system

Abstract

Provide a system. A method for loading a generative AI model; a means for storing user information in a database; means for receiving symptom details from a user; A means for inputting the received symptom data into a generative AI model to generate appropriate health advice; means for transmitting the generated health advice to a user's terminal; A system including:

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] People today are busy, and many face problems such as not having a nearby medical institution or not knowing what specific health measures to take. Since it is difficult to find a doctor who is familiar with all cases, there is a need for a system that can efficiently provide health management and preventive medicine advice from home. The purpose of this invention is to solve these problems and provide a system that allows users to easily receive health advice in their daily lives. [Means for solving the problem]

[0005] The present invention solves the above problems by providing a system including: means for loading a generative AI model; means for saving user information in a database; means for receiving detailed symptoms from a user; means for inputting the received symptom data into the generative AI model and generating appropriate health advice; and means for sending the generated health advice to the user's device. The system also includes means for periodically pushing health information and advice to the user's device based on the user information, means for automatically requesting additional information based on the user's symptom data, and means for providing an interactive interface for the user's symptom input, thereby enabling users to effectively manage their daily health.

[0006] A "generative AI model" is an artificial intelligence algorithm used to process user input data and generate appropriate health advice.

[0007] "User information" refers to personal data such as the user's age, gender, occupation, medical history, and medication status, which is used to customize health advice based on this information.

[0008] A "database" is a storage device and a system for managing information such as user information and symptom data that can be searched and retrieved as needed.

[0009] "Symptom data" is information about physical symptoms reported by a user, including specific symptoms such as headache or fever.

[0010] An "interface" is a means by which a user and a system exchange information, and can take the form of a screen display, an input form, or a dialogue format.

[0011] "Push notification" is a function that allows the system to actively send information and notifications to the user's device, and is used by users to receive regular health information and advice.

[0012] A "request for additional information" is an action in which the system prompts the user for further detailed information that may be needed based on the initial symptom data received from the user.

[0013] "Health advice" refers to advice on appropriate health management and preventive medicine that is generated by the generative AI model based on the user's symptom data and user information. [Brief explanation of the drawings]

[0014] [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 showing 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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention is a system that uses a generative AI model to provide preventive medicine and health management advice within the home, and can be implemented as follows.

[0036] System Overview

[0037] This system consists of a server, user terminals, and a network connecting them. The server is equipped with a generative AI model, a database, and a push notification function. The user terminal is provided with an interface for users to input information.

[0038] Program processing

[0039] server

[0040] 1. At startup, the server loads a generative AI model into memory. This model generates appropriate health advice based on symptom data provided by the user.

[0041] 2. The server initializes the database connection and saves and manages user information (age, gender, occupation, medical history, medication status, etc.). New and updated information sent by the user is also saved in this database.

[0042] 3. When a user reports a symptom, the server receives the data, inputs it into the AI ​​model for analysis, and generates health advice that is formatted and sent to the user's device.

[0043] Terminal

[0044] 1. When the device is used for the first time, it displays an interface that prompts the user to create an account and log in. User information is collected through an input form and sent to the server.

[0045] 2. The terminal provides an interface for the user to interactively input details of their symptoms. Once the user enters their symptoms, the data is sent to the server.

[0046] 3. The device receives health advice sent from the server and displays it in an easy-to-read format for the user. Specifically, advice such as "If you have lower back pain, correct your posture and stretch regularly. If your symptoms do not improve, we recommend that you see a doctor" is displayed.

[0047] User

[0048] 1. When using the service for the first time, the user enters account information, including name, age, gender, occupation, medical history, medication status, etc.

[0049] 2. When a symptom occurs, the user enters details of the symptom, for example, "I have had back pain for the past three days."

[0050] 3. The user takes appropriate action based on the health advice received through the device. For example, "If you have back pain, correct your posture and stretch regularly."

[0051] Specific examples

[0052] For example, suppose a 30-year-old male user registers an account and registers to the server that he works at a desk and has a medical history of back pain. If he experiences back pain while at work and uses the system to report, "My back pain has been going on for three days and is getting worse," the following is an example of how the system will process the situation.

[0053] 1. Initial setup: The user enters their account information on the device and sends it to the server, which stores it in a database.

[0054] 2. Symptom input: The user inputs on the terminal, "I have had back pain for three days and it is getting worse."

[0055] 3. Data transmission: The device transmits this symptom data to the server.

[0056] 4. Advice generation: The server inputs the received symptom data into the generative AI model for analysis. As a result, it generates advice such as, "If you have lower back pain, it is effective to correct your posture and stretch regularly. If the pain persists, we recommend that you see a doctor."

[0057] 5. Display advice: The terminal receives the generated advice and displays it to the user.

[0058] In this way, by using this system, users can easily receive health advice from home or work and take appropriate measures.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] server

[0062] The server loads the generative AI model into memory upon startup, allowing the AI ​​to generate appropriate health advice based on symptom data from the user.

[0063] Step 2:

[0064] server

[0065] The server initializes a database connection and prepares to load existing user information, including the user's age, gender, occupation, medical history, medication status, etc.

[0066] Step 3:

[0067] Terminal

[0068] When the device is used for the first time, it displays an interface that prompts the user to register an account, and provides a form for entering user information (such as name, age, gender, occupation, medical history, and medication status).

[0069] Step 4:

[0070] User

[0071] The user enters the necessary information on the device, such as name, age, gender, occupation, and other details, and fills out the input form.

[0072] Step 5:

[0073] Terminal

[0074] The terminal validates the information entered by the user and sends it to the server, checking that all required fields are filled in and for formatting mismatches.

[0075] Step 6:

[0076] server

[0077] The server stores the received user information in a database, creating an entry for a new user or updating the information for an existing user.

[0078] Step 7:

[0079] Terminal

[0080] The terminal provides an interface for the user to interactively input details of symptoms, displaying questions such as "Please tell us your specific symptoms."

[0081] Step 8:

[0082] User

[0083] The user enters specific symptoms, such as "I've had back pain for the past three days, and it's getting worse."

[0084] Step 9:

[0085] Terminal

[0086] The terminal sends the symptom data entered by the user to the server, which rechecks whether the data is complete before sending.

[0087] Step 10:

[0088] server

[0089] The server analyzes the received data and requests additional information if necessary, for example by sending the user a follow-up question such as "Does your back pain get worse with certain movements?"

[0090] Step 11:

[0091] server

[0092] The server inputs user information and symptom data into the generative AI model and generates optimal health advice, such as "It would be effective to correct your posture and stretch regularly. If the pain persists, we recommend that you see a doctor."

[0093] Step 12:

[0094] server

[0095] The server formats the generated health advice and prepares it for sending to the device, optionally checking for grammar and whether the same advice has been given before.

[0096] Step 13:

[0097] server

[0098] The server transmits the generated health advice to the terminal.

[0099] Step 14:

[0100] Terminal

[0101] The device displays the received health advice in a format that is easy for the user to view. The content of the advice is displayed on the device's interface.

[0102] Step 15:

[0103] server

[0104] The server periodically checks user data and pushes health information and advice to the user's device, suggesting new health information and seasonal advice.

[0105] Step 16:

[0106] Terminal

[0107] The device receives a push notification and notifies the user, for example, "To boost your immunity, make sure you get enough sleep and eat a balanced diet."

[0108] Example 1

[0109] 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."

[0110] Prior art requires users to visit a hospital or specialist to receive advice about their health condition or symptoms. This takes time and effort, and can delay early treatment. Furthermore, existing systems provide a poor user experience, making it difficult for users to conveniently receive health information. There is a demand for convenient and efficient advice from home, particularly in the areas of daily health management and preventive medicine.

[0111] 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.

[0112] In this invention, the server includes means for loading the generative AI model, means for saving user information in a database, means for receiving symptom details from the user, means for inputting the received symptom data into the generative AI model and generating appropriate health advice, means for transmitting the generated health advice to the user's terminal, means for displaying an interface for prompting the user to create an account or log in, means for providing an interface for interactively inputting the user's symptom details, and means for displaying the generated health advice in an easy-to-read format on the user's terminal. This enables users to easily obtain health information and advice from home or work and use it for daily health management and preventive medicine.

[0113] A "generative AI model" is an artificial intelligence model that analyzes symptom data from users and generates appropriate health advice.

[0114] A "database" is a system that stores and manages user information (age, gender, occupation, medical history, medication status, etc.).

[0115] "User information" refers to detailed information such as name, age, sex, occupation, medical history, medication status, etc. that is input by the user when using the service for the first time.

[0116] "Symptom Data" is information sent to the server when a user enters details about a particular symptom.

[0117] An "account creation interface" is a system that provides a screen and input form for a user to create an account and log in when using the service for the first time.

[0118] An "interactive interface" is a system that provides interactive screens and input forms for users to enter details of their symptoms.

[0119] "Health advice" is specific advice on health management and symptom improvement that is generated as a result of analysis by the generative AI model.

[0120] An "easy-to-read format" is a display format that is easy for users to understand and intuitively check information.

[0121] MODE FOR CARRYING OUT THE INVENTION

[0122] The present invention is a system that uses a generative AI model to provide preventive medicine and health management advice within the home. This system is composed of a server, a user terminal, and a network connecting these. Specific embodiments of the system are described below.

[0123] server

[0124] The server is equipped with a generative AI model, a database, and a push notification function. When the server starts up, it loads the generative AI model into memory. This model is used to generate appropriate health advice based on symptom data from users. Specifically, it loads a pre-trained model using TENSORFLOW (registered trademark) or PyTorch in a Python environment.

[0125] The server also initializes a connection to a database and stores and manages user information (such as age, gender, occupation, medical history, and medication status). Relational databases such as MySQL (registered trademark) and PostgreSQL are often used. New and updated information is updated in real time.

[0126] When a user reports symptoms, the server receives the data. The received data is formatted appropriately and input into the generative AI model. The generative AI model analyzes the data and generates health advice. The generated health advice is formatted and sent to the user's device. At this time, the data is encoded in JSON format or similar and sent over the network.

[0127] Terminal

[0128] When the device is used for the first time, it displays an interface that prompts the user to create an account and log in. User information is collected through an input form and sent to the server. Specifically, a web interface using HTML and JavaScript (registered trademark) or a mobile application UI is used.

[0129] The device also provides an interface for interactively inputting details of the user's symptoms. Once the user enters the symptoms, the data is sent to the server. The device receives health advice sent from the server and displays it in an easy-to-read format for the user. For example, the device may display advice such as, "If you have back pain, correct your posture and stretch regularly. If your symptoms do not improve, we recommend that you see a doctor."

[0130] User

[0131] When using the service for the first time, users enter their account information, including their name, age, gender, occupation, medical history, and medication status. This information is sent to the server via their device. When a symptom occurs, the user enters details, such as "I've had back pain for the past three days." The symptom data is sent to the server via the device interface.

[0132] The user takes appropriate action based on the health advice received through the device, such as "If you have back pain, correct your posture and stretch regularly."

[0133] Specific examples

[0134] For example, let's take the following example: a 30-year-old male user registers an account, and registers to the server that he works at a desk and has a medical history of back pain. He experiences back pain while at work, and uses the system to report, "My back pain has been going on for three days and is getting worse."

[0135] Prompt Sentence Examples

[0136] "I've had back pain for three days now and it's getting worse. What should I do?"

[0137] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0138] Step 1:

[0139] When the server starts up, it loads the generative AI model into memory. Specifically, it loads a pre-trained model using TensorFlow or PyTorch in a Python environment. The input is a file of the pre-trained AI model, and the output is the model loaded into memory.

[0140] Step 2:

[0141] The server initializes a connection to a database to store and manage user information. Specifically, it uses a relational database such as MySQL or PostgreSQL. It requires database connection information as input and establishes a database connection where user information is stored as output.

[0142] Step 3:

[0143] When the device is used for the first time, it displays an interface that prompts the user to create an account and log in. The account information provided by the user (e.g., name, age, gender, occupation, medical history, medication status) is collected as input, and this information is sent to the server as output and stored in a database.

[0144] Step 4:

[0145] The terminal provides an interface for interactively inputting the user's symptom details. It requires user-provided symptom data as input and sends the symptom details to the server as output.

[0146] Step 5:

[0147] The server receives the symptom data sent by the user and formats it appropriately. The input is the received symptom data, and the output is the formatted data to be input into the generative AI model.

[0148] Step 6:

[0149] The server inputs the formatted symptom data into a generative AI model for analysis. The input is the formatted symptom data, and the output is generated health advice.

[0150] Step 7:

[0151] The server encodes the generated health advice in JSON format or similar and sends it to the user's device. The generated health advice is the input, and the encoded data is sent over the network as the output.

[0152] Step 8:

[0153] The terminal receives health advice sent from the server and displays it in a user-friendly format. The input is the encoded data sent from the server, and the output is the decoded health advice.

[0154] Step 9:

[0155] The user takes appropriate action according to the health advice received through the device. The input is the health advice, and the output is expected to be the user's execution of the action.

[0156] Specific examples

[0157] For example, the following is a processing example in which a 30-year-old male user registers an account and registers to the server that he / she works at a desk and has a medical history of lower back pain.

[0158] Example prompt sentence:

[0159] "I've had back pain for three days now and it's getting worse. What should I do?"

[0160] This series of steps allows users to quickly receive specific health advice and take appropriate action.

[0161] (Application example 1)

[0162] 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."

[0163] Conventional health management systems only allow users to receive health advice at home using a generative AI model, but there are no systems that also provide in-store health management and recommend appropriate products. As a result, users are not provided with sufficient support when purchasing appropriate products based on their health status. The present invention aims to improve this issue by providing a system that supports in-store health management and helps users maintain and improve their health.

[0164] 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.

[0165] In this invention, the server includes means for loading the generative AI model, means for saving user information in a database, means for receiving symptom details from the user, means for inputting the received symptom data into the generative AI model and generating appropriate health advice, means for transmitting the generated health advice to the user's terminal, and means for recommending appropriate products in a store based on the user's symptom data, thereby enabling the user to receive support in purchasing appropriate products based on their health condition in a physical store.

[0166] A "generative AI model" is an artificial intelligence model that generates appropriate health advice based on symptom data from users.

[0167] The "server" is a computer system that has the functions of loading the generative AI model, storing and managing user information, analyzing symptom data to generate health advice, and sending it to the user's device.

[0168] "User information" is information related to the user's health condition, such as age, sex, occupation, medical history, and medication status.

[0169] A "database" is an information system for storing and managing user information and symptom data.

[0170] "Symptom data" is detailed information about a health condition or symptom reported by a user.

[0171] "Health advice" is a recommendation for health management and improvement that is generated by an AI model that analyzes symptom data.

[0172] A "user terminal" is a device through which a user inputs information and receives generated health advice.

[0173] "Push notification" is a function that automatically delivers health information and advice to user devices on a regular basis.

[0174] A "brick and mortar store" is a physical store where users visit and purchase products based on their health condition.

[0175] "Product recommendation" refers to the generative AI model suggesting appropriate products based on the user's symptom data.

[0176] "Symptom details" are specific descriptions of individual symptoms reported by the user.

[0177] The present invention is a system that uses a generative AI model to provide health management and preventive medicine advice in a physical store, and can be implemented as follows.

[0178] System Overview

[0179] This system consists of a server, a user device, and a network connecting them. The server is equipped with a generative AI model, a database, and a push notification function. The user device is provided with an interface for users to input information. It also includes a function to recommend appropriate products in physical stores based on the user's symptom data.

[0180] Hardware and software used

[0181] Server: The server runs on a Linux®-based operating system and uses web server software such as Apache® or Nginx. The database management system is MySQL or PostgreSQL. The generative AI model is implemented using TensorFlow or PyTorch.

[0182] User terminal: The user terminal is an ANDROID (registered trademark) or iOS device with a dedicated mobile application installed.

[0183] Network: The server and user terminals communicate via the Internet.

[0184] Program processing

[0185] server:

[0186] 1. At startup, the server loads the generative AI model into memory. This model generates appropriate health advice based on symptom data provided by the user.

[0187] 2. The server initializes the database connection and saves and manages user information (age, gender, occupation, medical history, medication status, etc.). New and updated information sent by the user is also saved in this database.

[0188] 3. When a user reports a symptom, the server receives the data, inputs it into the AI ​​model for analysis, and generates health advice that is formatted and sent to the user's device.

[0189] 4. In a brick-and-mortar environment, the server recommends appropriate products based on the user's symptom data.

[0190] Device:

[0191] 1. When the device is used for the first time, it displays an interface that prompts the user to create an account and log in. User information is collected through an input form and sent to the server.

[0192] 2. The terminal provides an interface for the user to interactively input details of their symptoms. Once the user enters their symptoms, the data is sent to the server.

[0193] 3. The device receives health advice sent from the server and displays it in an easy-to-read format for the user. Specifically, advice such as "If you have lower back pain, correct your posture and stretch regularly. If your symptoms do not improve, we recommend that you see a doctor" is displayed.

[0194] User:

[0195] 1. When using the service for the first time, the user enters account information, including name, age, gender, occupation, medical history, medication status, etc.

[0196] 2. When a symptom occurs, the user enters details of the symptom, for example, "I have had back pain for the past three days."

[0197] 3. The user takes appropriate action based on the health advice received through the device. For example, "If you have back pain, correct your posture and stretch regularly."

[0198] 4. In-store, use the app to learn more about the products recommended by the generative AI model and select the appropriate product.

[0199] Specific examples

[0200] For example, if a 30-year-old male user reports that he has recently been feeling tired, the generative AI model will provide advice such as, "Moderate exercise and a balanced diet are recommended, and vitamin B supplements are effective." The user can then receive this advice in a physical store and purchase the appropriate supplements.

[0201] Example prompt sentence:

[0202] "I get tired easily these days"

[0203] In this way, by using this system, users can receive support in purchasing appropriate products based on their health status in physical stores.

[0204] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0205] Step 1:

[0206] The server loads the generative AI model into memory when it starts up. As input, it requires the file path of the generative AI model, and by loading it into memory, it is ready to analyze the user's symptom data. As output, the generative AI model is deployed in memory.

[0207] Step 2:

[0208] The server initializes a database connection. As input, it requires database connection information (host, user, password, database name, etc.). It uses this connection information to establish a connection with a database management system (e.g., MySQL or PostgreSQL). As output, it establishes a database connection to store and manage user information and symptom data.

[0209] Step 3:

[0210] The user enters account information using the terminal when using the service for the first time. User information such as name, age, gender, occupation, medical history, and medication status is required as input. The terminal sends this information to the server, which stores it in a database. The user information is then saved in the database as output.

[0211] Step 4:

[0212] When a symptom occurs, the user inputs details of the symptom through the device. The input requires symptom data such as "I have had back pain for the past three days." The device sends this symptom data to the server, which then inputs the data into a generative AI model for analysis. Health advice is generated as the output of the analysis.

[0213] Step 5:

[0214] The server formats the generated health advice and sends it to the user's device. As input, it requires the advice output by the generative AI model. It formats this advice in a format that is easy for the user to understand and sends it to the device. As output, the health advice is displayed on the user's device.

[0215] Step 6:

[0216] The server recommends appropriate products based on the user's symptom data in the physical store. The user's symptom data and the generated health advice are required as input. The product recommendation algorithm operates based on the analysis results of the generative AI model and generates an appropriate product list. As output, the product recommendation list is displayed on the user's device.

[0217] Step 7:

[0218] The user checks the health advice and product recommendations received through the device and takes appropriate action. The input required is the advice and recommended product list sent from the server. The user makes decisions about health management and product purchases based on this. The output is the user's actions being promoted.

[0219] 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.

[0220] The present invention is a system that uses a generative AI model and an emotion engine to provide preventive medicine and health management advice within the home, and can be implemented as follows.

[0221] System Overview

[0222] This system consists of a server, user devices, and a network connecting them. The server is equipped with a generative AI model, a database, an emotion engine, and a push notification function. The user device is provided with an interface for users to input information.

[0223] Program processing

[0224] server

[0225] 1. The server loads the generative AI model and emotion engine into memory at startup. The generative AI model generates appropriate health advice based on symptom data from the user, and the emotion engine recognizes the user's emotions.

[0226] 2. The server initializes the database connection and saves and manages user information (age, gender, occupation, medical history, medication status, etc.). New and updated information sent by the user is also saved in this database.

[0227] 3. When a user reports symptoms, the server receives the data and analyzes the user's emotions using an emotion engine. The analysis results and symptom data are input into a generative AI model to generate health advice. The generated health advice is adjusted and formatted based on the user's emotions and then sent to the user's device.

[0228] Terminal

[0229] 1. When the device is used for the first time, it displays an interface that prompts the user to register an account and provides a form for entering user information (such as name, age, gender, occupation, medical history, and medication status).

[0230] 2. The terminal provides an interface for the user to interactively input details of their symptoms, displaying questions such as "Please tell us your specific symptoms."

[0231] User

[0232] 1. When using the service for the first time, the user enters account information, including name, age, gender, occupation, medical history, medication status, etc.

[0233] 2. When a symptom occurs, the user enters details of the symptom, such as, "I've had back pain for the past three days, and the pain is getting worse."

[0234] Specific examples

[0235] For example, suppose a 30-year-old male user registers an account and registers to the server that he works at a desk and has a medical history of back pain. If he experiences back pain while at work and uses the system to report, "My back pain has been going on for three days and is getting worse," the following is an example of how the system will process the situation.

[0236] 1. Initial setup: The user enters their account information on the device and sends it to the server, which stores it in a database.

[0237] 2. Symptom input: The user inputs on the terminal, "I have had back pain for three days and it is getting worse."

[0238] 3. Data transmission: The device transmits this symptom data to the server.

[0239] 4. Sentiment Analysis: The server uses an emotion engine to analyze the emotions from the user's input data, for example, to determine whether the user's input indicates stress or anxiety.

[0240] 5. Advice generation: The server inputs the user's symptom data and the results of emotion analysis into the generative AI model and performs analysis. As a result, it generates advice such as, "If you have lower back pain, it is effective to correct your posture and stretch regularly. If the pain persists, we recommend that you see a doctor."

[0241] 6. Advice Adjustment: The server adjusts the generated advice based on the user's emotions. For example, if the user is feeling stressed or anxious, the server may include additional advice such as "Take deep breaths to relax and try to create as relaxing an environment as possible."

[0242] 7. Display Advice: The device receives the generated advice and displays it in a user-friendly format. The content of the advice is displayed on the device interface.

[0243] Ongoing support

[0244] The server periodically checks user data and pushes health information and advice to the user's device. By providing new health information and seasonal advice, the system provides ongoing support for the user's health management.

[0245] In this way, by using this system, users can easily receive health advice from home or work, take appropriate measures, and receive detailed support based on their emotions.

[0246] The processing flow will be explained below.

[0247] Step 1:

[0248] server

[0249] The server loads the generative AI model and emotion engine into memory upon startup, allowing the AI ​​to generate appropriate health advice based on symptom data from the user and the emotion engine to recognize the user's emotions.

[0250] Step 2:

[0251] server

[0252] The server initializes a database connection and prepares to load existing user information, including the user's age, gender, occupation, medical history, medication status, etc.

[0253] Step 3:

[0254] Terminal

[0255] When the device is used for the first time, it displays an interface that prompts the user to register an account, and provides a form for entering user information (such as name, age, gender, occupation, medical history, and medication status).

[0256] Step 4:

[0257] User

[0258] The user enters the necessary information on the terminal, for example, filling out an input form with their name, age, sex, occupation, and details of their medical history.

[0259] Step 5:

[0260] Terminal

[0261] The terminal validates the information entered by the user and sends it to the server, checking that all required fields are filled in and that there are no formatting mismatches.

[0262] Step 6:

[0263] server

[0264] The server stores the received user information in a database, creating an entry for a new user or updating the information for an existing user.

[0265] Step 7:

[0266] Terminal

[0267] The terminal provides an interface for the user to interactively input details of symptoms, displaying questions such as "Please tell us your specific symptoms."

[0268] Step 8:

[0269] User

[0270] The user enters specific symptoms, such as "I've had back pain for the past three days, and it's getting worse."

[0271] Step 9:

[0272] Terminal

[0273] The terminal sends the symptom data entered by the user to the server, which rechecks whether the data is complete before sending.

[0274] Step 10:

[0275] server

[0276] The server inputs the received symptom data into an emotion engine for emotion analysis, for example, to determine whether the user's input indicates stress or anxiety.

[0277] Step 11:

[0278] server

[0279] The server inputs the analysis results and symptom data into a generative AI model to generate optimal health advice, such as "If you have lower back pain, it is effective to correct your posture and stretch regularly. If the pain persists, we recommend that you see a doctor."

[0280] Step 12:

[0281] server

[0282] The server adjusts the generated health advice based on the emotion engine's analysis. For example, if the user is feeling stressed, the server may include additional advice such as "Take deep breaths to relax and try to create as relaxing an environment as possible."

[0283] Step 13:

[0284] server

[0285] The server prepares the generated advice to be sent to the terminal, optionally checking for syntax and whether the same advice has been given previously.

[0286] Step 14:

[0287] server

[0288] The server transmits the generated health advice to the terminal.

[0289] Step 15:

[0290] Terminal

[0291] The device displays the received health advice in a user-friendly format, with the advice content clearly displayed on the device's interface.

[0292] Step 16:

[0293] server

[0294] The server periodically checks user data and pushes health information and advice to the user's device, suggesting new health information and seasonal advice.

[0295] Step 17:

[0296] Terminal

[0297] The device receives the push notification and notifies the user, for example, by displaying a notification that reads, "To boost your immunity, make sure you get enough sleep and eat a balanced diet."

[0298] Example 2

[0299] 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."

[0300] Given the current lack of easy ways to receive preventive medicine and health management advice at home, there is a need for a system that takes into account the user's emotions and provides detailed advice tailored to each individual's symptoms.

[0301] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for loading a generative AI model, means for saving user information in a database, means for receiving symptom details from the user, means for inputting the received symptom data into the generative AI model and generating appropriate health advice, means for adjusting the generated health advice based on the user's emotions, means for sending the adjusted health advice to the terminal, means for periodically checking user data and pushing health information and advice to the user's terminal, and means for analyzing the user's emotions and inputting them together with the symptom data into the generative AI model to generate health advice. This allows users to easily receive health advice from home or work, take appropriate measures, and receive detailed support based on their emotions.

[0302] A "generative AI model" is an artificial intelligence model that generates appropriate health advice based on symptom data from users.

[0303] "User information" refers to personal data such as the user's name, age, sex, occupation, medical history, and medication status.

[0304] A "database" is a system for storing and managing user information and symptom data.

[0305] "Symptom details" are information about specific physical ailments or discomforts reported by the user.

[0306] An "emotion engine" is a system that analyzes user input data and infers their emotions.

[0307] The "adjusting means" is a means having a function of correcting and optimizing the generated health advice in accordance with the user's emotions.

[0308] "Push notification" is a communication method in which a server sends information directly to a user's device and displays it on the screen.

[0309] "Emotion analysis" is the process of analyzing a user's emotional state (stress, anxiety, etc.) based on their symptom data and input.

[0310] The present invention is a system that provides preventive medicine and health management advice within the home. Its main components include a server, a user terminal, and a network connecting them. The server is equipped with a generative AI model, a database, an emotion engine, and a push notification function. The user terminal is provided with an interface for users to input information.

[0311] System Overview

[0312] server

[0313] When the server starts up, it loads the generative AI model and emotion engine into memory. The generative AI model generates appropriate health advice based on symptom data from the user, and the emotion engine recognizes the user's emotions. The server also initializes the database connection and saves and manages user information (age, gender, occupation, medical history, medication status, etc.). New and updated information sent by the user is also saved in this database.

[0314] When a user reports symptoms, the server receives the data and analyzes the user's emotions using an emotion engine. The analysis results and symptom data are input into a generative AI model to generate health advice. The generated health advice is adjusted and formatted based on the user's emotions and then sent to the user's device. The server also periodically checks user data and has the function of pushing health information and advice to the user's device.

[0315] Terminal

[0316] When the device is used for the first time, it displays an interface that prompts the user to register an account. It also provides a form for entering user information (such as name, age, gender, occupation, medical history, and medication status). The user enters this information and sends it to the server.

[0317] The terminal also provides an interface for the user to interactively input details of their symptoms. It displays questions such as "Please tell us your specific symptoms" and allows the user to input their symptoms. The input data is sent to the server.

[0318] User

[0319] When using the service for the first time, users enter their account information, including their name, age, gender, occupation, medical history, medication status, etc. When symptoms occur, they also enter details of the symptoms, such as "I've had back pain for the past three days, and the pain is getting worse."

[0320] Specific examples

[0321] For example, suppose a 30-year-old male desk worker registers an account and registers with the server that he has a history of back pain. If he experiences back pain while at work and uses the system to report, "My back pain has lasted for three days and is getting worse," the following process will occur.

[0322] 1. Initial setup: The user enters their name, age, occupation, medical history, etc. into the terminal and sends it to the server, which stores this information in a database.

[0323] 2. Symptom input: The user inputs "I have had lower back pain for three days and the pain is getting worse" into the terminal and submits it.

[0324] 3. Sentiment analysis and advice generation: The server analyzes the received data and determines whether the user is feeling stressed or anxious. The generative AI model generates health advice such as, "If you have lower back pain, it is effective to correct your posture and stretch regularly."

[0325] 4. Advice adjustment: Consider the user's feelings and add additional advice such as "Take deep breaths to relax and try to create as relaxing an environment as possible."

[0326] 5. Displaying Advice: The terminal displays advice from the server. The user receives advice such as, "If your lower back pain persists, maintaining proper posture and stretching are effective. Also, take deep breaths to relax and create a relaxing environment."

[0327] Prompt Sentence Examples

[0328] "I'm a 30-year-old desk worker with a medical history of lower back pain. I've had this pain for the past three days, and it's getting worse. What should I do?"

[0329] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0330] Step 1: Register and save your account information

[0331] Terminal

[0332] When the device is used for the first time, it displays an interface that prompts the user to register an account. The user enters information such as name, age, gender, occupation, medical history, and medication status into an input form. When the user presses the "Register" button, the device sends this information to the server in JSON format.

[0333] Input: User information such as name, age, gender, occupation, medical history, medication status, etc.

[0334] Output: User information data in JSON format

[0335] server

[0336] The server receives the received user information, stores it in a database, and notifies the terminal that the storage is complete.

[0337] Input: JSON format user information data

[0338] Output: User information saved to the database, and notification of completion

[0339] Step 2: Enter and submit symptom data

[0340] Terminal

[0341] When the user presses the "Report Symptoms" button, the device interactively asks the question, "Please tell us your specific symptoms." When the user enters their symptoms (e.g., "I've had lower back pain for three days and it's getting worse") and presses the send button, the device sends this information in JSON format to the server.

[0342] Input: Symptom details (e.g., "I've had back pain for three days now and it's getting worse")

[0343] Output: Symptom data in JSON format

[0344] Step 3: Sentiment analysis and advice generation

[0345] server

[0346] The server receives the symptom data sent from the device. Based on the received data, it uses an emotion engine to analyze the user's emotions. Specifically, it determines whether the user is showing stress or anxiety based on keywords and context in the text. The analysis results and symptom data are input into a generative AI model to generate health advice. For example, it generates advice such as, "If you have lower back pain, it is effective to correct your posture and stretch regularly."

[0347] Input: Symptom data in JSON format

[0348] Output: Health advice generated by the generative AI model

[0349] Step 4: Adjust and send advice

[0350] server

[0351] The generated advice is adjusted based on the user's emotions. For example, if the user is showing signs of stress or anxiety, additional advice such as "Take deep breaths to relax and try to create as relaxing an environment as possible" is included. The adjusted advice is sent to the device in JSON format.

[0352] Input: Advice generated by generative AI model, results of sentiment analysis

[0353] Output: Adjusted health advice (JSON format)

[0354] Step 5: Viewing Advice

[0355] Terminal

[0356] The device displays the advice received from the server. The advice is presented on the screen in a format that is easy for the user to understand. For example, it might say, "If you experience persistent lower back pain, it is effective to maintain proper posture and stretch regularly. Also, take deep breaths to relax and create a relaxing environment."

[0357] Input: Tailored health advice (JSON format)

[0358] Output: Advice displayed on the user's device screen

[0359] Step 6: Regular health information push notifications

[0360] server

[0361] The server periodically checks user data and generates new health information and seasonal advice, which are then sent to the user's device as push notifications.

[0362] Input: User data

[0363] Output: Health information and advice sent as push notifications

[0364] Terminal

[0365] The device will display the received push notification and provide the user with new health information and advice, such as a notification that reads, "Check out health management methods appropriate for the current season."

[0366] Input: Health information and advice sent as push notifications

[0367] Output: Notification displayed on the user's device screen

[0368] (Application example 2)

[0369] 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."

[0370] Conventional health management systems lacked detailed support for users because it was difficult to provide health advice that took into account the user's emotional state. Furthermore, health advice was general and lacked consideration for the user's individual health condition and emotions. As a result, users found it difficult to put the suggested advice into practice, which reduced the effectiveness of health management.

[0371] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for loading a generative AI model, means for saving user information in a database, means for receiving symptom details from the user, means for inputting the received symptom data into the generative AI model and generating appropriate health advice, means for transmitting the generated health advice to the user's terminal, means for analyzing the user's emotions using an emotion engine, and means for adjusting the advice based on the analyzed emotions. This makes it possible to provide detailed health advice that takes into account the user's emotional state.

[0372] A "generative AI model" is an artificial intelligence model that generates appropriate advice and information based on data collected from users.

[0373] "User information" refers to information about a user's personal information, such as age, gender, medical history, and health condition.

[0374] "Database" means a data storage system for storing and managing user information and symptom data.

[0375] The "emotion engine" is an artificial intelligence engine for analyzing emotions from user input data.

[0376] "Symptom Data" is data about specific health symptoms or conditions reported by a user.

[0377] "Health Advice" refers to specific instructions or suggestions based on the user's health status provided by a generative AI model.

[0378] "Push notification" is a function that automatically sends information from a server to a user's device.

[0379] "User's device" refers to an information device such as a smartphone, tablet, or PC used by the user.

[0380] "Analysis" is the process of processing data to derive useful information or conclusions.

[0381] System configuration

[0382] The present invention consists of a server, a user terminal, and a network connecting them. The server is equipped with a generative AI model, a database, an emotion engine, and a push notification function. The user terminal is provided with an interface for users to input information.

[0383] Hardware and Software

[0384] 1. Hardware

[0385] Smartphone (Android / iOS)

[0386] Tablet (Android / iOS)

[0387] Kiosk terminal installed in a physical store

[0388] server

[0389] Network: Wi-Fi / 4G / 5G

[0390] 2. Software

[0391] Server: Generative AI model (large-scale language model), emotion engine, database (MySQL)

[0392] Client application: Smartphone / tablet app (Flutter (registered trademark) / React Native)

[0393] Program processing

[0394] Server-side processing

[0395] When the server starts up, it loads the generative AI model and emotion engine into memory. The generative AI model generates appropriate health advice based on symptom data from the user, and the emotion engine analyzes the user's emotions. It initializes the database connection and saves and manages user information (age, gender, medical history, etc.). New and updated information sent by the user is also saved in this database.

[0396] When a user reports symptoms, the data is received and the emotion engine is used to analyze the user's emotions. The analysis results and symptom data are input into a generative AI model to generate health advice. The generated health advice is adjusted and formatted based on the user's emotions and then sent to the user's device.

[0397] Terminal side processing

[0398] When the device is used for the first time, it displays an interface that prompts the user to register an account. It provides a form for entering user information (name, age, gender, occupation, medical history, etc.). It provides an interface for entering details of the user's symptoms in an interactive format. It displays questions such as "Please tell us your specific symptoms." The user enters details of the symptoms when they occur. The entered information is sent to the server.

[0399] Specific examples

[0400] For example, if a user visiting a physical store types "I've been having an upset stomach lately" into their smartphone, that information is sent to the server. The server uses an emotion engine to analyze the emotion contained in the input sentence and determine, for example, that the person is feeling anxious or stressed. Next, the generative AI model generates health advice based on the analysis results and symptom data, such as "If you have an upset stomach, we recommend that you pay attention to your diet and drink plenty of fluids." Based on the results of the emotion analysis, the model also includes additional advice, such as "Relaxing can also help."

[0401] Prompt Sentence Examples

[0402] "A 30-year-old male user reports, 'I've had lower back pain for three days now and it's getting worse.'"

[0403] This makes it possible to provide detailed health advice that takes into account the user's emotional state.

[0404] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0405] Step 1:

[0406] When the server starts up, it loads the generative AI model and emotion engine into memory. This process makes the generative AI model and emotion engine immediately available for use. The input is the server startup operation, and the output is the generative AI model and emotion engine loaded into memory.

[0407] Step 2:

[0408] The server initializes the database connection and prepares to save user information. This operation efficiently stores and manages user information (age, gender, medical history, etc.). The input is the database connection initialization operation, and the output is the database with the established connection.

[0409] Step 3:

[0410] When a user first uses the service, they input their user information (such as name, age, gender, occupation, and medical history) into the terminal. This information is used to provide advice thereafter. The input is the user information, and the output is the user information data entered into the terminal.

[0411] Step 4:

[0412] The terminal sends user information to the server. The server receives this information and stores it in a database. The input is the user information data from the terminal, and the output is the user information data stored in the database.

[0413] Step 5:

[0414] When a symptom occurs, the user inputs specific details of the symptom into the terminal. For example, the user might input a specific symptom such as, "I've had lower back pain for three days and the pain is getting worse." The input is symptom information, and the output is the symptom data entered into the terminal.

[0415] Step 6:

[0416] The terminal sends the input symptom data to the server, which receives the data and stores it in a database. The input is the symptom data, and the output is the symptom data stored in the database.

[0417] Step 7:

[0418] The server uses an emotion engine to analyze the symptom data received from the user and determine the user's emotional state. The input is the symptom data, and the output is the analyzed emotion data.

[0419] Step 8:

[0420] The server inputs the user's symptom data and emotion data into the generative AI model and generates appropriate health advice. The input is symptom data and emotion data, and the output is the generated health advice.

[0421] Step 9:

[0422] The server adjusts the generated health advice based on the user's emotional data, for example by including additional advice on relaxation if stress or anxiety is detected. The input is the generated health advice and the emotional data, and the output is the adjusted health advice.

[0423] Step 10:

[0424] The server sends the tailored health advice to the user's terminal, which receives it and displays it to the user. The input is the tailored health advice, and the output is the terminal screen with the advice displayed.

[0425] 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.

[0426] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0427] 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.

[0428] [Second embodiment]

[0429] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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."

[0441] The present invention is a system that uses a generative AI model to provide preventive medicine and health management advice within the home, and can be implemented as follows.

[0442] System Overview

[0443] This system consists of a server, user terminals, and a network connecting them. The server is equipped with a generative AI model, a database, and a push notification function. The user terminal is provided with an interface for users to input information.

[0444] Program processing

[0445] server

[0446] 1. At startup, the server loads a generative AI model into memory. This model generates appropriate health advice based on symptom data provided by the user.

[0447] 2. The server initializes the database connection and saves and manages user information (age, gender, occupation, medical history, medication status, etc.). New and updated information sent by the user is also saved in this database.

[0448] 3. When a user reports a symptom, the server receives the data, inputs it into the AI ​​model for analysis, and generates health advice that is formatted and sent to the user's device.

[0449] Terminal

[0450] 1. When the device is used for the first time, it displays an interface that prompts the user to create an account and log in. User information is collected through an input form and sent to the server.

[0451] 2. The terminal provides an interface for the user to interactively input details of their symptoms. Once the user enters their symptoms, the data is sent to the server.

[0452] 3. The device receives health advice sent from the server and displays it in an easy-to-read format for the user. Specifically, advice such as "If you have lower back pain, correct your posture and stretch regularly. If your symptoms do not improve, we recommend that you see a doctor" is displayed.

[0453] User

[0454] 1. When using the service for the first time, the user enters account information, including name, age, gender, occupation, medical history, medication status, etc.

[0455] 2. When a symptom occurs, the user enters details of the symptom, for example, "I have had back pain for the past three days."

[0456] 3. The user takes appropriate action based on the health advice received through the device. For example, "If you have back pain, correct your posture and stretch regularly."

[0457] Specific examples

[0458] For example, suppose a 30-year-old male user registers an account and registers to the server that he works at a desk and has a medical history of back pain. If he experiences back pain while at work and uses the system to report, "My back pain has been going on for three days and is getting worse," the following is an example of how the system will process the situation.

[0459] 1. Initial setup: The user enters their account information on the device and sends it to the server, which stores it in a database.

[0460] 2. Symptom input: The user inputs on the terminal, "I have had back pain for three days and it is getting worse."

[0461] 3. Data transmission: The device transmits this symptom data to the server.

[0462] 4. Advice generation: The server inputs the received symptom data into the generative AI model for analysis. As a result, it generates advice such as, "If you have lower back pain, it is effective to correct your posture and stretch regularly. If the pain persists, we recommend that you see a doctor."

[0463] 5. Display advice: The terminal receives the generated advice and displays it to the user.

[0464] In this way, by using this system, users can easily receive health advice from home or work and take appropriate measures.

[0465] The processing flow will be explained below.

[0466] Step 1:

[0467] server

[0468] The server loads the generative AI model into memory upon startup, allowing the AI ​​to generate appropriate health advice based on symptom data from the user.

[0469] Step 2:

[0470] server

[0471] The server initializes a database connection and prepares to load existing user information, including the user's age, gender, occupation, medical history, medication status, etc.

[0472] Step 3:

[0473] Terminal

[0474] When the device is used for the first time, it displays an interface that prompts the user to register an account, and provides a form for entering user information (such as name, age, gender, occupation, medical history, and medication status).

[0475] Step 4:

[0476] User

[0477] The user enters the necessary information on the device, such as name, age, gender, occupation, and other details, and fills out the input form.

[0478] Step 5:

[0479] Terminal

[0480] The terminal validates the information entered by the user and sends it to the server, checking that all required fields are filled in and for formatting mismatches.

[0481] Step 6:

[0482] server

[0483] The server stores the received user information in a database, creating an entry for a new user or updating the information for an existing user.

[0484] Step 7:

[0485] Terminal

[0486] The terminal provides an interface for the user to interactively input details of symptoms, displaying questions such as "Please tell us your specific symptoms."

[0487] Step 8:

[0488] User

[0489] The user enters specific symptoms, such as "I've had back pain for the past three days, and it's getting worse."

[0490] Step 9:

[0491] Terminal

[0492] The terminal sends the symptom data entered by the user to the server, which rechecks whether the data is complete before sending.

[0493] Step 10:

[0494] server

[0495] The server analyzes the received data and requests additional information if necessary, for example by sending the user a follow-up question such as "Does your back pain get worse with certain movements?"

[0496] Step 11:

[0497] server

[0498] The server inputs user information and symptom data into the generative AI model and generates optimal health advice, such as "It would be effective to correct your posture and stretch regularly. If the pain persists, we recommend that you see a doctor."

[0499] Step 12:

[0500] server

[0501] The server formats the generated health advice and prepares it for sending to the device, optionally checking for grammar and whether the same advice has been given before.

[0502] Step 13:

[0503] server

[0504] The server transmits the generated health advice to the terminal.

[0505] Step 14:

[0506] Terminal

[0507] The device displays the received health advice in a format that is easy for the user to view. The content of the advice is displayed on the device's interface.

[0508] Step 15:

[0509] server

[0510] The server periodically checks user data and pushes health information and advice to the user's device, suggesting new health information and seasonal advice.

[0511] Step 16:

[0512] Terminal

[0513] The device receives a push notification and notifies the user, for example, "To boost your immunity, make sure you get enough sleep and eat a balanced diet."

[0514] Example 1

[0515] 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."

[0516] Prior art requires users to visit a hospital or specialist to receive advice about their health condition or symptoms. This takes time and effort, and can delay early treatment. Furthermore, existing systems provide a poor user experience, making it difficult for users to conveniently receive health information. There is a demand for convenient and efficient advice from home, particularly in the areas of daily health management and preventive medicine.

[0517] 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.

[0518] In this invention, the server includes means for loading the generative AI model, means for saving user information in a database, means for receiving symptom details from the user, means for inputting the received symptom data into the generative AI model and generating appropriate health advice, means for transmitting the generated health advice to the user's terminal, means for displaying an interface for prompting the user to create an account or log in, means for providing an interface for interactively inputting the user's symptom details, and means for displaying the generated health advice in an easy-to-read format on the user's terminal. This enables users to easily obtain health information and advice from home or work and use it for daily health management and preventive medicine.

[0519] A "generative AI model" is an artificial intelligence model that analyzes symptom data from users and generates appropriate health advice.

[0520] A "database" is a system that stores and manages user information (age, gender, occupation, medical history, medication status, etc.).

[0521] "User information" refers to detailed information such as name, age, sex, occupation, medical history, medication status, etc. that is input by the user when using the service for the first time.

[0522] "Symptom Data" is information sent to the server when a user enters details about a particular symptom.

[0523] An "account creation interface" is a system that provides a screen and input form for a user to create an account and log in when using the service for the first time.

[0524] An "interactive interface" is a system that provides interactive screens and input forms for users to enter details of their symptoms.

[0525] "Health advice" is specific advice on health management and symptom improvement that is generated as a result of analysis by the generative AI model.

[0526] An "easy-to-read format" is a display format that is easy for users to understand and intuitively check information.

[0527] MODE FOR CARRYING OUT THE INVENTION

[0528] The present invention is a system that uses a generative AI model to provide preventive medicine and health management advice within the home. This system is composed of a server, a user terminal, and a network connecting these. Specific embodiments of the system are described below.

[0529] server

[0530] The server is equipped with a generative AI model, a database, and a push notification function. When the server starts up, it loads the generative AI model into memory. This model generates appropriate health advice based on symptom data from users. Specifically, it loads a pre-trained model using TensorFlow or PyTorch in a Python environment.

[0531] The server also initializes a connection to a database and stores and manages user information (such as age, gender, occupation, medical history, and medication status). Relational databases such as MySQL or PostgreSQL are often used. New and updated information is updated in real time.

[0532] When a user reports symptoms, the server receives the data. The received data is formatted appropriately and input into the generative AI model. The generative AI model analyzes the data and generates health advice. The generated health advice is formatted and sent to the user's device. At this time, the data is encoded in JSON format or similar and sent over the network.

[0533] Terminal

[0534] When the device is used for the first time, it displays an interface that prompts the user to create an account and log in. User information is collected through an input form and sent to the server. Specifically, a web interface using HTML and JavaScript or a mobile application UI is used.

[0535] The device also provides an interface for interactively inputting details of the user's symptoms. Once the user enters the symptoms, the data is sent to the server. The device receives health advice sent from the server and displays it in an easy-to-read format for the user. For example, the device may display advice such as, "If you have back pain, correct your posture and stretch regularly. If your symptoms do not improve, we recommend that you see a doctor."

[0536] User

[0537] When using the service for the first time, users enter their account information, including their name, age, gender, occupation, medical history, and medication status. This information is sent to the server via their device. When a symptom occurs, the user enters details, such as "I've had back pain for the past three days." The symptom data is sent to the server via the device interface.

[0538] The user takes appropriate action based on the health advice received through the device, such as "If you have back pain, correct your posture and stretch regularly."

[0539] Specific examples

[0540] For example, let's take the following example: a 30-year-old male user registers an account, and registers to the server that he works at a desk and has a medical history of back pain. He experiences back pain while at work, and uses the system to report, "My back pain has been going on for three days and is getting worse."

[0541] Prompt Sentence Examples

[0542] "I've had back pain for three days now and it's getting worse. What should I do?"

[0543] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0544] Step 1:

[0545] When the server starts up, it loads the generative AI model into memory. Specifically, it loads a pre-trained model using TensorFlow or PyTorch in a Python environment. The input is a file of the pre-trained AI model, and the output is the model loaded into memory.

[0546] Step 2:

[0547] The server initializes a connection to a database to store and manage user information. Specifically, it uses a relational database such as MySQL or PostgreSQL. It requires database connection information as input and establishes a database connection where user information is stored as output.

[0548] Step 3:

[0549] When the device is used for the first time, it displays an interface that prompts the user to create an account and log in. The account information provided by the user (e.g., name, age, gender, occupation, medical history, medication status) is collected as input, and this information is sent to the server as output and stored in a database.

[0550] Step 4:

[0551] The terminal provides an interface for interactively inputting the user's symptom details. It requires user-provided symptom data as input and sends the symptom details to the server as output.

[0552] Step 5:

[0553] The server receives the symptom data sent by the user and formats it appropriately. The input is the received symptom data, and the output is the formatted data to be input into the generative AI model.

[0554] Step 6:

[0555] The server inputs the formatted symptom data into a generative AI model for analysis. The input is the formatted symptom data, and the output is generated health advice.

[0556] Step 7:

[0557] The server encodes the generated health advice in JSON format or similar and sends it to the user's device. The generated health advice is the input, and the encoded data is sent over the network as the output.

[0558] Step 8:

[0559] The terminal receives health advice sent from the server and displays it in a user-friendly format. The input is the encoded data sent from the server, and the output is the decoded health advice.

[0560] Step 9:

[0561] The user takes appropriate action according to the health advice received through the device. The input is the health advice, and the output is expected to be the user's execution of the action.

[0562] Specific examples

[0563] For example, the following is a processing example in which a 30-year-old male user registers an account and registers to the server that he / she works at a desk and has a medical history of lower back pain.

[0564] Example prompt sentence:

[0565] "I've had back pain for three days now and it's getting worse. What should I do?"

[0566] This series of steps allows users to quickly receive specific health advice and take appropriate action.

[0567] (Application example 1)

[0568] 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."

[0569] Conventional health management systems only allow users to receive health advice at home using a generative AI model, but there are no systems that also provide in-store health management and recommend appropriate products. As a result, users are not provided with sufficient support when purchasing appropriate products based on their health status. The present invention aims to improve this issue by providing a system that supports in-store health management and helps users maintain and improve their health.

[0570] 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.

[0571] In this invention, the server includes means for loading the generative AI model, means for saving user information in a database, means for receiving symptom details from the user, means for inputting the received symptom data into the generative AI model and generating appropriate health advice, means for transmitting the generated health advice to the user's terminal, and means for recommending appropriate products in a store based on the user's symptom data, thereby enabling the user to receive support in purchasing appropriate products based on their health condition in a physical store.

[0572] A "generative AI model" is an artificial intelligence model that generates appropriate health advice based on symptom data from users.

[0573] The "server" is a computer system that has the functions of loading the generative AI model, storing and managing user information, analyzing symptom data to generate health advice, and sending it to the user's device.

[0574] "User information" is information related to the user's health condition, such as age, sex, occupation, medical history, and medication status.

[0575] A "database" is an information system for storing and managing user information and symptom data.

[0576] "Symptom data" is detailed information about a health condition or symptom reported by a user.

[0577] "Health advice" is a recommendation for health management and improvement that is generated by an AI model that analyzes symptom data.

[0578] A "user terminal" is a device through which a user inputs information and receives generated health advice.

[0579] "Push notification" is a function that automatically delivers health information and advice to user devices on a regular basis.

[0580] A "brick and mortar store" is a physical store where users visit and purchase products based on their health condition.

[0581] "Product recommendation" refers to the generative AI model suggesting appropriate products based on the user's symptom data.

[0582] "Symptom details" are specific descriptions of individual symptoms reported by the user.

[0583] The present invention is a system that uses a generative AI model to provide health management and preventive medicine advice in a physical store, and can be implemented as follows.

[0584] System Overview

[0585] This system consists of a server, a user device, and a network connecting them. The server is equipped with a generative AI model, a database, and a push notification function. The user device is provided with an interface for users to input information. It also includes a function to recommend appropriate products in physical stores based on the user's symptom data.

[0586] Hardware and software used

[0587] Server: The server runs on a Linux-based operating system and uses web server software such as Apache or Nginx, MySQL or PostgreSQL as the database management system, and TensorFlow or PyTorch to implement the generative AI model.

[0588] User device: The user device is an Android or iOS device with a dedicated mobile application installed.

[0589] Network: The server and user terminals communicate via the Internet.

[0590] Program processing

[0591] server:

[0592] 1. At startup, the server loads the generative AI model into memory. This model generates appropriate health advice based on symptom data provided by the user.

[0593] 2. The server initializes the database connection and saves and manages user information (age, gender, occupation, medical history, medication status, etc.). New and updated information sent by the user is also saved in this database.

[0594] 3. When a user reports a symptom, the server receives the data, inputs it into the AI ​​model for analysis, and generates health advice that is formatted and sent to the user's device.

[0595] 4. In a brick-and-mortar environment, the server recommends appropriate products based on the user's symptom data.

[0596] Device:

[0597] 1. When the device is used for the first time, it displays an interface that prompts the user to create an account and log in. User information is collected through an input form and sent to the server.

[0598] 2. The terminal provides an interface for the user to interactively input details of their symptoms. Once the user enters their symptoms, the data is sent to the server.

[0599] 3. The device receives health advice sent from the server and displays it in an easy-to-read format for the user. Specifically, advice such as "If you have lower back pain, correct your posture and stretch regularly. If your symptoms do not improve, we recommend that you see a doctor" is displayed.

[0600] User:

[0601] 1. When using the service for the first time, the user enters account information, including name, age, gender, occupation, medical history, medication status, etc.

[0602] 2. When a symptom occurs, the user enters details of the symptom, for example, "I have had back pain for the past three days."

[0603] 3. The user takes appropriate action based on the health advice received through the device. For example, "If you have back pain, correct your posture and stretch regularly."

[0604] 4. In-store, use the app to learn more about the products recommended by the generative AI model and select the appropriate product.

[0605] Specific examples

[0606] For example, if a 30-year-old male user reports that he has recently been feeling tired, the generative AI model will provide advice such as, "Moderate exercise and a balanced diet are recommended, and vitamin B supplements are effective." The user can then receive this advice in a physical store and purchase the appropriate supplements.

[0607] Example prompt sentence:

[0608] "I get tired easily these days"

[0609] In this way, by using this system, users can receive support in purchasing appropriate products based on their health status in physical stores.

[0610] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0611] Step 1:

[0612] The server loads the generative AI model into memory when it starts up. As input, it requires the file path of the generative AI model, and by loading it into memory, it is ready to analyze the user's symptom data. As output, the generative AI model is deployed in memory.

[0613] Step 2:

[0614] The server initializes a database connection. As input, it requires database connection information (host, user, password, database name, etc.). It uses this connection information to establish a connection with a database management system (e.g., MySQL or PostgreSQL). As output, it establishes a database connection to store and manage user information and symptom data.

[0615] Step 3:

[0616] The user enters account information using the terminal when using the service for the first time. User information such as name, age, gender, occupation, medical history, and medication status is required as input. The terminal sends this information to the server, which stores it in a database. The user information is then saved in the database as output.

[0617] Step 4:

[0618] When a symptom occurs, the user inputs details of the symptom through the device. The input requires symptom data such as "I have had back pain for the past three days." The device sends this symptom data to the server, which then inputs the data into a generative AI model for analysis. Health advice is generated as the output of the analysis.

[0619] Step 5:

[0620] The server formats the generated health advice and sends it to the user's device. As input, it requires the advice output by the generative AI model. It formats this advice in a format that is easy for the user to understand and sends it to the device. As output, the health advice is displayed on the user's device.

[0621] Step 6:

[0622] The server recommends appropriate products based on the user's symptom data in the physical store. The user's symptom data and the generated health advice are required as input. The product recommendation algorithm operates based on the analysis results of the generative AI model and generates an appropriate product list. As output, the product recommendation list is displayed on the user's device.

[0623] Step 7:

[0624] The user checks the health advice and product recommendations received through the device and takes appropriate action. The input required is the advice and recommended product list sent from the server. The user makes decisions about health management and product purchases based on this. The output is the user's actions being promoted.

[0625] 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.

[0626] The present invention is a system that uses a generative AI model and an emotion engine to provide preventive medicine and health management advice within the home, and can be implemented as follows.

[0627] System Overview

[0628] This system consists of a server, user devices, and a network connecting them. The server is equipped with a generative AI model, a database, an emotion engine, and a push notification function. The user device is provided with an interface for users to input information.

[0629] Program processing

[0630] server

[0631] 1. The server loads the generative AI model and emotion engine into memory at startup. The generative AI model generates appropriate health advice based on symptom data from the user, and the emotion engine recognizes the user's emotions.

[0632] 2. The server initializes the database connection and saves and manages user information (age, gender, occupation, medical history, medication status, etc.). New and updated information sent by the user is also saved in this database.

[0633] 3. When a user reports symptoms, the server receives the data and analyzes the user's emotions using an emotion engine. The analysis results and symptom data are input into a generative AI model to generate health advice. The generated health advice is adjusted and formatted based on the user's emotions and then sent to the user's device.

[0634] Terminal

[0635] 1. When the device is used for the first time, it displays an interface that prompts the user to register an account and provides a form for entering user information (such as name, age, gender, occupation, medical history, and medication status).

[0636] 2. The terminal provides an interface for the user to interactively input details of their symptoms, displaying questions such as "Please tell us your specific symptoms."

[0637] User

[0638] 1. When using the service for the first time, the user enters account information, including name, age, gender, occupation, medical history, medication status, etc.

[0639] 2. When a symptom occurs, the user enters details of the symptom, such as, "I've had back pain for the past three days, and the pain is getting worse."

[0640] Specific examples

[0641] For example, suppose a 30-year-old male user registers an account and registers to the server that he works at a desk and has a medical history of back pain. If he experiences back pain while at work and uses the system to report, "My back pain has been going on for three days and is getting worse," the following is an example of how the system will process the situation.

[0642] 1. Initial setup: The user enters their account information on the device and sends it to the server, which stores it in a database.

[0643] 2. Symptom input: The user inputs on the terminal, "I have had back pain for three days and it is getting worse."

[0644] 3. Data transmission: The device transmits this symptom data to the server.

[0645] 4. Sentiment Analysis: The server uses an emotion engine to analyze the emotions from the user's input data, for example, to determine whether the user's input indicates stress or anxiety.

[0646] 5. Advice generation: The server inputs the user's symptom data and the results of emotion analysis into the generative AI model and performs analysis. As a result, it generates advice such as, "If you have lower back pain, it is effective to correct your posture and stretch regularly. If the pain persists, we recommend that you see a doctor."

[0647] 6. Advice Adjustment: The server adjusts the generated advice based on the user's emotions. For example, if the user is feeling stressed or anxious, the server may include additional advice such as "Take deep breaths to relax and try to create as relaxing an environment as possible."

[0648] 7. Display Advice: The device receives the generated advice and displays it in a user-friendly format. The content of the advice is displayed on the device interface.

[0649] Ongoing support

[0650] The server periodically checks user data and pushes health information and advice to the user's device. By providing new health information and seasonal advice, the system provides ongoing support for the user's health management.

[0651] In this way, by using this system, users can easily receive health advice from home or work, take appropriate measures, and receive detailed support based on their emotions.

[0652] The processing flow will be explained below.

[0653] Step 1:

[0654] server

[0655] The server loads the generative AI model and emotion engine into memory upon startup, allowing the AI ​​to generate appropriate health advice based on symptom data from the user and the emotion engine to recognize the user's emotions.

[0656] Step 2:

[0657] server

[0658] The server initializes a database connection and prepares to load existing user information, including the user's age, gender, occupation, medical history, medication status, etc.

[0659] Step 3:

[0660] Terminal

[0661] When the device is used for the first time, it displays an interface that prompts the user to register an account, and provides a form for entering user information (such as name, age, gender, occupation, medical history, and medication status).

[0662] Step 4:

[0663] User

[0664] The user enters the necessary information on the terminal, for example, filling out an input form with their name, age, sex, occupation, and details of their medical history.

[0665] Step 5:

[0666] Terminal

[0667] The terminal validates the information entered by the user and sends it to the server, checking that all required fields are filled in and that there are no formatting mismatches.

[0668] Step 6:

[0669] server

[0670] The server stores the received user information in a database, creating an entry for a new user or updating the information for an existing user.

[0671] Step 7:

[0672] Terminal

[0673] The terminal provides an interface for the user to interactively input details of symptoms, displaying questions such as "Please tell us your specific symptoms."

[0674] Step 8:

[0675] User

[0676] The user enters specific symptoms, such as "I've had back pain for the past three days, and it's getting worse."

[0677] Step 9:

[0678] Terminal

[0679] The terminal sends the symptom data entered by the user to the server, which rechecks whether the data is complete before sending.

[0680] Step 10:

[0681] server

[0682] The server inputs the received symptom data into an emotion engine for emotion analysis, for example, to determine whether the user's input indicates stress or anxiety.

[0683] Step 11:

[0684] server

[0685] The server inputs the analysis results and symptom data into a generative AI model to generate optimal health advice, such as "If you have lower back pain, it is effective to correct your posture and stretch regularly. If the pain persists, we recommend that you see a doctor."

[0686] Step 12:

[0687] server

[0688] The server adjusts the generated health advice based on the emotion engine's analysis. For example, if the user is feeling stressed, the server may include additional advice such as "Take deep breaths to relax and try to create as relaxing an environment as possible."

[0689] Step 13:

[0690] server

[0691] The server prepares the generated advice to be sent to the terminal, optionally checking for syntax and whether the same advice has been given previously.

[0692] Step 14:

[0693] server

[0694] The server transmits the generated health advice to the terminal.

[0695] Step 15:

[0696] Terminal

[0697] The device displays the received health advice in a user-friendly format, with the advice content clearly displayed on the device's interface.

[0698] Step 16:

[0699] server

[0700] The server periodically checks user data and pushes health information and advice to the user's device, suggesting new health information and seasonal advice.

[0701] Step 17:

[0702] Terminal

[0703] The device receives the push notification and notifies the user, for example, by displaying a notification that reads, "To boost your immunity, make sure you get enough sleep and eat a balanced diet."

[0704] Example 2

[0705] 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."

[0706] Given the current lack of easy ways to receive preventive medicine and health management advice at home, there is a need for a system that takes into account the user's emotions and provides detailed advice tailored to each individual's symptoms.

[0707] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for loading a generative AI model, means for saving user information in a database, means for receiving symptom details from the user, means for inputting the received symptom data into the generative AI model and generating appropriate health advice, means for adjusting the generated health advice based on the user's emotions, means for sending the adjusted health advice to the terminal, means for periodically checking user data and pushing health information and advice to the user's terminal, and means for analyzing the user's emotions and inputting them together with the symptom data into the generative AI model to generate health advice. This allows users to easily receive health advice from home or work, take appropriate measures, and receive detailed support based on their emotions.

[0708] A "generative AI model" is an artificial intelligence model that generates appropriate health advice based on symptom data from users.

[0709] "User information" refers to personal data such as the user's name, age, sex, occupation, medical history, and medication status.

[0710] A "database" is a system for storing and managing user information and symptom data.

[0711] "Symptom details" are information about specific physical ailments or discomforts reported by the user.

[0712] An "emotion engine" is a system that analyzes user input data and infers their emotions.

[0713] The "adjusting means" is a means having a function of correcting and optimizing the generated health advice in accordance with the user's emotions.

[0714] "Push notification" is a communication method in which a server sends information directly to a user's device and displays it on the screen.

[0715] "Emotion analysis" is the process of analyzing a user's emotional state (stress, anxiety, etc.) based on their symptom data and input.

[0716] The present invention is a system that provides preventive medicine and health management advice within the home. Its main components include a server, a user terminal, and a network connecting them. The server is equipped with a generative AI model, a database, an emotion engine, and a push notification function. The user terminal is provided with an interface for users to input information.

[0717] System Overview

[0718] server

[0719] When the server starts up, it loads the generative AI model and emotion engine into memory. The generative AI model generates appropriate health advice based on symptom data from the user, and the emotion engine recognizes the user's emotions. The server also initializes the database connection and saves and manages user information (age, gender, occupation, medical history, medication status, etc.). New and updated information sent by the user is also saved in this database.

[0720] When a user reports symptoms, the server receives the data and analyzes the user's emotions using an emotion engine. The analysis results and symptom data are input into a generative AI model to generate health advice. The generated health advice is adjusted and formatted based on the user's emotions and then sent to the user's device. The server also periodically checks user data and has the function of pushing health information and advice to the user's device.

[0721] Terminal

[0722] When the device is used for the first time, it displays an interface that prompts the user to register an account. It also provides a form for entering user information (such as name, age, gender, occupation, medical history, and medication status). The user enters this information and sends it to the server.

[0723] The terminal also provides an interface for the user to interactively input details of their symptoms. It displays questions such as "Please tell us your specific symptoms" and allows the user to input their symptoms. The input data is sent to the server.

[0724] User

[0725] When using the service for the first time, users enter their account information, including their name, age, gender, occupation, medical history, medication status, etc. When symptoms occur, they also enter details of the symptoms, such as "I've had back pain for the past three days, and the pain is getting worse."

[0726] Specific examples

[0727] For example, suppose a 30-year-old male desk worker registers an account and registers with the server that he has a history of back pain. If he experiences back pain while at work and uses the system to report, "My back pain has lasted for three days and is getting worse," the following process will occur.

[0728] 1. Initial setup: The user enters their name, age, occupation, medical history, etc. into the terminal and sends it to the server, which stores this information in a database.

[0729] 2. Symptom input: The user inputs "I have had lower back pain for three days and the pain is getting worse" into the terminal and submits it.

[0730] 3. Sentiment analysis and advice generation: The server analyzes the received data and determines whether the user is feeling stressed or anxious. The generative AI model generates health advice such as, "If you have lower back pain, it is effective to correct your posture and stretch regularly."

[0731] 4. Advice adjustment: Consider the user's feelings and add additional advice such as "Take deep breaths to relax and try to create as relaxing an environment as possible."

[0732] 5. Displaying Advice: The terminal displays advice from the server. The user receives advice such as, "If your lower back pain persists, maintaining proper posture and stretching are effective. Also, take deep breaths to relax and create a relaxing environment."

[0733] Prompt Sentence Examples

[0734] "I'm a 30-year-old desk worker with a medical history of lower back pain. I've had this pain for the past three days, and it's getting worse. What should I do?"

[0735] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0736] Step 1: Register and save your account information

[0737] Terminal

[0738] When the device is used for the first time, it displays an interface that prompts the user to register an account. The user enters information such as name, age, gender, occupation, medical history, and medication status into an input form. When the user presses the "Register" button, the device sends this information to the server in JSON format.

[0739] Input: User information such as name, age, gender, occupation, medical history, medication status, etc.

[0740] Output: User information data in JSON format

[0741] server

[0742] The server receives the received user information, stores it in a database, and notifies the terminal that the storage is complete.

[0743] Input: JSON format user information data

[0744] Output: User information saved to the database, and notification of completion

[0745] Step 2: Enter and submit symptom data

[0746] Terminal

[0747] When the user presses the "Report Symptoms" button, the device interactively asks the question, "Please tell us your specific symptoms." When the user enters their symptoms (e.g., "I've had lower back pain for three days and it's getting worse") and presses the send button, the device sends this information in JSON format to the server.

[0748] Input: Symptom details (e.g., "I've had back pain for three days now and it's getting worse")

[0749] Output: Symptom data in JSON format

[0750] Step 3: Sentiment analysis and advice generation

[0751] server

[0752] The server receives the symptom data sent from the device. Based on the received data, it uses an emotion engine to analyze the user's emotions. Specifically, it determines whether the user is showing stress or anxiety based on keywords and context in the text. The analysis results and symptom data are input into a generative AI model to generate health advice. For example, it generates advice such as, "If you have lower back pain, it is effective to correct your posture and stretch regularly."

[0753] Input: Symptom data in JSON format

[0754] Output: Health advice generated by the generative AI model

[0755] Step 4: Adjust and send advice

[0756] server

[0757] The generated advice is adjusted based on the user's emotions. For example, if the user is showing signs of stress or anxiety, additional advice such as "Take deep breaths to relax and try to create as relaxing an environment as possible" is included. The adjusted advice is sent to the device in JSON format.

[0758] Input: Advice generated by generative AI model, results of sentiment analysis

[0759] Output: Adjusted health advice (JSON format)

[0760] Step 5: Viewing Advice

[0761] Terminal

[0762] The device displays the advice received from the server. The advice is presented on the screen in a format that is easy for the user to understand. For example, it might say, "If you experience persistent lower back pain, it is effective to maintain proper posture and stretch regularly. Also, take deep breaths to relax and create a relaxing environment."

[0763] Input: Tailored health advice (JSON format)

[0764] Output: Advice displayed on the user's device screen

[0765] Step 6: Regular health information push notifications

[0766] server

[0767] The server periodically checks user data and generates new health information and seasonal advice, which are then sent to the user's device as push notifications.

[0768] Input: User data

[0769] Output: Health information and advice sent as push notifications

[0770] Terminal

[0771] The device will display the received push notification and provide the user with new health information and advice, such as a notification that reads, "Check out health management methods appropriate for the current season."

[0772] Input: Health information and advice sent as push notifications

[0773] Output: Notification displayed on the user's device screen

[0774] (Application example 2)

[0775] 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."

[0776] Conventional health management systems lacked detailed support for users because it was difficult to provide health advice that took into account the user's emotional state. Furthermore, health advice was general and lacked consideration for the user's individual health condition and emotions. As a result, users found it difficult to put the suggested advice into practice, which reduced the effectiveness of health management.

[0777] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for loading a generative AI model, means for saving user information in a database, means for receiving symptom details from the user, means for inputting the received symptom data into the generative AI model and generating appropriate health advice, means for transmitting the generated health advice to the user's terminal, means for analyzing the user's emotions using an emotion engine, and means for adjusting the advice based on the analyzed emotions. This makes it possible to provide detailed health advice that takes into account the user's emotional state.

[0778] A "generative AI model" is an artificial intelligence model that generates appropriate advice and information based on data collected from users.

[0779] "User information" refers to information about a user's personal information, such as age, gender, medical history, and health condition.

[0780] "Database" means a data storage system for storing and managing user information and symptom data.

[0781] The "emotion engine" is an artificial intelligence engine for analyzing emotions from user input data.

[0782] "Symptom Data" is data about specific health symptoms or conditions reported by a user.

[0783] "Health Advice" refers to specific instructions or suggestions based on the user's health status provided by a generative AI model.

[0784] "Push notification" is a function that automatically sends information from a server to a user's device.

[0785] "User's device" refers to an information device such as a smartphone, tablet, or PC used by the user.

[0786] "Analysis" is the process of processing data to derive useful information or conclusions.

[0787] System configuration

[0788] The present invention consists of a server, a user terminal, and a network connecting them. The server is equipped with a generative AI model, a database, an emotion engine, and a push notification function. The user terminal is provided with an interface for users to input information.

[0789] Hardware and Software

[0790] 1. Hardware

[0791] Smartphone (Android / iOS)

[0792] Tablet (Android / iOS)

[0793] Kiosk terminal installed in a physical store

[0794] server

[0795] Network: Wi-Fi / 4G / 5G

[0796] 2. Software

[0797] Server: Generative AI model (large-scale language model), emotion engine, database (MySQL)

[0798] Client application: Smartphone / tablet app (Flutter / React Native)

[0799] Program processing

[0800] Server-side processing

[0801] When the server starts up, it loads the generative AI model and emotion engine into memory. The generative AI model generates appropriate health advice based on symptom data from the user, and the emotion engine analyzes the user's emotions. It initializes the database connection and saves and manages user information (age, gender, medical history, etc.). New and updated information sent by the user is also saved in this database.

[0802] When a user reports symptoms, the data is received and the emotion engine is used to analyze the user's emotions. The analysis results and symptom data are input into a generative AI model to generate health advice. The generated health advice is adjusted and formatted based on the user's emotions and then sent to the user's device.

[0803] Terminal side processing

[0804] When the device is used for the first time, it displays an interface that prompts the user to register an account. It provides a form for entering user information (name, age, gender, occupation, medical history, etc.). It provides an interface for entering details of the user's symptoms in an interactive format. It displays questions such as "Please tell us your specific symptoms." The user enters details of the symptoms when they occur. The entered information is sent to the server.

[0805] Specific examples

[0806] For example, if a user visiting a physical store types "I've been having an upset stomach lately" into their smartphone, that information is sent to the server. The server uses an emotion engine to analyze the emotion contained in the input sentence and determine, for example, that the person is feeling anxious or stressed. Next, the generative AI model generates health advice based on the analysis results and symptom data, such as "If you have an upset stomach, we recommend that you pay attention to your diet and drink plenty of fluids." Based on the results of the emotion analysis, the model also includes additional advice, such as "Relaxing can also help."

[0807] Prompt Sentence Examples

[0808] "A 30-year-old male user reports, 'I've had lower back pain for three days now and it's getting worse.'"

[0809] This makes it possible to provide detailed health advice that takes into account the user's emotional state.

[0810] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0811] Step 1:

[0812] When the server starts up, it loads the generative AI model and emotion engine into memory. This process makes the generative AI model and emotion engine immediately available for use. The input is the server startup operation, and the output is the generative AI model and emotion engine loaded into memory.

[0813] Step 2:

[0814] The server initializes the database connection and prepares to save user information. This operation efficiently stores and manages user information (age, gender, medical history, etc.). The input is the database connection initialization operation, and the output is the database with the established connection.

[0815] Step 3:

[0816] When a user first uses the service, they input their user information (such as name, age, gender, occupation, and medical history) into the terminal. This information is used to provide advice thereafter. The input is the user information, and the output is the user information data entered into the terminal.

[0817] Step 4:

[0818] The terminal sends user information to the server. The server receives this information and stores it in a database. The input is the user information data from the terminal, and the output is the user information data stored in the database.

[0819] Step 5:

[0820] When a symptom occurs, the user inputs specific details of the symptom into the terminal. For example, the user might input a specific symptom such as, "I've had lower back pain for three days and the pain is getting worse." The input is symptom information, and the output is the symptom data entered into the terminal.

[0821] Step 6:

[0822] The terminal sends the input symptom data to the server, which receives the data and stores it in a database. The input is the symptom data, and the output is the symptom data stored in the database.

[0823] Step 7:

[0824] The server uses an emotion engine to analyze the symptom data received from the user and determine the user's emotional state. The input is the symptom data, and the output is the analyzed emotion data.

[0825] Step 8:

[0826] The server inputs the user's symptom data and emotion data into the generative AI model and generates appropriate health advice. The input is symptom data and emotion data, and the output is the generated health advice.

[0827] Step 9:

[0828] The server adjusts the generated health advice based on the user's emotional data, for example by including additional advice on relaxation if stress or anxiety is detected. The input is the generated health advice and the emotional data, and the output is the adjusted health advice.

[0829] Step 10:

[0830] The server sends the tailored health advice to the user's terminal, which receives it and displays it to the user. The input is the tailored health advice, and the output is the terminal screen with the advice displayed.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] [Third embodiment]

[0835] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0836] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0837] 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).

[0838] 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.

[0839] 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.

[0840] 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).

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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."

[0847] The present invention is a system that uses a generative AI model to provide preventive medicine and health management advice within the home, and can be implemented as follows.

[0848] System Overview

[0849] This system consists of a server, user terminals, and a network connecting them. The server is equipped with a generative AI model, a database, and a push notification function. The user terminal is provided with an interface for users to input information.

[0850] Program processing

[0851] server

[0852] 1. At startup, the server loads a generative AI model into memory. This model generates appropriate health advice based on symptom data provided by the user.

[0853] 2. The server initializes the database connection and saves and manages user information (age, gender, occupation, medical history, medication status, etc.). New and updated information sent by the user is also saved in this database.

[0854] 3. When a user reports a symptom, the server receives the data, inputs it into the AI ​​model for analysis, and generates health advice that is formatted and sent to the user's device.

[0855] Terminal

[0856] 1. When the device is used for the first time, it displays an interface that prompts the user to create an account and log in. User information is collected through an input form and sent to the server.

[0857] 2. The terminal provides an interface for the user to interactively input details of their symptoms. Once the user enters their symptoms, the data is sent to the server.

[0858] 3. The device receives health advice sent from the server and displays it in an easy-to-read format for the user. Specifically, advice such as "If you have lower back pain, correct your posture and stretch regularly. If your symptoms do not improve, we recommend that you see a doctor" is displayed.

[0859] User

[0860] 1. When using the service for the first time, the user enters account information, including name, age, gender, occupation, medical history, medication status, etc.

[0861] 2. When a symptom occurs, the user enters details of the symptom, for example, "I have had back pain for the past three days."

[0862] 3. The user takes appropriate action based on the health advice received through the device. For example, "If you have back pain, correct your posture and stretch regularly."

[0863] Specific examples

[0864] For example, suppose a 30-year-old male user registers an account and registers to the server that he works at a desk and has a medical history of back pain. If he experiences back pain while at work and uses the system to report, "My back pain has been going on for three days and is getting worse," the following is an example of how the system will process the situation.

[0865] 1. Initial setup: The user enters their account information on the device and sends it to the server, which stores it in a database.

[0866] 2. Symptom input: The user inputs on the terminal, "I have had back pain for three days and it is getting worse."

[0867] 3. Data transmission: The device transmits this symptom data to the server.

[0868] 4. Advice generation: The server inputs the received symptom data into the generative AI model for analysis. As a result, it generates advice such as, "If you have lower back pain, it is effective to correct your posture and stretch regularly. If the pain persists, we recommend that you see a doctor."

[0869] 5. Display advice: The terminal receives the generated advice and displays it to the user.

[0870] In this way, by using this system, users can easily receive health advice from home or work and take appropriate measures.

[0871] The processing flow will be explained below.

[0872] Step 1:

[0873] server

[0874] The server loads the generative AI model into memory upon startup, allowing the AI ​​to generate appropriate health advice based on symptom data from the user.

[0875] Step 2:

[0876] server

[0877] The server initializes a database connection and prepares to load existing user information, including the user's age, gender, occupation, medical history, medication status, etc.

[0878] Step 3:

[0879] Terminal

[0880] When the device is used for the first time, it displays an interface that prompts the user to register an account, and provides a form for entering user information (such as name, age, gender, occupation, medical history, and medication status).

[0881] Step 4:

[0882] User

[0883] The user enters the necessary information on the device, such as name, age, gender, occupation, and other details, and fills out the input form.

[0884] Step 5:

[0885] Terminal

[0886] The terminal validates the information entered by the user and sends it to the server, checking that all required fields are filled in and for formatting mismatches.

[0887] Step 6:

[0888] server

[0889] The server stores the received user information in a database, creating an entry for a new user or updating the information for an existing user.

[0890] Step 7:

[0891] Terminal

[0892] The terminal provides an interface for the user to interactively input details of symptoms, displaying questions such as "Please tell us your specific symptoms."

[0893] Step 8:

[0894] User

[0895] The user enters specific symptoms, such as "I've had back pain for the past three days, and it's getting worse."

[0896] Step 9:

[0897] Terminal

[0898] The terminal sends the symptom data entered by the user to the server, which rechecks whether the data is complete before sending.

[0899] Step 10:

[0900] server

[0901] The server analyzes the received data and requests additional information if necessary, for example by sending the user a follow-up question such as "Does your back pain get worse with certain movements?"

[0902] Step 11:

[0903] server

[0904] The server inputs user information and symptom data into the generative AI model and generates optimal health advice, such as "It would be effective to correct your posture and stretch regularly. If the pain persists, we recommend that you see a doctor."

[0905] Step 12:

[0906] server

[0907] The server formats the generated health advice and prepares it for sending to the device, optionally checking for grammar and whether the same advice has been given before.

[0908] Step 13:

[0909] server

[0910] The server transmits the generated health advice to the terminal.

[0911] Step 14:

[0912] Terminal

[0913] The device displays the received health advice in a format that is easy for the user to view. The content of the advice is displayed on the device's interface.

[0914] Step 15:

[0915] server

[0916] The server periodically checks user data and pushes health information and advice to the user's device, suggesting new health information and seasonal advice.

[0917] Step 16:

[0918] Terminal

[0919] The device receives a push notification and notifies the user, for example, "To boost your immunity, make sure you get enough sleep and eat a balanced diet."

[0920] Example 1

[0921] 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."

[0922] Prior art requires users to visit a hospital or specialist to receive advice about their health condition or symptoms. This takes time and effort, and can delay early treatment. Furthermore, existing systems provide a poor user experience, making it difficult for users to conveniently receive health information. There is a demand for convenient and efficient advice from home, particularly in the areas of daily health management and preventive medicine.

[0923] 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.

[0924] In this invention, the server includes means for loading the generative AI model, means for saving user information in a database, means for receiving symptom details from the user, means for inputting the received symptom data into the generative AI model and generating appropriate health advice, means for transmitting the generated health advice to the user's terminal, means for displaying an interface for prompting the user to create an account or log in, means for providing an interface for interactively inputting the user's symptom details, and means for displaying the generated health advice in an easy-to-read format on the user's terminal. This enables users to easily obtain health information and advice from home or work and use it for daily health management and preventive medicine.

[0925] A "generative AI model" is an artificial intelligence model that analyzes symptom data from users and generates appropriate health advice.

[0926] A "database" is a system that stores and manages user information (age, gender, occupation, medical history, medication status, etc.).

[0927] "User information" refers to detailed information such as name, age, sex, occupation, medical history, medication status, etc. that is input by the user when using the service for the first time.

[0928] "Symptom Data" is information sent to the server when a user enters details about a particular symptom.

[0929] An "account creation interface" is a system that provides a screen and input form for a user to create an account and log in when using the service for the first time.

[0930] An "interactive interface" is a system that provides interactive screens and input forms for users to enter details of their symptoms.

[0931] "Health advice" is specific advice on health management and symptom improvement that is generated as a result of analysis by the generative AI model.

[0932] An "easy-to-read format" is a display format that is easy for users to understand and intuitively check information.

[0933] MODE FOR CARRYING OUT THE INVENTION

[0934] The present invention is a system that uses a generative AI model to provide preventive medicine and health management advice within the home. This system is composed of a server, a user terminal, and a network connecting these. Specific embodiments of the system are described below.

[0935] server

[0936] The server is equipped with a generative AI model, a database, and a push notification function. When the server starts up, it loads the generative AI model into memory. This model generates appropriate health advice based on symptom data from users. Specifically, it loads a pre-trained model using TensorFlow or PyTorch in a Python environment.

[0937] The server also initializes a connection to a database and stores and manages user information (such as age, gender, occupation, medical history, and medication status). Relational databases such as MySQL or PostgreSQL are often used. New and updated information is updated in real time.

[0938] When a user reports symptoms, the server receives the data. The received data is formatted appropriately and input into the generative AI model. The generative AI model analyzes the data and generates health advice. The generated health advice is formatted and sent to the user's device. At this time, the data is encoded in JSON format or similar and sent over the network.

[0939] Terminal

[0940] When the device is used for the first time, it displays an interface that prompts the user to create an account and log in. User information is collected through an input form and sent to the server. Specifically, a web interface using HTML and JavaScript or a mobile application UI is used.

[0941] The device also provides an interface for interactively inputting details of the user's symptoms. Once the user enters the symptoms, the data is sent to the server. The device receives health advice sent from the server and displays it in an easy-to-read format for the user. For example, the device may display advice such as, "If you have back pain, correct your posture and stretch regularly. If your symptoms do not improve, we recommend that you see a doctor."

[0942] User

[0943] When using the service for the first time, users enter their account information, including their name, age, gender, occupation, medical history, and medication status. This information is sent to the server via their device. When a symptom occurs, the user enters details, such as "I've had back pain for the past three days." The symptom data is sent to the server via the device interface.

[0944] The user takes appropriate action based on the health advice received through the device, such as "If you have back pain, correct your posture and stretch regularly."

[0945] Specific examples

[0946] For example, let's take the following example: a 30-year-old male user registers an account, and registers to the server that he works at a desk and has a medical history of back pain. He experiences back pain while at work, and uses the system to report, "My back pain has been going on for three days and is getting worse."

[0947] Prompt Sentence Examples

[0948] "I've had back pain for three days now and it's getting worse. What should I do?"

[0949] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0950] Step 1:

[0951] When the server starts up, it loads the generative AI model into memory. Specifically, it loads a pre-trained model using TensorFlow or PyTorch in a Python environment. The input is a file of the pre-trained AI model, and the output is the model loaded into memory.

[0952] Step 2:

[0953] The server initializes a connection to a database to store and manage user information. Specifically, it uses a relational database such as MySQL or PostgreSQL. It requires database connection information as input and establishes a database connection where user information is stored as output.

[0954] Step 3:

[0955] When the device is used for the first time, it displays an interface that prompts the user to create an account and log in. The account information provided by the user (e.g., name, age, gender, occupation, medical history, medication status) is collected as input, and this information is sent to the server as output and stored in a database.

[0956] Step 4:

[0957] The terminal provides an interface for interactively inputting the user's symptom details. It requires user-provided symptom data as input and sends the symptom details to the server as output.

[0958] Step 5:

[0959] The server receives the symptom data sent by the user and formats it appropriately. The input is the received symptom data, and the output is the formatted data to be input into the generative AI model.

[0960] Step 6:

[0961] The server inputs the formatted symptom data into a generative AI model for analysis. The input is the formatted symptom data, and the output is generated health advice.

[0962] Step 7:

[0963] The server encodes the generated health advice in JSON format or similar and sends it to the user's device. The generated health advice is the input, and the encoded data is sent over the network as the output.

[0964] Step 8:

[0965] The terminal receives health advice sent from the server and displays it in a user-friendly format. The input is the encoded data sent from the server, and the output is the decoded health advice.

[0966] Step 9:

[0967] The user takes appropriate action according to the health advice received through the device. The input is the health advice, and the output is expected to be the user's execution of the action.

[0968] Specific examples

[0969] For example, the following is a processing example in which a 30-year-old male user registers an account and registers to the server that he / she works at a desk and has a medical history of lower back pain.

[0970] Example prompt sentence:

[0971] "I've had back pain for three days now and it's getting worse. What should I do?"

[0972] This series of steps allows users to quickly receive specific health advice and take appropriate action.

[0973] (Application example 1)

[0974] 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."

[0975] Conventional health management systems only allow users to receive health advice at home using a generative AI model, but there are no systems that also provide in-store health management and recommend appropriate products. As a result, users are not provided with sufficient support when purchasing appropriate products based on their health status. The present invention aims to improve this issue by providing a system that supports in-store health management and helps users maintain and improve their health.

[0976] 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.

[0977] In this invention, the server includes means for loading the generative AI model, means for saving user information in a database, means for receiving symptom details from the user, means for inputting the received symptom data into the generative AI model and generating appropriate health advice, means for transmitting the generated health advice to the user's terminal, and means for recommending appropriate products in a store based on the user's symptom data, thereby enabling the user to receive support in purchasing appropriate products based on their health condition in a physical store.

[0978] A "generative AI model" is an artificial intelligence model that generates appropriate health advice based on symptom data from users.

[0979] The "server" is a computer system that has the functions of loading the generative AI model, storing and managing user information, analyzing symptom data to generate health advice, and sending it to the user's device.

[0980] "User information" is information related to the user's health condition, such as age, sex, occupation, medical history, and medication status.

[0981] A "database" is an information system for storing and managing user information and symptom data.

[0982] "Symptom data" is detailed information about a health condition or symptom reported by a user.

[0983] "Health advice" is a recommendation for health management and improvement that is generated by an AI model that analyzes symptom data.

[0984] A "user terminal" is a device through which a user inputs information and receives generated health advice.

[0985] "Push notification" is a function that automatically delivers health information and advice to user devices on a regular basis.

[0986] A "brick and mortar store" is a physical store where users visit and purchase products based on their health condition.

[0987] "Product recommendation" refers to the generative AI model suggesting appropriate products based on the user's symptom data.

[0988] "Symptom details" are specific descriptions of individual symptoms reported by the user.

[0989] The present invention is a system that uses a generative AI model to provide health management and preventive medicine advice in a physical store, and can be implemented as follows.

[0990] System Overview

[0991] This system consists of a server, a user device, and a network connecting them. The server is equipped with a generative AI model, a database, and a push notification function. The user device is provided with an interface for users to input information. It also includes a function to recommend appropriate products in physical stores based on the user's symptom data.

[0992] Hardware and software used

[0993] Server: The server runs on a Linux-based operating system and uses web server software such as Apache or Nginx, MySQL or PostgreSQL as the database management system, and TensorFlow or PyTorch to implement the generative AI model.

[0994] User device: The user device is an Android or iOS device with a dedicated mobile application installed.

[0995] Network: The server and user terminals communicate via the Internet.

[0996] Program processing

[0997] server:

[0998] 1. At startup, the server loads the generative AI model into memory. This model generates appropriate health advice based on symptom data provided by the user.

[0999] 2. The server initializes the database connection and saves and manages user information (age, gender, occupation, medical history, medication status, etc.). New and updated information sent by the user is also saved in this database.

[1000] 3. When a user reports a symptom, the server receives the data, inputs it into the AI ​​model for analysis, and generates health advice that is formatted and sent to the user's device.

[1001] 4. In a brick-and-mortar environment, the server recommends appropriate products based on the user's symptom data.

[1002] Device:

[1003] 1. When the device is used for the first time, it displays an interface that prompts the user to create an account and log in. User information is collected through an input form and sent to the server.

[1004] 2. The terminal provides an interface for the user to interactively input details of their symptoms. Once the user enters their symptoms, the data is sent to the server.

[1005] 3. The device receives health advice sent from the server and displays it in an easy-to-read format for the user. Specifically, advice such as "If you have lower back pain, correct your posture and stretch regularly. If your symptoms do not improve, we recommend that you see a doctor" is displayed.

[1006] User:

[1007] 1. When using the service for the first time, the user enters account information, including name, age, gender, occupation, medical history, medication status, etc.

[1008] 2. When a symptom occurs, the user enters details of the symptom, for example, "I have had back pain for the past three days."

[1009] 3. The user takes appropriate action based on the health advice received through the device. For example, "If you have back pain, correct your posture and stretch regularly."

[1010] 4. In-store, use the app to learn more about the products recommended by the generative AI model and select the appropriate product.

[1011] Specific examples

[1012] For example, if a 30-year-old male user reports that he has recently been feeling tired, the generative AI model will provide advice such as, "Moderate exercise and a balanced diet are recommended, and vitamin B supplements are effective." The user can then receive this advice in a physical store and purchase the appropriate supplements.

[1013] Example prompt sentence:

[1014] "I get tired easily these days"

[1015] In this way, by using this system, users can receive support in purchasing appropriate products based on their health status in physical stores.

[1016] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1017] Step 1:

[1018] The server loads the generative AI model into memory when it starts up. As input, it requires the file path of the generative AI model, and by loading it into memory, it is ready to analyze the user's symptom data. As output, the generative AI model is deployed in memory.

[1019] Step 2:

[1020] The server initializes a database connection. As input, it requires database connection information (host, user, password, database name, etc.). It uses this connection information to establish a connection with a database management system (e.g., MySQL or PostgreSQL). As output, it establishes a database connection to store and manage user information and symptom data.

[1021] Step 3:

[1022] The user enters account information using the terminal when using the service for the first time. User information such as name, age, gender, occupation, medical history, and medication status is required as input. The terminal sends this information to the server, which stores it in a database. The user information is then saved in the database as output.

[1023] Step 4:

[1024] When a symptom occurs, the user inputs details of the symptom through the device. The input requires symptom data such as "I have had back pain for the past three days." The device sends this symptom data to the server, which then inputs the data into a generative AI model for analysis. Health advice is generated as the output of the analysis.

[1025] Step 5:

[1026] The server formats the generated health advice and sends it to the user's device. As input, it requires the advice output by the generative AI model. It formats this advice in a format that is easy for the user to understand and sends it to the device. As output, the health advice is displayed on the user's device.

[1027] Step 6:

[1028] The server recommends appropriate products based on the user's symptom data in the physical store. The user's symptom data and the generated health advice are required as input. The product recommendation algorithm operates based on the analysis results of the generative AI model and generates an appropriate product list. As output, the product recommendation list is displayed on the user's device.

[1029] Step 7:

[1030] The user checks the health advice and product recommendations received through the device and takes appropriate action. The input required is the advice and recommended product list sent from the server. The user makes decisions about health management and product purchases based on this. The output is the user's actions being promoted.

[1031] 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.

[1032] The present invention is a system that uses a generative AI model and an emotion engine to provide preventive medicine and health management advice within the home, and can be implemented as follows.

[1033] System Overview

[1034] This system consists of a server, user devices, and a network connecting them. The server is equipped with a generative AI model, a database, an emotion engine, and a push notification function. The user device is provided with an interface for users to input information.

[1035] Program processing

[1036] server

[1037] 1. The server loads the generative AI model and emotion engine into memory at startup. The generative AI model generates appropriate health advice based on symptom data from the user, and the emotion engine recognizes the user's emotions.

[1038] 2. The server initializes the database connection and saves and manages user information (age, gender, occupation, medical history, medication status, etc.). New and updated information sent by the user is also saved in this database.

[1039] 3. When a user reports symptoms, the server receives the data and analyzes the user's emotions using an emotion engine. The analysis results and symptom data are input into a generative AI model to generate health advice. The generated health advice is adjusted and formatted based on the user's emotions and then sent to the user's device.

[1040] Terminal

[1041] 1. When the device is used for the first time, it displays an interface that prompts the user to register an account and provides a form for entering user information (such as name, age, gender, occupation, medical history, and medication status).

[1042] 2. The terminal provides an interface for the user to interactively input details of their symptoms, displaying questions such as "Please tell us your specific symptoms."

[1043] User

[1044] 1. When using the service for the first time, the user enters account information, including name, age, gender, occupation, medical history, medication status, etc.

[1045] 2. When a symptom occurs, the user enters details of the symptom, such as, "I've had back pain for the past three days, and the pain is getting worse."

[1046] Specific examples

[1047] For example, suppose a 30-year-old male user registers an account and registers to the server that he works at a desk and has a medical history of back pain. If he experiences back pain while at work and uses the system to report, "My back pain has been going on for three days and is getting worse," the following is an example of how the system will process the situation.

[1048] 1. Initial setup: The user enters their account information on the device and sends it to the server, which stores it in a database.

[1049] 2. Symptom input: The user inputs on the terminal, "I have had back pain for three days and it is getting worse."

[1050] 3. Data transmission: The device transmits this symptom data to the server.

[1051] 4. Sentiment Analysis: The server uses an emotion engine to analyze the emotions from the user's input data, for example, to determine whether the user's input indicates stress or anxiety.

[1052] 5. Advice generation: The server inputs the user's symptom data and the results of emotion analysis into the generative AI model and performs analysis. As a result, it generates advice such as, "If you have lower back pain, it is effective to correct your posture and stretch regularly. If the pain persists, we recommend that you see a doctor."

[1053] 6. Advice Adjustment: The server adjusts the generated advice based on the user's emotions. For example, if the user is feeling stressed or anxious, the server may include additional advice such as "Take deep breaths to relax and try to create as relaxing an environment as possible."

[1054] 7. Display Advice: The device receives the generated advice and displays it in a user-friendly format. The content of the advice is displayed on the device interface.

[1055] Ongoing support

[1056] The server periodically checks user data and pushes health information and advice to the user's device. By providing new health information and seasonal advice, the system provides ongoing support for the user's health management.

[1057] In this way, by using this system, users can easily receive health advice from home or work, take appropriate measures, and receive detailed support based on their emotions.

[1058] The processing flow will be explained below.

[1059] Step 1:

[1060] server

[1061] The server loads the generative AI model and emotion engine into memory upon startup, allowing the AI ​​to generate appropriate health advice based on symptom data from the user and the emotion engine to recognize the user's emotions.

[1062] Step 2:

[1063] server

[1064] The server initializes a database connection and prepares to load existing user information, including the user's age, gender, occupation, medical history, medication status, etc.

[1065] Step 3:

[1066] Terminal

[1067] When the device is used for the first time, it displays an interface that prompts the user to register an account, and provides a form for entering user information (such as name, age, gender, occupation, medical history, and medication status).

[1068] Step 4:

[1069] User

[1070] The user enters the necessary information on the terminal, for example, filling out an input form with their name, age, sex, occupation, and details of their medical history.

[1071] Step 5:

[1072] Terminal

[1073] The terminal validates the information entered by the user and sends it to the server, checking that all required fields are filled in and that there are no formatting mismatches.

[1074] Step 6:

[1075] server

[1076] The server stores the received user information in a database, creating an entry for a new user or updating the information for an existing user.

[1077] Step 7:

[1078] Terminal

[1079] The terminal provides an interface for the user to interactively input details of symptoms, displaying questions such as "Please tell us your specific symptoms."

[1080] Step 8:

[1081] User

[1082] The user enters specific symptoms, such as "I've had back pain for the past three days, and it's getting worse."

[1083] Step 9:

[1084] Terminal

[1085] The terminal sends the symptom data entered by the user to the server, which rechecks whether the data is complete before sending.

[1086] Step 10:

[1087] server

[1088] The server inputs the received symptom data into an emotion engine for emotion analysis, for example, to determine whether the user's input indicates stress or anxiety.

[1089] Step 11:

[1090] server

[1091] The server inputs the analysis results and symptom data into a generative AI model to generate optimal health advice, such as "If you have lower back pain, it is effective to correct your posture and stretch regularly. If the pain persists, we recommend that you see a doctor."

[1092] Step 12:

[1093] server

[1094] The server adjusts the generated health advice based on the emotion engine's analysis. For example, if the user is feeling stressed, the server may include additional advice such as "Take deep breaths to relax and try to create as relaxing an environment as possible."

[1095] Step 13:

[1096] server

[1097] The server prepares the generated advice to be sent to the terminal, optionally checking for syntax and whether the same advice has been given previously.

[1098] Step 14:

[1099] server

[1100] The server transmits the generated health advice to the terminal.

[1101] Step 15:

[1102] Terminal

[1103] The device displays the received health advice in a user-friendly format, with the advice content clearly displayed on the device's interface.

[1104] Step 16:

[1105] server

[1106] The server periodically checks user data and pushes health information and advice to the user's device, suggesting new health information and seasonal advice.

[1107] Step 17:

[1108] Terminal

[1109] The device receives the push notification and notifies the user, for example, by displaying a notification that reads, "To boost your immunity, make sure you get enough sleep and eat a balanced diet."

[1110] Example 2

[1111] 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."

[1112] Given the current lack of easy ways to receive preventive medicine and health management advice at home, there is a need for a system that takes into account the user's emotions and provides detailed advice tailored to each individual's symptoms.

[1113] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for loading a generative AI model, means for saving user information in a database, means for receiving symptom details from the user, means for inputting the received symptom data into the generative AI model and generating appropriate health advice, means for adjusting the generated health advice based on the user's emotions, means for sending the adjusted health advice to the terminal, means for periodically checking user data and pushing health information and advice to the user's terminal, and means for analyzing the user's emotions and inputting them together with the symptom data into the generative AI model to generate health advice. This allows users to easily receive health advice from home or work, take appropriate measures, and receive detailed support based on their emotions.

[1114] A "generative AI model" is an artificial intelligence model that generates appropriate health advice based on symptom data from users.

[1115] "User information" refers to personal data such as the user's name, age, sex, occupation, medical history, and medication status.

[1116] A "database" is a system for storing and managing user information and symptom data.

[1117] "Symptom details" are information about specific physical ailments or discomforts reported by the user.

[1118] An "emotion engine" is a system that analyzes user input data and infers their emotions.

[1119] The "adjusting means" is a means having a function of correcting and optimizing the generated health advice in accordance with the user's emotions.

[1120] "Push notification" is a communication method in which a server sends information directly to a user's device and displays it on the screen.

[1121] "Emotion analysis" is the process of analyzing a user's emotional state (stress, anxiety, etc.) based on their symptom data and input.

[1122] The present invention is a system that provides preventive medicine and health management advice within the home. Its main components include a server, a user terminal, and a network connecting them. The server is equipped with a generative AI model, a database, an emotion engine, and a push notification function. The user terminal is provided with an interface for users to input information.

[1123] System Overview

[1124] server

[1125] When the server starts up, it loads the generative AI model and emotion engine into memory. The generative AI model generates appropriate health advice based on symptom data from the user, and the emotion engine recognizes the user's emotions. The server also initializes the database connection and saves and manages user information (age, gender, occupation, medical history, medication status, etc.). New and updated information sent by the user is also saved in this database.

[1126] When a user reports symptoms, the server receives the data and analyzes the user's emotions using an emotion engine. The analysis results and symptom data are input into a generative AI model to generate health advice. The generated health advice is adjusted and formatted based on the user's emotions and then sent to the user's device. The server also periodically checks user data and has the function of pushing health information and advice to the user's device.

[1127] Terminal

[1128] When the device is used for the first time, it displays an interface that prompts the user to register an account. It also provides a form for entering user information (such as name, age, gender, occupation, medical history, and medication status). The user enters this information and sends it to the server.

[1129] The terminal also provides an interface for the user to interactively input details of their symptoms. It displays questions such as "Please tell us your specific symptoms" and allows the user to input their symptoms. The input data is sent to the server.

[1130] User

[1131] When using the service for the first time, users enter their account information, including their name, age, gender, occupation, medical history, medication status, etc. When symptoms occur, they also enter details of the symptoms, such as "I've had back pain for the past three days, and the pain is getting worse."

[1132] Specific examples

[1133] For example, suppose a 30-year-old male desk worker registers an account and registers with the server that he has a history of back pain. If he experiences back pain while at work and uses the system to report, "My back pain has lasted for three days and is getting worse," the following process will occur.

[1134] 1. Initial setup: The user enters their name, age, occupation, medical history, etc. into the terminal and sends it to the server, which stores this information in a database.

[1135] 2. Symptom input: The user inputs "I have had lower back pain for three days and the pain is getting worse" into the terminal and submits it.

[1136] 3. Sentiment analysis and advice generation: The server analyzes the received data and determines whether the user is feeling stressed or anxious. The generative AI model generates health advice such as, "If you have lower back pain, it is effective to correct your posture and stretch regularly."

[1137] 4. Advice adjustment: Consider the user's feelings and add additional advice such as "Take deep breaths to relax and try to create as relaxing an environment as possible."

[1138] 5. Displaying Advice: The terminal displays advice from the server. The user receives advice such as, "If your lower back pain persists, maintaining proper posture and stretching are effective. Also, take deep breaths to relax and create a relaxing environment."

[1139] Prompt Sentence Examples

[1140] "I'm a 30-year-old desk worker with a medical history of lower back pain. I've had this pain for the past three days, and it's getting worse. What should I do?"

[1141] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1142] Step 1: Register and save your account information

[1143] Terminal

[1144] When the device is used for the first time, it displays an interface that prompts the user to register an account. The user enters information such as name, age, gender, occupation, medical history, and medication status into an input form. When the user presses the "Register" button, the device sends this information to the server in JSON format.

[1145] Input: User information such as name, age, gender, occupation, medical history, medication status, etc.

[1146] Output: User information data in JSON format

[1147] server

[1148] The server receives the received user information, stores it in a database, and notifies the terminal that the storage is complete.

[1149] Input: JSON format user information data

[1150] Output: User information saved to the database, and notification of completion

[1151] Step 2: Enter and submit symptom data

[1152] Terminal

[1153] When the user presses the "Report Symptoms" button, the device interactively asks the question, "Please tell us your specific symptoms." When the user enters their symptoms (e.g., "I've had lower back pain for three days and it's getting worse") and presses the send button, the device sends this information in JSON format to the server.

[1154] Input: Symptom details (e.g., "I've had back pain for three days now and it's getting worse")

[1155] Output: Symptom data in JSON format

[1156] Step 3: Sentiment analysis and advice generation

[1157] server

[1158] The server receives the symptom data sent from the device. Based on the received data, it uses an emotion engine to analyze the user's emotions. Specifically, it determines whether the user is showing stress or anxiety based on keywords and context in the text. The analysis results and symptom data are input into a generative AI model to generate health advice. For example, it generates advice such as, "If you have lower back pain, it is effective to correct your posture and stretch regularly."

[1159] Input: Symptom data in JSON format

[1160] Output: Health advice generated by the generative AI model

[1161] Step 4: Adjust and send advice

[1162] server

[1163] The generated advice is adjusted based on the user's emotions. For example, if the user is showing signs of stress or anxiety, additional advice such as "Take deep breaths to relax and try to create as relaxing an environment as possible" is included. The adjusted advice is sent to the device in JSON format.

[1164] Input: Advice generated by generative AI model, results of sentiment analysis

[1165] Output: Adjusted health advice (JSON format)

[1166] Step 5: Viewing Advice

[1167] Terminal

[1168] The device displays the advice received from the server. The advice is presented on the screen in a format that is easy for the user to understand. For example, it might say, "If you experience persistent lower back pain, it is effective to maintain proper posture and stretch regularly. Also, take deep breaths to relax and create a relaxing environment."

[1169] Input: Tailored health advice (JSON format)

[1170] Output: Advice displayed on the user's device screen

[1171] Step 6: Regular health information push notifications

[1172] server

[1173] The server periodically checks user data and generates new health information and seasonal advice, which are then sent to the user's device as push notifications.

[1174] Input: User data

[1175] Output: Health information and advice sent as push notifications

[1176] Terminal

[1177] The device will display the received push notification and provide the user with new health information and advice, such as a notification that reads, "Check out health management methods appropriate for the current season."

[1178] Input: Health information and advice sent as push notifications

[1179] Output: Notification displayed on the user's device screen

[1180] (Application example 2)

[1181] 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."

[1182] Conventional health management systems lacked detailed support for users because it was difficult to provide health advice that took into account the user's emotional state. Furthermore, health advice was general and lacked consideration for the user's individual health condition and emotions. As a result, users found it difficult to put the suggested advice into practice, which reduced the effectiveness of health management.

[1183] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for loading a generative AI model, means for saving user information in a database, means for receiving symptom details from the user, means for inputting the received symptom data into the generative AI model and generating appropriate health advice, means for transmitting the generated health advice to the user's terminal, means for analyzing the user's emotions using an emotion engine, and means for adjusting the advice based on the analyzed emotions. This makes it possible to provide detailed health advice that takes into account the user's emotional state.

[1184] A "generative AI model" is an artificial intelligence model that generates appropriate advice and information based on data collected from users.

[1185] "User information" refers to information about a user's personal information, such as age, gender, medical history, and health condition.

[1186] "Database" means a data storage system for storing and managing user information and symptom data.

[1187] The "emotion engine" is an artificial intelligence engine for analyzing emotions from user input data.

[1188] "Symptom Data" is data about specific health symptoms or conditions reported by a user.

[1189] "Health Advice" refers to specific instructions or suggestions based on the user's health status provided by a generative AI model.

[1190] "Push notification" is a function that automatically sends information from a server to a user's device.

[1191] "User's device" refers to an information device such as a smartphone, tablet, or PC used by the user.

[1192] "Analysis" is the process of processing data to derive useful information or conclusions.

[1193] System configuration

[1194] The present invention consists of a server, a user terminal, and a network connecting them. The server is equipped with a generative AI model, a database, an emotion engine, and a push notification function. The user terminal is provided with an interface for users to input information.

[1195] Hardware and Software

[1196] 1. Hardware

[1197] Smartphone (Android / iOS)

[1198] Tablet (Android / iOS)

[1199] Kiosk terminal installed in a physical store

[1200] server

[1201] Network: Wi-Fi / 4G / 5G

[1202] 2. Software

[1203] Server: Generative AI model (large-scale language model), emotion engine, database (MySQL)

[1204] Client application: Smartphone / tablet app (Flutter / React Native)

[1205] Program processing

[1206] Server-side processing

[1207] When the server starts up, it loads the generative AI model and emotion engine into memory. The generative AI model generates appropriate health advice based on symptom data from the user, and the emotion engine analyzes the user's emotions. It initializes the database connection and saves and manages user information (age, gender, medical history, etc.). New and updated information sent by the user is also saved in this database.

[1208] When a user reports symptoms, the data is received and the emotion engine is used to analyze the user's emotions. The analysis results and symptom data are input into a generative AI model to generate health advice. The generated health advice is adjusted and formatted based on the user's emotions and then sent to the user's device.

[1209] Terminal side processing

[1210] When the device is used for the first time, it displays an interface that prompts the user to register an account. It provides a form for entering user information (name, age, gender, occupation, medical history, etc.). It provides an interface for entering details of the user's symptoms in an interactive format. It displays questions such as "Please tell us your specific symptoms." The user enters details of the symptoms when they occur. The entered information is sent to the server.

[1211] Specific examples

[1212] For example, if a user visiting a physical store types "I've been having an upset stomach lately" into their smartphone, that information is sent to the server. The server uses an emotion engine to analyze the emotion contained in the input sentence and determine, for example, that the person is feeling anxious or stressed. Next, the generative AI model generates health advice based on the analysis results and symptom data, such as "If you have an upset stomach, we recommend that you pay attention to your diet and drink plenty of fluids." Based on the results of the emotion analysis, the model also includes additional advice, such as "Relaxing can also help."

[1213] Prompt Sentence Examples

[1214] "A 30-year-old male user reports, 'I've had lower back pain for three days now and it's getting worse.'"

[1215] This makes it possible to provide detailed health advice that takes into account the user's emotional state.

[1216] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1217] Step 1:

[1218] When the server starts up, it loads the generative AI model and emotion engine into memory. This process makes the generative AI model and emotion engine immediately available for use. The input is the server startup operation, and the output is the generative AI model and emotion engine loaded into memory.

[1219] Step 2:

[1220] The server initializes the database connection and prepares to save user information. This operation efficiently stores and manages user information (age, gender, medical history, etc.). The input is the database connection initialization operation, and the output is the database with the established connection.

[1221] Step 3:

[1222] When a user first uses the service, they input their user information (such as name, age, gender, occupation, and medical history) into the terminal. This information is used to provide advice thereafter. The input is the user information, and the output is the user information data entered into the terminal.

[1223] Step 4:

[1224] The terminal sends user information to the server. The server receives this information and stores it in a database. The input is the user information data from the terminal, and the output is the user information data stored in the database.

[1225] Step 5:

[1226] When a symptom occurs, the user inputs specific details of the symptom into the terminal. For example, the user might input a specific symptom such as, "I've had lower back pain for three days and the pain is getting worse." The input is symptom information, and the output is the symptom data entered into the terminal.

[1227] Step 6:

[1228] The terminal sends the input symptom data to the server, which receives the data and stores it in a database. The input is the symptom data, and the output is the symptom data stored in the database.

[1229] Step 7:

[1230] The server uses an emotion engine to analyze the symptom data received from the user and determine the user's emotional state. The input is the symptom data, and the output is the analyzed emotion data.

[1231] Step 8:

[1232] The server inputs the user's symptom data and emotion data into the generative AI model and generates appropriate health advice. The input is symptom data and emotion data, and the output is the generated health advice.

[1233] Step 9:

[1234] The server adjusts the generated health advice based on the user's emotional data, for example by including additional advice on relaxation if stress or anxiety is detected. The input is the generated health advice and the emotional data, and the output is the adjusted health advice.

[1235] Step 10:

[1236] The server sends the tailored health advice to the user's terminal, which receives it and displays it to the user. The input is the tailored health advice, and the output is the terminal screen with the advice displayed.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] [Fourth embodiment]

[1241] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1242] 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.

[1243] 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).

[1244] 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.

[1245] 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.

[1246] 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).

[1247] 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.

[1248] 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.

[1249] 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.

[1250] 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.

[1251] 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.

[1252] 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.

[1253] 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."

[1254] The present invention is a system that uses a generative AI model to provide preventive medicine and health management advice within the home, and can be implemented as follows.

[1255] System Overview

[1256] This system consists of a server, user terminals, and a network connecting them. The server is equipped with a generative AI model, a database, and a push notification function. The user terminal is provided with an interface for users to input information.

[1257] Program processing

[1258] server

[1259] 1. At startup, the server loads a generative AI model into memory. This model generates appropriate health advice based on symptom data provided by the user.

[1260] 2. The server initializes the database connection and saves and manages user information (age, gender, occupation, medical history, medication status, etc.). New and updated information sent by the user is also saved in this database.

[1261] 3. When a user reports a symptom, the server receives the data, inputs it into the AI ​​model for analysis, and generates health advice that is formatted and sent to the user's device.

[1262] Terminal

[1263] 1. When the device is used for the first time, it displays an interface that prompts the user to create an account and log in. User information is collected through an input form and sent to the server.

[1264] 2. The terminal provides an interface for the user to interactively input details of their symptoms. Once the user enters their symptoms, the data is sent to the server.

[1265] 3. The device receives health advice sent from the server and displays it in an easy-to-read format for the user. Specifically, advice such as "If you have lower back pain, correct your posture and stretch regularly. If your symptoms do not improve, we recommend that you see a doctor" is displayed.

[1266] User

[1267] 1. When using the service for the first time, the user enters account information, including name, age, gender, occupation, medical history, medication status, etc.

[1268] 2. When a symptom occurs, the user enters details of the symptom, for example, "I have had back pain for the past three days."

[1269] 3. The user takes appropriate action based on the health advice received through the device. For example, "If you have back pain, correct your posture and stretch regularly."

[1270] Specific examples

[1271] For example, suppose a 30-year-old male user registers an account and registers to the server that he works at a desk and has a medical history of back pain. If he experiences back pain while at work and uses the system to report, "My back pain has been going on for three days and is getting worse," the following is an example of how the system will process the situation.

[1272] 1. Initial setup: The user enters their account information on the device and sends it to the server, which stores it in a database.

[1273] 2. Symptom input: The user inputs on the terminal, "I have had back pain for three days and it is getting worse."

[1274] 3. Data transmission: The device transmits this symptom data to the server.

[1275] 4. Advice generation: The server inputs the received symptom data into the generative AI model for analysis. As a result, it generates advice such as, "If you have lower back pain, it is effective to correct your posture and stretch regularly. If the pain persists, we recommend that you see a doctor."

[1276] 5. Display advice: The terminal receives the generated advice and displays it to the user.

[1277] In this way, by using this system, users can easily receive health advice from home or work and take appropriate measures.

[1278] The processing flow will be explained below.

[1279] Step 1:

[1280] server

[1281] The server loads the generative AI model into memory upon startup, allowing the AI ​​to generate appropriate health advice based on symptom data from the user.

[1282] Step 2:

[1283] server

[1284] The server initializes a database connection and prepares to load existing user information, including the user's age, gender, occupation, medical history, medication status, etc.

[1285] Step 3:

[1286] Terminal

[1287] When the device is used for the first time, it displays an interface that prompts the user to register an account, and provides a form for entering user information (such as name, age, gender, occupation, medical history, and medication status).

[1288] Step 4:

[1289] User

[1290] The user enters the necessary information on the device, such as name, age, gender, occupation, and other details, and fills out the input form.

[1291] Step 5:

[1292] Terminal

[1293] The terminal validates the information entered by the user and sends it to the server, checking that all required fields are filled in and for formatting mismatches.

[1294] Step 6:

[1295] server

[1296] The server stores the received user information in a database, creating an entry for a new user or updating the information for an existing user.

[1297] Step 7:

[1298] Terminal

[1299] The terminal provides an interface for the user to interactively input details of symptoms, displaying questions such as "Please tell us your specific symptoms."

[1300] Step 8:

[1301] User

[1302] The user enters specific symptoms, such as "I've had back pain for the past three days, and it's getting worse."

[1303] Step 9:

[1304] Terminal

[1305] The terminal sends the symptom data entered by the user to the server, which rechecks whether the data is complete before sending.

[1306] Step 10:

[1307] server

[1308] The server analyzes the received data and requests additional information if necessary, for example by sending the user a follow-up question such as "Does your back pain get worse with certain movements?"

[1309] Step 11:

[1310] server

[1311] The server inputs user information and symptom data into the generative AI model and generates optimal health advice, such as "It would be effective to correct your posture and stretch regularly. If the pain persists, we recommend that you see a doctor."

[1312] Step 12:

[1313] server

[1314] The server formats the generated health advice and prepares it for sending to the device, optionally checking for grammar and whether the same advice has been given before.

[1315] Step 13:

[1316] server

[1317] The server transmits the generated health advice to the terminal.

[1318] Step 14:

[1319] Terminal

[1320] The device displays the received health advice in a format that is easy for the user to view. The content of the advice is displayed on the device's interface.

[1321] Step 15:

[1322] server

[1323] The server periodically checks user data and pushes health information and advice to the user's device, suggesting new health information and seasonal advice.

[1324] Step 16:

[1325] Terminal

[1326] The device receives a push notification and notifies the user, for example, "To boost your immunity, make sure you get enough sleep and eat a balanced diet."

[1327] Example 1

[1328] 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."

[1329] Prior art requires users to visit a hospital or specialist to receive advice about their health condition or symptoms. This takes time and effort, and can delay early treatment. Furthermore, existing systems provide a poor user experience, making it difficult for users to conveniently receive health information. There is a demand for convenient and efficient advice from home, particularly in the areas of daily health management and preventive medicine.

[1330] 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.

[1331] In this invention, the server includes means for loading the generative AI model, means for saving user information in a database, means for receiving symptom details from the user, means for inputting the received symptom data into the generative AI model and generating appropriate health advice, means for transmitting the generated health advice to the user's terminal, means for displaying an interface for prompting the user to create an account or log in, means for providing an interface for interactively inputting the user's symptom details, and means for displaying the generated health advice in an easy-to-read format on the user's terminal. This enables users to easily obtain health information and advice from home or work and use it for daily health management and preventive medicine.

[1332] A "generative AI model" is an artificial intelligence model that analyzes symptom data from users and generates appropriate health advice.

[1333] A "database" is a system that stores and manages user information (age, gender, occupation, medical history, medication status, etc.).

[1334] "User information" refers to detailed information such as name, age, sex, occupation, medical history, medication status, etc. that is input by the user when using the service for the first time.

[1335] "Symptom Data" is information sent to the server when a user enters details about a particular symptom.

[1336] An "account creation interface" is a system that provides a screen and input form for a user to create an account and log in when using the service for the first time.

[1337] An "interactive interface" is a system that provides interactive screens and input forms for users to enter details of their symptoms.

[1338] "Health advice" is specific advice on health management and symptom improvement that is generated as a result of analysis by the generative AI model.

[1339] An "easy-to-read format" is a display format that is easy for users to understand and intuitively check information.

[1340] MODE FOR CARRYING OUT THE INVENTION

[1341] The present invention is a system that uses a generative AI model to provide preventive medicine and health management advice within the home. This system is composed of a server, a user terminal, and a network connecting these. Specific embodiments of the system are described below.

[1342] server

[1343] The server is equipped with a generative AI model, a database, and a push notification function. When the server starts up, it loads the generative AI model into memory. This model generates appropriate health advice based on symptom data from users. Specifically, it loads a pre-trained model using TensorFlow or PyTorch in a Python environment.

[1344] The server also initializes a connection to a database and stores and manages user information (such as age, gender, occupation, medical history, and medication status). Relational databases such as MySQL or PostgreSQL are often used. New and updated information is updated in real time.

[1345] When a user reports symptoms, the server receives the data. The received data is formatted appropriately and input into the generative AI model. The generative AI model analyzes the data and generates health advice. The generated health advice is formatted and sent to the user's device. At this time, the data is encoded in JSON format or similar and sent over the network.

[1346] Terminal

[1347] When the device is used for the first time, it displays an interface that prompts the user to create an account and log in. User information is collected through an input form and sent to the server. Specifically, a web interface using HTML and JavaScript or a mobile application UI is used.

[1348] The device also provides an interface for interactively inputting details of the user's symptoms. Once the user enters the symptoms, the data is sent to the server. The device receives health advice sent from the server and displays it in an easy-to-read format for the user. For example, the device may display advice such as, "If you have back pain, correct your posture and stretch regularly. If your symptoms do not improve, we recommend that you see a doctor."

[1349] User

[1350] When using the service for the first time, users enter their account information, including their name, age, gender, occupation, medical history, and medication status. This information is sent to the server via their device. When a symptom occurs, the user enters details, such as "I've had back pain for the past three days." The symptom data is sent to the server via the device interface.

[1351] The user takes appropriate action based on the health advice received through the device, such as "If you have back pain, correct your posture and stretch regularly."

[1352] Specific examples

[1353] For example, let's take the following example: a 30-year-old male user registers an account, and registers to the server that he works at a desk and has a medical history of back pain. He experiences back pain while at work, and uses the system to report, "My back pain has been going on for three days and is getting worse."

[1354] Prompt Sentence Examples

[1355] "I've had back pain for three days now and it's getting worse. What should I do?"

[1356] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1357] Step 1:

[1358] When the server starts up, it loads the generative AI model into memory. Specifically, it loads a pre-trained model using TensorFlow or PyTorch in a Python environment. The input is a file of the pre-trained AI model, and the output is the model loaded into memory.

[1359] Step 2:

[1360] The server initializes a connection to a database to store and manage user information. Specifically, it uses a relational database such as MySQL or PostgreSQL. It requires database connection information as input and establishes a database connection where user information is stored as output.

[1361] Step 3:

[1362] When the device is used for the first time, it displays an interface that prompts the user to create an account and log in. The account information provided by the user (e.g., name, age, gender, occupation, medical history, medication status) is collected as input, and this information is sent to the server as output and stored in a database.

[1363] Step 4:

[1364] The terminal provides an interface for interactively inputting the user's symptom details. It requires user-provided symptom data as input and sends the symptom details to the server as output.

[1365] Step 5:

[1366] The server receives the symptom data sent by the user and formats it appropriately. The input is the received symptom data, and the output is the formatted data to be input into the generative AI model.

[1367] Step 6:

[1368] The server inputs the formatted symptom data into a generative AI model for analysis. The input is the formatted symptom data, and the output is generated health advice.

[1369] Step 7:

[1370] The server encodes the generated health advice in JSON format or similar and sends it to the user's device. The generated health advice is the input, and the encoded data is sent over the network as the output.

[1371] Step 8:

[1372] The terminal receives health advice sent from the server and displays it in a user-friendly format. The input is the encoded data sent from the server, and the output is the decoded health advice.

[1373] Step 9:

[1374] The user takes appropriate action according to the health advice received through the device. The input is the health advice, and the output is expected to be the user's execution of the action.

[1375] Specific examples

[1376] For example, the following is a processing example in which a 30-year-old male user registers an account and registers to the server that he / she works at a desk and has a medical history of lower back pain.

[1377] Example prompt sentence:

[1378] "I've had back pain for three days now and it's getting worse. What should I do?"

[1379] This series of steps allows users to quickly receive specific health advice and take appropriate action.

[1380] (Application example 1)

[1381] 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."

[1382] Conventional health management systems only allow users to receive health advice at home using a generative AI model, but there are no systems that also provide in-store health management and recommend appropriate products. As a result, users are not provided with sufficient support when purchasing appropriate products based on their health status. The present invention aims to improve this issue by providing a system that supports in-store health management and helps users maintain and improve their health.

[1383] 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.

[1384] In this invention, the server includes means for loading the generative AI model, means for saving user information in a database, means for receiving symptom details from the user, means for inputting the received symptom data into the generative AI model and generating appropriate health advice, means for transmitting the generated health advice to the user's terminal, and means for recommending appropriate products in a store based on the user's symptom data, thereby enabling the user to receive support in purchasing appropriate products based on their health condition in a physical store.

[1385] A "generative AI model" is an artificial intelligence model that generates appropriate health advice based on symptom data from users.

[1386] The "server" is a computer system that has the functions of loading the generative AI model, storing and managing user information, analyzing symptom data to generate health advice, and sending it to the user's device.

[1387] "User information" is information related to the user's health condition, such as age, sex, occupation, medical history, and medication status.

[1388] A "database" is an information system for storing and managing user information and symptom data.

[1389] "Symptom data" is detailed information about a health condition or symptom reported by a user.

[1390] "Health advice" is a recommendation for health management and improvement that is generated by an AI model that analyzes symptom data.

[1391] A "user terminal" is a device through which a user inputs information and receives generated health advice.

[1392] "Push notification" is a function that automatically delivers health information and advice to user devices on a regular basis.

[1393] A "brick and mortar store" is a physical store where users visit and purchase products based on their health condition.

[1394] "Product recommendation" refers to the generative AI model suggesting appropriate products based on the user's symptom data.

[1395] "Symptom details" are specific descriptions of individual symptoms reported by the user.

[1396] The present invention is a system that uses a generative AI model to provide health management and preventive medicine advice in a physical store, and can be implemented as follows.

[1397] System Overview

[1398] This system consists of a server, a user device, and a network connecting them. The server is equipped with a generative AI model, a database, and a push notification function. The user device is provided with an interface for users to input information. It also includes a function to recommend appropriate products in physical stores based on the user's symptom data.

[1399] Hardware and software used

[1400] Server: The server runs on a Linux-based operating system and uses web server software such as Apache or Nginx, MySQL or PostgreSQL as the database management system, and TensorFlow or PyTorch to implement the generative AI model.

[1401] User device: The user device is an Android or iOS device with a dedicated mobile application installed.

[1402] Network: The server and user terminals communicate via the Internet.

[1403] Program processing

[1404] server:

[1405] 1. At startup, the server loads the generative AI model into memory. This model generates appropriate health advice based on symptom data provided by the user.

[1406] 2. The server initializes the database connection and saves and manages user information (age, gender, occupation, medical history, medication status, etc.). New and updated information sent by the user is also saved in this database.

[1407] 3. When a user reports a symptom, the server receives the data, inputs it into the AI ​​model for analysis, and generates health advice that is formatted and sent to the user's device.

[1408] 4. In a brick-and-mortar environment, the server recommends appropriate products based on the user's symptom data.

[1409] Device:

[1410] 1. When the device is used for the first time, it displays an interface that prompts the user to create an account and log in. User information is collected through an input form and sent to the server.

[1411] 2. The terminal provides an interface for the user to interactively input details of their symptoms. Once the user enters their symptoms, the data is sent to the server.

[1412] 3. The device receives health advice sent from the server and displays it in an easy-to-read format for the user. Specifically, advice such as "If you have lower back pain, correct your posture and stretch regularly. If your symptoms do not improve, we recommend that you see a doctor" is displayed.

[1413] User:

[1414] 1. When using the service for the first time, the user enters account information, including name, age, gender, occupation, medical history, medication status, etc.

[1415] 2. When a symptom occurs, the user enters details of the symptom, for example, "I have had back pain for the past three days."

[1416] 3. The user takes appropriate action based on the health advice received through the device. For example, "If you have back pain, correct your posture and stretch regularly."

[1417] 4. In-store, use the app to learn more about the products recommended by the generative AI model and select the appropriate product.

[1418] Specific examples

[1419] For example, if a 30-year-old male user reports that he has recently been feeling tired, the generative AI model will provide advice such as, "Moderate exercise and a balanced diet are recommended, and vitamin B supplements are effective." The user can then receive this advice in a physical store and purchase the appropriate supplements.

[1420] Example prompt sentence:

[1421] "I get tired easily these days"

[1422] In this way, by using this system, users can receive support in purchasing appropriate products based on their health status in physical stores.

[1423] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1424] Step 1:

[1425] The server loads the generative AI model into memory when it starts up. As input, it requires the file path of the generative AI model, and by loading it into memory, it is ready to analyze the user's symptom data. As output, the generative AI model is deployed in memory.

[1426] Step 2:

[1427] The server initializes a database connection. As input, it requires database connection information (host, user, password, database name, etc.). It uses this connection information to establish a connection with a database management system (e.g., MySQL or PostgreSQL). As output, it establishes a database connection to store and manage user information and symptom data.

[1428] Step 3:

[1429] The user enters account information using the terminal when using the service for the first time. User information such as name, age, gender, occupation, medical history, and medication status is required as input. The terminal sends this information to the server, which stores it in a database. The user information is then saved in the database as output.

[1430] Step 4:

[1431] When a symptom occurs, the user inputs details of the symptom through the device. The input requires symptom data such as "I have had back pain for the past three days." The device sends this symptom data to the server, which then inputs the data into a generative AI model for analysis. Health advice is generated as the output of the analysis.

[1432] Step 5:

[1433] The server formats the generated health advice and sends it to the user's device. As input, it requires the advice output by the generative AI model. It formats this advice in a format that is easy for the user to understand and sends it to the device. As output, the health advice is displayed on the user's device.

[1434] Step 6:

[1435] The server recommends appropriate products based on the user's symptom data in the physical store. The user's symptom data and the generated health advice are required as input. The product recommendation algorithm operates based on the analysis results of the generative AI model and generates an appropriate product list. As output, the product recommendation list is displayed on the user's device.

[1436] Step 7:

[1437] The user checks the health advice and product recommendations received through the device and takes appropriate action. The input required is the advice and recommended product list sent from the server. The user makes decisions about health management and product purchases based on this. The output is the user's actions being promoted.

[1438] 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.

[1439] The present invention is a system that uses a generative AI model and an emotion engine to provide preventive medicine and health management advice within the home, and can be implemented as follows.

[1440] System Overview

[1441] This system consists of a server, user devices, and a network connecting them. The server is equipped with a generative AI model, a database, an emotion engine, and a push notification function. The user device is provided with an interface for users to input information.

[1442] Program processing

[1443] server

[1444] 1. The server loads the generative AI model and emotion engine into memory at startup. The generative AI model generates appropriate health advice based on symptom data from the user, and the emotion engine recognizes the user's emotions.

[1445] 2. The server initializes the database connection and saves and manages user information (age, gender, occupation, medical history, medication status, etc.). New and updated information sent by the user is also saved in this database.

[1446] 3. When a user reports symptoms, the server receives the data and analyzes the user's emotions using an emotion engine. The analysis results and symptom data are input into a generative AI model to generate health advice. The generated health advice is adjusted and formatted based on the user's emotions and then sent to the user's device.

[1447] Terminal

[1448] 1. When the device is used for the first time, it displays an interface that prompts the user to register an account and provides a form for entering user information (such as name, age, gender, occupation, medical history, and medication status).

[1449] 2. The terminal provides an interface for the user to interactively input details of their symptoms, displaying questions such as "Please tell us your specific symptoms."

[1450] User

[1451] 1. When using the service for the first time, the user enters account information, including name, age, gender, occupation, medical history, medication status, etc.

[1452] 2. When a symptom occurs, the user enters details of the symptom, such as, "I've had back pain for the past three days, and the pain is getting worse."

[1453] Specific examples

[1454] For example, suppose a 30-year-old male user registers an account and registers to the server that he works at a desk and has a medical history of back pain. If he experiences back pain while at work and uses the system to report, "My back pain has been going on for three days and is getting worse," the following is an example of how the system will process the situation.

[1455] 1. Initial setup: The user enters their account information on the device and sends it to the server, which stores it in a database.

[1456] 2. Symptom input: The user inputs on the terminal, "I have had back pain for three days and it is getting worse."

[1457] 3. Data transmission: The device transmits this symptom data to the server.

[1458] 4. Sentiment Analysis: The server uses an emotion engine to analyze the emotions from the user's input data, for example, to determine whether the user's input indicates stress or anxiety.

[1459] 5. Advice generation: The server inputs the user's symptom data and the results of emotion analysis into the generative AI model and performs analysis. As a result, it generates advice such as, "If you have lower back pain, it is effective to correct your posture and stretch regularly. If the pain persists, we recommend that you see a doctor."

[1460] 6. Advice Adjustment: The server adjusts the generated advice based on the user's emotions. For example, if the user is feeling stressed or anxious, the server may include additional advice such as "Take deep breaths to relax and try to create as relaxing an environment as possible."

[1461] 7. Display Advice: The device receives the generated advice and displays it in a user-friendly format. The content of the advice is displayed on the device interface.

[1462] Ongoing support

[1463] The server periodically checks user data and pushes health information and advice to the user's device. By providing new health information and seasonal advice, the system provides ongoing support for the user's health management.

[1464] In this way, by using this system, users can easily receive health advice from home or work, take appropriate measures, and receive detailed support based on their emotions.

[1465] The processing flow will be explained below.

[1466] Step 1:

[1467] server

[1468] The server loads the generative AI model and emotion engine into memory upon startup, allowing the AI ​​to generate appropriate health advice based on symptom data from the user and the emotion engine to recognize the user's emotions.

[1469] Step 2:

[1470] server

[1471] The server initializes a database connection and prepares to load existing user information, including the user's age, gender, occupation, medical history, medication status, etc.

[1472] Step 3:

[1473] Terminal

[1474] When the device is used for the first time, it displays an interface that prompts the user to register an account, and provides a form for entering user information (such as name, age, gender, occupation, medical history, and medication status).

[1475] Step 4:

[1476] User

[1477] The user enters the necessary information on the terminal, for example, filling out an input form with their name, age, sex, occupation, and details of their medical history.

[1478] Step 5:

[1479] Terminal

[1480] The terminal validates the information entered by the user and sends it to the server, checking that all required fields are filled in and that there are no formatting mismatches.

[1481] Step 6:

[1482] server

[1483] The server stores the received user information in a database, creating an entry for a new user or updating the information for an existing user.

[1484] Step 7:

[1485] Terminal

[1486] The terminal provides an interface for the user to interactively input details of symptoms, displaying questions such as "Please tell us your specific symptoms."

[1487] Step 8:

[1488] User

[1489] The user enters specific symptoms, such as "I've had back pain for the past three days, and it's getting worse."

[1490] Step 9:

[1491] Terminal

[1492] The terminal sends the symptom data entered by the user to the server, which rechecks whether the data is complete before sending.

[1493] Step 10:

[1494] server

[1495] The server inputs the received symptom data into an emotion engine for emotion analysis, for example, to determine whether the user's input indicates stress or anxiety.

[1496] Step 11:

[1497] server

[1498] The server inputs the analysis results and symptom data into a generative AI model to generate optimal health advice, such as "If you have lower back pain, it is effective to correct your posture and stretch regularly. If the pain persists, we recommend that you see a doctor."

[1499] Step 12:

[1500] server

[1501] The server adjusts the generated health advice based on the emotion engine's analysis. For example, if the user is feeling stressed, the server may include additional advice such as "Take deep breaths to relax and try to create as relaxing an environment as possible."

[1502] Step 13:

[1503] server

[1504] The server prepares the generated advice to be sent to the terminal, optionally checking for syntax and whether the same advice has been given previously.

[1505] Step 14:

[1506] server

[1507] The server transmits the generated health advice to the terminal.

[1508] Step 15:

[1509] Terminal

[1510] The device displays the received health advice in a user-friendly format, with the advice content clearly displayed on the device's interface.

[1511] Step 16:

[1512] server

[1513] The server periodically checks user data and pushes health information and advice to the user's device, suggesting new health information and seasonal advice.

[1514] Step 17:

[1515] Terminal

[1516] The device receives the push notification and notifies the user, for example, by displaying a notification that reads, "To boost your immunity, make sure you get enough sleep and eat a balanced diet."

[1517] Example 2

[1518] 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."

[1519] Given the current lack of easy ways to receive preventive medicine and health management advice at home, there is a need for a system that takes into account the user's emotions and provides detailed advice tailored to each individual's symptoms.

[1520] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for loading a generative AI model, means for saving user information in a database, means for receiving symptom details from the user, means for inputting the received symptom data into the generative AI model and generating appropriate health advice, means for adjusting the generated health advice based on the user's emotions, means for sending the adjusted health advice to the terminal, means for periodically checking user data and pushing health information and advice to the user's terminal, and means for analyzing the user's emotions and inputting them together with the symptom data into the generative AI model to generate health advice. This allows users to easily receive health advice from home or work, take appropriate measures, and receive detailed support based on their emotions.

[1521] A "generative AI model" is an artificial intelligence model that generates appropriate health advice based on symptom data from users.

[1522] "User information" refers to personal data such as the user's name, age, sex, occupation, medical history, and medication status.

[1523] A "database" is a system for storing and managing user information and symptom data.

[1524] "Symptom details" are information about specific physical ailments or discomforts reported by the user.

[1525] An "emotion engine" is a system that analyzes user input data and infers their emotions.

[1526] The "adjusting means" is a means having a function of correcting and optimizing the generated health advice in accordance with the user's emotions.

[1527] "Push notification" is a communication method in which a server sends information directly to a user's device and displays it on the screen.

[1528] "Emotion analysis" is the process of analyzing a user's emotional state (stress, anxiety, etc.) based on their symptom data and input.

[1529] The present invention is a system that provides preventive medicine and health management advice within the home. Its main components include a server, a user terminal, and a network connecting them. The server is equipped with a generative AI model, a database, an emotion engine, and a push notification function. The user terminal is provided with an interface for users to input information.

[1530] System Overview

[1531] server

[1532] When the server starts up, it loads the generative AI model and emotion engine into memory. The generative AI model generates appropriate health advice based on symptom data from the user, and the emotion engine recognizes the user's emotions. The server also initializes the database connection and saves and manages user information (age, gender, occupation, medical history, medication status, etc.). New and updated information sent by the user is also saved in this database.

[1533] When a user reports symptoms, the server receives the data and analyzes the user's emotions using an emotion engine. The analysis results and symptom data are input into a generative AI model to generate health advice. The generated health advice is adjusted and formatted based on the user's emotions and then sent to the user's device. The server also periodically checks user data and has the function of pushing health information and advice to the user's device.

[1534] Terminal

[1535] When the device is used for the first time, it displays an interface that prompts the user to register an account. It also provides a form for entering user information (such as name, age, gender, occupation, medical history, and medication status). The user enters this information and sends it to the server.

[1536] The terminal also provides an interface for the user to interactively input details of their symptoms. It displays questions such as "Please tell us your specific symptoms" and allows the user to input their symptoms. The input data is sent to the server.

[1537] User

[1538] When using the service for the first time, users enter their account information, including their name, age, gender, occupation, medical history, medication status, etc. When symptoms occur, they also enter details of the symptoms, such as "I've had back pain for the past three days, and the pain is getting worse."

[1539] Specific examples

[1540] For example, suppose a 30-year-old male desk worker registers an account and registers with the server that he has a history of back pain. If he experiences back pain while at work and uses the system to report, "My back pain has lasted for three days and is getting worse," the following process will occur.

[1541] 1. Initial setup: The user enters their name, age, occupation, medical history, etc. into the terminal and sends it to the server, which stores this information in a database.

[1542] 2. Symptom input: The user inputs "I have had lower back pain for three days and the pain is getting worse" into the terminal and submits it.

[1543] 3. Sentiment analysis and advice generation: The server analyzes the received data and determines whether the user is feeling stressed or anxious. The generative AI model generates health advice such as, "If you have lower back pain, it is effective to correct your posture and stretch regularly."

[1544] 4. Advice adjustment: Consider the user's feelings and add additional advice such as "Take deep breaths to relax and try to create as relaxing an environment as possible."

[1545] 5. Displaying Advice: The terminal displays advice from the server. The user receives advice such as, "If your lower back pain persists, maintaining proper posture and stretching are effective. Also, take deep breaths to relax and create a relaxing environment."

[1546] Prompt Sentence Examples

[1547] "I'm a 30-year-old desk worker with a medical history of lower back pain. I've had this pain for the past three days, and it's getting worse. What should I do?"

[1548] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1549] Step 1: Register and save your account information

[1550] Terminal

[1551] When the device is used for the first time, it displays an interface that prompts the user to register an account. The user enters information such as name, age, gender, occupation, medical history, and medication status into an input form. When the user presses the "Register" button, the device sends this information to the server in JSON format.

[1552] Input: User information such as name, age, gender, occupation, medical history, medication status, etc.

[1553] Output: User information data in JSON format

[1554] server

[1555] The server receives the received user information, stores it in a database, and notifies the terminal that the storage is complete.

[1556] Input: JSON format user information data

[1557] Output: User information saved to the database, and notification of completion

[1558] Step 2: Enter and submit symptom data

[1559] Terminal

[1560] When the user presses the "Report Symptoms" button, the device interactively asks the question, "Please tell us your specific symptoms." When the user enters their symptoms (e.g., "I've had lower back pain for three days and it's getting worse") and presses the send button, the device sends this information in JSON format to the server.

[1561] Input: Symptom details (e.g., "I've had back pain for three days now and it's getting worse")

[1562] Output: Symptom data in JSON format

[1563] Step 3: Sentiment analysis and advice generation

[1564] server

[1565] The server receives the symptom data sent from the device. Based on the received data, it uses an emotion engine to analyze the user's emotions. Specifically, it determines whether the user is showing stress or anxiety based on keywords and context in the text. The analysis results and symptom data are input into a generative AI model to generate health advice. For example, it generates advice such as, "If you have lower back pain, it is effective to correct your posture and stretch regularly."

[1566] Input: Symptom data in JSON format

[1567] Output: Health advice generated by the generative AI model

[1568] Step 4: Adjust and send advice

[1569] server

[1570] The generated advice is adjusted based on the user's emotions. For example, if the user is showing signs of stress or anxiety, additional advice such as "Take deep breaths to relax and try to create as relaxing an environment as possible" is included. The adjusted advice is sent to the device in JSON format.

[1571] Input: Advice generated by generative AI model, results of sentiment analysis

[1572] Output: Adjusted health advice (JSON format)

[1573] Step 5: Viewing Advice

[1574] Terminal

[1575] The device displays the advice received from the server. The advice is presented on the screen in a format that is easy for the user to understand. For example, it might say, "If you experience persistent lower back pain, it is effective to maintain proper posture and stretch regularly. Also, take deep breaths to relax and create a relaxing environment."

[1576] Input: Tailored health advice (JSON format)

[1577] Output: Advice displayed on the user's device screen

[1578] Step 6: Regular health information push notifications

[1579] server

[1580] The server periodically checks user data and generates new health information and seasonal advice, which are then sent to the user's device as push notifications.

[1581] Input: User data

[1582] Output: Health information and advice sent as push notifications

[1583] Terminal

[1584] The device will display the received push notification and provide the user with new health information and advice, such as a notification that reads, "Check out health management methods appropriate for the current season."

[1585] Input: Health information and advice sent as push notifications

[1586] Output: Notification displayed on the user's device screen

[1587] (Application example 2)

[1588] 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."

[1589] Conventional health management systems lacked detailed support for users because it was difficult to provide health advice that took into account the user's emotional state. Furthermore, health advice was general and lacked consideration for the user's individual health condition and emotions. As a result, users found it difficult to put the suggested advice into practice, which reduced the effectiveness of health management.

[1590] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for loading a generative AI model, means for saving user information in a database, means for receiving symptom details from the user, means for inputting the received symptom data into the generative AI model and generating appropriate health advice, means for transmitting the generated health advice to the user's terminal, means for analyzing the user's emotions using an emotion engine, and means for adjusting the advice based on the analyzed emotions. This makes it possible to provide detailed health advice that takes into account the user's emotional state.

[1591] A "generative AI model" is an artificial intelligence model that generates appropriate advice and information based on data collected from users.

[1592] "User information" refers to information about a user's personal information, such as age, gender, medical history, and health condition.

[1593] "Database" means a data storage system for storing and managing user information and symptom data.

[1594] The "emotion engine" is an artificial intelligence engine for analyzing emotions from user input data.

[1595] "Symptom Data" is data about specific health symptoms or conditions reported by a user.

[1596] "Health Advice" refers to specific instructions or suggestions based on the user's health status provided by a generative AI model.

[1597] "Push notification" is a function that automatically sends information from a server to a user's device.

[1598] "User's device" refers to an information device such as a smartphone, tablet, or PC used by the user.

[1599] "Analysis" is the process of processing data to derive useful information or conclusions.

[1600] System configuration

[1601] The present invention consists of a server, a user terminal, and a network connecting them. The server is equipped with a generative AI model, a database, an emotion engine, and a push notification function. The user terminal is provided with an interface for users to input information.

[1602] Hardware and Software

[1603] 1. Hardware

[1604] Smartphone (Android / iOS)

[1605] Tablet (Android / iOS)

[1606] Kiosk terminal installed in a physical store

[1607] server

[1608] Network: Wi-Fi / 4G / 5G

[1609] 2. Software

[1610] Server: Generative AI model (large-scale language model), emotion engine, database (MySQL)

[1611] Client application: Smartphone / tablet app (Flutter / React Native)

[1612] Program processing

[1613] Server-side processing

[1614] When the server starts up, it loads the generative AI model and emotion engine into memory. The generative AI model generates appropriate health advice based on symptom data from the user, and the emotion engine analyzes the user's emotions. It initializes the database connection and saves and manages user information (age, gender, medical history, etc.). New and updated information sent by the user is also saved in this database.

[1615] When a user reports symptoms, the data is received and the emotion engine is used to analyze the user's emotions. The analysis results and symptom data are input into a generative AI model to generate health advice. The generated health advice is adjusted and formatted based on the user's emotions and then sent to the user's device.

[1616] Terminal side processing

[1617] When the device is used for the first time, it displays an interface that prompts the user to register an account. It provides a form for entering user information (name, age, gender, occupation, medical history, etc.). It provides an interface for entering details of the user's symptoms in an interactive format. It displays questions such as "Please tell us your specific symptoms." The user enters details of the symptoms when they occur. The entered information is sent to the server.

[1618] Specific examples

[1619] For example, if a user visiting a physical store types "I've been having an upset stomach lately" into their smartphone, that information is sent to the server. The server uses an emotion engine to analyze the emotion contained in the input sentence and determine, for example, that the person is feeling anxious or stressed. Next, the generative AI model generates health advice based on the analysis results and symptom data, such as "If you have an upset stomach, we recommend that you pay attention to your diet and drink plenty of fluids." Based on the results of the emotion analysis, the model also includes additional advice, such as "Relaxing can also help."

[1620] Prompt Sentence Examples

[1621] "A 30-year-old male user reports, 'I've had lower back pain for three days now and it's getting worse.'"

[1622] This makes it possible to provide detailed health advice that takes into account the user's emotional state.

[1623] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1624] Step 1:

[1625] When the server starts up, it loads the generative AI model and emotion engine into memory. This process makes the generative AI model and emotion engine immediately available for use. The input is the server startup operation, and the output is the generative AI model and emotion engine loaded into memory.

[1626] Step 2:

[1627] The server initializes the database connection and prepares to save user information. This operation efficiently stores and manages user information (age, gender, medical history, etc.). The input is the database connection initialization operation, and the output is the database with the established connection.

[1628] Step 3:

[1629] When a user first uses the service, they input their user information (such as name, age, gender, occupation, and medical history) into the terminal. This information is used to provide advice thereafter. The input is the user information, and the output is the user information data entered into the terminal.

[1630] Step 4:

[1631] The terminal sends user information to the server. The server receives this information and stores it in a database. The input is the user information data from the terminal, and the output is the user information data stored in the database.

[1632] Step 5:

[1633] When a symptom occurs, the user inputs specific details of the symptom into the terminal. For example, the user might input a specific symptom such as, "I've had lower back pain for three days and the pain is getting worse." The input is symptom information, and the output is the symptom data entered into the terminal.

[1634] Step 6:

[1635] The terminal sends the input symptom data to the server, which receives the data and stores it in a database. The input is the symptom data, and the output is the symptom data stored in the database.

[1636] Step 7:

[1637] The server uses an emotion engine to analyze the symptom data received from the user and determine the user's emotional state. The input is the symptom data, and the output is the analyzed emotion data.

[1638] Step 8:

[1639] The server inputs the user's symptom data and emotion data into the generative AI model and generates appropriate health advice. The input is symptom data and emotion data, and the output is the generated health advice.

[1640] Step 9:

[1641] The server adjusts the generated health advice based on the user's emotional data, for example by including additional advice on relaxation if stress or anxiety is detected. The input is the generated health advice and the emotional data, and the output is the adjusted health advice.

[1642] Step 10:

[1643] The server sends the tailored health advice to the user's terminal, which receives it and displays it to the user. The input is the tailored health advice, and the output is the terminal screen with the advice displayed.

[1644] 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.

[1645] 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.

[1646] 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.

[1647] 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.

[1648] 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.

[1649] 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.

[1650] 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).

[1651] 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.

[1652] 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."

[1653] 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.

[1654] 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).

[1655] 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.

[1656] 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.

[1657] 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.

[1658] 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.

[1659] 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.

[1660] 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.

[1661] 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.

[1662] 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.

[1663] 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.

[1664] 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.

[1665] The following is further disclosed regarding the above embodiment.

[1666] (Claim 1)

[1667] a means for loading the generative AI model;

[1668] a means for storing user information in a database;

[1669] means for receiving symptom details from a user;

[1670] A means for inputting the received symptom data into a generative AI model to generate appropriate health advice;

[1671] means for transmitting the generated health advice to a user's terminal;

[1672] A system including:

[1673] (Claim 2)

[1674] A means for periodically sending push notifications of health information and advice to the user's device based on user information is provided.

[1675] 10. The system of claim 1.

[1676] (Claim 3)

[1677] means for automatically requesting additional information based on the user's symptom data;

[1678] 10. The system of claim 1.

[1679] (Claim 4)

[1680] means for providing an interactive interface as a user symptom input interface;

[1681] 10. The system of claim 1.

[1682] "Example 1"

[1683] (Claim 1)

[1684] a means for loading the generative AI model;

[1685] a means for storing user information in a database;

[1686] means for receiving symptom details from a user;

[1687] A means for inputting the received symptom data into a generative AI model to generate appropriate health advice;

[1688] means for transmitting the generated health advice to a user's terminal;

[1689] A means for displaying an interface that prompts the user to create an account or log in;

[1690] means for providing an interface for interactively inputting details of a user's symptoms;

[1691] a means for displaying the generated health advice in an easy-to-read format on a user's terminal;

[1692] A system including:

[1693] (Claim 2)

[1694] The system according to claim 1, further comprising means for periodically pushing health information and advice to the user's terminal based on the user information.

[1695] (Claim 3)

[1696] 10. The system of claim 1, further comprising means for automatically requesting additional information based on the user's symptom data.

[1697] "Application Example 1"

[1698] (Claim 1)

[1699] a means for loading the generative AI model;

[1700] a means for storing user information in a database;

[1701] means for receiving symptom details from a user;

[1702] A means for inputting the received symptom data into a generative AI model to generate appropriate health advice;

[1703] means for transmitting the generated health advice to a user's terminal;

[1704] A means for recommending appropriate products in-store based on the user's symptom data;

[1705] A system including:

[1706] (Claim 2)

[1707] A means for periodically sending push notifications of health information and advice to the user's device based on user information is provided.

[1708] 10. The system of claim 1.

[1709] (Claim 3)

[1710] means for automatically requesting additional information based on the user's symptom data;

[1711] 10. The system of claim 1.

[1712] "Example 2: Combining Emotion Engines"

[1713] (Claim 1)

[1714] a means for loading the generative AI model;

[1715] a means for storing user information in a database;

[1716] means for receiving symptom details from a user;

[1717] A means for inputting the received symptom data into a generative AI model to generate appropriate health advice;

[1718] means for adjusting the generated health advice based on the user's emotions;

[1719] means for transmitting the tailored health advice to the device;

[1720] A system including:

[1721] (Claim 2)

[1722] It has a means to periodically check user data and push health information and advice to the user's device.

[1723] 10. The system of claim 1.

[1724] (Claim 3)

[1725] It has a means for analyzing the user's emotions and inputting them into a generative AI model along with symptom data to generate health advice.

[1726] 10. The system of claim 1.

[1727] "Application example 2 when combining emotion engines"

[1728] (Claim 1)

[1729] a means for loading the generative AI model;

[1730] a means for storing user information in a database;

[1731] means for receiving symptom details from a user;

[1732] A means for inputting the received symptom data into a generative AI model to generate appropriate health advice;

[1733] means for transmitting the generated health advice to a user's terminal;

[1734] means for analyzing a user's emotions using an emotion engine;

[1735] a means for tailoring advice based on the analyzed sentiment;

[1736] A system including:

[1737] (Claim 2)

[1738] The system according to claim 1, further comprising means for periodically sending push notifications of health information and advice to the user's terminal based on the user information and emotion analysis results.

[1739] (Claim 3)

[1740] 10. The system of claim 1, further comprising means for automatically requesting additional information based on the user's symptom data and sentiment analysis results. [Explanation of symbols]

[1741] 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 means for loading the generative AI model; a means for storing user information in a database; means for receiving symptom details from a user; A means for inputting the received symptom data into a generative AI model to generate appropriate health advice; means for transmitting the generated health advice to a user's terminal; A system including:

2. A means for periodically sending push notifications of health information and advice to the user's device based on user information is provided. The system of claim 1 .

3. means for automatically requesting additional information based on the user's symptom data; The system of claim 1 .

4. means for providing an interactive interface as a user symptom input interface; The system of claim 1 .

Images