction of the type of cold based on the latest prevalence data and the proposal of appropriate countermeasures. Furthermore, the accuracy of the analysis results can be improved by using means for prompting the user for additional input and means for extracting information.

[0077] "User" refers to an individual or user who uses the system to input their symptoms and receive appropriate countermeasure information.

[0078] "Terminal device" refers to an electronic device used by a user to input symptoms, including mobile information terminals such as smartphones and tablets.

[0079] "Computer" refers to a central processing unit that analyzes symptom data received from the user and generates appropriate countermeasure information.

[0080] "Information reference means" refers to the function of referencing symptom data and epidemic data in order to predict the type of disease currently prevalent based on the analysis results.

[0081] "Measure selection means" refers to a function that selects effective measures to be provided to users based on the predicted type of disease.

[0082] "Notification means" refers to the means for notifying users of the selected countermeasure information and indicating specific countermeasures.

[0083] "Symptom data" refers to data entered by users that indicates specific health conditions, such as cough, sore throat, or slight fever.

[0084] "Epidemic data" refers to database information provided by medical institutions and health authorities that shows the current status of epidemics of diseases.

[0085] "Information extraction means" refers to the function of analyzing symptom data received from the user and extracting important information such as keywords from it.

[0086] The present invention relates to a system that allows a user to easily input their symptoms and quickly proposes effective cold prevention measures based on the input. The system includes a terminal device for receiving the user's input, a computer for analyzing the received data, a means for predicting the type of cold by referring to a trend database, and a means for notifying the user of the recommended measures.

[0087] Using a terminal

[0088] Users can launch a dedicated application on their smartphone, tablet, or other electronic device and enter specific symptoms such as "cough, sore throat, slight fever" into the application's input screen. The entered data is verified by the program, and an interface prompting for additional input is displayed if necessary.

[0089] Computer-based data analysis

[0090] The symptom data collected by the terminal device is sent to a computer via a network. The computer extracts keywords from the received data using a text analysis library (such as Python's NLTK). For example, keywords such as "cough," "sore throat," and "low-grade fever" are extracted. This clarifies the main points of the symptom data.

[0091] Browse epidemic data

[0092] The computer uses the extracted keywords to search a symptom database, which contains information on the common cold, flu, and other infectious diseases. It also consults an epidemic database to obtain the latest epidemic information for each region. The epidemic database is based on information provided by medical institutions and health authorities.

[0093] Cold type prediction

[0094] The computer then combines and analyzes the received symptom data and epidemic data, and uses statistical models (e.g., logistic regression models) and machine learning algorithms to predict the type of cold. For example, it can predict "influenza type A."

[0095] Selection of effective measures

[0096] Based on the predicted cold type, the computer selects effective countermeasures from a database. The countermeasures include specific advice such as how to get rest, recommended over-the-counter medications, and, if necessary, seeking medical attention. This allows users to take prompt and appropriate action.

[0097] User Notification

[0098] The selected countermeasure information is then sent back to the terminal device via the network. The terminal device then notifies the user of the received countermeasure information and displays specific countermeasures. The notification may be displayed as a pop-up message or as detailed information in a specific section within the application.

[0099] Specific examples

[0100] For example, if user A enters symptoms such as "sneezing, runny nose, slight fever," the process will proceed as follows:

[0101] 1. The user enters the symptoms at the terminal.

[0102] 2. The input data is sent to the computer.

[0103] 3. The computer analyzes the data and extracts keywords.

[0104] 4. The computer performs the analysis by referencing the symptom database and the prevalence database.

[0105] 5. Predict that the possibility of the illness is "common cold."

[0106] 6. The computer selects the following countermeasures: "Drink plenty of fluids, dress warmly, and take vitamin C."

[0107] 7. Countermeasure information is sent to the terminal device and notified to the user.

[0108] 8. The user must act in accordance with the displayed countermeasure information.

[0109] Prompt Sentence Examples

[0110] "Please explain a system in which a user inputs symptoms such as 'sneezing, runny nose, slight fever' into a smartphone, and a server analyzes the data to predict the type of cold they have. Please also explain how the system notifies the user of effective measures to treat the cold."

[0111] This will enable users to quickly receive optimal cold treatment, which is expected to help prevent the disease from becoming severe and the spread of colds.

[0112] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0113] Step 1:

[0114] The user operates the terminal device to input the symptoms.

[0115] Specifically, the user launches a dedicated application on their smartphone or tablet device and enters specific symptoms such as "cough, sore throat, slight fever" on the input screen. This information is stored as input data on the terminal device. The input data is in JSON format, and is saved as, for example, "{ 'symptoms': ['cough', 'sore throat', 'slight fever']}".

[0116] Step 2:

[0117] The terminal device transmits input data to the server.

[0118] The terminal device sends the data entered by the user to the server via the HTTPS protocol. The input data is packaged in JSON format on the terminal device and sent to the server via a secure communication channel. For example, the data "{ 'Symptoms': ['Cough', 'Sore throat', 'Mild fever']}" is sent to the server. Input: Symptom data in JSON format, Output: Data sent to the server.

[0119] Step 3:

[0120] The server receives and parses the input data.

[0121] The server extracts keywords from the received data using a text analysis library (e.g., Python's NLTK). Specifically, it uses an analysis algorithm to extract symptom keywords such as "cough," "sore throat," and "low-grade fever." Input: Symptom data sent to the server. Output: Extracted keywords.

[0122] Step 4:

[0123] The server searches the symptom database.

[0124] The server searches a symptom database based on the extracted keywords. The database contains information on various colds and infectious diseases, and lists candidate diseases that match the keywords. For example, "cough," "sore throat," and "slight fever" match the symptoms of a common cold. Input: extracted keywords, Output: candidate disease list.

[0125] Step 5:

[0126] The server consults the trend database.

[0127] The server references epidemic databases provided by medical institutions and health authorities. The epidemic database records the latest cold epidemic status and classifies data by region. This reference identifies the type of cold currently prevalent in that region. Input: Regional information and extracted keywords, Output: Epidemic data.

[0128] Step 6:

[0129] The server integrates and analyzes symptom data and trend data to predict the type of cold.

[0130] The server combines symptom data and epidemic data and analyzes them using statistical models and machine learning algorithms. For example, a logistic regression model may be used to make a prediction, resulting in "influenza type A." Input: symptom data and epidemic data, output: predicted type of cold.

[0131] Step 7:

[0132] The server selects effective countermeasures based on the type of cold.

[0133] The server selects effective countermeasure information from the countermeasure database based on the predicted type of cold. For example, if "Influenza A" is predicted, specific advice including how to get enough rest, over-the-counter medication recommendations, and visiting a medical institution will be selected. Input: Predicted type of cold, Output: Countermeasure information.

[0134] Step 8:

[0135] The server transmits the selected countermeasure information to the terminal device.

[0136] The server then packages the selected countermeasure information into JSON format again and sends it to the user's terminal device. Input: Selected countermeasure information, Output: Countermeasure information sent to the terminal device

[0137] Step 9:

[0138] The terminal device displays the countermeasure information to the user.

[0139] The terminal device displays the countermeasure information received from the server in a pop-up or in a specific section within the application. The notification includes a specific message such as, "The current outbreak may be influenza type A. Please get plenty of rest and use over-the-counter anti-influenza medication." Input: Countermeasure information from the server, Output: Notification message to the user.

[0140] The above is the flow of the specific processing steps of the system, which allows users to quickly and accurately take optimal measures against colds.

[0141] (Application example 1)

[0142] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0143] While early detection and treatment of cold and flu symptoms is important, it is difficult to obtain accurate predictions and appropriate treatment by simply inputting symptoms based on personal judgment. Furthermore, when purchasing medicines as a treatment, the effort required to quickly find and purchase the appropriate products can be a problem. There is a need for a system that can solve these problems and enable users to quickly and accurately predict the type of cold they have, and easily purchase effective treatments and necessary medicines.

[0144] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0145] In this invention, the server includes a device for a user to input symptoms, a server that receives and analyzes the input symptom data, database reference means for predicting the type of cold that is currently prevalent based on the analysis results, treatment selection means for selecting effective treatments based on the predicted type of cold, display means for notifying the user of the selected treatments, purchase link generation means for suggesting medicines corresponding to the predicted type of cold and providing a purchase link for the medicines, and electronic payment processing means for the user to purchase medicines electronically within the application. This enables users to quickly and accurately analyze their own symptoms and easily purchase optimal treatments and necessary medicines.

[0146] "User" means an individual who uses the system to enter their symptoms.

[0147] "Devices for inputting symptoms" are electronic devices such as smartphones and tablets that users use to input their symptoms.

[0148] The "server that receives and analyzes symptom data" is a central processing unit that receives symptom data entered by the user and analyzes the data.

[0149] The "database reference means" is a means by which the server refers to a database prepared in advance in order to predict the type of cold based on symptom data.

[0150] The "measure selection means" is a function for selecting the most effective countermeasure based on the predicted type of cold.

[0151] "Display means" means an output device such as a display or monitor used to notify the user of the selected measure.

[0152] The "purchase link generation means" is a function for proposing medicines corresponding to the predicted type of cold and generating a purchase link for the medicines.

[0153] "Electronic payment processing means" means a function that allows users to purchase medicines electronically within the application.

[0154] "Cold type" is a classification of illnesses that characterizes specific symptoms such as colds and influenza.

[0155] "Effective measures" are the defenses and treatments that are most appropriate for the predicted type of cold.

[0156] "Medicines" are drugs used to treat or prevent illnesses such as colds and flu.

[0157] This invention is a system that includes a device where a user inputs their symptoms, a server that receives and analyzes the input symptom data, and display means. Based on the symptoms input by the user, the system predicts the type of cold while referring to the latest epidemic data, and suggests effective countermeasures against it.

[0158] First, a smartphone or tablet is used as a device for users to input their symptoms. Users enter specific symptoms, such as "cough, sore throat, slight fever," into the device's input screen. This input data is then sent to a server.

[0159] The server analyzes the received symptom data. Specifically, the server extracts keywords from the symptom data and references a database based on keywords such as "cough," "sore throat," and "low-grade fever." The database contains data on common colds, influenza, and other infectious diseases.

[0160] The server then references an epidemic database, which records the latest cold epidemic information provided by medical institutions and health authorities. The server combines and analyzes the symptom data and epidemic data to predict the type of cold currently prevalent. Based on the results, the server selects the most effective countermeasures. For example, if influenza type A is predicted, the server will recommend the need for rest and the use of over-the-counter anti-influenza medication. If necessary, the server can also recommend a visit to a medical institution.

[0161] Another distinctive feature of this system is that it also includes a means for suggesting medications that correspond to the predicted type of cold and generating and providing a purchase link for them. The server creates a purchase link for the suggested medication and displays it to the user. The user can use this purchase link to purchase the medication via electronic commerce directly within the application.

[0162] The above process includes the following elements to clarify the hardware and software used:

[0163] Smartphones and tablets are used as devices for entering symptoms.

[0164] The server acts as an analysis engine, analyzing symptom data and referencing the database.

[0165] The database is used to manage symptom data and prevalence data.

[0166] An electronic trading system is used to generate and provide the purchase links.

[0167] For example, if a user inputs symptoms such as "cough, slight fever, sore throat," the server analyzes this and predicts that it is influenza A. As a countermeasure, the server suggests "using over-the-counter anti-influenza medication" and generates a purchase link to display to the user. The user can click the displayed link to purchase the medication electronically within the application.

[0168] An example of a prompt sentence is as follows:

[0169] Health Wallet App

[0170] Symptoms: cough, slight fever, sore throat

[0171] It predicts the best course of action for this condition and provides links to where you can purchase the necessary medication.

[0172] As described above, this invention enables users to quickly and accurately analyze their own symptoms and easily purchase optimal measures and necessary medicines.

[0173] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0174] Step 1:

[0175] The user inputs symptoms into a device (smartphone or tablet) for inputting symptoms. The user enters specific symptoms such as "cough, sore throat, slight fever" into the input screen. This input data is sent from the device to the server.

[0176] Input: User-entered symptom data

[0177] Output: Symptom data sent to the server

[0178] Step 2:

[0179] The server analyzes the received symptom data. The data analysis engine extracts keywords from the symptom data, such as "cough," "sore throat," and "low-grade fever." These keywords are used to search the symptom database.

[0180] Input: Symptom data

[0181] Output: Extracted keyword data

[0182] Step 3:

[0183] The server uses the database reference means to search the symptom database and the epidemic database, and combines the symptom and epidemic information to predict the type of cold currently prevalent, for example, "influenza type A."

[0184] Input: Extracted keyword data

[0185] Output: Predicted cold type

[0186] Step 4:

[0187] The server uses the countermeasure selection means to select effective countermeasures based on the predicted type of cold, such as "get enough rest and use over-the-counter anti-influenza medicine."

[0188] Input: Predicted cold type

[0189] Output: Selected countermeasure information

[0190] Step 5:

[0191] The server uses the purchase link generation means to suggest medicines that correspond to the predicted type of cold and generate a purchase link for them. The generated link is notified to the user.

[0192] Input: Selected countermeasure information

[0193] Output: Generated purchase link

[0194] Step 6:

[0195] The terminal displays the purchase link received from the server to the user, and the user can purchase the medicine electronically within the application by clicking the displayed link.

[0196] Input: Generated purchase link

[0197] Output: Displayed purchase link and user purchase action

[0198] Step 7:

[0199] The server processes the user's purchase request and settles the payment for the medicine using an electronic payment processing means.

[0200] Input: User purchase request

[0201] Output: Purchase completion notification

[0202] The above are the specific processing steps of the system based on the application example.

[0203] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0204] This invention relates to a system that predicts the type of cold currently prevalent based on the symptoms entered by the user and suggests the best countermeasures for it. Furthermore, by combining it with an emotion engine that recognizes the user's emotions and adjusts countermeasures based on the emotions, it provides more personalized health management support.

[0205] First, the user uses a device (smartphone, tablet, PC, etc.) to input their symptoms. They enter specific symptoms such as "cough," "sore throat," and "slight fever" into the device's input screen. The device then sends the input symptom data to the server.

[0206] The server analyzes the received symptom data and identifies the type of cold based on the analysis results. Specifically, the server extracts keywords from the symptom data and predicts the type of cold, such as "common cold" or "influenza," by referring to the symptom database and trend database.

[0207] Furthermore, the server is equipped with an emotion engine that analyzes the user's emotions based on the user's input data, voice input, facial expression data, etc. The results of this analysis are used in conjunction with the countermeasure selection means to adjust countermeasures in accordance with the user's emotions.

[0208] For example, if a user's emotions indicate "anxiety" or "stress," the emotion engine will take that information into account and suggest relaxation strategies (e.g., having a hot drink or taking deep breaths). On the other hand, if a user expresses positive emotions such as "elation" or "relief," it will suggest more traditional strategies.

[0209] To give a concrete example, consider the following cases:

[0210] 1. User B enters symptoms such as "sneezing, runny nose, slight fever" into the device.

[0211] 2. The terminal sends the entered data to the server.

[0212] 3. The server analyzes the data and, by referring to the symptom database and epidemic database, predicts that the likelihood of the illness is a "common cold."

[0213] 4. The server further analyzes User B's voice input and facial expression data and confirms that he / she is expressing strong feelings of "anxiety."

[0214] 5. Based on the results of this analysis, the emotion engine selects measures to promote relaxation in addition to the usual measures.

[0215] 6. The server selects countermeasures such as "staying well hydrated, wearing warm clothes, replenishing vitamin C, and taking deep breaths to relax" and sends the information to the device.

[0216] 7. User B checks the countermeasure information displayed on the device and takes action.

[0217] As a result, users can not only receive measures to address their symptoms, but also receive personalized measures that take into account their emotional state at the time. This not only helps prevent and quickly alleviate colds, but also provides emotional care.

[0218] The processing flow will be explained below.

[0219] Step 1:

[0220] The user enters symptoms into the device, for example, specific symptoms such as "cough, sore throat, slight fever."

[0221] Step 2:

[0222] The device formats the symptom data entered and sends it to the server, including information such as the user ID, symptoms, and the date and time of entry.

[0223] Step 3:

[0224] The server analyzes the received symptom data. Specifically, the server extracts keywords from the symptom data, such as "cough," "sore throat," and "low-grade fever."

[0225] Step 4:

[0226] The server uses a combination of keywords to search a symptom database, which contains information about the common cold, flu, and other infectious diseases.

[0227] Step 5:

[0228] The server references an epidemic database, which records the latest cold epidemic information provided by medical institutions and health authorities.

[0229] Step 6:

[0230] The server compares the symptom data with the epidemic data and predicts the type of cold currently circulating. For example, it determines that the symptom matches influenza type A.

[0231] Step 7:

[0232] Based on the predicted type of cold, the server searches a database of countermeasures and selects the most effective countermeasures, such as "getting rest, taking anti-influenza medicine, and staying hydrated."

[0233] Step 8:

[0234] The server uses an emotion engine to analyze the user's emotional state, recognizing emotions from voice input and facial expression data and identifying emotions such as "anxiety" or "stress."

[0235] Step 9:

[0236] The server adjusts countermeasures based on the analyzed emotional data. For example, if the user is feeling anxious, it adds countermeasures to promote relaxation (such as drinking a warm drink or taking deep breaths).

[0237] Step 10:

[0238] The server sends the selected countermeasure information to the terminal, which includes the cold type prediction result and the adjusted countermeasure.

[0239] Step 11:

[0240] The device displays the received countermeasure information to the user, visually providing specific countermeasures. For example, it displays the message, "The current outbreak may be influenza type A. Get plenty of rest and use over-the-counter anti-influenza medication. Also, drink warm drinks and take deep breaths to relax."

[0241] Step 12:

[0242] Users can take action based on the displayed countermeasure information, and in some cases can ask more detailed questions to obtain additional countermeasure information.

[0243] Through each of these steps, users can not only find the best solution for their symptoms, but also personalized solutions based on their emotional state, helping them to prevent and quickly recover from colds and take care of their emotions.

[0244] Example 2

[0245] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0246] In recent years, with the variety of colds and fluctuating epidemics, it is important for users to take appropriate measures. However, conventional health management systems are unable to propose measures that take into account the user's emotional state, and they are not adequately attuned to the psychological aspects of the user. This can reduce the effectiveness of health management and lower user satisfaction.

[0247] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0248] In this invention, the server includes a terminal for a user to input symptoms, a server that receives and analyzes the input symptom data, database reference means for predicting the type of cold currently prevalent based on the analysis results, countermeasure selection means for selecting a countermeasure based on the predicted type of cold, display means for notifying the user of the selected countermeasure, emotion analysis means for analyzing the user's input data, voice input, and facial expression data to recognize the user's emotion, and means for adjusting the countermeasure in consideration of the emotion data obtained by the emotion analysis means. This enables the user to receive not only appropriate countermeasures for their symptoms but also personalized health management that takes into account their emotional state.

[0249] A "terminal" is an electronic device that allows users to input symptoms, and includes devices such as smartphones, tablets, and personal computers.

[0250] A "server" is a computer system for receiving and analyzing symptom data sent by a user.

[0251] The "database reference means" is a function in which the server refers to the symptom database and the epidemic database and predicts the type of cold that is currently prevalent based on the analysis results.

[0252] The "measure selection means" is a function for selecting effective measures based on the predicted type of cold.

[0253] "Display means" refers to a system for notifying users of the selected measures, and includes the device's screen display and notification functions.

[0254] "Emotion analysis means" is a function for recognizing a user's emotions by analyzing the user's input data, voice input, and facial expression data.

[0255] The "measure adjustment means" is a function that adjusts measures according to the emotional state of the user, taking into consideration the emotional data obtained by the emotion analysis means.

[0256] This invention relates to a system that predicts the type of cold currently prevalent based on the symptoms entered by the user and suggests the best countermeasures for it. Furthermore, by combining it with an emotion engine that recognizes the user's emotions and adjusts countermeasures based on the emotions, it provides more personalized health management support.

[0257] First, the user uses a device to input their symptoms. Devices include smartphones, tablets, and PCs. The user inputs specific symptoms such as "cough," "sore throat," and "slight fever" into the device's input screen. The device then sends the input symptom data to the server.

[0258] The server analyzes the received symptom data. This analysis uses natural language processing (NLP) technology to extract important keywords from the user's input. The server uses these keywords to refer to a symptom database and a trend database, and uses database reference methods to predict the type of cold, such as "common cold" or "influenza." The specific technology used is a Python NLP library (e.g., spaCy or NLTK).

[0259] Additionally, the server incorporates an emotion analyzer, which is used to analyze user input data, voice input, and facial expression data to identify the user's emotions. The server uses this data to run a speech recognition model (e.g., Google Cloud Speech-to-Text API) or a facial recognition model (e.g., OpenCV or Dlib) to obtain emotion data.

[0260] The countermeasure selection means selects effective countermeasures based on the type of cold predicted by the server. At this time, a means for adjusting the countermeasures is activated taking into account the emotional data obtained by the emotion analysis means. For example, if the user is feeling "anxiety" or "stress," countermeasures to promote relaxation (such as drinking a warm drink or taking deep breaths) are suggested.

[0261] The server then sends the selected countermeasure information to the terminal and notifies the user using the terminal's display. The user can then check the countermeasure information displayed on the terminal and take action.

[0262] Specific examples

[0263] User B enters symptoms such as "sneezing, runny nose, slight fever" into the device. The device sends the entered data to the server. The server analyzes the data and, by referring to a symptom database and an epidemic database, predicts that the illness is likely a "common cold." The server then analyzes User B's voice input and facial expression data and determines that the emotion of "anxiety" is strong. Based on the results of this analysis, the emotion analysis means selects measures to encourage relaxation in addition to the usual measures. The server selects measures such as "adequate hydration, warm clothing, vitamin C supplementation, and deep breathing to relax" and sends the information to the device. User B checks the measures displayed on the device and puts them into action.

[0264] Prompt Sentence Examples

[0265] Below are some example prompts to input to the generative AI model:

[0266] If the user enters symptoms such as "sneezing, runny nose, slight fever," suggest recommended measures to address them. If the user expresses an emotion of "anxiety," suggest measures to promote relaxation.

[0267] As a result, this system can provide users with personalized health management that takes into account not only symptoms but also emotional states.

[0268] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0269] Step 1:

[0270] The user uses a terminal to enter symptoms.

[0271] Entered symptom data: The user enters specific symptoms such as "cough," "sore throat," and "slight fever" into the device's input screen.

[0272] Specific operation: The user accesses a dedicated application or web page on the device and enters the required information into the symptom entry form.

[0273] Step 2:

[0274] The terminal transmits the input symptom data to the server.

[0275] Input Data: Symptom data entered by the user.

[0276] Output Data: The symptom data that is sent to the server.

[0277] Specific operation: The terminal temporarily stores the data entered by the user and sends it to the server's API endpoint using the HTTPS protocol.

[0278] Step 3:

[0279] The server analyzes the received symptom data.

[0280] Input data: Symptom data sent from the device.

[0281] Output: Parsed keywords.

[0282] What it does: The server uses a Python script to run a natural language processing (NLP) library (e.g., spaCy or NLTK) to extract important keywords from the symptom data.

[0283] Step 4:

[0284] The server refers to a symptom database and an epidemic database to predict the type of cold.

[0285] Input data: Parsed keywords.

[0286] Output data: Predicted cold type.

[0287] Specific operation: The server uses an SQL query to search the symptom database and trend database, obtain data that matches the keywords, and identify the type of cold.

[0288] Step 5:

[0289] The server's emotion analysis means analyzes the user's input data, voice input, and facial expression data to recognize emotions.

[0290] Input data: User symptom data, voice input data, and facial expression data.

[0291] Output data: Parsed user sentiment.

[0292] What it does: The server runs a speech recognition model (e.g., Google Cloud Speech-to-Text API) and a facial recognition model (e.g., OpenCV or Dlib) to extract the user's emotions from the input data.

[0293] Step 6:

[0294] The server selects and adjusts countermeasures based on the predicted cold type and emotion data.

[0295] Input data: predicted cold type, parsed emotion data.

[0296] Output data: Selected and adjusted measures.

[0297] What happens: The server uses a business rules engine (e.g., Drools) to evaluate the analysis data and select and adjust the optimal countermeasure.

[0298] Step 7:

[0299] The server transmits the selected countermeasure information to the terminal, and the terminal notifies the user.

[0300] Input data: Selected countermeasure information.

[0301] Output data: Countermeasure information displayed on the device and notifications to the user.

[0302] Specific operation: The server generates countermeasure information in JSON format and sends it to the device. The device displays the received countermeasure information on the screen and notifies the user using the notification function.

[0303] (Application example 2)

[0304] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0305] Conventional health management systems predict the type of cold and suggest treatments based on symptom data entered by the user, but because they do not take the user's emotions into account, they are unable to provide fully personalized treatments or address the user's mental health condition. This makes it difficult for users to effectively manage their health when they are feeling anxious or stressed. Furthermore, they are unable to provide appropriate treatments when there are insufficient input data, making it difficult to effectively support users' health management.

[0306] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0307] In this invention, the server includes input means for the user to input symptoms, analysis means for receiving and analyzing the input symptom data, prediction means for predicting the type of cold that is currently prevalent, selection means for selecting effective countermeasures based on the predicted type of cold, notification means for notifying the user of the countermeasures, emotion analysis means for recognizing and analyzing the user's emotion, and adjustment means for adjusting the countermeasures based on the user's emotion, thereby making it possible to provide personalized healthcare that also takes into account the user's emotional state.

[0308] "User" means a person who utilizes the system to input symptom and emotion data.

[0309] "Symptoms" are physical symptoms experienced by a user when feeling unwell.

[0310] "Input means" refers to a device or interface that allows the user to input symptoms and emotions.

[0311] The "analysis means" is a means for analyzing the received symptom data and performing processing to identify the type of cold.

[0312] The "prediction means" is a means for predicting the type of cold currently prevalent based on the analysis results.

[0313] The "selection method" is a mechanism for selecting effective countermeasures based on the predicted type of cold.

[0314] "Notification means" refers to the means used to notify users of the selected measures.

[0315] "Emotion analysis means" refers to a means for recognizing a user's emotions by analyzing the user's input data, voice input, facial expression data, etc.

[0316] "Adjustment measures" are measures to appropriately adjust countermeasures based on recognized user sentiment.

[0317] This invention is a system that analyzes symptom data and emotion data entered by a user using a device such as a smartphone, predicts the type of cold that is currently prevalent, and suggests countermeasures. This system is implemented based on the following configuration and process.

[0318] System Configuration

[0319] The system mainly consists of the following hardware and software:

[0320] 1. Input means: A device such as a smartphone or tablet that allows users to input their symptoms and feelings.

[0321] 2. Analysis method: A server for receiving and analyzing data. Python and Flask are used.

[0322] 3. Prediction tool: Database reference function to predict the type of cold that is prevalent.

[0323] 4. Selection method: An algorithm for selecting effective measures.

[0324] 5. Notification means: A display screen to inform the user of the selected measures.

[0325] 6. Sentiment analysis tools: Generative AI models for analyzing user emotions, and emotion recognition technologies using OpenCV and TensorFlow.

[0326] 7. Adjustment measures: Algorithms to adjust measures based on user sentiment.

[0327] Program processing

[0328] 1. Data input and transmission: Users input their symptoms and emotions through a smartphone application. This data is transmitted to the server via the input means.

[0329] 2. Data analysis: The server analyzes the received data using Python scripts and compares it with the symptom database and the prevalence database. Flask acts as a web server, receiving and analyzing the data in real time.

[0330] 3. Cold type prediction: Based on the analysis results, we predict which type of cold is prevalent. The prediction method is based on an algorithm that includes information from the symptom database.

[0331] 4. Emotion Analysis: The TensorFlow model analyzes the user's voice input and facial expression data to recognize the user's emotions. OpenCV is used to capture facial expressions using a camera and perform analysis.

[0332] 5. Selection and adjustment of countermeasures: The selection method selects effective countermeasures based on the predicted cold type, and the adjustment method adjusts the countermeasures according to the results of user sentiment analysis.

[0333] 6. Notification of measures: The final selected measures will be displayed to the user through notification means.

[0334] Specific examples

[0335] For example, a user can input symptoms such as "sneezing, runny nose, slight fever" into a smartphone application. The device then sends the input data to a server. The server analyzes the data and, by referencing a symptom database and an epidemic database, predicts the likelihood of a "common cold."

[0336] Furthermore, the system analyzes the user's voice input and facial expression data to determine whether they are experiencing strong feelings of anxiety. As a result, it suggests measures to promote relaxation in addition to the usual measures. Specific instructions such as "drink plenty of water, dress warmly, replenish vitamin C, and take deep breaths to relax" are displayed on the notification screen.

[0337] Prompt Sentence Examples

[0338] If you enter "I have a headache and a sore throat" through the user interface:

[0339] "Analyzes input symptom data and predicts the corresponding type of cold."

[0340] If you say "I have a cough, a slight fever, and feel tired" using voice input:

[0341] "Analyzes voice-input symptoms and predicts the type of cold they are experiencing."

[0342] This allows users to receive measures appropriate to their symptoms and receive personalized health management support that takes into account their emotions at the time.

[0343] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0344] Step 1:

[0345] Users launch the application on their smartphones and input their symptoms and emotions. The user interface provides text fields and checkboxes for entering specific symptoms such as cough, sore throat, and slight fever. It also displays options for entering emotions. The input data is sent to the server in JSON format.

[0346] Input: User-entered symptom and emotion data

[0347] Output: Sending symptom and emotion data in JSON format

[0348] Step 2:

[0349] The server analyzes the received JSON data using an analysis tool. It uses a Python script to extract keywords from the symptom data and compare them with the symptom database and the epidemic database. This allows it to predict which colds are prevalent.

[0350] Input: Symptom and emotion data in JSON format

[0351] Output: Parsed symptom data and cold type

[0352] Step 3:

[0353] After predicting the type of cold, the server also analyzes the user's emotional data using an emotion analysis method. The user's voice and facial expression data are analyzed using a TensorFlow model to determine the type of emotion. Specifically, the voice data is converted into text using speech recognition software, and facial expression data is captured using OpenCV.

[0354] Input: Voice data and facial expression data

[0355] Output: Parsed emotion data

[0356] Step 4:

[0357] The server uses a selection method to select the most appropriate countermeasure based on the type of cold and emotional data. A Python algorithm lists effective countermeasures for each type of cold and adjusts them according to the user's emotional state. For example, if the user is feeling "anxious," countermeasures that promote relaxation will be prioritized.

[0358] Input: Parsed symptom data and emotion data

[0359] Output: Adjusted list of measures

[0360] Step 5:

[0361] The server then sends the final list of selected measures to the user's smartphone via a notification method. Specific measures are displayed as text on the user interface, and notifications are also sent. Users can use this information to manage their own health.

[0362] Input: Adjusted countermeasure list

[0363] Output: Countermeasure information notified to the user

[0364] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0365] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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 voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0366] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0367] [Second embodiment]

[0368] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0369] 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.

[0370] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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).

[0371] 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.

[0372] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0373] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0374] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0375] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0376] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0377] 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.

[0378] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0379] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0380] The present invention relates to a system that includes a device where a user inputs their symptoms, a server that receives and analyzes the input symptom data, and a display means. Based on the symptoms input by the user, the system predicts the type of cold while referring to the latest epidemic data, and suggests effective countermeasures against it.

[0381] First, the user uses a device (such as a smartphone or tablet) to input their symptoms. The user enters their specific symptoms, such as "cough, sore throat, slight fever," into the device's input screen. The device then sends this input data to the server.

[0382] The server analyzes the symptom data it receives. Specifically, the server extracts keywords from the symptom data and searches a symptom database based on keywords such as "cough," "sore throat," and "mild fever." The symptom database contains data on common colds, influenza, and other infectious diseases.

[0383] The server then refers to a trend database, which contains the latest cold epidemic information provided by medical institutions and health authorities. The server then combines and analyzes the symptom data and trend data to predict the type of cold currently circulating.

[0384] The server then selects the most effective countermeasures based on the predicted type of cold. For example, if influenza A is predicted, it will recommend the need for rest and the use of over-the-counter anti-influenza medication. If necessary, it can also recommend a medical visit.

[0385] The selected countermeasure information is sent from the server to the device. The device notifies the user of this information and displays specific countermeasures. For example, a message such as "Influenza A may be the current epidemic. Get plenty of rest and use over-the-counter anti-influenza medication" may be displayed.

[0386] To give a concrete example, the following cases can be considered:

[0387] 1. User A enters symptoms such as "sneezing, runny nose, slight fever" into the device.

[0388] 2. The terminal sends the entered data to the server.

[0389] 3. The server analyzes the data and, by referring to the symptom database and epidemic database, predicts the likelihood of a "common cold."

[0390] 4. The server selects countermeasures such as "sufficient hydration, warm clothing, and vitamin C supplementation" and sends the information to the device.

[0391] 5. User A checks the countermeasure information displayed on the device and takes action.

[0392] In this way, this system allows users to quickly and accurately find the best measures for their symptoms, which is expected to lead to early cold prevention measures, prevent the illness from becoming severe, and help curb the spread of colds.

[0393] The processing flow will be explained below.

[0394] Step 1:

[0395] The user enters symptoms into the device, for example, specific symptoms such as "cough, sore throat, slight fever."

[0396] Step 2:

[0397] The device formats the symptom data entered and sends it to the server, including information such as the user ID, symptoms, and the date and time of entry.

[0398] Step 3:

[0399] The server analyzes the received symptom data. Specifically, the server extracts keywords from the symptom data, such as "cough," "sore throat," and "low-grade fever."

[0400] Step 4:

[0401] The server uses a combination of keywords to search a symptom database, which contains information about the common cold, flu, and other infectious diseases.

[0402] Step 5:

[0403] The server references an epidemic database, which records the latest cold epidemic information provided by medical institutions and health authorities.

[0404] Step 6:

[0405] The server compares the symptom data with the epidemic data and predicts the type of cold currently circulating. For example, it determines that the symptom matches influenza type A.

[0406] Step 7:

[0407] Based on the predicted type of cold, the server searches a database of countermeasures and selects the most effective countermeasures, such as "getting rest, taking anti-influenza medicine, and staying hydrated."

[0408] Step 8:

[0409] The server then sends the selected countermeasure information to the terminal, including the cold type prediction results and countermeasures.

[0410] Step 9:

[0411] The device displays the received countermeasure information to the user, visually providing specific countermeasures. For example, it may say, "The current epidemic may be influenza type A. Get plenty of rest and use over-the-counter anti-influenza medication."

[0412] Step 10:

[0413] Users can take action based on the displayed countermeasure information, and in some cases can ask additional questions or provide further details.

[0414] Through each of the above steps, users can quickly and accurately find the optimal measures, which can help prevent colds and improve their condition quickly.

[0415] Example 1

[0416] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0417] Conventional cold prevention systems often took a long time to analyze the symptom data entered by the user and predict the appropriate type of cold and countermeasures. Furthermore, they lacked a mechanism for quickly and accurately proposing effective countermeasures based on the prevalent type of cold, resulting in delays before users could take appropriate measures. Furthermore, if the symptom data entered by the user contained insufficient information, the system lacked a function to prompt for additional input to compensate, which created a risk of reducing the accuracy of the analysis results. Additionally, the accuracy of keyword extraction based on symptom data was incomplete, which sometimes led to an inability to make an appropriate diagnosis.

[0418] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0419] In this invention, the server includes a terminal device for users to input symptoms, a computer that receives and analyzes the input symptom data, information reference means for predicting the type of disease currently prevalent based on the analysis results, means for selecting effective countermeasures based on the predicted type of disease, and notification means for notifying the user of the selected countermeasures. This enables rapid and accurate analysis of symptom data, enabling accurate prediction of the type of cold based on the latest prevalence data and the proposal of appropriate countermeasures. Furthermore, the accuracy of the analysis results can be improved by using means for prompting the user for additional input and means for extracting information.

[0420] "User" refers to an individual or user who uses the system to input their symptoms and receive appropriate countermeasure information.

[0421] "Terminal device" refers to an electronic device used by a user to input symptoms, including mobile information terminals such as smartphones and tablets.

[0422] "Computer" refers to a central processing unit that analyzes symptom data received from the user and generates appropriate countermeasure information.

[0423] "Information reference means" refers to the function of referencing symptom data and epidemic data in order to predict the type of disease currently prevalent based on the analysis results.

[0424] "Measure selection means" refers to a function that selects effective measures to be provided to users based on the predicted type of disease.

[0425] "Notification means" refers to the means for notifying users of the selected countermeasure information and indicating specific countermeasures.

[0426] "Symptom data" refers to data entered by users that indicates specific health conditions, such as cough, sore throat, or slight fever.

[0427] "Epidemic data" refers to database information provided by medical institutions and health authorities that shows the current status of epidemics of diseases.

[0428] "Information extraction means" refers to the function of analyzing symptom data received from the user and extracting important information such as keywords from it.

[0429] The present invention relates to a system that allows a user to easily input their symptoms and quickly proposes effective cold prevention measures based on the input. The system includes a terminal device for receiving the user's input, a computer for analyzing the received data, a means for predicting the type of cold by referring to a trend database, and a means for notifying the user of the recommended measures.

[0430] Using a terminal

[0431] Users can launch a dedicated application on their smartphone, tablet, or other electronic device and enter specific symptoms such as "cough, sore throat, slight fever" into the application's input screen. The entered data is verified by the program, and an interface prompting for additional input is displayed if necessary.

[0432] Computer-based data analysis

[0433] The symptom data collected by the terminal device is sent to a computer via a network. The computer extracts keywords from the received data using a text analysis library (such as Python's NLTK). For example, keywords such as "cough," "sore throat," and "low-grade fever" are extracted. This clarifies the main points of the symptom data.

[0434] Browse epidemic data

[0435] The computer uses the extracted keywords to search a symptom database, which contains information on the common cold, flu, and other infectious diseases. It also consults an epidemic database to obtain the latest epidemic information for each region. The epidemic database is based on information provided by medical institutions and health authorities.

[0436] Cold type prediction

[0437] The computer then combines and analyzes the received symptom data and epidemic data, and uses statistical models (e.g., logistic regression models) and machine learning algorithms to predict the type of cold. For example, it can predict "influenza type A."

[0438] Selection of effective measures

[0439] Based on the predicted cold type, the computer selects effective countermeasures from a database. The countermeasures include specific advice such as how to get rest, recommended over-the-counter medications, and, if necessary, seeking medical attention. This allows users to take prompt and appropriate action.

[0440] User Notification

[0441] The selected countermeasure information is then sent back to the terminal device via the network. The terminal device then notifies the user of the received countermeasure information and displays specific countermeasures. The notification may be displayed as a pop-up message or as detailed information in a specific section within the application.

[0442] Specific examples

[0443] For example, if user A enters symptoms such as "sneezing, runny nose, slight fever," the process will proceed as follows:

[0444] 1. The user enters the symptoms at the terminal.

[0445] 2. The input data is sent to the computer.

[0446] 3. The computer analyzes the data and extracts keywords.

[0447] 4. The computer performs the analysis by referencing the symptom database and the prevalence database.

[0448] 5. Predict that the possibility of the illness is "common cold."

[0449] 6. The computer selects the following countermeasures: "Drink plenty of fluids, dress warmly, and take vitamin C."

[0450] 7. Countermeasure information is sent to the terminal device and notified to the user.

[0451] 8. The user must act in accordance with the displayed countermeasure information.

[0452] Prompt Sentence Examples

[0453] "Please explain a system in which a user inputs symptoms such as 'sneezing, runny nose, slight fever' into a smartphone, and a server analyzes the data to predict the type of cold they have. Please also explain how the system notifies the user of effective measures to treat the cold."

[0454] This will enable users to quickly receive optimal cold treatment, which is expected to help prevent the disease from becoming severe and the spread of colds.

[0455] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0456] Step 1:

[0457] The user operates the terminal device to input the symptoms.

[0458] Specifically, the user launches a dedicated application on their smartphone or tablet device and enters specific symptoms such as "cough, sore throat, slight fever" on the input screen. This information is stored as input data on the terminal device. The input data is in JSON format, and is saved as, for example, "{ 'symptoms': ['cough', 'sore throat', 'slight fever']}".

[0459] Step 2:

[0460] The terminal device transmits input data to the server.

[0461] The terminal device sends the data entered by the user to the server via the HTTPS protocol. The input data is packaged in JSON format on the terminal device and sent to the server via a secure communication channel. For example, the data "{ 'Symptoms': ['Cough', 'Sore throat', 'Mild fever']}" is sent to the server. Input: Symptom data in JSON format, Output: Data sent to the server.

[0462] Step 3:

[0463] The server receives and parses the input data.

[0464] The server extracts keywords from the received data using a text analysis library (e.g., Python's NLTK). Specifically, it uses an analysis algorithm to extract symptom keywords such as "cough," "sore throat," and "low-grade fever." Input: Symptom data sent to the server. Output: Extracted keywords.

[0465] Step 4:

[0466] The server searches the symptom database.

[0467] The server searches a symptom database based on the extracted keywords. The database contains information on various colds and infectious diseases, and lists candidate diseases that match the keywords. For example, "cough," "sore throat," and "slight fever" match the symptoms of a common cold. Input: extracted keywords, Output: candidate disease list.

[0468] Step 5:

[0469] The server consults the trend database.

[0470] The server references epidemic databases provided by medical institutions and health authorities. The epidemic database records the latest cold epidemic status and classifies data by region. This reference identifies the type of cold currently prevalent in that region. Input: Regional information and extracted keywords, Output: Epidemic data.

[0471] Step 6:

[0472] The server integrates and analyzes symptom data and trend data to predict the type of cold.

[0473] The server combines symptom data and epidemic data and analyzes them using statistical models and machine learning algorithms. For example, a logistic regression model may be used to make a prediction, resulting in "influenza type A." Input: symptom data and epidemic data, output: predicted type of cold.

[0474] Step 7:

[0475] The server selects effective countermeasures based on the type of cold.

[0476] The server selects effective countermeasure information from the countermeasure database based on the predicted type of cold. For example, if "Influenza A" is predicted, specific advice including how to get enough rest, over-the-counter medication recommendations, and visiting a medical institution will be selected. Input: Predicted type of cold, Output: Countermeasure information.

[0477] Step 8:

[0478] The server transmits the selected countermeasure information to the terminal device.

[0479] The server then packages the selected countermeasure information into JSON format again and sends it to the user's terminal device. Input: Selected countermeasure information, Output: Countermeasure information sent to the terminal device

[0480] Step 9:

[0481] The terminal device displays the countermeasure information to the user.

[0482] The terminal device displays the countermeasure information received from the server in a pop-up or in a specific section within the application. The notification includes a specific message such as, "The current outbreak may be influenza type A. Please get plenty of rest and use over-the-counter anti-influenza medication." Input: Countermeasure information from the server, Output: Notification message to the user.

[0483] The above is the flow of the specific processing steps of the system, which allows users to quickly and accurately take optimal measures against colds.

[0484] (Application example 1)

[0485] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0486] While early detection and treatment of cold and flu symptoms is important, it is difficult to obtain accurate predictions and appropriate treatment by simply inputting symptoms based on personal judgment. Furthermore, when purchasing medicines as a treatment, the effort required to quickly find and purchase the appropriate products can be a problem. There is a need for a system that can solve these problems and enable users to quickly and accurately predict the type of cold they have, and easily purchase effective treatments and necessary medicines.

[0487] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0488] In this invention, the server includes a device for a user to input symptoms, a server that receives and analyzes the input symptom data, database reference means for predicting the type of cold that is currently prevalent based on the analysis results, treatment selection means for selecting effective treatments based on the predicted type of cold, display means for notifying the user of the selected treatments, purchase link generation means for suggesting medicines corresponding to the predicted type of cold and providing a purchase link for the medicines, and electronic payment processing means for the user to purchase medicines electronically within the application. This enables users to quickly and accurately analyze their own symptoms and easily purchase optimal treatments and necessary medicines.

[0489] "User" means an individual who uses the system to enter their symptoms.

[0490] "Devices for inputting symptoms" are electronic devices such as smartphones and tablets that users use to input their symptoms.

[0491] The "server that receives and analyzes symptom data" is a central processing unit that receives symptom data entered by the user and analyzes the data.

[0492] The "database reference means" is a means by which the server refers to a database prepared in advance in order to predict the type of cold based on symptom data.

[0493] The "measure selection means" is a function for selecting the most effective countermeasure based on the predicted type of cold.

[0494] "Display means" means an output device such as a display or monitor used to notify the user of the selected measure.

[0495] The "purchase link generation means" is a function for proposing medicines corresponding to the predicted type of cold and generating a purchase link for the medicines.

[0496] "Electronic payment processing means" means a function that allows users to purchase medicines electronically within the application.

[0497] "Cold type" is a classification of illnesses that characterizes specific symptoms such as colds and influenza.

[0498] "Effective measures" are the defenses and treatments that are most appropriate for the predicted type of cold.

[0499] "Medicines" are drugs used to treat or prevent illnesses such as colds and flu.

[0500] This invention is a system that includes a device where a user inputs their symptoms, a server that receives and analyzes the input symptom data, and display means. Based on the symptoms input by the user, the system predicts the type of cold while referring to the latest epidemic data, and suggests effective countermeasures against it.

[0501] First, a smartphone or tablet is used as a device for users to input their symptoms. Users enter specific symptoms, such as "cough, sore throat, slight fever," into the device's input screen. This input data is then sent to a server.

[0502] The server analyzes the received symptom data. Specifically, the server extracts keywords from the symptom data and references a database based on keywords such as "cough," "sore throat," and "low-grade fever." The database contains data on common colds, influenza, and other infectious diseases.

[0503] The server then references an epidemic database, which records the latest cold epidemic information provided by medical institutions and health authorities. The server combines and analyzes the symptom data and epidemic data to predict the type of cold currently prevalent. Based on the results, the server selects the most effective countermeasures. For example, if influenza type A is predicted, the server will recommend the need for rest and the use of over-the-counter anti-influenza medication. If necessary, the server can also recommend a visit to a medical institution.

[0504] Another distinctive feature of this system is that it also includes a means for suggesting medications that correspond to the predicted type of cold and generating and providing a purchase link for them. The server creates a purchase link for the suggested medication and displays it to the user. The user can use this purchase link to purchase the medication via electronic commerce directly within the application.

[0505] The above process includes the following elements to clarify the hardware and software used:

[0506] Smartphones and tablets are used as devices for entering symptoms.

[0507] The server acts as an analysis engine, analyzing symptom data and referencing the database.

[0508] The database is used to manage symptom data and prevalence data.

[0509] An electronic trading system is used to generate and provide the purchase links.

[0510] For example, if a user inputs symptoms such as "cough, slight fever, sore throat," the server analyzes this and predicts that it is influenza A. As a countermeasure, the server suggests "using over-the-counter anti-influenza medication" and generates a purchase link to display to the user. The user can click the displayed link to purchase the medication electronically within the application.

[0511] An example of a prompt sentence is as follows:

[0512] Health Wallet App

[0513] Symptoms: cough, slight fever, sore throat

[0514] It predicts the best course of action for this condition and provides links to where you can purchase the necessary medication.

[0515] As described above, this invention enables users to quickly and accurately analyze their own symptoms and easily purchase optimal measures and necessary medicines.

[0516] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0517] Step 1:

[0518] The user inputs symptoms into a device (smartphone or tablet) for inputting symptoms. The user enters specific symptoms such as "cough, sore throat, slight fever" into the input screen. This input data is sent from the device to the server.

[0519] Input: User-entered symptom data

[0520] Output: Symptom data sent to the server

[0521] Step 2:

[0522] The server analyzes the received symptom data. The data analysis engine extracts keywords from the symptom data, such as "cough," "sore throat," and "low-grade fever." These keywords are used to search the symptom database.

[0523] Input: Symptom data

[0524] Output: Extracted keyword data

[0525] Step 3:

[0526] The server uses the database reference means to search the symptom database and the epidemic database, and combines the symptom and epidemic information to predict the type of cold currently prevalent, for example, "influenza type A."

[0527] Input: Extracted keyword data

[0528] Output: Predicted cold type

[0529] Step 4:

[0530] The server uses the countermeasure selection means to select effective countermeasures based on the predicted type of cold, such as "get enough rest and use over-the-counter anti-influenza medicine."

[0531] Input: Predicted cold type

[0532] Output: Selected countermeasure information

[0533] Step 5:

[0534] The server uses the purchase link generation means to suggest medicines that correspond to the predicted type of cold and generate a purchase link for them. The generated link is notified to the user.

[0535] Input: Selected countermeasure information

[0536] Output: Generated purchase link

[0537] Step 6:

[0538] The terminal displays the purchase link received from the server to the user, and the user can purchase the medicine electronically within the application by clicking the displayed link.

[0539] Input: Generated purchase link

[0540] Output: Displayed purchase link and user purchase action

[0541] Step 7:

[0542] The server processes the user's purchase request and settles the payment for the medicine using an electronic payment processing means.

[0543] Input: User purchase request

[0544] Output: Purchase completion notification

[0545] The above are the specific processing steps of the system based on the application example.

[0546] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0547] This invention relates to a system that predicts the type of cold currently prevalent based on the symptoms entered by the user and suggests the best countermeasures for it. Furthermore, by combining it with an emotion engine that recognizes the user's emotions and adjusts countermeasures based on the emotions, it provides more personalized health management support.

[0548] First, the user uses a device (smartphone, tablet, PC, etc.) to input their symptoms. They enter specific symptoms such as "cough," "sore throat," and "slight fever" into the device's input screen. The device then sends the input symptom data to the server.

[0549] The server analyzes the received symptom data and identifies the type of cold based on the analysis results. Specifically, the server extracts keywords from the symptom data and predicts the type of cold, such as "common cold" or "influenza," by referring to the symptom database and trend database.

[0550] Furthermore, the server is equipped with an emotion engine that analyzes the user's emotions based on the user's input data, voice input, facial expression data, etc. The results of this analysis are used in conjunction with the countermeasure selection means to adjust countermeasures in accordance with the user's emotions.

[0551] For example, if a user's emotions indicate "anxiety" or "stress," the emotion engine will take that information into account and suggest relaxation strategies (e.g., having a hot drink or taking deep breaths). On the other hand, if a user expresses positive emotions such as "elation" or "relief," it will suggest more traditional strategies.

[0552] To give a concrete example, consider the following cases:

[0553] 1. User B enters symptoms such as "sneezing, runny nose, slight fever" into the device.

[0554] 2. The terminal sends the entered data to the server.

[0555] 3. The server analyzes the data and, by referring to the symptom database and epidemic database, predicts that the likelihood of the illness is a "common cold."

[0556] 4. The server further analyzes User B's voice input and facial expression data and confirms that he / she is expressing strong feelings of "anxiety."

[0557] 5. Based on the results of this analysis, the emotion engine selects measures to promote relaxation in addition to the usual measures.

[0558] 6. The server selects countermeasures such as "staying well hydrated, wearing warm clothes, replenishing vitamin C, and taking deep breaths to relax" and sends the information to the device.

[0559] 7. User B checks the countermeasure information displayed on the device and takes action.

[0560] As a result, users can not only receive measures to address their symptoms, but also receive personalized measures that take into account their emotional state at the time. This not only helps prevent and quickly alleviate colds, but also provides emotional care.

[0561] The processing flow will be explained below.

[0562] Step 1:

[0563] The user enters symptoms into the device, for example, specific symptoms such as "cough, sore throat, slight fever."

[0564] Step 2:

[0565] The device formats the symptom data entered and sends it to the server, including information such as the user ID, symptoms, and the date and time of entry.

[0566] Step 3:

[0567] The server analyzes the received symptom data. Specifically, the server extracts keywords from the symptom data, such as "cough," "sore throat," and "low-grade fever."

[0568] Step 4:

[0569] The server uses a combination of keywords to search a symptom database, which contains information about the common cold, flu, and other infectious diseases.

[0570] Step 5:

[0571] The server references an epidemic database, which records the latest cold epidemic information provided by medical institutions and health authorities.

[0572] Step 6:

[0573] The server compares the symptom data with the epidemic data and predicts the type of cold currently circulating. For example, it determines that the symptom matches influenza type A.

[0574] Step 7:

[0575] Based on the predicted type of cold, the server searches a database of countermeasures and selects the most effective countermeasures, such as "getting rest, taking anti-influenza medicine, and staying hydrated."

[0576] Step 8:

[0577] The server uses an emotion engine to analyze the user's emotional state, recognizing emotions from voice input and facial expression data and identifying emotions such as "anxiety" or "stress."

[0578] Step 9:

[0579] The server adjusts countermeasures based on the analyzed emotional data. For example, if the user is feeling anxious, it adds countermeasures to promote relaxation (such as drinking a warm drink or taking deep breaths).

[0580] Step 10:

[0581] The server sends the selected countermeasure information to the terminal, which includes the cold type prediction result and the adjusted countermeasure.

[0582] Step 11:

[0583] The device displays the received countermeasure information to the user, visually providing specific countermeasures. For example, it displays the message, "The current outbreak may be influenza type A. Get plenty of rest and use over-the-counter anti-influenza medication. Also, drink warm drinks and take deep breaths to relax."

[0584] Step 12:

[0585] Users can take action based on the displayed countermeasure information, and in some cases can ask more detailed questions to obtain additional countermeasure information.

[0586] Through each of these steps, users can not only find the best solution for their symptoms, but also personalized solutions based on their emotional state, helping them to prevent and quickly recover from colds and take care of their emotions.

[0587] Example 2

[0588] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0589] In recent years, with the variety of colds and fluctuating epidemics, it is important for users to take appropriate measures. However, conventional health management systems are unable to propose measures that take into account the user's emotional state, and they are not adequately attuned to the psychological aspects of the user. This can reduce the effectiveness of health management and lower user satisfaction.

[0590] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0591] In this invention, the server includes a terminal for a user to input symptoms, a server that receives and analyzes the input symptom data, database reference means for predicting the type of cold currently prevalent based on the analysis results, countermeasure selection means for selecting a countermeasure based on the predicted type of cold, display means for notifying the user of the selected countermeasure, emotion analysis means for analyzing the user's input data, voice input, and facial expression data to recognize the user's emotion, and means for adjusting the countermeasure in consideration of the emotion data obtained by the emotion analysis means. This enables the user to receive not only appropriate countermeasures for their symptoms but also personalized health management that takes into account their emotional state.

[0592] A "terminal" is an electronic device that allows users to input symptoms, and includes devices such as smartphones, tablets, and personal computers.

[0593] A "server" is a computer system for receiving and analyzing symptom data sent by a user.

[0594] The "database reference means" is a function in which the server refers to the symptom database and the epidemic database and predicts the type of cold that is currently prevalent based on the analysis results.

[0595] The "measure selection means" is a function for selecting effective measures based on the predicted type of cold.

[0596] "Display means" refers to a system for notifying users of the selected measures, and includes the device's screen display and notification functions.

[0597] "Emotion analysis means" is a function for recognizing a user's emotions by analyzing the user's input data, voice input, and facial expression data.

[0598] The "measure adjustment means" is a function that adjusts measures according to the emotional state of the user, taking into consideration the emotional data obtained by the emotion analysis means.

[0599] This invention relates to a system that predicts the type of cold currently prevalent based on the symptoms entered by the user and suggests the best countermeasures for it. Furthermore, by combining it with an emotion engine that recognizes the user's emotions and adjusts countermeasures based on the emotions, it provides more personalized health management support.

[0600] First, the user uses a device to input their symptoms. Devices include smartphones, tablets, and PCs. The user inputs specific symptoms such as "cough," "sore throat," and "slight fever" into the device's input screen. The device then sends the input symptom data to the server.

[0601] The server analyzes the received symptom data. This analysis uses natural language processing (NLP) technology to extract important keywords from the user's input. The server uses these keywords to refer to a symptom database and a trend database, and uses database reference methods to predict the type of cold, such as "common cold" or "influenza." The specific technology used is a Python NLP library (e.g., spaCy or NLTK).

[0602] Additionally, the server incorporates an emotion analyzer, which is used to analyze user input data, voice input, and facial expression data to identify the user's emotions. The server uses this data to run a speech recognition model (e.g., Google Cloud Speech-to-Text API) or a facial recognition model (e.g., OpenCV or Dlib) to obtain emotion data.

[0603] The countermeasure selection means selects effective countermeasures based on the type of cold predicted by the server. At this time, a means for adjusting the countermeasures is activated taking into account the emotional data obtained by the emotion analysis means. For example, if the user is feeling "anxiety" or "stress," countermeasures to promote relaxation (such as drinking a warm drink or taking deep breaths) are suggested.

[0604] The server then sends the selected countermeasure information to the terminal and notifies the user using the terminal's display. The user can then check the countermeasure information displayed on the terminal and take action.

[0605] Specific examples

[0606] User B enters symptoms such as "sneezing, runny nose, slight fever" into the device. The device sends the entered data to the server. The server analyzes the data and, by referring to a symptom database and an epidemic database, predicts that the illness is likely a "common cold." The server then analyzes User B's voice input and facial expression data and determines that the emotion of "anxiety" is strong. Based on the results of this analysis, the emotion analysis means selects measures to encourage relaxation in addition to the usual measures. The server selects measures such as "adequate hydration, warm clothing, vitamin C supplementation, and deep breathing to relax" and sends the information to the device. User B checks the measures displayed on the device and puts them into action.

[0607] Prompt Sentence Examples

[0608] Below are some example prompts to input to the generative AI model:

[0609] If the user enters symptoms such as "sneezing, runny nose, slight fever," suggest recommended measures to address them. If the user expresses an emotion of "anxiety," suggest measures to promote relaxation.

[0610] As a result, this system can provide users with personalized health management that takes into account not only symptoms but also emotional states.

[0611] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0612] Step 1:

[0613] The user uses a terminal to enter symptoms.

[0614] Entered symptom data: The user enters specific symptoms such as "cough," "sore throat," and "slight fever" into the device's input screen.

[0615] Specific operation: The user accesses a dedicated application or web page on the device and enters the required information into the symptom entry form.

[0616] Step 2:

[0617] The terminal transmits the input symptom data to the server.

[0618] Input Data: Symptom data entered by the user.

[0619] Output Data: The symptom data that is sent to the server.

[0620] Specific operation: The terminal temporarily stores the data entered by the user and sends it to the server's API endpoint using the HTTPS protocol.

[0621] Step 3:

[0622] The server analyzes the received symptom data.

[0623] Input data: Symptom data sent from the device.

[0624] Output: Parsed keywords.

[0625] What it does: The server uses a Python script to run a natural language processing (NLP) library (e.g., spaCy or NLTK) to extract important keywords from the symptom data.

[0626] Step 4:

[0627] The server refers to a symptom database and an epidemic database to predict the type of cold.

[0628] Input data: Parsed keywords.

[0629] Output data: Predicted cold type.

[0630] Specific operation: The server uses an SQL query to search the symptom database and trend database, obtain data that matches the keywords, and identify the type of cold.

[0631] Step 5:

[0632] The server's emotion analysis means analyzes the user's input data, voice input, and facial expression data to recognize emotions.

[0633] Input data: User symptom data, voice input data, and facial expression data.

[0634] Output data: Parsed user sentiment.

[0635] What it does: The server runs a speech recognition model (e.g., Google Cloud Speech-to-Text API) and a facial recognition model (e.g., OpenCV or Dlib) to extract the user's emotions from the input data.

[0636] Step 6:

[0637] The server selects and adjusts countermeasures based on the predicted cold type and emotion data.

[0638] Input data: predicted cold type, parsed emotion data.

[0639] Output data: Selected and adjusted measures.

[0640] What happens: The server uses a business rules engine (e.g., Drools) to evaluate the analysis data and select and adjust the optimal countermeasure.

[0641] Step 7:

[0642] The server transmits the selected countermeasure information to the terminal, and the terminal notifies the user.

[0643] Input data: Selected countermeasure information.

[0644] Output data: Countermeasure information displayed on the device and notifications to the user.

[0645] Specific operation: The server generates countermeasure information in JSON format and sends it to the device. The device displays the received countermeasure information on the screen and notifies the user using the notification function.

[0646] (Application example 2)

[0647] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0648] Conventional health management systems predict the type of cold and suggest treatments based on symptom data entered by the user, but because they do not take the user's emotions into account, they are unable to provide fully personalized treatments or address the user's mental health condition. This makes it difficult for users to effectively manage their health when they are feeling anxious or stressed. Furthermore, they are unable to provide appropriate treatments when there are insufficient input data, making it difficult to effectively support users' health management.

[0649] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0650] In this invention, the server includes input means for the user to input symptoms, analysis means for receiving and analyzing the input symptom data, prediction means for predicting the type of cold that is currently prevalent, selection means for selecting effective countermeasures based on the predicted type of cold, notification means for notifying the user of the countermeasures, emotion analysis means for recognizing and analyzing the user's emotion, and adjustment means for adjusting the countermeasures based on the user's emotion, thereby making it possible to provide personalized healthcare that also takes into account the user's emotional state.

[0651] "User" means a person who utilizes the system to input symptom and emotion data.

[0652] "Symptoms" are physical symptoms experienced by a user when feeling unwell.

[0653] "Input means" refers to a device or interface that allows the user to input symptoms and emotions.

[0654] The "analysis means" is a means for analyzing the received symptom data and performing processing to identify the type of cold.

[0655] The "prediction means" is a means for predicting the type of cold currently prevalent based on the analysis results.

[0656] The "selection method" is a mechanism for selecting effective countermeasures based on the predicted type of cold.

[0657] "Notification means" refers to the means used to notify users of the selected measures.

[0658] "Emotion analysis means" refers to a means for recognizing a user's emotions by analyzing the user's input data, voice input, facial expression data, etc.

[0659] "Adjustment measures" are measures to appropriately adjust countermeasures based on recognized user sentiment.

[0660] This invention is a system that analyzes symptom data and emotion data entered by a user using a device such as a smartphone, predicts the type of cold that is currently prevalent, and suggests countermeasures. This system is implemented based on the following configuration and process.

[0661] System Configuration

[0662] The system mainly consists of the following hardware and software:

[0663] 1. Input means: A device such as a smartphone or tablet that allows users to input their symptoms and feelings.

[0664] 2. Analysis method: A server for receiving and analyzing data. Python and Flask are used.

[0665] 3. Prediction tool: Database reference function to predict the type of cold that is prevalent.

[0666] 4. Selection method: An algorithm for selecting effective measures.

[0667] 5. Notification means: A display screen to inform the user of the selected measures.

[0668] 6. Sentiment analysis tools: Generative AI models for analyzing user emotions, and emotion recognition technologies using OpenCV and TensorFlow.

[0669] 7. Adjustment measures: Algorithms to adjust measures based on user sentiment.

[0670] Program processing

[0671] 1. Data input and transmission: Users input their symptoms and emotions through a smartphone application. This data is transmitted to the server via the input means.

[0672] 2. Data analysis: The server analyzes the received data using Python scripts and compares it with the symptom database and the prevalence database. Flask acts as a web server, receiving and analyzing the data in real time.

[0673] 3. Cold type prediction: Based on the analysis results, we predict which type of cold is prevalent. The prediction method is based on an algorithm that includes information from the symptom database.

[0674] 4. Emotion Analysis: The TensorFlow model analyzes the user's voice input and facial expression data to recognize the user's emotions. OpenCV is used to capture facial expressions using a camera and perform analysis.

[0675] 5. Selection and adjustment of countermeasures: The selection method selects effective countermeasures based on the predicted cold type, and the adjustment method adjusts the countermeasures according to the results of user sentiment analysis.

[0676] 6. Notification of measures: The final selected measures will be displayed to the user through notification means.

[0677] Specific examples

[0678] For example, a user can input symptoms such as "sneezing, runny nose, slight fever" into a smartphone application. The device then sends the input data to a server. The server analyzes the data and, by referencing a symptom database and an epidemic database, predicts the likelihood of a "common cold."

[0679] Furthermore, the system analyzes the user's voice input and facial expression data to determine whether they are experiencing strong feelings of anxiety. As a result, it suggests measures to promote relaxation in addition to the usual measures. Specific instructions such as "drink plenty of water, dress warmly, replenish vitamin C, and take deep breaths to relax" are displayed on the notification screen.

[0680] Prompt Sentence Examples

[0681] If you enter "I have a headache and a sore throat" through the user interface:

[0682] "Analyzes input symptom data and predicts the corresponding type of cold."

[0683] If you say "I have a cough, a slight fever, and feel tired" using voice input:

[0684] "Analyzes voice-input symptoms and predicts the type of cold they are experiencing."

[0685] This allows users to receive measures appropriate to their symptoms and receive personalized health management support that takes into account their emotions at the time.

[0686] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0687] Step 1:

[0688] Users launch the application on their smartphones and input their symptoms and emotions. The user interface provides text fields and checkboxes for entering specific symptoms such as cough, sore throat, and slight fever. It also displays options for entering emotions. The input data is sent to the server in JSON format.

[0689] Input: User-entered symptom and emotion data

[0690] Output: Sending symptom and emotion data in JSON format

[0691] Step 2:

[0692] The server analyzes the received JSON data using an analysis tool. It uses a Python script to extract keywords from the symptom data and compare them with the symptom database and the epidemic database. This allows it to predict which colds are prevalent.

[0693] Input: Symptom and emotion data in JSON format

[0694] Output: Parsed symptom data and cold type

[0695] Step 3:

[0696] After predicting the type of cold, the server also analyzes the user's emotional data using an emotion analysis method. The user's voice and facial expression data are analyzed using a TensorFlow model to determine the type of emotion. Specifically, the voice data is converted into text using speech recognition software, and facial expression data is captured using OpenCV.

[0697] Input: Voice data and facial expression data

[0698] Output: Parsed emotion data

[0699] Step 4:

[0700] The server uses a selection method to select the most appropriate countermeasure based on the type of cold and emotional data. A Python algorithm lists effective countermeasures for each type of cold and adjusts them according to the user's emotional state. For example, if the user is feeling "anxious," countermeasures that promote relaxation will be prioritized.

[0701] Input: Parsed symptom data and emotion data

[0702] Output: Adjusted list of measures

[0703] Step 5:

[0704] The server then sends the final list of selected measures to the user's smartphone via a notification method. Specific measures are displayed as text on the user interface, and notifications are also sent. Users can use this information to manage their own health.

[0705] Input: Adjusted countermeasure list

[0706] Output: Countermeasure information notified to the user

[0707] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0708] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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 voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0709] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0710] [Third embodiment]

[0711] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0712] 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.

[0713] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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).

[0714] 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.

[0715] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0716] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0717] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0718] 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.

[0719] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0720] 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.

[0721] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0722] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0723] The present invention relates to a system that includes a device where a user inputs their symptoms, a server that receives and analyzes the input symptom data, and a display means. Based on the symptoms input by the user, the system predicts the type of cold while referring to the latest epidemic data, and suggests effective countermeasures against it.

[0724] First, the user uses a device (such as a smartphone or tablet) to input their symptoms. The user enters their specific symptoms, such as "cough, sore throat, slight fever," into the device's input screen. The device then sends this input data to the server.

[0725] The server analyzes the symptom data it receives. Specifically, the server extracts keywords from the symptom data and searches a symptom database based on keywords such as "cough," "sore throat," and "mild fever." The symptom database contains data on common colds, influenza, and other infectious diseases.

[0726] The server then refers to a trend database, which contains the latest cold epidemic information provided by medical institutions and health authorities. The server then combines and analyzes the symptom data and trend data to predict the type of cold currently circulating.

[0727] The server then selects the most effective countermeasures based on the predicted type of cold. For example, if influenza A is predicted, it will recommend the need for rest and the use of over-the-counter anti-influenza medication. If necessary, it can also recommend a medical visit.

[0728] The selected countermeasure information is sent from the server to the device. The device notifies the user of this information and displays specific countermeasures. For example, a message such as "Influenza A may be the current epidemic. Get plenty of rest and use over-the-counter anti-influenza medication" may be displayed.

[0729] To give a concrete example, the following cases can be considered:

[0730] 1. User A enters symptoms such as "sneezing, runny nose, slight fever" into the device.

[0731] 2. The terminal sends the entered data to the server.

[0732] 3. The server analyzes the data and, by referring to the symptom database and epidemic database, predicts the likelihood of a "common cold."

[0733] 4. The server selects countermeasures such as "sufficient hydration, warm clothing, and vitamin C supplementation" and sends the information to the device.

[0734] 5. User A checks the countermeasure information displayed on the device and takes action.

[0735] In this way, this system allows users to quickly and accurately find the best measures for their symptoms, which is expected to lead to early cold prevention measures, prevent the illness from becoming severe, and help curb the spread of colds.

[0736] The processing flow will be explained below.

[0737] Step 1:

[0738] The user enters symptoms into the device, for example, specific symptoms such as "cough, sore throat, slight fever."

[0739] Step 2:

[0740] The device formats the symptom data entered and sends it to the server, including information such as the user ID, symptoms, and the date and time of entry.

[0741] Step 3:

[0742] The server analyzes the received symptom data. Specifically, the server extracts keywords from the symptom data, such as "cough," "sore throat," and "low-grade fever."

[0743] Step 4:

[0744] The server uses a combination of keywords to search a symptom database, which contains information about the common cold, flu, and other infectious diseases.

[0745] Step 5:

[0746] The server references an epidemic database, which records the latest cold epidemic information provided by medical institutions and health authorities.

[0747] Step 6:

[0748] The server compares the symptom data with the epidemic data and predicts the type of cold currently circulating. For example, it determines that the symptom matches influenza type A.

[0749] Step 7:

[0750] Based on the predicted type of cold, the server searches a database of countermeasures and selects the most effective countermeasures, such as "getting rest, taking anti-influenza medicine, and staying hydrated."

[0751] Step 8:

[0752] The server then sends the selected countermeasure information to the terminal, including the cold type prediction results and countermeasures.

[0753] Step 9:

[0754] The device displays the received countermeasure information to the user, visually providing specific countermeasures. For example, it may say, "The current epidemic may be influenza type A. Get plenty of rest and use over-the-counter anti-influenza medication."

[0755] Step 10:

[0756] Users can take action based on the displayed countermeasure information, and in some cases can ask additional questions or provide further details.

[0757] Through each of the above steps, users can quickly and accurately find the optimal measures, which can help prevent colds and improve their condition quickly.

[0758] Example 1

[0759] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0760] Conventional cold prevention systems often took a long time to analyze the symptom data entered by the user and predict the appropriate type of cold and countermeasures. Furthermore, they lacked a mechanism for quickly and accurately proposing effective countermeasures based on the prevalent type of cold, resulting in delays before users could take appropriate measures. Furthermore, if the symptom data entered by the user contained insufficient information, the system lacked a function to prompt for additional input to compensate, which created a risk of reducing the accuracy of the analysis results. Additionally, the accuracy of keyword extraction based on symptom data was incomplete, which sometimes led to an inability to make an appropriate diagnosis.

[0761] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0762] In this invention, the server includes a terminal device for users to input symptoms, a computer that receives and analyzes the input symptom data, information reference means for predicting the type of disease currently prevalent based on the analysis results, means for selecting effective countermeasures based on the predicted type of disease, and notification means for notifying the user of the selected countermeasures. This enables rapid and accurate analysis of symptom data, enabling accurate prediction of the type of cold based on the latest prevalence data and the proposal of appropriate countermeasures. Furthermore, the accuracy of the analysis results can be improved by using means for prompting the user for additional input and means for extracting information.

[0763] "User" refers to an individual or user who uses the system to input their symptoms and receive appropriate countermeasure information.

[0764] "Terminal device" refers to an electronic device used by a user to input symptoms, including mobile information terminals such as smartphones and tablets.

[0765] "Computer" refers to a central processing unit that analyzes symptom data received from the user and generates appropriate countermeasure information.

[0766] "Information reference means" refers to the function of referencing symptom data and epidemic data in order to predict the type of disease currently prevalent based on the analysis results.

[0767] "Measure selection means" refers to a function that selects effective measures to be provided to users based on the predicted type of disease.

[0768] "Notification means" refers to the means for notifying users of the selected countermeasure information and indicating specific countermeasures.

[0769] "Symptom data" refers to data entered by users that indicates specific health conditions, such as cough, sore throat, or slight fever.

[0770] "Epidemic data" refers to database information provided by medical institutions and health authorities that shows the current status of epidemics of diseases.

[0771] "Information extraction means" refers to the function of analyzing symptom data received from the user and extracting important information such as keywords from it.

[0772] The present invention relates to a system that allows a user to easily input their symptoms and quickly proposes effective cold prevention measures based on the input. The system includes a terminal device for receiving the user's input, a computer for analyzing the received data, a means for predicting the type of cold by referring to a trend database, and a means for notifying the user of the recommended measures.

[0773] Using a terminal

[0774] Users can launch a dedicated application on their smartphone, tablet, or other electronic device and enter specific symptoms such as "cough, sore throat, slight fever" into the application's input screen. The entered data is verified by the program, and an interface prompting for additional input is displayed if necessary.

[0775] Computer-based data analysis

[0776] The symptom data collected by the terminal device is sent to a computer via a network. The computer extracts keywords from the received data using a text analysis library (such as Python's NLTK). For example, keywords such as "cough," "sore throat," and "low-grade fever" are extracted. This clarifies the main points of the symptom data.

[0777] Browse epidemic data

[0778] The computer uses the extracted keywords to search a symptom database, which contains information on the common cold, flu, and other infectious diseases. It also consults an epidemic database to obtain the latest epidemic information for each region. The epidemic database is based on information provided by medical institutions and health authorities.

[0779] Cold type prediction

[0780] The computer then combines and analyzes the received symptom data and epidemic data, and uses statistical models (e.g., logistic regression models) and machine learning algorithms to predict the type of cold. For example, it can predict "influenza type A."

[0781] Selection of effective measures

[0782] Based on the predicted cold type, the computer selects effective countermeasures from a database. The countermeasures include specific advice such as how to get rest, recommended over-the-counter medications, and, if necessary, seeking medical attention. This allows users to take prompt and appropriate action.

[0783] User Notification

[0784] The selected countermeasure information is then sent back to the terminal device via the network. The terminal device then notifies the user of the received countermeasure information and displays specific countermeasures. The notification may be displayed as a pop-up message or as detailed information in a specific section within the application.

[0785] Specific examples

[0786] For example, if user A enters symptoms such as "sneezing, runny nose, slight fever," the process will proceed as follows:

[0787] 1. The user enters the symptoms at the terminal.

[0788] 2. The input data is sent to the computer.

[0789] 3. The computer analyzes the data and extracts keywords.

[0790] 4. The computer performs the analysis by referencing the symptom database and the prevalence database.

[0791] 5. Predict that the possibility of the illness is "common cold."

[0792] 6. The computer selects the following countermeasures: "Drink plenty of fluids, dress warmly, and take vitamin C."

[0793] 7. Countermeasure information is sent to the terminal device and notified to the user.

[0794] 8. The user must act in accordance with the displayed countermeasure information.

[0795] Prompt Sentence Examples

[0796] "Please explain a system in which a user inputs symptoms such as 'sneezing, runny nose, slight fever' into a smartphone, and a server analyzes the data to predict the type of cold they have. Please also explain how the system notifies the user of effective measures to treat the cold."

[0797] This will enable users to quickly receive optimal cold treatment, which is expected to help prevent the disease from becoming severe and the spread of colds.

[0798] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0799] Step 1:

[0800] The user operates the terminal device to input the symptoms.

[0801] Specifically, the user launches a dedicated application on their smartphone or tablet device and enters specific symptoms such as "cough, sore throat, slight fever" on the input screen. This information is stored as input data on the terminal device. The input data is in JSON format, and is saved as, for example, "{ 'symptoms': ['cough', 'sore throat', 'slight fever']}".

[0802] Step 2:

[0803] The terminal device transmits input data to the server.

[0804] The terminal device sends the data entered by the user to the server via the HTTPS protocol. The input data is packaged in JSON format on the terminal device and sent to the server via a secure communication channel. For example, the data "{ 'Symptoms': ['Cough', 'Sore throat', 'Mild fever']}" is sent to the server. Input: Symptom data in JSON format, Output: Data sent to the server.

[0805] Step 3:

[0806] The server receives and parses the input data.

[0807] The server extracts keywords from the received data using a text analysis library (e.g., Python's NLTK). Specifically, it uses an analysis algorithm to extract symptom keywords such as "cough," "sore throat," and "low-grade fever." Input: Symptom data sent to the server. Output: Extracted keywords.

[0808] Step 4:

[0809] The server searches the symptom database.

[0810] The server searches a symptom database based on the extracted keywords. The database contains information on various colds and infectious diseases, and lists candidate diseases that match the keywords. For example, "cough," "sore throat," and "slight fever" match the symptoms of a common cold. Input: extracted keywords, Output: candidate disease list.

[0811] Step 5:

[0812] The server consults the trend database.

[0813] The server references epidemic databases provided by medical institutions and health authorities. The epidemic database records the latest cold epidemic status and classifies data by region. This reference identifies the type of cold currently prevalent in that region. Input: Regional information and extracted keywords, Output: Epidemic data.

[0814] Step 6:

[0815] The server integrates and analyzes symptom data and trend data to predict the type of cold.

[0816] The server combines symptom data and epidemic data and analyzes them using statistical models and machine learning algorithms. For example, a logistic regression model may be used to make a prediction, resulting in "influenza type A." Input: symptom data and epidemic data, output: predicted type of cold.

[0817] Step 7:

[0818] The server selects effective countermeasures based on the type of cold.

[0819] The server selects effective countermeasure information from the countermeasure database based on the predicted type of cold. For example, if "Influenza A" is predicted, specific advice including how to get enough rest, over-the-counter medication recommendations, and visiting a medical institution will be selected. Input: Predicted type of cold, Output: Countermeasure information.

[0820] Step 8:

[0821] The server transmits the selected countermeasure information to the terminal device.

[0822] The server then packages the selected countermeasure information into JSON format again and sends it to the user's terminal device. Input: Selected countermeasure information, Output: Countermeasure information sent to the terminal device

[0823] Step 9:

[0824] The terminal device displays the countermeasure information to the user.

[0825] The terminal device displays the countermeasure information received from the server in a pop-up or in a specific section within the application. The notification includes a specific message such as, "The current outbreak may be influenza type A. Please get plenty of rest and use over-the-counter anti-influenza medication." Input: Countermeasure information from the server, Output: Notification message to the user.

[0826] The above is the flow of the specific processing steps of the system, which allows users to quickly and accurately take optimal measures against colds.

[0827] (Application example 1)

[0828] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0829] While early detection and treatment of cold and flu symptoms is important, it is difficult to obtain accurate predictions and appropriate treatment by simply inputting symptoms based on personal judgment. Furthermore, when purchasing medicines as a treatment, the effort required to quickly find and purchase the appropriate products can be a problem. There is a need for a system that can solve these problems and enable users to quickly and accurately predict the type of cold they have, and easily purchase effective treatments and necessary medicines.

[0830] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0831] In this invention, the server includes a device for a user to input symptoms, a server that receives and analyzes the input symptom data, database reference means for predicting the type of cold that is currently prevalent based on the analysis results, treatment selection means for selecting effective treatments based on the predicted type of cold, display means for notifying the user of the selected treatments, purchase link generation means for suggesting medicines corresponding to the predicted type of cold and providing a purchase link for the medicines, and electronic payment processing means for the user to purchase medicines electronically within the application. This enables users to quickly and accurately analyze their own symptoms and easily purchase optimal treatments and necessary medicines.

[0832] "User" means an individual who uses the system to enter their symptoms.

[0833] "Devices for inputting symptoms" are electronic devices such as smartphones and tablets that users use to input their symptoms.

[0834] The "server that receives and analyzes symptom data" is a central processing unit that receives symptom data entered by the user and analyzes the data.

[0835] The "database reference means" is a means by which the server refers to a database prepared in advance in order to predict the type of cold based on symptom data.

[0836] The "measure selection means" is a function for selecting the most effective countermeasure based on the predicted type of cold.

[0837] "Display means" means an output device such as a display or monitor used to notify the user of the selected measure.

[0838] The "purchase link generation means" is a function for proposing medicines corresponding to the predicted type of cold and generating a purchase link for the medicines.

[0839] "Electronic payment processing means" means a function that allows users to purchase medicines electronically within the application.

[0840] "Cold type" is a classification of illnesses that characterizes specific symptoms such as colds and influenza.

[0841] "Effective measures" are the defenses and treatments that are most appropriate for the predicted type of cold.

[0842] "Medicines" are drugs used to treat or prevent illnesses such as colds and flu.

[0843] This invention is a system that includes a device where a user inputs their symptoms, a server that receives and analyzes the input symptom data, and display means. Based on the symptoms input by the user, the system predicts the type of cold while referring to the latest epidemic data, and suggests effective countermeasures against it.

[0844] First, a smartphone or tablet is used as a device for users to input their symptoms. Users enter specific symptoms, such as "cough, sore throat, slight fever," into the device's input screen. This input data is then sent to a server.

[0845] The server analyzes the received symptom data. Specifically, the server extracts keywords from the symptom data and references a database based on keywords such as "cough," "sore throat," and "low-grade fever." The database contains data on common colds, influenza, and other infectious diseases.

[0846] The server then references an epidemic database, which records the latest cold epidemic information provided by medical institutions and health authorities. The server combines and analyzes the symptom data and epidemic data to predict the type of cold currently prevalent. Based on the results, the server selects the most effective countermeasures. For example, if influenza type A is predicted, the server will recommend the need for rest and the use of over-the-counter anti-influenza medication. If necessary, the server can also recommend a visit to a medical institution.

[0847] Another distinctive feature of this system is that it also includes a means for suggesting medications that correspond to the predicted type of cold and generating and providing a purchase link for them. The server creates a purchase link for the suggested medication and displays it to the user. The user can use this purchase link to purchase the medication via electronic commerce directly within the application.

[0848] The above process includes the following elements to clarify the hardware and software used:

[0849] Smartphones and tablets are used as devices for entering symptoms.

[0850] The server acts as an analysis engine, analyzing symptom data and referencing the database.

[0851] The database is used to manage symptom data and prevalence data.

[0852] An electronic trading system is used to generate and provide the purchase links.

[0853] For example, if a user inputs symptoms such as "cough, slight fever, sore throat," the server analyzes this and predicts that it is influenza A. As a countermeasure, the server suggests "using over-the-counter anti-influenza medication" and generates a purchase link to display to the user. The user can click the displayed link to purchase the medication electronically within the application.

[0854] An example of a prompt sentence is as follows:

[0855] Health Wallet App

[0856] Symptoms: cough, slight fever, sore throat

[0857] It predicts the best course of action for this condition and provides links to where you can purchase the necessary medication.

[0858] As described above, this invention enables users to quickly and accurately analyze their own symptoms and easily purchase optimal measures and necessary medicines.

[0859] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0860] Step 1:

[0861] The user inputs symptoms into a device (smartphone or tablet) for inputting symptoms. The user enters specific symptoms such as "cough, sore throat, slight fever" into the input screen. This input data is sent from the device to the server.

[0862] Input: User-entered symptom data

[0863] Output: Symptom data sent to the server

[0864] Step 2:

[0865] The server analyzes the received symptom data. The data analysis engine extracts keywords from the symptom data, such as "cough," "sore throat," and "low-grade fever." These keywords are used to search the symptom database.

[0866] Input: Symptom data

[0867] Output: Extracted keyword data

[0868] Step 3:

[0869] The server uses the database reference means to search the symptom database and the epidemic database, and combines the symptom and epidemic information to predict the type of cold currently prevalent, for example, "influenza type A."

[0870] Input: Extracted keyword data

[0871] Output: Predicted cold type

[0872] Step 4:

[0873] The server uses the countermeasure selection means to select effective countermeasures based on the predicted type of cold, such as "get enough rest and use over-the-counter anti-influenza medicine."

[0874] Input: Predicted cold type

[0875] Output: Selected countermeasure information

[0876] Step 5:

[0877] The server uses the purchase link generation means to suggest medicines that correspond to the predicted type of cold and generate a purchase link for them. The generated link is notified to the user.

[0878] Input: Selected countermeasure information

[0879] Output: Generated purchase link

[0880] Step 6:

[0881] The terminal displays the purchase link received from the server to the user, and the user can purchase the medicine electronically within the application by clicking the displayed link.

[0882] Input: Generated purchase link

[0883] Output: Displayed purchase link and user purchase action

[0884] Step 7:

[0885] The server processes the user's purchase request and settles the payment for the medicine using an electronic payment processing means.

[0886] Input: User purchase request

[0887] Output: Purchase completion notification

[0888] The above are the specific processing steps of the system based on the application example.

[0889] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0890] This invention relates to a system that predicts the type of cold currently prevalent based on the symptoms entered by the user and suggests the best countermeasures for it. Furthermore, by combining it with an emotion engine that recognizes the user's emotions and adjusts countermeasures based on the emotions, it provides more personalized health management support.

[0891] First, the user uses a device (smartphone, tablet, PC, etc.) to input their symptoms. They enter specific symptoms such as "cough," "sore throat," and "slight fever" into the device's input screen. The device then sends the input symptom data to the server.

[0892] The server analyzes the received symptom data and identifies the type of cold based on the analysis results. Specifically, the server extracts keywords from the symptom data and predicts the type of cold, such as "common cold" or "influenza," by referring to the symptom database and trend database.

[0893] Furthermore, the server is equipped with an emotion engine that analyzes the user's emotions based on the user's input data, voice input, facial expression data, etc. The results of this analysis are used in conjunction with the countermeasure selection means to adjust countermeasures in accordance with the user's emotions.

[0894] For example, if a user's emotions indicate "anxiety" or "stress," the emotion engine will take that information into account and suggest relaxation strategies (e.g., having a hot drink or taking deep breaths). On the other hand, if a user expresses positive emotions such as "elation" or "relief," it will suggest more traditional strategies.

[0895] To give a concrete example, consider the following cases:

[0896] 1. User B enters symptoms such as "sneezing, runny nose, slight fever" into the device.

[0897] 2. The terminal sends the entered data to the server.

[0898] 3. The server analyzes the data and, by referring to the symptom database and epidemic database, predicts that the likelihood of the illness is a "common cold."

[0899] 4. The server further analyzes User B's voice input and facial expression data and confirms that he / she is expressing strong feelings of "anxiety."

[0900] 5. Based on the results of this analysis, the emotion engine selects measures to promote relaxation in addition to the usual measures.

[0901] 6. The server selects countermeasures such as "staying well hydrated, wearing warm clothes, replenishing vitamin C, and taking deep breaths to relax" and sends the information to the device.

[0902] 7. User B checks the countermeasure information displayed on the device and takes action.

[0903] As a result, users can not only receive measures to address their symptoms, but also receive personalized measures that take into account their emotional state at the time. This not only helps prevent and quickly alleviate colds, but also provides emotional care.

[0904] The processing flow will be explained below.

[0905] Step 1:

[0906] The user enters symptoms into the device, for example, specific symptoms such as "cough, sore throat, slight fever."

[0907] Step 2:

[0908] The device formats the symptom data entered and sends it to the server, including information such as the user ID, symptoms, and the date and time of entry.

[0909] Step 3:

[0910] The server analyzes the received symptom data. Specifically, the server extracts keywords from the symptom data, such as "cough," "sore throat," and "low-grade fever."

[0911] Step 4:

[0912] The server uses a combination of keywords to search a symptom database, which contains information about the common cold, flu, and other infectious diseases.

[0913] Step 5:

[0914] The server references an epidemic database, which records the latest cold epidemic information provided by medical institutions and health authorities.

[0915] Step 6:

[0916] The server compares the symptom data with the epidemic data and predicts the type of cold currently circulating. For example, it determines that the symptom matches influenza type A.

[0917] Step 7:

[0918] Based on the predicted type of cold, the server searches a database of countermeasures and selects the most effective countermeasures, such as "getting rest, taking anti-influenza medicine, and staying hydrated."

[0919] Step 8:

[0920] The server uses an emotion engine to analyze the user's emotional state, recognizing emotions from voice input and facial expression data and identifying emotions such as "anxiety" or "stress."

[0921] Step 9:

[0922] The server adjusts countermeasures based on the analyzed emotional data. For example, if the user is feeling anxious, it adds countermeasures to promote relaxation (such as drinking a warm drink or taking deep breaths).

[0923] Step 10:

[0924] The server sends the selected countermeasure information to the terminal, which includes the cold type prediction result and the adjusted countermeasure.

[0925] Step 11:

[0926] The device displays the received countermeasure information to the user, visually providing specific countermeasures. For example, it displays the message, "The current outbreak may be influenza type A. Get plenty of rest and use over-the-counter anti-influenza medication. Also, drink warm drinks and take deep breaths to relax."

[0927] Step 12:

[0928] Users can take action based on the displayed countermeasure information, and in some cases can ask more detailed questions to obtain additional countermeasure information.

[0929] Through each of these steps, users can not only find the best solution for their symptoms, but also personalized solutions based on their emotional state, helping them to prevent and quickly recover from colds and take care of their emotions.

[0930] Example 2

[0931] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0932] In recent years, with the variety of colds and fluctuating epidemics, it is important for users to take appropriate measures. However, conventional health management systems are unable to propose measures that take into account the user's emotional state, and they are not adequately attuned to the psychological aspects of the user. This can reduce the effectiveness of health management and lower user satisfaction.

[0933] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0934] In this invention, the server includes a terminal for a user to input symptoms, a server that receives and analyzes the input symptom data, database reference means for predicting the type of cold currently prevalent based on the analysis results, countermeasure selection means for selecting a countermeasure based on the predicted type of cold, display means for notifying the user of the selected countermeasure, emotion analysis means for analyzing the user's input data, voice input, and facial expression data to recognize the user's emotion, and means for adjusting the countermeasure in consideration of the emotion data obtained by the emotion analysis means. This enables the user to receive not only appropriate countermeasures for their symptoms but also personalized health management that takes into account their emotional state.

[0935] A "terminal" is an electronic device that allows users to input symptoms, and includes devices such as smartphones, tablets, and personal computers.

[0936] A "server" is a computer system for receiving and analyzing symptom data sent by a user.

[0937] The "database reference means" is a function in which the server refers to the symptom database and the epidemic database and predicts the type of cold that is currently prevalent based on the analysis results.

[0938] The "measure selection means" is a function for selecting effective measures based on the predicted type of cold.

[0939] "Display means" refers to a system for notifying users of the selected measures, and includes the device's screen display and notification functions.

[0940] "Emotion analysis means" is a function for recognizing a user's emotions by analyzing the user's input data, voice input, and facial expression data.

[0941] The "measure adjustment means" is a function that adjusts measures according to the emotional state of the user, taking into consideration the emotional data obtained by the emotion analysis means.

[0942] This invention relates to a system that predicts the type of cold currently prevalent based on the symptoms entered by the user and suggests the best countermeasures for it. Furthermore, by combining it with an emotion engine that recognizes the user's emotions and adjusts countermeasures based on the emotions, it provides more personalized health management support.

[0943] First, the user uses a device to input their symptoms. Devices include smartphones, tablets, and PCs. The user inputs specific symptoms such as "cough," "sore throat," and "slight fever" into the device's input screen. The device then sends the input symptom data to the server.

[0944] The server analyzes the received symptom data. This analysis uses natural language processing (NLP) technology to extract important keywords from the user's input. The server uses these keywords to refer to a symptom database and a trend database, and uses database reference methods to predict the type of cold, such as "common cold" or "influenza." The specific technology used is a Python NLP library (e.g., spaCy or NLTK).

[0945] Additionally, the server incorporates an emotion analyzer, which is used to analyze user input data, voice input, and facial expression data to identify the user's emotions. The server uses this data to run a speech recognition model (e.g., Google Cloud Speech-to-Text API) or a facial recognition model (e.g., OpenCV or Dlib) to obtain emotion data.

[0946] The countermeasure selection means selects effective countermeasures based on the type of cold predicted by the server. At this time, a means for adjusting the countermeasures is activated taking into account the emotional data obtained by the emotion analysis means. For example, if the user is feeling "anxiety" or "stress," countermeasures to promote relaxation (such as drinking a warm drink or taking deep breaths) are suggested.

[0947] The server then sends the selected countermeasure information to the terminal and notifies the user using the terminal's display. The user can then check the countermeasure information displayed on the terminal and take action.

[0948] Specific examples

[0949] User B enters symptoms such as "sneezing, runny nose, slight fever" into the device. The device sends the entered data to the server. The server analyzes the data and, by referring to a symptom database and an epidemic database, predicts that the illness is likely a "common cold." The server then analyzes User B's voice input and facial expression data and determines that the emotion of "anxiety" is strong. Based on the results of this analysis, the emotion analysis means selects measures to encourage relaxation in addition to the usual measures. The server selects measures such as "adequate hydration, warm clothing, vitamin C supplementation, and deep breathing to relax" and sends the information to the device. User B checks the measures displayed on the device and puts them into action.

[0950] Prompt Sentence Examples

[0951] Below are some example prompts to input to the generative AI model:

[0952] If the user enters symptoms such as "sneezing, runny nose, slight fever," suggest recommended measures to address them. If the user expresses an emotion of "anxiety," suggest measures to promote relaxation.

[0953] As a result, this system can provide users with personalized health management that takes into account not only symptoms but also emotional states.

[0954] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0955] Step 1:

[0956] The user uses a terminal to enter symptoms.

[0957] Entered symptom data: The user enters specific symptoms such as "cough," "sore throat," and "slight fever" into the device's input screen.

[0958] Specific operation: The user accesses a dedicated application or web page on the device and enters the required information into the symptom entry form.

[0959] Step 2:

[0960] The terminal transmits the input symptom data to the server.

[0961] Input Data: Symptom data entered by the user.

[0962] Output Data: The symptom data that is sent to the server.

[0963] Specific operation: The terminal temporarily stores the data entered by the user and sends it to the server's API endpoint using the HTTPS protocol.

[0964] Step 3:

[0965] The server analyzes the received symptom data.

[0966] Input data: Symptom data sent from the device.

[0967] Output: Parsed keywords.

[0968] What it does: The server uses a Python script to run a natural language processing (NLP) library (e.g., spaCy or NLTK) to extract important keywords from the symptom data.

[0969] Step 4:

[0970] The server refers to a symptom database and an epidemic database to predict the type of cold.

[0971] Input data: Parsed keywords.

[0972] Output data: Predicted cold type.

[0973] Specific operation: The server uses an SQL query to search the symptom database and trend database, obtain data that matches the keywords, and identify the type of cold.

[0974] Step 5:

[0975] The server's emotion analysis means analyzes the user's input data, voice input, and facial expression data to recognize emotions.

[0976] Input data: User symptom data, voice input data, and facial expression data.

[0977] Output data: Parsed user sentiment.

[0978] What it does: The server runs a speech recognition model (e.g., Google Cloud Speech-to-Text API) and a facial recognition model (e.g., OpenCV or Dlib) to extract the user's emotions from the input data.

[0979] Step 6:

[0980] The server selects and adjusts countermeasures based on the predicted cold type and emotion data.

[0981] Input data: predicted cold type, parsed emotion data.

[0982] Output data: Selected and adjusted measures.

[0983] What happens: The server uses a business rules engine (e.g., Drools) to evaluate the analysis data and select and adjust the optimal countermeasure.

[0984] Step 7:

[0985] The server transmits the selected countermeasure information to the terminal, and the terminal notifies the user.

[0986] Input data: Selected countermeasure information.

[0987] Output data: Countermeasure information displayed on the device and notifications to the user.

[0988] Specific operation: The server generates countermeasure information in JSON format and sends it to the device. The device displays the received countermeasure information on the screen and notifies the user using the notification function.

[0989] (Application example 2)

[0990] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0991] Conventional health management systems predict the type of cold and suggest treatments based on symptom data entered by the user, but because they do not take the user's emotions into account, they are unable to provide fully personalized treatments or address the user's mental health condition. This makes it difficult for users to effectively manage their health when they are feeling anxious or stressed. Furthermore, they are unable to provide appropriate treatments when there are insufficient input data, making it difficult to effectively support users' health management.

[0992] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0993] In this invention, the server includes input means for the user to input symptoms, analysis means for receiving and analyzing the input symptom data, prediction means for predicting the type of cold that is currently prevalent, selection means for selecting effective countermeasures based on the predicted type of cold, notification means for notifying the user of the countermeasures, emotion analysis means for recognizing and analyzing the user's emotion, and adjustment means for adjusting the countermeasures based on the user's emotion, thereby making it possible to provide personalized healthcare that also takes into account the user's emotional state.

[0994] "User" means a person who utilizes the system to input symptom and emotion data.

[0995] "Symptoms" are physical symptoms experienced by a user when feeling unwell.

[0996] "Input means" refers to a device or interface that allows the user to input symptoms and emotions.

[0997] The "analysis means" is a means for analyzing the received symptom data and performing processing to identify the type of cold.

[0998] The "prediction means" is a means for predicting the type of cold currently prevalent based on the analysis results.

[0999] The "selection method" is a mechanism for selecting effective countermeasures based on the predicted type of cold.

[1000] "Notification means" refers to the means used to notify users of the selected measures.

[1001] "Emotion analysis means" refers to a means for recognizing a user's emotions by analyzing the user's input data, voice input, facial expression data, etc.

[1002] "Adjustment measures" are measures to appropriately adjust countermeasures based on recognized user sentiment.

[1003] This invention is a system that analyzes symptom data and emotion data entered by a user using a device such as a smartphone, predicts the type of cold that is currently prevalent, and suggests countermeasures. This system is implemented based on the following configuration and process.

[1004] System Configuration

[1005] The system mainly consists of the following hardware and software:

[1006] 1. Input means: A device such as a smartphone or tablet that allows users to input their symptoms and feelings.

[1007] 2. Analysis method: A server for receiving and analyzing data. Python and Flask are used.

[1008] 3. Prediction tool: Database reference function to predict the type of cold that is prevalent.

[1009] 4. Selection method: An algorithm for selecting effective measures.

[1010] 5. Notification means: A display screen to inform the user of the selected measures.

[1011] 6. Sentiment analysis tools: Generative AI models for analyzing user emotions, and emotion recognition technologies using OpenCV and TensorFlow.

[1012] 7. Adjustment measures: Algorithms to adjust measures based on user sentiment.

[1013] Program processing

[1014] 1. Data input and transmission: Users input their symptoms and emotions through a smartphone application. This data is transmitted to the server via the input means.

[1015] 2. Data analysis: The server analyzes the received data using Python scripts and compares it with the symptom database and the prevalence database. Flask acts as a web server, receiving and analyzing the data in real time.

[1016] 3. Cold type prediction: Based on the analysis results, we predict which type of cold is prevalent. The prediction method is based on an algorithm that includes information from the symptom database.

[1017] 4. Emotion Analysis: The TensorFlow model analyzes the user's voice input and facial expression data to recognize the user's emotions. OpenCV is used to capture facial expressions using a camera and perform analysis.

[1018] 5. Selection and adjustment of countermeasures: The selection method selects effective countermeasures based on the predicted cold type, and the adjustment method adjusts the countermeasures according to the results of user sentiment analysis.

[1019] 6. Notification of measures: The final selected measures will be displayed to the user through notification means.

[1020] Specific examples

[1021] For example, a user can input symptoms such as "sneezing, runny nose, slight fever" into a smartphone application. The device then sends the input data to a server. The server analyzes the data and, by referencing a symptom database and an epidemic database, predicts the likelihood of a "common cold."

[1022] Furthermore, the system analyzes the user's voice input and facial expression data to determine whether they are experiencing strong feelings of anxiety. As a result, it suggests measures to promote relaxation in addition to the usual measures. Specific instructions such as "drink plenty of water, dress warmly, replenish vitamin C, and take deep breaths to relax" are displayed on the notification screen.

[1023] Prompt Sentence Examples

[1024] If you enter "I have a headache and a sore throat" through the user interface:

[1025] "Analyzes input symptom data and predicts the corresponding type of cold."

[1026] If you say "I have a cough, a slight fever, and feel tired" using voice input:

[1027] "Analyzes voice-input symptoms and predicts the type of cold they are experiencing."

[1028] This allows users to receive measures appropriate to their symptoms and receive personalized health management support that takes into account their emotions at the time.

[1029] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1030] Step 1:

[1031] Users launch the application on their smartphones and input their symptoms and emotions. The user interface provides text fields and checkboxes for entering specific symptoms such as cough, sore throat, and slight fever. It also displays options for entering emotions. The input data is sent to the server in JSON format.

[1032] Input: User-entered symptom and emotion data

[1033] Output: Sending symptom and emotion data in JSON format

[1034] Step 2:

[1035] The server analyzes the received JSON data using an analysis tool. It uses a Python script to extract keywords from the symptom data and compare them with the symptom database and the epidemic database. This allows it to predict which colds are prevalent.

[1036] Input: Symptom and emotion data in JSON format

[1037] Output: Parsed symptom data and cold type

[1038] Step 3:

[1039] After predicting the type of cold, the server also analyzes the user's emotional data using an emotion analysis method. The user's voice and facial expression data are analyzed using a TensorFlow model to determine the type of emotion. Specifically, the voice data is converted into text using speech recognition software, and facial expression data is captured using OpenCV.

[1040] Input: Voice data and facial expression data

[1041] Output: Parsed emotion data

[1042] Step 4:

[1043] The server uses a selection method to select the most appropriate countermeasure based on the type of cold and emotional data. A Python algorithm lists effective countermeasures for each type of cold and adjusts them according to the user's emotional state. For example, if the user is feeling "anxious," countermeasures that promote relaxation will be prioritized.

[1044] Input: Parsed symptom data and emotion data

[1045] Output: Adjusted list of measures

[1046] Step 5:

[1047] The server then sends the final list of selected measures to the user's smartphone via a notification method. Specific measures are displayed as text on the user interface, and notifications are also sent. Users can use this information to manage their own health.

[1048] Input: Adjusted countermeasure list

[1049] Output: Countermeasure information notified to the user

[1050] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1051] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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 voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1052] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1053] [Fourth embodiment]

[1054] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1055] 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.

[1056] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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).

[1057] 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.

[1058] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1059] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1060] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1061] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1062] 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.

[1063] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1064] 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.

[1065] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1066] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1067] The present invention relates to a system that includes a device where a user inputs their symptoms, a server that receives and analyzes the input symptom data, and a display means. Based on the symptoms input by the user, the system predicts the type of cold while referring to the latest epidemic data, and suggests effective countermeasures against it.

[1068] First, the user uses a device (such as a smartphone or tablet) to input their symptoms. The user enters their specific symptoms, such as "cough, sore throat, slight fever," into the device's input screen. The device then sends this input data to the server.

[1069] The server analyzes the symptom data it receives. Specifically, the server extracts keywords from the symptom data and searches a symptom database based on keywords such as "cough," "sore throat," and "mild fever." The symptom database contains data on common colds, influenza, and other infectious diseases.

[1070] The server then refers to a trend database, which contains the latest cold epidemic information provided by medical institutions and health authorities. The server then combines and analyzes the symptom data and trend data to predict the type of cold currently circulating.

[1071] The server then selects the most effective countermeasures based on the predicted type of cold. For example, if influenza A is predicted, it will recommend the need for rest and the use of over-the-counter anti-influenza medication. If necessary, it can also recommend a medical visit.

[1072] The selected countermeasure information is sent from the server to the device. The device notifies the user of this information and displays specific countermeasures. For example, a message such as "Influenza A may be the current epidemic. Get plenty of rest and use over-the-counter anti-influenza medication" may be displayed.

[1073] To give a concrete example, the following cases can be considered:

[1074] 1. User A enters symptoms such as "sneezing, runny nose, slight fever" into the device.

[1075] 2. The terminal sends the entered data to the server.

[1076] 3. The server analyzes the data and, by referring to the symptom database and epidemic database, predicts the likelihood of a "common cold."

[1077] 4. The server selects countermeasures such as "sufficient hydration, warm clothing, and vitamin C supplementation" and sends the information to the device.

[1078] 5. User A checks the countermeasure information displayed on the device and takes action.

[1079] In this way, this system allows users to quickly and accurately find the best measures for their symptoms, which is expected to lead to early cold prevention measures, prevent the illness from becoming severe, and help curb the spread of colds.

[1080] The processing flow will be explained below.

[1081] Step 1:

[1082] The user enters symptoms into the device, for example, specific symptoms such as "cough, sore throat, slight fever."

[1083] Step 2:

[1084] The device formats the symptom data entered and sends it to the server, including information such as the user ID, symptoms, and the date and time of entry.

[1085] Step 3:

[1086] The server analyzes the received symptom data. Specifically, the server extracts keywords from the symptom data, such as "cough," "sore throat," and "low-grade fever."

[1087] Step 4:

[1088] The server uses a combination of keywords to search a symptom database, which contains information about the common cold, flu, and other infectious diseases.

[1089] Step 5:

[1090] The server references an epidemic database, which records the latest cold epidemic information provided by medical institutions and health authorities.

[1091] Step 6:

[1092] The server compares the symptom data with the epidemic data and predicts the type of cold currently circulating. For example, it determines that the symptom matches influenza type A.

[1093] Step 7:

[1094] Based on the predicted type of cold, the server searches a database of countermeasures and selects the most effective countermeasures, such as "getting rest, taking anti-influenza medicine, and staying hydrated."

[1095] Step 8:

[1096] The server then sends the selected countermeasure information to the terminal, including the cold type prediction results and countermeasures.

[1097] Step 9:

[1098] The device displays the received countermeasure information to the user, visually providing specific countermeasures. For example, it may say, "The current epidemic may be influenza type A. Get plenty of rest and use over-the-counter anti-influenza medication."

[1099] Step 10:

[1100] Users can take action based on the displayed countermeasure information, and in some cases can ask additional questions or provide further details.

[1101] Through each of the above steps, users can quickly and accurately find the optimal measures, which can help prevent colds and improve their condition quickly.

[1102] Example 1

[1103] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1104] Conventional cold prevention systems often took a long time to analyze the symptom data entered by the user and predict the appropriate type of cold and countermeasures. Furthermore, they lacked a mechanism for quickly and accurately proposing effective countermeasures based on the prevalent type of cold, resulting in delays before users could take appropriate measures. Furthermore, if the symptom data entered by the user contained insufficient information, the system lacked a function to prompt for additional input to compensate, which created a risk of reducing the accuracy of the analysis results. Additionally, the accuracy of keyword extraction based on symptom data was incomplete, which sometimes led to an inability to make an appropriate diagnosis.

[1105] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1106] In this invention, the server includes a terminal device for users to input symptoms, a computer that receives and analyzes the input symptom data, information reference means for predicting the type of disease currently prevalent based on the analysis results, means for selecting effective countermeasures based on the predicted type of disease, and notification means for notifying the user of the selected countermeasures. This enables rapid and accurate analysis of symptom data, enabling accurate prediction of the type of cold based on the latest prevalence data and the proposal of appropriate countermeasures. Furthermore, the accuracy of the analysis results can be improved by using means for prompting the user for additional input and means for extracting information.

[1107] "User" refers to an individual or user who uses the system to input their symptoms and receive appropriate countermeasure information.

[1108] "Terminal device" refers to an electronic device used by a user to input symptoms, including mobile information terminals such as smartphones and tablets.

[1109] "Computer" refers to a central processing unit that analyzes symptom data received from the user and generates appropriate countermeasure information.

[1110] "Information reference means" refers to the function of referencing symptom data and epidemic data in order to predict the type of disease currently prevalent based on the analysis results.

[1111] "Measure selection means" refers to a function that selects effective measures to be provided to users based on the predicted type of disease.

[1112] "Notification means" refers to the means for notifying users of the selected countermeasure information and indicating specific countermeasures.

[1113] "Symptom data" refers to data entered by users that indicates specific health conditions, such as cough, sore throat, or slight fever.

[1114] "Epidemic data" refers to database information provided by medical institutions and health authorities that shows the current status of epidemics of diseases.

[1115] "Information extraction means" refers to the function of analyzing symptom data received from the user and extracting important information such as keywords from it.

[1116] The present invention relates to a system that allows a user to easily input their symptoms and quickly proposes effective cold prevention measures based on the input. The system includes a terminal device for receiving the user's input, a computer for analyzing the received data, a means for predicting the type of cold by referring to a trend database, and a means for notifying the user of the recommended measures.

[1117] Using a terminal

[1118] Users can launch a dedicated application on their smartphone, tablet, or other electronic device and enter specific symptoms such as "cough, sore throat, slight fever" into the application's input screen. The entered data is verified by the program, and an interface prompting for additional input is displayed if necessary.

[1119] Computer-based data analysis

[1120] The symptom data collected by the terminal device is sent to a computer via a network. The computer extracts keywords from the received data using a text analysis library (such as Python's NLTK). For example, keywords such as "cough," "sore throat," and "low-grade fever" are extracted. This clarifies the main points of the symptom data.

[1121] Browse epidemic data

[1122] The computer uses the extracted keywords to search a symptom database, which contains information on the common cold, flu, and other infectious diseases. It also consults an epidemic database to obtain the latest epidemic information for each region. The epidemic database is based on information provided by medical institutions and health authorities.

[1123] Cold type prediction

[1124] The computer then combines and analyzes the received symptom data and epidemic data, and uses statistical models (e.g., logistic regression models) and machine learning algorithms to predict the type of cold. For example, it can predict "influenza type A."

[1125] Selection of effective measures

[1126] Based on the predicted cold type, the computer selects effective countermeasures from a database. The countermeasures include specific advice such as how to get rest, recommended over-the-counter medications, and, if necessary, seeking medical attention. This allows users to take prompt and appropriate action.

[1127] User Notification

[1128] The selected countermeasure information is then sent back to the terminal device via the network. The terminal device then notifies the user of the received countermeasure information and displays specific countermeasures. The notification may be displayed as a pop-up message or as detailed information in a specific section within the application.

[1129] Specific examples

[1130] For example, if user A enters symptoms such as "sneezing, runny nose, slight fever," the process will proceed as follows:

[1131] 1. The user enters the symptoms at the terminal.

[1132] 2. The input data is sent to the computer.

[1133] 3. The computer analyzes the data and extracts keywords.

[1134] 4. The computer performs the analysis by referencing the symptom database and the prevalence database.

[1135] 5. Predict that the possibility of the illness is "common cold."

[1136] 6. The computer selects the following countermeasures: "Drink plenty of fluids, dress warmly, and take vitamin C."

[1137] 7. Countermeasure information is sent to the terminal device and notified to the user.

[1138] 8. The user must act in accordance with the displayed countermeasure information.

[1139] Prompt Sentence Examples

[1140] "Please explain a system in which a user inputs symptoms such as 'sneezing, runny nose, slight fever' into a smartphone, and a server analyzes the data to predict the type of cold they have. Please also explain how the system notifies the user of effective measures to treat the cold."

[1141] This will enable users to quickly receive optimal cold treatment, which is expected to help prevent the disease from becoming severe and the spread of colds.

[1142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1143] Step 1:

[1144] The user operates the terminal device to input the symptoms.

[1145] Specifically, the user launches a dedicated application on their smartphone or tablet device and enters specific symptoms such as "cough, sore throat, slight fever" on the input screen. This information is stored as input data on the terminal device. The input data is in JSON format, and is saved as, for example, "{ 'symptoms': ['cough', 'sore throat', 'slight fever']}".

[1146] Step 2:

[1147] The terminal device transmits input data to the server.

[1148] The terminal device sends the data entered by the user to the server via the HTTPS protocol. The input data is packaged in JSON format on the terminal device and sent to the server via a secure communication channel. For example, the data "{ 'Symptoms': ['Cough', 'Sore throat', 'Mild fever']}" is sent to the server. Input: Symptom data in JSON format, Output: Data sent to the server.

[1149] Step 3:

[1150] The server receives and parses the input data.

[1151] The server extracts keywords from the received data using a text analysis library (e.g., Python's NLTK). Specifically, it uses an analysis algorithm to extract symptom keywords such as "cough," "sore throat," and "low-grade fever." Input: Symptom data sent to the server. Output: Extracted keywords.

[1152] Step 4:

[1153] The server searches the symptom database.

[1154] The server searches a symptom database based on the extracted keywords. The database contains information on various colds and infectious diseases, and lists candidate diseases that match the keywords. For example, "cough," "sore throat," and "slight fever" match the symptoms of a common cold. Input: extracted keywords, Output: candidate disease list.

[1155] Step 5:

[1156] The server consults the trend database.

[1157] The server references epidemic databases provided by medical institutions and health authorities. The epidemic database records the latest cold epidemic status and classifies data by region. This reference identifies the type of cold currently prevalent in that region. Input: Regional information and extracted keywords, Output: Epidemic data.

[1158] Step 6:

[1159] The server integrates and analyzes symptom data and trend data to predict the type of cold.

[1160] The server combines symptom data and epidemic data and analyzes them using statistical models and machine learning algorithms. For example, a logistic regression model may be used to make a prediction, resulting in "influenza type A." Input: symptom data and epidemic data, output: predicted type of cold.

[1161] Step 7:

[1162] The server selects effective countermeasures based on the type of cold.

[1163] The server selects effective countermeasure information from the countermeasure database based on the predicted type of cold. For example, if "Influenza A" is predicted, specific advice including how to get enough rest, over-the-counter medication recommendations, and visiting a medical institution will be selected. Input: Predicted type of cold, Output: Countermeasure information.

[1164] Step 8:

[1165] The server transmits the selected countermeasure information to the terminal device.

[1166] The server then packages the selected countermeasure information into JSON format again and sends it to the user's terminal device. Input: Selected countermeasure information, Output: Countermeasure information sent to the terminal device

[1167] Step 9:

[1168] The terminal device displays the countermeasure information to the user.

[1169] The terminal device displays the countermeasure information received from the server in a pop-up or in a specific section within the application. The notification includes a specific message such as, "The current outbreak may be influenza type A. Please get plenty of rest and use over-the-counter anti-influenza medication." Input: Countermeasure information from the server, Output: Notification message to the user.

[1170] The above is the flow of the specific processing steps of the system, which allows users to quickly and accurately take optimal measures against colds.

[1171] (Application example 1)

[1172] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1173] While early detection and treatment of cold and flu symptoms is important, it is difficult to obtain accurate predictions and appropriate treatment by simply inputting symptoms based on personal judgment. Furthermore, when purchasing medicines as a treatment, the effort required to quickly find and purchase the appropriate products can be a problem. There is a need for a system that can solve these problems and enable users to quickly and accurately predict the type of cold they have, and easily purchase effective treatments and necessary medicines.

[1174] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1175] In this invention, the server includes a device for a user to input symptoms, a server that receives and analyzes the input symptom data, database reference means for predicting the type of cold that is currently prevalent based on the analysis results, treatment selection means for selecting effective treatments based on the predicted type of cold, display means for notifying the user of the selected treatments, purchase link generation means for suggesting medicines corresponding to the predicted type of cold and providing a purchase link for the medicines, and electronic payment processing means for the user to purchase medicines electronically within the application. This enables users to quickly and accurately analyze their own symptoms and easily purchase optimal treatments and necessary medicines.

[1176] "User" means an individual who uses the system to enter their symptoms.

[1177] "Devices for inputting symptoms" are electronic devices such as smartphones and tablets that users use to input their symptoms.

[1178] The "server that receives and analyzes symptom data" is a central processing unit that receives symptom data entered by the user and analyzes the data.

[1179] The "database reference means" is a means by which the server refers to a database prepared in advance in order to predict the type of cold based on symptom data.

[1180] The "measure selection means" is a function for selecting the most effective countermeasure based on the predicted type of cold.

[1181] "Display means" means an output device such as a display or monitor used to notify the user of the selected measure.

[1182] The "purchase link generation means" is a function for proposing medicines corresponding to the predicted type of cold and generating a purchase link for the medicines.

[1183] "Electronic payment processing means" means a function that allows users to purchase medicines electronically within the application.

[1184] "Cold type" is a classification of illnesses that characterizes specific symptoms such as colds and influenza.

[1185] "Effective measures" are the defenses and treatments that are most appropriate for the predicted type of cold.

[1186] "Medicines" are drugs used to treat or prevent illnesses such as colds and flu.

[1187] This invention is a system that includes a device where a user inputs their symptoms, a server that receives and analyzes the input symptom data, and display means. Based on the symptoms input by the user, the system predicts the type of cold while referring to the latest epidemic data, and suggests effective countermeasures against it.

[1188] First, a smartphone or tablet is used as a device for users to input their symptoms. Users enter specific symptoms, such as "cough, sore throat, slight fever," into the device's input screen. This input data is then sent to a server.

[1189] The server analyzes the received symptom data. Specifically, the server extracts keywords from the symptom data and references a database based on keywords such as "cough," "sore throat," and "low-grade fever." The database contains data on common colds, influenza, and other infectious diseases.

[1190] The server then references an epidemic database, which records the latest cold epidemic information provided by medical institutions and health authorities. The server combines and analyzes the symptom data and epidemic data to predict the type of cold currently prevalent. Based on the results, the server selects the most effective countermeasures. For example, if influenza type A is predicted, the server will recommend the need for rest and the use of over-the-counter anti-influenza medication. If necessary, the server can also recommend a visit to a medical institution.

[1191] Another distinctive feature of this system is that it also includes a means for suggesting medications that correspond to the predicted type of cold and generating and providing a purchase link for them. The server creates a purchase link for the suggested medication and displays it to the user. The user can use this purchase link to purchase the medication via electronic commerce directly within the application.

[1192] The above process includes the following elements to clarify the hardware and software used:

[1193] Smartphones and tablets are used as devices for entering symptoms.

[1194] The server acts as an analysis engine, analyzing symptom data and referencing the database.

[1195] The database is used to manage symptom data and prevalence data.

[1196] An electronic trading system is used to generate and provide the purchase links.

[1197] For example, if a user inputs symptoms such as "cough, slight fever, sore throat," the server analyzes this and predicts that it is influenza A. As a countermeasure, the server suggests "using over-the-counter anti-influenza medication" and generates a purchase link to display to the user. The user can click the displayed link to purchase the medication electronically within the application.

[1198] An example of a prompt sentence is as follows:

[1199] Health Wallet App

[1200] Symptoms: cough, slight fever, sore throat

[1201] It predicts the best course of action for this condition and provides links to where you can purchase the necessary medication.

[1202] As described above, this invention enables users to quickly and accurately analyze their own symptoms and easily purchase optimal measures and necessary medicines.

[1203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1204] Step 1:

[1205] The user inputs symptoms into a device (smartphone or tablet) for inputting symptoms. The user enters specific symptoms such as "cough, sore throat, slight fever" into the input screen. This input data is sent from the device to the server.

[1206] Input: User-entered symptom data

[1207] Output: Symptom data sent to the server

[1208] Step 2:

[1209] The server analyzes the received symptom data. The data analysis engine extracts keywords from the symptom data, such as "cough," "sore throat," and "low-grade fever." These keywords are used to search the symptom database.

[1210] Input: Symptom data

[1211] Output: Extracted keyword data

[1212] Step 3:

[1213] The server uses the database reference means to search the symptom database and the epidemic database, and combines the symptom and epidemic information to predict the type of cold currently prevalent, for example, "influenza type A."

[1214] Input: Extracted keyword data

[1215] Output: Predicted cold type

[1216] Step 4:

[1217] The server uses the countermeasure selection means to select effective countermeasures based on the predicted type of cold, such as "get enough rest and use over-the-counter anti-influenza medicine."

[1218] Input: Predicted cold type

[1219] Output: Selected countermeasure information

[1220] Step 5:

[1221] The server uses the purchase link generation means to suggest medicines that correspond to the predicted type of cold and generate a purchase link for them. The generated link is notified to the user.

[1222] Input: Selected countermeasure information

[1223] Output: Generated purchase link

[1224] Step 6:

[1225] The terminal displays the purchase link received from the server to the user, and the user can purchase the medicine electronically within the application by clicking the displayed link.

[1226] Input: Generated purchase link

[1227] Output: Displayed purchase link and user purchase action

[1228] Step 7:

[1229] The server processes the user's purchase request and settles the payment for the medicine using an electronic payment processing means.

[1230] Input: User purchase request

[1231] Output: Purchase completion notification

[1232] The above are the specific processing steps of the system based on the application example.

[1233] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1234] This invention relates to a system that predicts the type of cold currently prevalent based on the symptoms entered by the user and suggests the best countermeasures for it. Furthermore, by combining it with an emotion engine that recognizes the user's emotions and adjusts countermeasures based on the emotions, it provides more personalized health management support.

[1235] First, the user uses a device (smartphone, tablet, PC, etc.) to input their symptoms. They enter specific symptoms such as "cough," "sore throat," and "slight fever" into the device's input screen. The device then sends the input symptom data to the server.

[1236] The server analyzes the received symptom data and identifies the type of cold based on the analysis results. Specifically, the server extracts keywords from the symptom data and predicts the type of cold, such as "common cold" or "influenza," by referring to the symptom database and trend database.

[1237] Furthermore, the server is equipped with an emotion engine that analyzes the user's emotions based on the user's input data, voice input, facial expression data, etc. The results of this analysis are used in conjunction with the countermeasure selection means to adjust countermeasures in accordance with the user's emotions.

[1238] For example, if a user's emotions indicate "anxiety" or "stress," the emotion engine will take that information into account and suggest relaxation strategies (e.g., having a hot drink or taking deep breaths). On the other hand, if a user expresses positive emotions such as "elation" or "relief," it will suggest more traditional strategies.

[1239] To give a concrete example, consider the following cases:

[1240] 1. User B enters symptoms such as "sneezing, runny nose, slight fever" into the device.

[1241] 2. The terminal sends the entered data to the server.

[1242] 3. The server analyzes the data and, by referring to the symptom database and epidemic database, predicts that the likelihood of the illness is a "common cold."

[1243] 4. The server further analyzes User B's voice input and facial expression data and confirms that he / she is expressing strong feelings of "anxiety."

[1244] 5. Based on the results of this analysis, the emotion engine selects measures to promote relaxation in addition to the usual measures.

[1245] 6. The server selects countermeasures such as "staying well hydrated, wearing warm clothes, replenishing vitamin C, and taking deep breaths to relax" and sends the information to the device.

[1246] 7. User B checks the countermeasure information displayed on the device and takes action.

[1247] As a result, users can not only receive measures to address their symptoms, but also receive personalized measures that take into account their emotional state at the time. This not only helps prevent and quickly alleviate colds, but also provides emotional care.

[1248] The processing flow will be explained below.

[1249] Step 1:

[1250] The user enters symptoms into the device, for example, specific symptoms such as "cough, sore throat, slight fever."

[1251] Step 2:

[1252] The device formats the symptom data entered and sends it to the server, including information such as the user ID, symptoms, and the date and time of entry.

[1253] Step 3:

[1254] The server analyzes the received symptom data. Specifically, the server extracts keywords from the symptom data, such as "cough," "sore throat," and "low-grade fever."

[1255] Step 4:

[1256] The server uses a combination of keywords to search a symptom database, which contains information about the common cold, flu, and other infectious diseases.

[1257] Step 5:

[1258] The server references an epidemic database, which records the latest cold epidemic information provided by medical institutions and health authorities.

[1259] Step 6:

[1260] The server compares the symptom data with the epidemic data and predicts the type of cold currently circulating. For example, it determines that the symptom matches influenza type A.

[1261] Step 7:

[1262] Based on the predicted type of cold, the server searches a database of countermeasures and selects the most effective countermeasures, such as "getting rest, taking anti-influenza medicine, and staying hydrated."

[1263] Step 8:

[1264] The server uses an emotion engine to analyze the user's emotional state, recognizing emotions from voice input and facial expression data and identifying emotions such as "anxiety" or "stress."

[1265] Step 9:

[1266] The server adjusts countermeasures based on the analyzed emotional data. For example, if the user is feeling anxious, it adds countermeasures to promote relaxation (such as drinking a warm drink or taking deep breaths).

[1267] Step 10:

[1268] The server sends the selected countermeasure information to the terminal, which includes the cold type prediction result and the adjusted countermeasure.

[1269] Step 11:

[1270] The device displays the received countermeasure information to the user, visually providing specific countermeasures. For example, it displays the message, "The current outbreak may be influenza type A. Get plenty of rest and use over-the-counter anti-influenza medication. Also, drink warm drinks and take deep breaths to relax."

[1271] Step 12:

[1272] Users can take action based on the displayed countermeasure information, and in some cases can ask more detailed questions to obtain additional countermeasure information.

[1273] Through each of these steps, users can not only find the best solution for their symptoms, but also personalized solutions based on their emotional state, helping them to prevent and quickly recover from colds and take care of their emotions.

[1274] Example 2

[1275] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1276] In recent years, with the variety of colds and fluctuating epidemics, it is important for users to take appropriate measures. However, conventional health management systems are unable to propose measures that take into account the user's emotional state, and they are not adequately attuned to the psychological aspects of the user. This can reduce the effectiveness of health management and lower user satisfaction.

[1277] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1278] In this invention, the server includes a terminal for a user to input symptoms, a server that receives and analyzes the input symptom data, database reference means for predicting the type of cold currently prevalent based on the analysis results, countermeasure selection means for selecting a countermeasure based on the predicted type of cold, display means for notifying the user of the selected countermeasure, emotion analysis means for analyzing the user's input data, voice input, and facial expression data to recognize the user's emotion, and means for adjusting the countermeasure in consideration of the emotion data obtained by the emotion analysis means. This enables the user to receive not only appropriate countermeasures for their symptoms but also personalized health management that takes into account their emotional state.

[1279] A "terminal" is an electronic device that allows users to input symptoms, and includes devices such as smartphones, tablets, and personal computers.

[1280] A "server" is a computer system for receiving and analyzing symptom data sent by a user.

[1281] The "database reference means" is a function in which the server refers to the symptom database and the epidemic database and predicts the type of cold that is currently prevalent based on the analysis results.

[1282] The "measure selection means" is a function for selecting effective measures based on the predicted type of cold.

[1283] "Display means" refers to a system for notifying users of the selected measures, and includes the device's screen display and notification functions.

[1284] "Emotion analysis means" is a function for recognizing a user's emotions by analyzing the user's input data, voice input, and facial expression data.

[1285] The "measure adjustment means" is a function that adjusts measures according to the emotional state of the user, taking into consideration the emotional data obtained by the emotion analysis means.

[1286] This invention relates to a system that predicts the type of cold currently prevalent based on the symptoms entered by the user and suggests the best countermeasures for it. Furthermore, by combining it with an emotion engine that recognizes the user's emotions and adjusts countermeasures based on the emotions, it provides more personalized health management support.

[1287] First, the user uses a device to input their symptoms. Devices include smartphones, tablets, and PCs. The user inputs specific symptoms such as "cough," "sore throat," and "slight fever" into the device's input screen. The device then sends the input symptom data to the server.

[1288] The server analyzes the received symptom data. This analysis uses natural language processing (NLP) technology to extract important keywords from the user's input. The server uses these keywords to refer to a symptom database and a trend database, and uses database reference methods to predict the type of cold, such as "common cold" or "influenza." The specific technology used is a Python NLP library (e.g., spaCy or NLTK).

[1289] Additionally, the server incorporates an emotion analyzer, which is used to analyze user input data, voice input, and facial expression data to identify the user's emotions. The server uses this data to run a speech recognition model (e.g., Google Cloud Speech-to-Text API) or a facial recognition model (e.g., OpenCV or Dlib) to obtain emotion data.

[1290] The countermeasure selection means selects effective countermeasures based on the type of cold predicted by the server. At this time, a means for adjusting the countermeasures is activated taking into account the emotional data obtained by the emotion analysis means. For example, if the user is feeling "anxiety" or "stress," countermeasures to promote relaxation (such as drinking a warm drink or taking deep breaths) are suggested.

[1291] The server then sends the selected countermeasure information to the terminal and notifies the user using the terminal's display. The user can then check the countermeasure information displayed on the terminal and take action.

[1292] Specific examples

[1293] User B enters symptoms such as "sneezing, runny nose, slight fever" into the device. The device sends the entered data to the server. The server analyzes the data and, by referring to a symptom database and an epidemic database, predicts that the illness is likely a "common cold." The server then analyzes User B's voice input and facial expression data and determines that the emotion of "anxiety" is strong. Based on the results of this analysis, the emotion analysis means selects measures to encourage relaxation in addition to the usual measures. The server selects measures such as "adequate hydration, warm clothing, vitamin C supplementation, and deep breathing to relax" and sends the information to the device. User B checks the measures displayed on the device and puts them into action.

[1294] Prompt Sentence Examples

[1295] Below are some example prompts to input to the generative AI model:

[1296] If the user enters symptoms such as "sneezing, runny nose, slight fever," suggest recommended measures to address them. If the user expresses an emotion of "anxiety," suggest measures to promote relaxation.

[1297] As a result, this system can provide users with personalized health management that takes into account not only symptoms but also emotional states.

[1298] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1299] Step 1:

[1300] The user uses a terminal to enter symptoms.

[1301] Entered symptom data: The user enters specific symptoms such as "cough," "sore throat," and "slight fever" into the device's input screen.

[1302] Specific operation: The user accesses a dedicated application or web page on the device and enters the required information into the symptom entry form.

[1303] Step 2:

[1304] The terminal transmits the input symptom data to the server.

[1305] Input Data: Symptom data entered by the user.

[1306] Output Data: The symptom data that is sent to the server.

[1307] Specific operation: The terminal temporarily stores the data entered by the user and sends it to the server's API endpoint using the HTTPS protocol.

[1308] Step 3:

[1309] The server analyzes the received symptom data.

[1310] Input data: Symptom data sent from the device.

[1311] Output: Parsed keywords.

[1312] What it does: The server uses a Python script to run a natural language processing (NLP) library (e.g., spaCy or NLTK) to extract important keywords from the symptom data.

[1313] Step 4:

[1314] The server refers to a symptom database and an epidemic database to predict the type of cold.

[1315] Input data: Parsed keywords.

[1316] Output data: Predicted cold type.

[1317] Specific operation: The server uses an SQL query to search the symptom database and trend database, obtain data that matches the keywords, and identify the type of cold.

[1318] Step 5:

[1319] The server's emotion analysis means analyzes the user's input data, voice input, and facial expression data to recognize emotions.

[1320] Input data: User symptom data, voice input data, and facial expression data.

[1321] Output data: Parsed user sentiment.

[1322] What it does: The server runs a speech recognition model (e.g., Google Cloud Speech-to-Text API) and a facial recognition model (e.g., OpenCV or Dlib) to extract the user's emotions from the input data.

[1323] Step 6:

[1324] The server selects and adjusts countermeasures based on the predicted cold type and emotion data.

[1325] Input data: predicted cold type, parsed emotion data.

[1326] Output data: Selected and adjusted measures.

[1327] What happens: The server uses a business rules engine (e.g., Drools) to evaluate the analysis data and select and adjust the optimal countermeasure.

[1328] Step 7:

[1329] The server transmits the selected countermeasure information to the terminal, and the terminal notifies the user.

[1330] Input data: Selected countermeasure information.

[1331] Output data: Countermeasure information displayed on the device and notifications to the user.

[1332] Specific operation: The server generates countermeasure information in JSON format and sends it to the device. The device displays the received countermeasure information on the screen and notifies the user using the notification function.

[1333] (Application example 2)

[1334] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1335] Conventional health management systems predict the type of cold and suggest treatments based on symptom data entered by the user, but because they do not take the user's emotions into account, they are unable to provide fully personalized treatments or address the user's mental health condition. This makes it difficult for users to effectively manage their health when they are feeling anxious or stressed. Furthermore, they are unable to provide appropriate treatments when there are insufficient input data, making it difficult to effectively support users' health management.

[1336] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1337] In this invention, the server includes input means for the user to input symptoms, analysis means for receiving and analyzing the input symptom data, prediction means for predicting the type of cold that is currently prevalent, selection means for selecting effective countermeasures based on the predicted type of cold, notification means for notifying the user of the countermeasures, emotion analysis means for recognizing and analyzing the user's emotion, and adjustment means for adjusting the countermeasures based on the user's emotion, thereby making it possible to provide personalized healthcare that also takes into account the user's emotional state.

[1338] "User" means a person who utilizes the system to input symptom and emotion data.

[1339] "Symptoms" are physical symptoms experienced by a user when feeling unwell.

[1340] "Input means" refers to a device or interface that allows the user to input symptoms and emotions.

[1341] The "analysis means" is a means for analyzing the received symptom data and performing processing to identify the type of cold.

[1342] The "prediction means" is a means for predicting the type of cold currently prevalent based on the analysis results.

[1343] The "selection method" is a mechanism for selecting effective countermeasures based on the predicted type of cold.

[1344] "Notification means" refers to the means used to notify users of the selected measures.

[1345] "Emotion analysis means" refers to a means for recognizing a user's emotions by analyzing the user's input data, voice input, facial expression data, etc.

[1346] "Adjustment measures" are measures to appropriately adjust countermeasures based on recognized user sentiment.

[1347] This invention is a system that analyzes symptom data and emotion data entered by a user using a device such as a smartphone, predicts the type of cold that is currently prevalent, and suggests countermeasures. This system is implemented based on the following configuration and process.

[1348] System Configuration

[1349] The system mainly consists of the following hardware and software:

[1350] 1. Input means: A device such as a smartphone or tablet that allows users to input their symptoms and feelings.

[1351] 2. Analysis method: A server for receiving and analyzing data. Python and Flask are used.

[1352] 3. Prediction tool: Database reference function to predict the type of cold that is prevalent.

[1353] 4. Selection method: An algorithm for selecting effective measures.

[1354] 5. Notification means: A display screen to inform the user of the selected measures.

[1355] 6. Sentiment analysis tools: Generative AI models for analyzing user emotions, and emotion recognition technologies using OpenCV and TensorFlow.

[1356] 7. Adjustment measures: Algorithms to adjust measures based on user sentiment.

[1357] Program processing

[1358] 1. Data input and transmission: Users input their symptoms and emotions through a smartphone application. This data is transmitted to the server via the input means.

[1359] 2. Data analysis: The server analyzes the received data using Python scripts and compares it with the symptom database and the prevalence database. Flask acts as a web server, receiving and analyzing the data in real time.

[1360] 3. Cold type prediction: Based on the analysis results, we predict which type of cold is prevalent. The prediction method is based on an algorithm that includes information from the symptom database.

[1361] 4. Emotion Analysis: The TensorFlow model analyzes the user's voice input and facial expression data to recognize the user's emotions. OpenCV is used to capture facial expressions using a camera and perform analysis.

[1362] 5. Selection and adjustment of countermeasures: The selection method selects effective countermeasures based on the predicted cold type, and the adjustment method adjusts the countermeasures according to the results of user sentiment analysis.

[1363] 6. Notification of measures: The final selected measures will be displayed to the user through notification means.

[1364] Specific examples

[1365] For example, a user can input symptoms such as "sneezing, runny nose, slight fever" into a smartphone application. The device then sends the input data to a server. The server analyzes the data and, by referencing a symptom database and an epidemic database, predicts the likelihood of a "common cold."

[1366] Furthermore, the system analyzes the user's voice input and facial expression data to determine whether they are experiencing strong feelings of anxiety. As a result, it suggests measures to promote relaxation in addition to the usual measures. Specific instructions such as "drink plenty of water, dress warmly, replenish vitamin C, and take deep breaths to relax" are displayed on the notification screen.

[1367] Prompt Sentence Examples

[1368] If you enter "I have a headache and a sore throat" through the user interface:

[1369] "Analyzes input symptom data and predicts the corresponding type of cold."

[1370] If you say "I have a cough, a slight fever, and feel tired" using voice input:

[1371] "Analyzes voice-input symptoms and predicts the type of cold they are experiencing."

[1372] This allows users to receive measures appropriate to their symptoms and receive personalized health management support that takes into account their emotions at the time.

[1373] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1374] Step 1:

[1375] Users launch the application on their smartphones and input their symptoms and emotions. The user interface provides text fields and checkboxes for entering specific symptoms such as cough, sore throat, and slight fever. It also displays options for entering emotions. The input data is sent to the server in JSON format.

[1376] Input: User-entered symptom and emotion data

[1377] Output: Sending symptom and emotion data in JSON format

[1378] Step 2:

[1379] The server analyzes the received JSON data using an analysis tool. It uses a Python script to extract keywords from the symptom data and compare them with the symptom database and the epidemic database. This allows it to predict which colds are prevalent.

[1380] Input: Symptom and emotion data in JSON format

[1381] Output: Parsed symptom data and cold type

[1382] Step 3:

[1383] After predicting the type of cold, the server also analyzes the user's emotional data using an emotion analysis method. The user's voice and facial expression data are analyzed using a TensorFlow model to determine the type of emotion. Specifically, the voice data is converted into text using speech recognition software, and facial expression data is captured using OpenCV.

[1384] Input: Voice data and facial expression data

[1385] Output: Parsed emotion data

[1386] Step 4:

[1387] The server uses a selection method to select the most appropriate countermeasure based on the type of cold and emotional data. A Python algorithm lists effective countermeasures for each type of cold and adjusts them according to the user's emotional state. For example, if the user is feeling "anxious," countermeasures that promote relaxation will be prioritized.

[1388] Input: Parsed symptom data and emotion data

[1389] Output: Adjusted list of measures

[1390] Step 5:

[1391] The server then sends the final list of selected measures to the user's smartphone via a notification method. Specific measures are displayed as text on the user interface, and notifications are also sent. Users can use this information to manage their own health.

[1392] Input: Adjusted countermeasure list

[1393] Output: Countermeasure information notified to the user

[1394] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1395] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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 voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1396] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1397] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1398] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1399] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1400] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1401] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1402] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1403] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1404] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1405] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1406] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1407] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1408] 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.

[1409] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1410] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1411] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1412] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1413] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1414] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1415] The following is further disclosed regarding the above embodiment.

[1416] (Claim 1)

[1417] a device for a user to input symptoms;

[1418] a server that receives and analyzes the input symptom data;

[1419] a database reference means for predicting the type of cold currently prevalent based on the analysis results;

[1420] a countermeasure selection means for selecting an effective countermeasure based on the predicted type of cold;

[1421] a display means for notifying the user of the selected measures;

[1422] A system including:

[1423] (Claim 2)

[1424] Including a means to prompt the user for additional input if the data entered by the user is insufficient.

[1425] 10. The system of claim 1.

[1426] (Claim 3)

[1427] a keyword extraction means for the server to analyze input data;

[1428] 10. The system of claim 1.

[1429] "Example 1"

[1430] (Claim 1)

[1431] a terminal device for a user to input symptoms;

[1432] a computer that receives and analyzes the input symptom data;

[1433] an information reference means for predicting the type of currently prevalent disease based on the analysis results;

[1434] A means for selecting effective countermeasures based on the type of disease predicted;

[1435] a notification means for notifying users of the selected measures;

[1436] A system including:

[1437] (Claim 2)

[1438] Including a means to prompt the user for additional input if the data entered by the user is insufficient.

[1439] 10. The system of claim 1.

[1440] (Claim 3)

[1441] including information extraction means for the computer to analyze input data;

[1442] 10. The system of claim 1.

[1443] "Application Example 1"

[1444] (Claim 1)

[1445] a device for a user to input symptoms;

[1446] a server that receives and analyzes the input symptom data;

[1447] a database reference means for predicting the type of cold currently prevalent based on the analysis results;

[1448] a countermeasure selection means for selecting an effective countermeasure based on the predicted type of cold;

[1449] a display means for notifying the user of the selected measures;

[1450] a purchase link generating means for suggesting medicines corresponding to the predicted type of cold and providing a purchase link for the medicines;

[1451] an electronic payment processing means for allowing a user to purchase medicines electronically within the application;

[1452] A system including:

[1453] (Claim 2)

[1454] 2. The system of claim 1, further comprising means for prompting a user for additional input if the input data is insufficient.

[1455] (Claim 3)

[1456] 10. The system of claim 1, wherein the server comprises a keyword extraction means for analyzing the input data.

[1457] "Example 2: Combining Emotion Engines"

[1458] (Claim 1)

[1459] a terminal for users to input symptoms;

[1460] a server that receives and analyzes the input symptom data;

[1461] a database reference means for predicting the type of cold currently prevalent based on the analysis results;

[1462] a countermeasure selection means for selecting a countermeasure based on the predicted type of cold;

[1463] a display means for notifying the user of the selected measures;

[1464] an emotion analysis means for analyzing user input data, voice input, and facial expression data to recognize user emotions;

[1465] means for adjusting the countermeasures in consideration of the emotion data obtained by the emotion analysis means;

[1466] A system including:

[1467] (Claim 2)

[1468] 2. The system of claim 1, further comprising means for prompting a user for additional input if the input data is insufficient.

[1469] (Claim 3)

[1470] 10. The system of claim 1, wherein the server comprises a keyword extraction means for analyzing the input data.

[1471] "Application example 2 when combining emotion engines"

[1472] (Claim 1)

[1473] an input means for a user to input symptoms;

[1474] an analysis means for receiving and analyzing input symptom data;

[1475] a prediction means for predicting the type of cold currently prevalent based on the analysis results;

[1476] A selection means for selecting an effective countermeasure based on the predicted type of cold;

[1477] a notification means for notifying users of the selected measures;

[1478] An emotion analysis means for recognizing and analyzing the emotion of a user;

[1479] an adjustment means for adjusting the countermeasures based on user sentiment;

[1480] A system including:

[1481] (Claim 2)

[1482] Including a means to prompt the user for additional input if the data entered by the user is insufficient.

[1483] 10. The system of claim 1.

[1484] (Claim 3)

[1485] a keyword extraction means for the server to analyze input data;

[1486] 10. The system of claim 1. [Explanation of symbols]

[1487] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a device for a user to input symptoms; a server that receives and analyzes the input symptom data; a database reference means for predicting the type of cold currently prevalent based on the analysis results; a countermeasure selection means for selecting an effective countermeasure based on the predicted type of cold; a display means for notifying the user of the selected measures; A system including:

2. Including a means to prompt the user for additional input if the data entered by the user is insufficient. The system of claim 1 .

3. a keyword extraction means for the server to analyze input data; The system of claim 1 .

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