System

Abstract

A system is provided.SOLUTION: A system for providing personalized advice to a user, the system comprising: an input device for inputting health information of the user; a transmitting device for transmitting the health information to a server; a receiving device for receiving and storing the health information in the server; an analyzing device including a AI engine for analyzing the health information in the server; and a providing device for providing the personalized advice generated in the analyzing device to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional medical systems have made it difficult for users to understand their own health status in real time and receive appropriate advice. This makes it difficult to detect and respond to signs of illness early, and also hinders the efficient use of medical resources. There are also limitations to the technological means available for providing personalized medical services. There is a need to solve these problems. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following means: an input means for inputting a user's health data and a transmission means for transmitting the health data to a server. Furthermore, the server is provided with a receiving means for receiving and storing the health data, and an analysis means including an AI engine for analyzing the health data. This results in a system equipped with a provision means for providing users with personalized advice generated by the analysis means. This system allows users to obtain health insights in real time and receive advice tailored to their individual needs. It also promotes efficient use of medical resources and improves users' health management.

[0006] "User" means an individual or healthcare provider who utilizes the system to input health data and obtain personalized advice.

[0007] "Health data" refers to data that includes information about the user's health condition, such as the user's heart rate, number of steps, blood pressure, and body temperature.

[0008] "Input means" refers to devices and software interfaces that allow users to input health data into the system.

[0009] The "transmission means" includes a communication function for transmitting the health data acquired by the input means to the server.

[0010] A "server" is a remote computer system that receives and analyzes health data.

[0011] The "receiving means" has the function of receiving and storing the health data sent from the transmitting means.

[0012] The "analysis means" includes an AI engine and its accompanying software for analyzing the health data received by the receiving means.

[0013] An "AI engine" is a software component that uses machine learning algorithms to analyze health data and generate personalized advice.

[0014] The "provision means" refers to a function for providing the user with the individualized advice generated by the analysis means, and specifically, displays the advice on the user's terminal.

[0015] The "display means" is a part of the providing means, and has a function of displaying the personalized advice on the terminal in a form that can be visually recognized by the user.

[0016] "Personalized advice" refers to specific suggestions or recommendations generated by the analytical means based on each user's health status. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention provides a system that allows users to manage their own health data in real time and receive personalized advice. This system includes a terminal, a server, and an AI engine for analyzing the user's health data. Specific embodiments of the present invention will be described below.

[0039] First, a user inputs their health data using a device such as a smartphone or wearable device. This health data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through the input means on the device.

[0040] Next, the device sends the collected health data to the server. The data to be sent is converted into a data format such as JSON. The device then sends an HTTP POST request over the Internet to send the data to the server. The data is sent using common communication methods such as Wi-Fi or mobile data communication.

[0041] The server receives this data at a specified endpoint, where it is parsed and stored in a database by the receiving means, allowing the server to centralize the health data of all users.

[0042] The stored data is then analyzed by an AI engine on the server, which uses machine learning algorithms to analyze the user's health data and generate specific insights, such as how a user's heart rate changes during exercise or how their dietary habits affect their health.

[0043] Once the analysis is complete, the AI ​​engine generates personalized advice, including specific behavioral suggestions and health management recommendations, such as "drink more water" or "walk 30 minutes every day." This advice is then sent to the user's device via the server's provisioning means. The advice is then displayed on the device's user interface.

[0044] As a concrete example, after a user finishes their morning jog, they send their heart rate and step count via smartphone to a server, which receives and analyzes the data. The AI ​​engine then evaluates whether the user's heart rate is within an appropriate range and whether the number of steps has reached the target. Based on the analysis results, advice such as "Always drink water after jogging" is displayed on the user's smartphone.

[0045] This allows users to understand their health status in real time and receive personalized, accurate advice, making health management easier, while healthcare providers can use this data to provide more accurate diagnosis and treatment.

[0046] The processing flow will be explained below.

[0047] Step 1:

[0048] The user opens their smartphone and launches a dedicated application, which uses the wearable device to collect health data such as heart rate and number of steps.

[0049] Step 2:

[0050] The device captures the health data entered by the user within the application, which organizes the data in a standard format such as JSON.

[0051] Step 3:

[0052] The device sends the captured health data to a specified API endpoint on the server using an HTTP POST request.

[0053] Step 4:

[0054] The server receives the data at the API endpoint, which is parsed from the request body in JSON format.

[0055] Step 5:

[0056] The server stores the parsed data in an internal database. Specifically, data is managed for each user ID, and past data is also accumulated.

[0057] Step 6:

[0058] The server calls the AI ​​engine to analyze the stored data, and the AI ​​engine then begins analysis based on the health data.

[0059] Step 7:

[0060] The server-based AI engine uses machine learning algorithms to analyze health data, generating health insights and risk assessments.

[0061] Step 8:

[0062] The AI ​​engine generates personalized advice based on the analysis results, such as "Try to relax" if your heart rate is high.

[0063] Step 9:

[0064] The server prepares the generated advice for transmission to the user's terminal, and converts the data to be transmitted into a format that is easy for the user to understand.

[0065] Step 10:

[0066] The server sends the generated advice to the terminal as an HTTP response, using data personalized based on the user ID.

[0067] Step 11:

[0068] The device displays the advice received from the server within the application, and the specific advice is presented visually on the user interface.

[0069] Step 12:

[0070] The user can then review the displayed advice and adjust their behavior based on it, for example by taking specific actions such as drinking water or doing stretches.

[0071] In this way, the system can assist users in managing their health in real time and provide appropriate advice to improve their health status.

[0072] Example 1

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

[0074] In recent years, there has been an increasing demand for healthcare systems that allow users to monitor their health status in real time and receive personalized advice. However, current systems lack consistent and efficient collection, transmission, analysis, and provision of health data, and there are particular issues with the accuracy and immediacy of personalized advice. The present invention aims to solve these problems and provide a system that allows users to manage their health status in real time and receive appropriate advice.

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

[0076] In this invention, the server includes an input means for inputting a user's health data, a transmission means for transmitting the health data to the server, a receiving means for receiving and storing the health data in the server, an analysis means including an AI engine for analyzing the health data in the server, a provision means for providing the user with personalized advice generated by the analysis means, a means for converting data collected by the analysis means into JSON format and transmitting it, and a display means for displaying the personalized advice provided by the provision means. This enables efficient collection, transmission, and analysis of health data and immediate provision of personalized advice.

[0077] "Input means" means a device or method by which a user inputs their health data into the system.

[0078] "Transmitting means" refers to a device or method for transmitting the input health data to the server.

[0079] "Receiving means" refers to a device or method by which the server receives and stores health data sent from the user.

[0080] "Analysis means" refers to a device or method for analyzing health data stored on the server, and includes an AI engine.

[0081] The "providing means" is a device or method for providing the user with the personalized advice generated by the analyzing means.

[0082] "Means for converting to JSON format" refers to a device or method for converting collected health data into JSON format and transmitting it.

[0083] "Display means" refers to a device or method for displaying the provided personalized advice on the user's terminal.

[0084] "Health data" refers to data that indicates the user's health condition, such as heart rate, number of steps, blood pressure, and body temperature.

[0085] "Server" means a computer system that receives, stores, and analyzes health data submitted by a user and generates and provides personalized advice.

[0086] A "user" is an individual who utilizes the system to input their health data and receive personalized advice.

[0087] The present invention provides a system for users to manage their own health data in real time and receive personalized advice. The system includes a terminal, a server, and an AI engine for analyzing the user's health data.

[0088] First, a user inputs their own health data using a device such as a smartphone or wearable device. This health data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through the input means on the device.

[0089] Next, the device sends the collected health data to the server. The data to be sent is converted into a data format such as JSON. The device then sends an HTTP POST request over the Internet to send the data to the server. The data is sent using common communication methods such as Wi-Fi or mobile data communication.

[0090] The server receives this data at a specified endpoint, where it is parsed and stored in a database by the receiving means, allowing the server to centralize the health data of all users.

[0091] The stored data is analyzed by an AI engine on the server. This analysis method uses machine learning algorithms to analyze the user's health data and generate specific insights. For example, the AI ​​engine is implemented using Python and analyzes the collected data using frameworks such as TensorFlow and PyTorch. For example, it extracts information such as how a user's heart rate changes during exercise and how their dietary habits affect their health.

[0092] Once the analysis is complete, the AI ​​engine generates personalized advice, including specific behavioral suggestions and health management recommendations, such as "drink more water" or "walk 30 minutes every day." This advice is sent to the user's device by the server's provisioning means, and is displayed on the device's user interface.

[0093] Examples:

[0094] After a user finishes their morning jog, they send their heart rate and step count to a server via their smartphone. The server receives and analyzes the data. The AI ​​engine then analyzes the data and evaluates whether the user's heart rate is within an appropriate range and whether the number of steps has reached the target. Based on the analysis results, advice such as "Always drink water after jogging" is displayed on the user's smartphone.

[0095] Example prompt for generative AI model:

[0096] "When a user finishes jogging and enters their heart rate and number of steps, please explain the specific steps for sending that data to the server, analyzing the data on the server, and generating advice."

[0097] This allows users to easily manage their health by understanding their health status in real time and receiving personalized and accurate advice, while healthcare providers can also use this data to provide more accurate diagnosis and treatment.

[0098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0099] Step 1:

[0100] The user enters their own health data. The user opens the smartphone app and manually enters data such as heart rate, steps, blood pressure, and body temperature, or checks data automatically transferred from the wearable device. This provides the health data collected by the input method.

[0101] Input: Data entered by the user, such as heart rate, steps, blood pressure, and temperature

[0102] Output: Health data collected by input means

[0103] Step 2:

[0104] The device sends the collected health data to a server. The app on the device converts the collected data into JSON format and sends it to a specified endpoint on the server via the Internet using an HTTP POST request. Wi-Fi or mobile data communication is used as the transmission method.

[0105] Input: Health data converted to JSON format

[0106] Output: Health data sent to the server via HTTP POST request

[0107] Step 3:

[0108] The server receives and stores health data. The data received at the specified endpoint is parsed in JSON format and stored in a database. Specifically, the server's API receives an HTTP request, parses it, and stores it in a database (such as MongoDB or MySQL).

[0109] Input: Health data sent in an HTTP POST request

[0110] Output: Parsed health data stored in a database

[0111] Step 4:

[0112] An AI engine on the server analyzes the health data, using machine learning algorithms to extract insights about the user's health. The AI ​​engine is implemented using Python and performs analysis using frameworks such as TensorFlow and PyTorch.

[0113] Input: Health data stored in a database

[0114] Output: User health insights

[0115] Step 5:

[0116] The server generates personalized advice. Based on the analysis results, specific behavioral suggestions and health management recommendations are generated. For example, advice such as "drink more water" or "walk 30 minutes every day" is generated.

[0117] Input: User health insights

[0118] Output: personalized advice

[0119] Step 6:

[0120] The server sends the generated advice to the user's terminal using an HTTP POST request, and the terminal displays the received advice on its user interface.

[0121] Input: personalized advice

[0122] Output: Advice displayed on the user's terminal

[0123] Examples:

[0124] For example, when a user finishes jogging and enters their heart rate and number of steps, the device converts this data into JSON format and sends it to the server. The server receives and stores the data, and the AI ​​engine analyzes it. Based on the analysis results, advice such as "Always drink water after jogging" is generated and displayed on the user's smartphone.

[0125] Example prompt for generative AI model:

[0126] "When a user finishes jogging and enters their heart rate and number of steps, please explain the specific steps for sending that data to the server, analyzing the data on the server, and generating advice."

[0127] (Application example 1)

[0128] 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."

[0129] Conventional health management systems can collect and analyze users' health data, but often only provide personalized advice based on the results. This creates the challenge of making it difficult for users to find the health-related products and services they need. In particular, there is a need for a system that can automatically recommend products that are optimal for a user's health condition.

[0130] 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.

[0131] In this invention, the server includes an input means for inputting a user's health data, a transmission means for transmitting the health data to the server, a reception means for receiving and storing the health data, an analysis means including an AI engine for analyzing the health data, and a provision means for providing the user with personalized advice and product recommendations generated by the analysis means, thereby enabling the user to manage their own health and efficiently find products that are optimal for their health condition.

[0132] "Input means" refers to a mechanism by which a user inputs their own health data, and is implemented using a terminal such as a smartphone or wearable device.

[0133] The "transmission means" is a function that transmits health data from the user's device to the server, and uses Wi-Fi or mobile data communication.

[0134] The "receiving means" is a function by which the server receives and stores health data sent from the user's terminal.

[0135] The "analysis means" is a function that analyzes the received health data using an AI engine installed on the server.

[0136] The "provision means" is a mechanism for providing the user with personalized advice and product recommendations generated by the analysis means.

[0137] The "display means" is a function that displays the personalized advice and product recommendations on the user's terminal.

[0138] "Health insights" are insightful health information generated based on a user's past health data.

[0139] "Product recommendations" are health-related products recommended by the analysis means based on the user's health data.

[0140] The present invention is a system for managing a user's health data in real time and providing personalized advice and product recommendations. Specific embodiments for carrying out the present invention will be described below.

[0141] First, a user inputs their health data using a device such as a smartphone or wearable device. This health data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through the input means on the device.

[0142] Next, the device sends the collected health data to the server. The data to be sent is converted into a data format such as JSON. The device then sends an HTTP POST request over the Internet to send the data to the server. The data is sent using common communication methods such as Wi-Fi or mobile data communication.

[0143] The server receives this data at a specified endpoint, where it is parsed and stored in a database by the receiving means, allowing the server to centralize the health data of all users.

[0144] The stored data is analyzed by an AI engine on the server. This analysis method uses machine learning algorithms to analyze the user's health data and generate specific insights and product recommendations. For example, it extracts information such as how a user's heart rate changes during exercise and how their dietary habits affect their health. Based on this information, it then recommends related products such as vitamin supplements, fitness equipment, and health foods that are best suited to the user.

[0145] Once the analysis is complete, the AI ​​engine generates personalized advice and product recommendations. This advice includes specific behavioral suggestions and health management recommendations. For example, in addition to advice such as "drink more water" and "walk 30 minutes daily," product recommendations such as "take vitamin supplement A" are also made. This advice and product recommendations are sent to the user's device by the server's provisioning means. The sent advice and product recommendations are then displayed on the device's user interface.

[0146] As a concrete example, after a user finishes their morning jog, they send their heart rate and step count via smartphone to a server, which receives and analyzes the data. The AI ​​engine then analyzes the data and evaluates whether the user's heart rate is within an appropriate range and whether the number of steps has reached the target. Based on the analysis results, product recommendations such as "We recommend taking Vitamin Supplement A" are displayed on the user's smartphone, along with advice such as "Always drink water after jogging."

[0147] Example prompt sentence:

[0148] Input data:

[0149] {

[0150] "user_id": "user123",

[0151] "heart_rate": 70,

[0152] "steps": 5000,

[0153] "blood pressure": "120 / 80",

[0154] "body_temperature": 36.5

[0155] }

[0156] Generated advice:

[0157] "Your heart rate is within the normal range. Walk a few more steps to reach your goal for today. I also recommend taking Vitamin Supplement A."

[0158] Recommended products:

[0159] ["Vitamin Supplement A", "Fitness Equipment B", "Health Food C"]

[0160] This will allow users to understand their health status in real time and receive personalized health management advice and related product recommendations, making daily health management easier. Healthcare providers can also use this data to provide more accurate diagnosis and treatment.

[0161] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0162] Step 1:

[0163] Users input their own health data using a device such as a smartphone or wearable device. The input data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through the device's input means. The input data is temporarily stored in the device's memory.

[0164] Step 2:

[0165] The device sends the collected health data to the server. The data to be sent is converted into a data format such as JSON. The device then sends an HTTP POST request over the Internet to send the data to the server. Wi-Fi or mobile data communication is used as the transmission method. Specifically, the device sets the header and body of the HTTP request and performs the transmission procedure.

[0166] Step 3:

[0167] The server receives this data at the specified endpoint. The received data is parsed by the receiving means and stored in a database. Specifically, the data in JSON format is parsed, mapped to each field in the database, and stored. This allows the server to centrally manage the health data of all users.

[0168] Step 4:

[0169] An AI engine on the server analyzes the stored health data. The analysis method uses machine learning algorithms to analyze the user's health data and generate specific insights and product recommendations. Specific calculations include extracting insights based on heart rate trends, progress toward step goal achievement, and past trends, and recommending products based on these. For example, evaluations include whether the heart rate is within an appropriate range and whether the step goal has been achieved.

[0170] Step 5:

[0171] The server's provision means sends the personalized advice and product recommendations generated by the analysis means to the user's device. Specific data includes health advice and a product list in JSON format. This is then sent back to the user's device as an HTTP response. The reception means receives the response, parses it as necessary, and displays it on the user interface.

[0172] Step 6:

[0173] The device displays the received advice and product recommendations on the user interface. Specifically, the application displays personalized advice and product lists in a format that is easy for users to understand. Through this display, users can check suggested actions and products tailored to their health condition.

[0174] 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.

[0175] The present invention provides a system for users to manage their own health data and emotional state in real time and receive personalized advice. This system includes a terminal, a server, an AI engine, and an emotion engine. Specific embodiments for implementing the present invention will be described below.

[0176] First, a user inputs their health data using a device such as a smartphone or wearable device. This health data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through input means on the device. Users can also express their emotional state using voice input or facial expressions on the device.

[0177] The device then transmits the collected health and emotion data to a server. The data is converted into a standard format, such as JSON. The device then sends an HTTP POST request over the Internet to transmit the data to the server. The transmission method uses common communication methods, such as Wi-Fi or mobile data.

[0178] The server receives this data at the specified endpoint. The received data is parsed from the request body in JSON format. The parsed data is saved in an internal database and managed for each user ID.

[0179] The stored data is analyzed by an AI engine and an emotion engine on the server. The AI ​​engine analyzes the health data and generates specific insights. The emotion engine detects the user's emotional state from their voice, facial expressions, text input, etc. and combines this data to evaluate the user's overall condition.

[0180] Once the analysis is complete, the AI ​​engine and emotion engine generate personalized advice, including suggested actions based on health status and relaxation techniques corresponding to emotional states. For example, if a person's heart rate is high, the engine will generate advice such as "Try to relax." If the person's emotional state indicates stress, the engine will add a suggestion such as "stretch."

[0181] The generated advice is sent to the user's terminal by the server's providing means, and the sent advice is displayed on the terminal's user interface in a format that is easy for the user to understand.

[0182] As a specific example, after a user finishes their morning jog, they send their heart rate and step count to a server via their smartphone, which then receives and analyzes the data. At the same time, if the user records their emotional state through voice input, this data is also analyzed. The AI ​​engine and emotion engine work together to evaluate whether the user's heart rate is within an appropriate range and whether the number of steps has reached the target. In addition, because the user's emotional state is taken into consideration, specific and comprehensive advice such as "Always drink water after jogging, and take deep breaths if you feel stressed" is displayed on the user's smartphone.

[0183] In this way, users can understand their own health and emotional state in real time and receive personalized and accurate advice, enabling them to take comprehensive health management in their daily lives.

[0184] The processing flow will be explained below.

[0185] Step 1:

[0186] The user opens their smartphone and launches a dedicated application. The wearable device collects health data such as heart rate and number of steps taken. In addition, the user uses voice input to record their current emotional state (e.g., stressed, relaxed, etc.).

[0187] Step 2:

[0188] The device captures user-entered health data such as heart rate and step count, as well as emotional data obtained from voice input, and converts this data into standard formats such as JSON.

[0189] Step 3:

[0190] The device sends the captured health and emotion data to the server's designated API endpoint using an HTTP POST request.

[0191] Step 4:

[0192] The server receives health and emotion data via the API endpoint. The received data is parsed in JSON format and stored in an internal database. Data is managed by user ID, and past data is also accumulated.

[0193] Step 5:

[0194] The server calls the AI ​​engine and emotion engine to analyze the stored data. The AI ​​engine analyzes the health data and generates specific health insights, such as assessing whether your heart rate is high or low, or whether your step count is reaching your target.

[0195] Step 6:

[0196] The emotion engine on the server analyzes emotion data obtained through voice input or other means, and uses machine learning algorithms to identify the user's emotional state, such as "stressed" or "relaxed."

[0197] Step 7:

[0198] The AI ​​engine and emotion engine combine their respective analysis results. Based on the combined data, personalized advice is generated for the user. For example, if the heart rate is high and the emotional state indicates stress, advice such as "take deep breaths to relax" is generated.

[0199] Step 8:

[0200] The server prepares the generated personalized advice for sending to the user's device. The advice is formatted in a way that is easy for the user to understand, such as JSON format.

[0201] Step 9:

[0202] The server sends the generated advice to the device as an HTTP response, with personalized data based on the user ID.

[0203] Step 10:

[0204] The device displays the advice received from the server within the application, and the specific advice is presented visually on the user interface.

[0205] Step 11:

[0206] The user can then review the displayed advice and adjust their behavior accordingly, such as taking deep breaths to relax, drinking water, or stretching.

[0207] In this way, the system comprehensively analyzes the user's health and emotional data and provides personalized advice in real time, helping the user manage their overall health.

[0208] Example 2

[0209] 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."

[0210] Conventional health management systems collect and analyze users' health data using only a limited range of information, making it difficult to provide advice that takes into account their emotional state. Furthermore, the generation of personalized advice is insufficient, limiting their effectiveness in comprehensive health management. This makes it difficult for users to appropriately address specific health issues or stress states, and often results in only general advice being provided.

[0211] 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 a receiving means for receiving and storing the user's health data and emotion data, an analyzing means including an AI engine and an emotion engine for analyzing the health data and emotion data, and a providing means for providing the user with personalized advice generated by the analyzing means. This makes it possible to grasp not only the user's health state but also their emotional state in real time and provide comprehensive and personalized advice.

[0212] "Health data" refers to information including physiological indicators such as the user's heart rate, number of steps, blood pressure, and body temperature.

[0213] "Emotion data" is information indicating the emotional state of the user obtained from voice, facial expression, text input, and the like.

[0214] "Input means" refers to devices or software that allow users to input health data and emotional data. Specifically, this refers to smartphones, wearable devices, etc.

[0215] "Transmission means" refers to the functions and devices that allow the device to transmit health data and emotion data to the server. This includes Wi-Fi and mobile data communications.

[0216] "Receiving means" refers to a function or device that allows the server to receive and store health data and emotion data transmitted from the terminal.

[0217] "Analysis means" refers to the functions and devices used to analyze the health data and emotion data received by the server. Specifically, it includes an AI engine and an emotion engine.

[0218] An "AI engine" is an algorithm or program that analyzes health data and generates specific insights.

[0219] An "emotion engine" is an algorithm or program that analyzes emotional states from voice, facial expressions, text input, etc.

[0220] The "provision means" refers to a function or device for providing the user with the personalized advice generated by the analysis means.

[0221] The "display means" is a function or device for displaying the advice generated by the providing means on the user's terminal.

[0222] "Health insights" are various insights and advice obtained by analyzing a user's health data.

[0223] "Emotional insights" are various insights and advice obtained by analyzing a user's emotional data.

[0224] "Real-time" means that data is collected, analyzed, and advice is provided without delay.

[0225] MODE FOR CARRYING OUT THE INVENTION

[0226] The present invention provides a system for users to manage their own health data and emotional state in real time and receive personalized advice. This system includes a terminal, a server, an AI engine, and an emotion engine. Specific embodiments are described below.

[0227] Terminal

[0228] Users input their own health data using devices such as smartphones or wearable devices, including heart rate, number of steps, blood pressure, and body temperature. Users can also record their emotional state through voice input and facial expression recognition using the microphone and camera on their devices.

[0229] server

[0230] The health and emotion data sent from the device is sent to a server via the Internet. Specifically, the device converts the data into JSON format and sends it to the server via an HTTP POST request. Wi-Fi or mobile data communication is used as the communication method.

[0231] The server receives the data at the specified endpoint and parses it in JSON format. The parsed data is stored in a relational database system such as MySQL or PostgreSQL and managed by user ID.

[0232] AI engine and emotion engine

[0233] The data stored on the server is analyzed by an AI engine and an emotion engine. The AI ​​engine analyzes the health data and generates specific health insights. For example, it analyzes heart rate and step count data to evaluate the amount of exercise and fatigue level for the day. The AI ​​engine uses Python libraries such as TensorFlow and PyTorch to build and run models.

[0234] The emotion engine uses voice and facial recognition technology to detect the user's emotional state. Voice input is analyzed based on the user's tone of voice and the way they speak, while facial recognition is based on the user's facial expressions captured by the camera. This allows the engine to assess the user's stress level and mood.

[0235] Means of provision and display

[0236] Once the analysis is complete, the AI ​​and emotion engines generate personalized advice, including suggested actions based on the user's health and emotional state. For example, if your heart rate is high, they might suggest "try to relax," or if your emotional state indicates stress, they might suggest "take deep breaths."

[0237] The generated advice is sent from the server to the device and displayed on the device's user interface, which can be in the form of a notification, an app dashboard, a widget, or the like.

[0238] Specific examples

[0239] For example, after a user finishes jogging in the morning, they can use their smartphone to input their heart rate and step count data. At the same time, they can record their emotional state by voice input, such as "I feel a little tired from jogging today." The device then sends this data to the server.

[0240] The server receives the data and begins analysis. The AI ​​engine analyzes the heart rate data, and the emotion engine detects "fatigue" and "stress." The server generates advice such as "Don't forget to hydrate after jogging. If you feel tired, take a deep breath and relax," and sends it to the device. The device displays the received advice to the user as a notification.

[0241] Prompt Sentence Examples

[0242] "When a user sends heart rate and step count data from their smartphone and records their emotional state through voice input, the AI ​​and emotion engine will analyze it and generate optimal advice."

[0243] In this way, users can understand their own health and emotional state in real time and receive personalized advice, enabling comprehensive health management in daily life.

[0244] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0245] Step 1:

[0246] The user inputs health and emotional data.

[0247] Input: Users use smartphones or wearable devices to input health data such as heart rate, steps, blood pressure, and temperature, as well as record emotional state through voice input and facial expressions.

[0248] Data processing: Health and emotional data is converted into digital form within the app.

[0249] Output: The converted digital data is stored in a temporary database on the smartphone.

[0250] Step 2:

[0251] The device sends the data to the server.

[0252] Input: Health and emotion data collected in step 1.

[0253] Data processing: The device converts the collected data into JSON format and sends it to the server using an HTTP POST request. The device connects to the Internet via Wi-Fi or mobile data.

[0254] Output: JSON formatted data sent to the server.

[0255] Step 3:

[0256] The server receives the data and stores it in a database.

[0257] Input: Health and emotion data sent from the device in JSON format.

[0258] Data processing: The server receives the data, parses it from JSON format, and stores it in a relational database (e.g., MySQL or PostgreSQL) along with the user ID.

[0259] Output: Structured data stored in a database.

[0260] Step 4:

[0261] An AI engine and emotion engine on the server analyze the data.

[0262] Input: Health and emotion data stored in a database.

[0263] Data Computation: The AI ​​engine analyzes health data and generates health insights based on, for example, heart rate and step count, while the emotion engine analyzes emotional states using voice and facial recognition technologies, specifically using Python libraries such as TensorFlow and PyTorch.

[0264] Output: Generated health and sentiment insights.

[0265] Step 5:

[0266] The server generates personalized advice.

[0267] Input: Health and sentiment insights generated in step 4.

[0268] Data Computation: Based on the analysis results, personalized advice is generated based on the user's situation, including specific suggested actions that take into account both their health and emotional state.

[0269] Output: personalized advice.

[0270] Step 6:

[0271] The server sends the advice to the terminal.

[0272] Input: The personalized advice generated in step 5.

[0273] Data processing: The server converts the personalized advice into JSON format and sends it to the device as an HTTP response using the secure HTTPS protocol.

[0274] Output: Advice data sent to the terminal.

[0275] Step 7:

[0276] The terminal displays the advice to the user.

[0277] Input: Advice data sent by the server.

[0278] Data processing: The device parses the received advice and displays it in the user interface, which could be a notification, an in-app dashboard, or a widget.

[0279] Output: The advice that is displayed to the user.

[0280] This process allows users to manage their health and emotional state in real time and receive accurate, personalized advice.

[0281] (Application example 2)

[0282] 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."

[0283] The main purpose of conventional health management systems was to provide consistent advice based on the user's health data, but they had limitations in providing personalized advice that took into account the user's emotional state, or in suggesting meal plans that adapted to the user's health and emotional state. This resulted in a lack of motivation for users to actually follow the advice, and the health management system was not as effective as it could be.

[0284] The specific processing by the specific 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 input means for inputting the user's health data and emotional state, transmission means for transmitting the health data and emotional state to the server, reception means for receiving and storing the health data and emotional state, analysis means including an AI engine and an emotion engine for analyzing the health data and emotional state, provision means for providing the user with personalized advice generated by the analysis means, and suggestion means for proposing an optimal meal menu based on the advice. As a result, appropriate action suggestions and meal menus are provided in real time based on the user's health state and emotional state, making it possible to improve the effectiveness of health management.

[0285] "User's health data" refers to various data related to the user's health condition, such as the user's heart rate, number of steps, blood pressure, and body temperature.

[0286] "Emotional state" is data that indicates the user's mental and psychological state, and is collected through voice input and facial expression recognition.

[0287] "Input means" refers to a means by which a user inputs health data and emotional state using a smartphone, wearable device, or the like.

[0288] The "transmission means" is a means for transmitting the input health data and emotional state to the server via a network.

[0289] The "receiving means" is a means for receiving, on the server, the health data and emotional state sent via the transmitting means.

[0290] The "AI Engine" is an artificial intelligence engine that analyzes received health data and generates specific insights and suggestions.

[0291] The "emotion engine" is an artificial intelligence engine that analyzes the received emotional state and evaluates the user's psychological state.

[0292] "Analysis means" refers to means for analyzing health data and emotional states, including an AI engine and an emotion engine.

[0293] The "provision means" is a means for providing the user with the personalized advice or suggestions generated by the analysis means.

[0294] The "suggestion means" is a means for proposing an optimal meal menu based on the advice generated by the analysis means.

[0295] The "display means" is a means for displaying the advice or suggestion generated by the providing means on the user's terminal.

[0296] "Health Insights" are useful information and advice derived from a user's health data and emotional state.

[0297] A "meal menu" is a balanced meal option suggested based on the user's health data and emotional state.

[0298] The present invention provides a system for managing a user's health data and emotional state in real time and providing advice, including personalized meal menu suggestions. The system includes a terminal, a server, an AI engine, and an emotion engine.

[0299] First, a user inputs their health data and emotional state using a device such as a smartphone or wearable device. Health data includes heart rate, number of steps, body temperature, blood pressure, etc. This data is collected through input means on the device. Users can also input their emotional state using voice input or facial recognition.

[0300] The collected health and emotion data is converted into a standard format such as JSON and sent from the device to the server. Transmission is via common communication methods such as Wi-Fi or mobile data. The server receives the data at a specified endpoint and receives parsed data in JSON format from the request body. The received data is stored in an internal database and managed by user ID.

[0301] Next, the AI ​​engine and emotion engine running on the server analyze the received health and emotion data. The AI ​​engine analyzes the health data and generates specific insights, such as evaluating calorie consumption and nutritional balance. The emotion engine detects the user's emotional state from their voice and facial expressions and combines this data to generate a comprehensive evaluation. This generates personalized advice that takes into account the user's health and emotional state.

[0302] The generated advice is transmitted from the server to the user's terminal. The providing means also includes a suggesting means for suggesting meal menus. The optimal meal menu is suggested based on the user's health data and emotional state. For example, a meal with a relaxing effect is suggested to a user who has a high heart rate and is feeling stressed.

[0303] As a concrete example, consider the case where a user sends their heart rate and step count to a server via their device after jogging. If the user reports "I feel stressed" through voice input, the server analyzes the received data and comprehensively evaluates the user's health and emotional state. Specific advice such as "Your heart rate is high, so we recommend you drink some herbal tea to relax. Also, consider choosing a grilled chicken and salad set for a balanced diet" is generated and displayed on the user's device.

[0304] Examples of prompts that may be used include:

[0305] "Please suggest a meal menu that will have a relaxing effect for a user who is experiencing high heart rate and feeling stressed."

[0306] This invention allows users to understand their own health data and emotional state in real time, receive advice including personalized meal menu suggestions, and enable more appropriate health management.

[0307] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0308] Step 1:

[0309] Users use smartphones or wearable devices to input health data (heart rate, number of steps, body temperature, blood pressure, etc.) and emotional state data (voice input and facial expression recognition). The input data is collected by the device's application. Input data: heart rate, number of steps, body temperature, blood pressure, voice input, facial expression data. Output data: health data and emotional data formatted within the device.

[0310] Step 2:

[0311] The health and emotion data collected by the device is converted into JSON format and sent to the server. The data is sent via Wi-Fi or mobile data. Input data: Formatted health and emotion data. Output data: Transmitted data in JSON format.

[0312] Step 3:

[0313] The server receives the data at the specified endpoint and parses it from JSON format. The received data is stored in an internal database and managed for each user ID. Input data: Transmitted data in JSON format. Output data: Parsed health data and emotion data, stored in the internal database.

[0314] Step 4:

[0315] The AI ​​engine on the server analyzes the health data and generates specific insights such as calorie consumption and nutritional balance. Input data: Parsed health data. Output data: Health insights (calorie consumption, nutritional balance, etc.).

[0316] Step 5:

[0317] The emotion engine on the server analyzes the emotion data and evaluates the emotional state from the voice and facial expressions. Input data: Parsed emotion data. Output data: Evaluation result of the emotional state.

[0318] Step 6:

[0319] The server integrates the analysis results of the AI ​​engine and the emotion engine to generate personalized advice based on the user's health and emotional state. Input data: health insights, emotional state assessment results. Output data: personalized advice.

[0320] Step 7:

[0321] The server proposes the optimal meal menu to the user based on the personalized advice. Input data: personalized advice. Output data: proposed meal menu.

[0322] Step 8:

[0323] The server sends the generated advice and suggested meal menu to the user's terminal. Input data: suggested meal menu and advice. Output data: advice and meal menu sent to the user's terminal.

[0324] Step 9:

[0325] The advice and suggested meal menu received by the terminal are displayed on the user interface. Information is presented in a form that is easy for the user to understand. Input data: Received advice and meal menu. Output data: Advice and meal menu displayed on the user interface.

[0326] Example prompt sentence:

[0327] "Please suggest a meal menu that will have a relaxing effect for a user who is experiencing high heart rate and feeling stressed."

[0328] 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.

[0329] 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.

[0330] 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.

[0331] [Second embodiment]

[0332] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0333] 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.

[0334] 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).

[0335] 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.

[0336] 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.

[0337] 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).

[0338] 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.

[0339] 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.

[0340] 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.

[0341] 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.

[0342] In the smart glasses 214, 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.

[0343] 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."

[0344] The present invention provides a system that allows users to manage their own health data in real time and receive personalized advice. This system includes a terminal, a server, and an AI engine for analyzing the user's health data. Specific embodiments of the present invention will be described below.

[0345] First, a user inputs their health data using a device such as a smartphone or wearable device. This health data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through the input means on the device.

[0346] Next, the device sends the collected health data to the server. The data to be sent is converted into a data format such as JSON. The device then sends an HTTP POST request over the Internet to send the data to the server. The data is sent using common communication methods such as Wi-Fi or mobile data communication.

[0347] The server receives this data at a specified endpoint, where it is parsed and stored in a database by the receiving means, allowing the server to centralize the health data of all users.

[0348] The stored data is then analyzed by an AI engine on the server, which uses machine learning algorithms to analyze the user's health data and generate specific insights, such as how a user's heart rate changes during exercise or how their dietary habits affect their health.

[0349] Once the analysis is complete, the AI ​​engine generates personalized advice, including specific behavioral suggestions and health management recommendations, such as "drink more water" or "walk 30 minutes every day." This advice is then sent to the user's device via the server's provisioning means. The advice is then displayed on the device's user interface.

[0350] As a concrete example, after a user finishes their morning jog, they send their heart rate and step count via smartphone to a server, which receives and analyzes the data. The AI ​​engine then evaluates whether the user's heart rate is within an appropriate range and whether the number of steps has reached the target. Based on the analysis results, advice such as "Always drink water after jogging" is displayed on the user's smartphone.

[0351] This allows users to understand their health status in real time and receive personalized, accurate advice, making health management easier, while healthcare providers can use this data to provide more accurate diagnosis and treatment.

[0352] The processing flow will be explained below.

[0353] Step 1:

[0354] The user opens their smartphone and launches a dedicated application, which uses the wearable device to collect health data such as heart rate and number of steps.

[0355] Step 2:

[0356] The device captures the health data entered by the user within the application, which organizes the data in a standard format such as JSON.

[0357] Step 3:

[0358] The device sends the captured health data to a specified API endpoint on the server using an HTTP POST request.

[0359] Step 4:

[0360] The server receives the data at the API endpoint, which is parsed from the request body in JSON format.

[0361] Step 5:

[0362] The server stores the parsed data in an internal database. Specifically, data is managed for each user ID, and past data is also accumulated.

[0363] Step 6:

[0364] The server calls the AI ​​engine to analyze the stored data, and the AI ​​engine then begins analysis based on the health data.

[0365] Step 7:

[0366] The server-based AI engine uses machine learning algorithms to analyze health data, generating health insights and risk assessments.

[0367] Step 8:

[0368] The AI ​​engine generates personalized advice based on the analysis results, such as "Try to relax" if your heart rate is high.

[0369] Step 9:

[0370] The server prepares the generated advice for transmission to the user's terminal, and converts the data to be transmitted into a format that is easy for the user to understand.

[0371] Step 10:

[0372] The server sends the generated advice to the terminal as an HTTP response, using data personalized based on the user ID.

[0373] Step 11:

[0374] The device displays the advice received from the server within the application, and the specific advice is presented visually on the user interface.

[0375] Step 12:

[0376] The user can then review the displayed advice and adjust their behavior based on it, for example by taking specific actions such as drinking water or doing stretches.

[0377] In this way, the system can assist users in managing their health in real time and provide appropriate advice to improve their health status.

[0378] Example 1

[0379] 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."

[0380] In recent years, there has been an increasing demand for healthcare systems that allow users to monitor their health status in real time and receive personalized advice. However, current systems lack consistent and efficient collection, transmission, analysis, and provision of health data, and there are particular issues with the accuracy and immediacy of personalized advice. The present invention aims to solve these problems and provide a system that allows users to manage their health status in real time and receive appropriate advice.

[0381] 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.

[0382] In this invention, the server includes an input means for inputting a user's health data, a transmission means for transmitting the health data to the server, a receiving means for receiving and storing the health data in the server, an analysis means including an AI engine for analyzing the health data in the server, a provision means for providing the user with personalized advice generated by the analysis means, a means for converting data collected by the analysis means into JSON format and transmitting it, and a display means for displaying the personalized advice provided by the provision means. This enables efficient collection, transmission, and analysis of health data and immediate provision of personalized advice.

[0383] "Input means" means a device or method by which a user inputs their health data into the system.

[0384] "Transmitting means" refers to a device or method for transmitting the input health data to the server.

[0385] "Receiving means" refers to a device or method by which the server receives and stores health data sent from the user.

[0386] "Analysis means" refers to a device or method for analyzing health data stored on the server, and includes an AI engine.

[0387] The "providing means" is a device or method for providing the user with the personalized advice generated by the analyzing means.

[0388] "Means for converting to JSON format" refers to a device or method for converting collected health data into JSON format and transmitting it.

[0389] "Display means" refers to a device or method for displaying the provided personalized advice on the user's terminal.

[0390] "Health data" refers to data that indicates the user's health condition, such as heart rate, number of steps, blood pressure, and body temperature.

[0391] "Server" means a computer system that receives, stores, and analyzes health data submitted by a user and generates and provides personalized advice.

[0392] A "user" is an individual who utilizes the system to input their health data and receive personalized advice.

[0393] The present invention provides a system for users to manage their own health data in real time and receive personalized advice. The system includes a terminal, a server, and an AI engine for analyzing the user's health data.

[0394] First, a user inputs their own health data using a device such as a smartphone or wearable device. This health data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through the input means on the device.

[0395] Next, the device sends the collected health data to the server. The data to be sent is converted into a data format such as JSON. The device then sends an HTTP POST request over the Internet to send the data to the server. The data is sent using common communication methods such as Wi-Fi or mobile data communication.

[0396] The server receives this data at a specified endpoint, where it is parsed and stored in a database by the receiving means, allowing the server to centralize the health data of all users.

[0397] The stored data is analyzed by an AI engine on the server. This analysis method uses machine learning algorithms to analyze the user's health data and generate specific insights. For example, the AI ​​engine is implemented using Python and analyzes the collected data using frameworks such as TensorFlow and PyTorch. For example, it extracts information such as how a user's heart rate changes during exercise and how their dietary habits affect their health.

[0398] Once the analysis is complete, the AI ​​engine generates personalized advice, including specific behavioral suggestions and health management recommendations, such as "drink more water" or "walk 30 minutes every day." This advice is sent to the user's device by the server's provisioning means, and is displayed on the device's user interface.

[0399] Examples:

[0400] After a user finishes their morning jog, they send their heart rate and step count to a server via their smartphone. The server receives and analyzes the data. The AI ​​engine then analyzes the data and evaluates whether the user's heart rate is within an appropriate range and whether the number of steps has reached the target. Based on the analysis results, advice such as "Always drink water after jogging" is displayed on the user's smartphone.

[0401] Example prompt for generative AI model:

[0402] "When a user finishes jogging and enters their heart rate and number of steps, please explain the specific steps for sending that data to the server, analyzing the data on the server, and generating advice."

[0403] This allows users to easily manage their health by understanding their health status in real time and receiving personalized and accurate advice, while healthcare providers can also use this data to provide more accurate diagnosis and treatment.

[0404] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0405] Step 1:

[0406] The user enters their own health data. The user opens the smartphone app and manually enters data such as heart rate, steps, blood pressure, and body temperature, or checks data automatically transferred from the wearable device. This provides the health data collected by the input method.

[0407] Input: Data entered by the user, such as heart rate, steps, blood pressure, and temperature

[0408] Output: Health data collected by input means

[0409] Step 2:

[0410] The device sends the collected health data to a server. The app on the device converts the collected data into JSON format and sends it to a specified endpoint on the server via the Internet using an HTTP POST request. Wi-Fi or mobile data communication is used as the transmission method.

[0411] Input: Health data converted to JSON format

[0412] Output: Health data sent to the server via HTTP POST request

[0413] Step 3:

[0414] The server receives and stores health data. The data received at the specified endpoint is parsed in JSON format and stored in a database. Specifically, the server's API receives an HTTP request, parses it, and stores it in a database (such as MongoDB or MySQL).

[0415] Input: Health data sent in an HTTP POST request

[0416] Output: Parsed health data stored in a database

[0417] Step 4:

[0418] An AI engine on the server analyzes the health data, using machine learning algorithms to extract insights about the user's health. The AI ​​engine is implemented using Python and performs analysis using frameworks such as TensorFlow and PyTorch.

[0419] Input: Health data stored in a database

[0420] Output: User health insights

[0421] Step 5:

[0422] The server generates personalized advice. Based on the analysis results, specific behavioral suggestions and health management recommendations are generated. For example, advice such as "drink more water" or "walk 30 minutes every day" is generated.

[0423] Input: User health insights

[0424] Output: personalized advice

[0425] Step 6:

[0426] The server sends the generated advice to the user's terminal using an HTTP POST request, and the terminal displays the received advice on its user interface.

[0427] Input: personalized advice

[0428] Output: Advice displayed on the user's terminal

[0429] Examples:

[0430] For example, when a user finishes jogging and enters their heart rate and number of steps, the device converts this data into JSON format and sends it to the server. The server receives and stores the data, and the AI ​​engine analyzes it. Based on the analysis results, advice such as "Always drink water after jogging" is generated and displayed on the user's smartphone.

[0431] Example prompt for generative AI model:

[0432] "When a user finishes jogging and enters their heart rate and number of steps, please explain the specific steps for sending that data to the server, analyzing the data on the server, and generating advice."

[0433] (Application example 1)

[0434] 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."

[0435] Conventional health management systems can collect and analyze users' health data, but often only provide personalized advice based on the results. This creates the challenge of making it difficult for users to find the health-related products and services they need. In particular, there is a need for a system that can automatically recommend products that are optimal for a user's health condition.

[0436] 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.

[0437] In this invention, the server includes an input means for inputting a user's health data, a transmission means for transmitting the health data to the server, a reception means for receiving and storing the health data, an analysis means including an AI engine for analyzing the health data, and a provision means for providing the user with personalized advice and product recommendations generated by the analysis means, thereby enabling the user to manage their own health and efficiently find products that are optimal for their health condition.

[0438] "Input means" refers to a mechanism by which a user inputs their own health data, and is implemented using a terminal such as a smartphone or wearable device.

[0439] The "transmission means" is a function that transmits health data from the user's device to the server, and uses Wi-Fi or mobile data communication.

[0440] The "receiving means" is a function by which the server receives and stores health data sent from the user's terminal.

[0441] The "analysis means" is a function that analyzes the received health data using an AI engine installed on the server.

[0442] The "provision means" is a mechanism for providing the user with personalized advice and product recommendations generated by the analysis means.

[0443] The "display means" is a function that displays the personalized advice and product recommendations on the user's terminal.

[0444] "Health insights" are insightful health information generated based on a user's past health data.

[0445] "Product recommendations" are health-related products recommended by the analysis means based on the user's health data.

[0446] The present invention is a system for managing a user's health data in real time and providing personalized advice and product recommendations. Specific embodiments for carrying out the present invention will be described below.

[0447] First, a user inputs their health data using a device such as a smartphone or wearable device. This health data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through the input means on the device.

[0448] Next, the device sends the collected health data to the server. The data to be sent is converted into a data format such as JSON. The device then sends an HTTP POST request over the Internet to send the data to the server. The data is sent using common communication methods such as Wi-Fi or mobile data communication.

[0449] The server receives this data at a specified endpoint, where it is parsed and stored in a database by the receiving means, allowing the server to centralize the health data of all users.

[0450] The stored data is analyzed by an AI engine on the server. This analysis method uses machine learning algorithms to analyze the user's health data and generate specific insights and product recommendations. For example, it extracts information such as how a user's heart rate changes during exercise and how their dietary habits affect their health. Based on this information, it then recommends related products such as vitamin supplements, fitness equipment, and health foods that are best suited to the user.

[0451] Once the analysis is complete, the AI ​​engine generates personalized advice and product recommendations. This advice includes specific behavioral suggestions and health management recommendations. For example, in addition to advice such as "drink more water" and "walk 30 minutes daily," product recommendations such as "take vitamin supplement A" are also made. This advice and product recommendations are sent to the user's device by the server's provisioning means. The sent advice and product recommendations are then displayed on the device's user interface.

[0452] As a concrete example, after a user finishes their morning jog, they send their heart rate and step count via smartphone to a server, which receives and analyzes the data. The AI ​​engine then analyzes the data and evaluates whether the user's heart rate is within an appropriate range and whether the number of steps has reached the target. Based on the analysis results, product recommendations such as "We recommend taking Vitamin Supplement A" are displayed on the user's smartphone, along with advice such as "Always drink water after jogging."

[0453] Example prompt sentence:

[0454] Input data:

[0455] {

[0456] "user_id": "user123",

[0457] "heart_rate": 70,

[0458] "steps": 5000,

[0459] "blood pressure": "120 / 80",

[0460] "body_temperature": 36.5

[0461] }

[0462] Generated advice:

[0463] "Your heart rate is within the normal range. Walk a few more steps to reach your goal for today. I also recommend taking Vitamin Supplement A."

[0464] Recommended products:

[0465] ["Vitamin Supplement A", "Fitness Equipment B", "Health Food C"]

[0466] This will allow users to understand their health status in real time and receive personalized health management advice and related product recommendations, making daily health management easier. Healthcare providers can also use this data to provide more accurate diagnosis and treatment.

[0467] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0468] Step 1:

[0469] Users input their own health data using a device such as a smartphone or wearable device. The input data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through the device's input means. The input data is temporarily stored in the device's memory.

[0470] Step 2:

[0471] The device sends the collected health data to the server. The data to be sent is converted into a data format such as JSON. The device then sends an HTTP POST request over the Internet to send the data to the server. Wi-Fi or mobile data communication is used as the transmission method. Specifically, the device sets the header and body of the HTTP request and performs the transmission procedure.

[0472] Step 3:

[0473] The server receives this data at the specified endpoint. The received data is parsed by the receiving means and stored in a database. Specifically, the data in JSON format is parsed, mapped to each field in the database, and stored. This allows the server to centrally manage the health data of all users.

[0474] Step 4:

[0475] An AI engine on the server analyzes the stored health data. The analysis method uses machine learning algorithms to analyze the user's health data and generate specific insights and product recommendations. Specific calculations include extracting insights based on heart rate trends, progress toward step goal achievement, and past trends, and recommending products based on these. For example, evaluations include whether the heart rate is within an appropriate range and whether the step goal has been achieved.

[0476] Step 5:

[0477] The server's provision means sends the personalized advice and product recommendations generated by the analysis means to the user's device. Specific data includes health advice and a product list in JSON format. This is then sent back to the user's device as an HTTP response. The reception means receives the response, parses it as necessary, and displays it on the user interface.

[0478] Step 6:

[0479] The device displays the received advice and product recommendations on the user interface. Specifically, the application displays personalized advice and product lists in a format that is easy for users to understand. Through this display, users can check suggested actions and products tailored to their health condition.

[0480] 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.

[0481] The present invention provides a system for users to manage their own health data and emotional state in real time and receive personalized advice. This system includes a terminal, a server, an AI engine, and an emotion engine. Specific embodiments for implementing the present invention will be described below.

[0482] First, a user inputs their health data using a device such as a smartphone or wearable device. This health data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through input means on the device. Users can also express their emotional state using voice input or facial expressions on the device.

[0483] The device then transmits the collected health and emotion data to a server. The data is converted into a standard format, such as JSON. The device then sends an HTTP POST request over the Internet to transmit the data to the server. The transmission method uses common communication methods, such as Wi-Fi or mobile data.

[0484] The server receives this data at the specified endpoint. The received data is parsed from the request body in JSON format. The parsed data is saved in an internal database and managed for each user ID.

[0485] The stored data is analyzed by an AI engine and an emotion engine on the server. The AI ​​engine analyzes the health data and generates specific insights. The emotion engine detects the user's emotional state from their voice, facial expressions, text input, etc. and combines this data to evaluate the user's overall condition.

[0486] Once the analysis is complete, the AI ​​engine and emotion engine generate personalized advice, including suggested actions based on health status and relaxation techniques corresponding to emotional states. For example, if a person's heart rate is high, the engine will generate advice such as "Try to relax." If the person's emotional state indicates stress, the engine will add a suggestion such as "stretch."

[0487] The generated advice is sent to the user's terminal by the server's providing means, and the sent advice is displayed on the terminal's user interface in a format that is easy for the user to understand.

[0488] As a specific example, after a user finishes their morning jog, they send their heart rate and step count to a server via their smartphone, which then receives and analyzes the data. At the same time, if the user records their emotional state through voice input, this data is also analyzed. The AI ​​engine and emotion engine work together to evaluate whether the user's heart rate is within an appropriate range and whether the number of steps has reached the target. In addition, because the user's emotional state is taken into consideration, specific and comprehensive advice such as "Always drink water after jogging, and take deep breaths if you feel stressed" is displayed on the user's smartphone.

[0489] In this way, users can understand their own health and emotional state in real time and receive personalized and accurate advice, enabling them to take comprehensive health management in their daily lives.

[0490] The processing flow will be explained below.

[0491] Step 1:

[0492] The user opens their smartphone and launches a dedicated application. The wearable device collects health data such as heart rate and number of steps taken. In addition, the user uses voice input to record their current emotional state (e.g., stressed, relaxed, etc.).

[0493] Step 2:

[0494] The device captures user-entered health data such as heart rate and step count, as well as emotional data obtained from voice input, and converts this data into standard formats such as JSON.

[0495] Step 3:

[0496] The device sends the captured health and emotion data to the server's designated API endpoint using an HTTP POST request.

[0497] Step 4:

[0498] The server receives health and emotion data via the API endpoint. The received data is parsed in JSON format and stored in an internal database. Data is managed by user ID, and past data is also accumulated.

[0499] Step 5:

[0500] The server calls the AI ​​engine and emotion engine to analyze the stored data. The AI ​​engine analyzes the health data and generates specific health insights, such as assessing whether your heart rate is high or low, or whether your step count is reaching your target.

[0501] Step 6:

[0502] The emotion engine on the server analyzes emotion data obtained through voice input or other means, and uses machine learning algorithms to identify the user's emotional state, such as "stressed" or "relaxed."

[0503] Step 7:

[0504] The AI ​​engine and emotion engine combine their respective analysis results. Based on the combined data, personalized advice is generated for the user. For example, if the heart rate is high and the emotional state indicates stress, advice such as "take deep breaths to relax" is generated.

[0505] Step 8:

[0506] The server prepares the generated personalized advice for sending to the user's device. The advice is formatted in a way that is easy for the user to understand, such as JSON format.

[0507] Step 9:

[0508] The server sends the generated advice to the device as an HTTP response, with personalized data based on the user ID.

[0509] Step 10:

[0510] The device displays the advice received from the server within the application, and the specific advice is presented visually on the user interface.

[0511] Step 11:

[0512] The user can then review the displayed advice and adjust their behavior accordingly, such as taking deep breaths to relax, drinking water, or stretching.

[0513] In this way, the system comprehensively analyzes the user's health and emotional data and provides personalized advice in real time, helping the user manage their overall health.

[0514] Example 2

[0515] 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."

[0516] Conventional health management systems collect and analyze users' health data using only a limited range of information, making it difficult to provide advice that takes into account their emotional state. Furthermore, the generation of personalized advice is insufficient, limiting their effectiveness in comprehensive health management. This makes it difficult for users to appropriately address specific health issues or stress states, and often results in only general advice being provided.

[0517] 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 a receiving means for receiving and storing the user's health data and emotion data, an analyzing means including an AI engine and an emotion engine for analyzing the health data and emotion data, and a providing means for providing the user with personalized advice generated by the analyzing means. This makes it possible to grasp not only the user's health state but also their emotional state in real time and provide comprehensive and personalized advice.

[0518] "Health data" refers to information including physiological indicators such as the user's heart rate, number of steps, blood pressure, and body temperature.

[0519] "Emotion data" is information indicating the emotional state of the user obtained from voice, facial expression, text input, and the like.

[0520] "Input means" refers to devices or software that allow users to input health data and emotional data. Specifically, this refers to smartphones, wearable devices, etc.

[0521] "Transmission means" refers to the functions and devices that allow the device to transmit health data and emotion data to the server. This includes Wi-Fi and mobile data communications.

[0522] "Receiving means" refers to a function or device that allows the server to receive and store health data and emotion data transmitted from the terminal.

[0523] "Analysis means" refers to the functions and devices used to analyze the health data and emotion data received by the server. Specifically, it includes an AI engine and an emotion engine.

[0524] An "AI engine" is an algorithm or program that analyzes health data and generates specific insights.

[0525] An "emotion engine" is an algorithm or program that analyzes emotional states from voice, facial expressions, text input, etc.

[0526] The "provision means" refers to a function or device for providing the user with the personalized advice generated by the analysis means.

[0527] The "display means" is a function or device for displaying the advice generated by the providing means on the user's terminal.

[0528] "Health insights" are various insights and advice obtained by analyzing a user's health data.

[0529] "Emotional insights" are various insights and advice obtained by analyzing a user's emotional data.

[0530] "Real-time" means that data is collected, analyzed, and advice is provided without delay.

[0531] MODE FOR CARRYING OUT THE INVENTION

[0532] The present invention provides a system for users to manage their own health data and emotional state in real time and receive personalized advice. This system includes a terminal, a server, an AI engine, and an emotion engine. Specific embodiments are described below.

[0533] Terminal

[0534] Users input their own health data using devices such as smartphones or wearable devices, including heart rate, number of steps, blood pressure, and body temperature. Users can also record their emotional state through voice input and facial expression recognition using the microphone and camera on their devices.

[0535] server

[0536] The health and emotion data sent from the device is sent to a server via the Internet. Specifically, the device converts the data into JSON format and sends it to the server via an HTTP POST request. Wi-Fi or mobile data communication is used as the communication method.

[0537] The server receives the data at the specified endpoint and parses it in JSON format. The parsed data is stored in a relational database system such as MySQL or PostgreSQL and managed by user ID.

[0538] AI engine and emotion engine

[0539] The data stored on the server is analyzed by an AI engine and an emotion engine. The AI ​​engine analyzes the health data and generates specific health insights. For example, it analyzes heart rate and step count data to evaluate the amount of exercise and fatigue level for the day. The AI ​​engine uses Python libraries such as TensorFlow and PyTorch to build and run models.

[0540] The emotion engine uses voice and facial recognition technology to detect the user's emotional state. Voice input is analyzed based on the user's tone of voice and the way they speak, while facial recognition is based on the user's facial expressions captured by the camera. This allows the engine to assess the user's stress level and mood.

[0541] Means of provision and display

[0542] Once the analysis is complete, the AI ​​and emotion engines generate personalized advice, including suggested actions based on the user's health and emotional state. For example, if your heart rate is high, they might suggest "try to relax," or if your emotional state indicates stress, they might suggest "take deep breaths."

[0543] The generated advice is sent from the server to the device and displayed on the device's user interface, which can be in the form of a notification, an app dashboard, a widget, or the like.

[0544] Specific examples

[0545] For example, after a user finishes jogging in the morning, they can use their smartphone to input their heart rate and step count data. At the same time, they can record their emotional state by voice input, such as "I feel a little tired from jogging today." The device then sends this data to the server.

[0546] The server receives the data and begins analysis. The AI ​​engine analyzes the heart rate data, and the emotion engine detects "fatigue" and "stress." The server generates advice such as "Don't forget to hydrate after jogging. If you feel tired, take a deep breath and relax," and sends it to the device. The device displays the received advice to the user as a notification.

[0547] Prompt Sentence Examples

[0548] "When a user sends heart rate and step count data from their smartphone and records their emotional state through voice input, the AI ​​and emotion engine will analyze it and generate optimal advice."

[0549] In this way, users can understand their own health and emotional state in real time and receive personalized advice, enabling comprehensive health management in daily life.

[0550] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0551] Step 1:

[0552] The user inputs health and emotional data.

[0553] Input: Users use smartphones or wearable devices to input health data such as heart rate, steps, blood pressure, and temperature, as well as record emotional state through voice input and facial expressions.

[0554] Data processing: Health and emotional data is converted into digital form within the app.

[0555] Output: The converted digital data is stored in a temporary database on the smartphone.

[0556] Step 2:

[0557] The device sends the data to the server.

[0558] Input: Health and emotion data collected in step 1.

[0559] Data processing: The device converts the collected data into JSON format and sends it to the server using an HTTP POST request. The device connects to the Internet via Wi-Fi or mobile data.

[0560] Output: JSON formatted data sent to the server.

[0561] Step 3:

[0562] The server receives the data and stores it in a database.

[0563] Input: Health and emotion data sent from the device in JSON format.

[0564] Data processing: The server receives the data, parses it from JSON format, and stores it in a relational database (e.g., MySQL or PostgreSQL) along with the user ID.

[0565] Output: Structured data stored in a database.

[0566] Step 4:

[0567] An AI engine and emotion engine on the server analyze the data.

[0568] Input: Health and emotion data stored in a database.

[0569] Data Computation: The AI ​​engine analyzes health data and generates health insights based on, for example, heart rate and step count, while the emotion engine analyzes emotional states using voice and facial recognition technologies, specifically using Python libraries such as TensorFlow and PyTorch.

[0570] Output: Generated health and sentiment insights.

[0571] Step 5:

[0572] The server generates personalized advice.

[0573] Input: Health and sentiment insights generated in step 4.

[0574] Data Computation: Based on the analysis results, personalized advice is generated based on the user's situation, including specific suggested actions that take into account both their health and emotional state.

[0575] Output: personalized advice.

[0576] Step 6:

[0577] The server sends the advice to the terminal.

[0578] Input: The personalized advice generated in step 5.

[0579] Data processing: The server converts the personalized advice into JSON format and sends it to the device as an HTTP response using the secure HTTPS protocol.

[0580] Output: Advice data sent to the terminal.

[0581] Step 7:

[0582] The terminal displays the advice to the user.

[0583] Input: Advice data sent by the server.

[0584] Data processing: The device parses the received advice and displays it in the user interface, which could be a notification, an in-app dashboard, or a widget.

[0585] Output: The advice that is displayed to the user.

[0586] This process allows users to manage their health and emotional state in real time and receive accurate, personalized advice.

[0587] (Application example 2)

[0588] 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."

[0589] The main purpose of conventional health management systems was to provide consistent advice based on the user's health data, but they had limitations in providing personalized advice that took into account the user's emotional state, or in suggesting meal plans that adapted to the user's health and emotional state. This resulted in a lack of motivation for users to actually follow the advice, and the health management system was not as effective as it could be.

[0590] The specific processing by the specific 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 input means for inputting the user's health data and emotional state, transmission means for transmitting the health data and emotional state to the server, reception means for receiving and storing the health data and emotional state, analysis means including an AI engine and an emotion engine for analyzing the health data and emotional state, provision means for providing the user with personalized advice generated by the analysis means, and suggestion means for proposing an optimal meal menu based on the advice. As a result, appropriate action suggestions and meal menus are provided in real time based on the user's health state and emotional state, making it possible to improve the effectiveness of health management.

[0591] "User's health data" refers to various data related to the user's health condition, such as the user's heart rate, number of steps, blood pressure, and body temperature.

[0592] "Emotional state" is data that indicates the user's mental and psychological state, and is collected through voice input and facial expression recognition.

[0593] "Input means" refers to a means by which a user inputs health data and emotional state using a smartphone, wearable device, or the like.

[0594] The "transmission means" is a means for transmitting the input health data and emotional state to the server via a network.

[0595] The "receiving means" is a means for receiving, on the server, the health data and emotional state sent via the transmitting means.

[0596] The "AI Engine" is an artificial intelligence engine that analyzes received health data and generates specific insights and suggestions.

[0597] The "emotion engine" is an artificial intelligence engine that analyzes the received emotional state and evaluates the user's psychological state.

[0598] "Analysis means" refers to means for analyzing health data and emotional states, including an AI engine and an emotion engine.

[0599] The "provision means" is a means for providing the user with the personalized advice or suggestions generated by the analysis means.

[0600] The "suggestion means" is a means for proposing an optimal meal menu based on the advice generated by the analysis means.

[0601] The "display means" is a means for displaying the advice or suggestion generated by the providing means on the user's terminal.

[0602] "Health Insights" are useful information and advice derived from a user's health data and emotional state.

[0603] A "meal menu" is a balanced meal option suggested based on the user's health data and emotional state.

[0604] The present invention provides a system for managing a user's health data and emotional state in real time and providing advice, including personalized meal menu suggestions. The system includes a terminal, a server, an AI engine, and an emotion engine.

[0605] First, a user inputs their health data and emotional state using a device such as a smartphone or wearable device. Health data includes heart rate, number of steps, body temperature, blood pressure, etc. This data is collected through input means on the device. Users can also input their emotional state using voice input or facial recognition.

[0606] The collected health and emotion data is converted into a standard format such as JSON and sent from the device to the server. Transmission is via common communication methods such as Wi-Fi or mobile data. The server receives the data at a specified endpoint and receives parsed data in JSON format from the request body. The received data is stored in an internal database and managed by user ID.

[0607] Next, the AI ​​engine and emotion engine running on the server analyze the received health and emotion data. The AI ​​engine analyzes the health data and generates specific insights, such as evaluating calorie consumption and nutritional balance. The emotion engine detects the user's emotional state from their voice and facial expressions and combines this data to generate a comprehensive evaluation. This generates personalized advice that takes into account the user's health and emotional state.

[0608] The generated advice is transmitted from the server to the user's terminal. The providing means also includes a suggesting means for suggesting meal menus. The optimal meal menu is suggested based on the user's health data and emotional state. For example, a meal with a relaxing effect is suggested to a user who has a high heart rate and is feeling stressed.

[0609] As a concrete example, consider the case where a user sends their heart rate and step count to a server via their device after jogging. If the user reports "I feel stressed" through voice input, the server analyzes the received data and comprehensively evaluates the user's health and emotional state. Specific advice such as "Your heart rate is high, so we recommend you drink some herbal tea to relax. Also, consider choosing a grilled chicken and salad set for a balanced diet" is generated and displayed on the user's device.

[0610] Examples of prompts that may be used include:

[0611] "Please suggest a meal menu that will have a relaxing effect for a user who is experiencing high heart rate and feeling stressed."

[0612] This invention allows users to understand their own health data and emotional state in real time, receive advice including personalized meal menu suggestions, and enable more appropriate health management.

[0613] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0614] Step 1:

[0615] Users use smartphones or wearable devices to input health data (heart rate, number of steps, body temperature, blood pressure, etc.) and emotional state data (voice input and facial expression recognition). The input data is collected by the device's application. Input data: heart rate, number of steps, body temperature, blood pressure, voice input, facial expression data. Output data: health data and emotional data formatted within the device.

[0616] Step 2:

[0617] The health and emotion data collected by the device is converted into JSON format and sent to the server. The data is sent via Wi-Fi or mobile data. Input data: Formatted health and emotion data. Output data: Transmitted data in JSON format.

[0618] Step 3:

[0619] The server receives the data at the specified endpoint and parses it from JSON format. The received data is stored in an internal database and managed for each user ID. Input data: Transmitted data in JSON format. Output data: Parsed health data and emotion data, stored in the internal database.

[0620] Step 4:

[0621] The AI ​​engine on the server analyzes the health data and generates specific insights such as calorie consumption and nutritional balance. Input data: Parsed health data. Output data: Health insights (calorie consumption, nutritional balance, etc.).

[0622] Step 5:

[0623] The emotion engine on the server analyzes the emotion data and evaluates the emotional state from the voice and facial expressions. Input data: Parsed emotion data. Output data: Evaluation result of the emotional state.

[0624] Step 6:

[0625] The server integrates the analysis results of the AI ​​engine and the emotion engine to generate personalized advice based on the user's health and emotional state. Input data: health insights, emotional state assessment results. Output data: personalized advice.

[0626] Step 7:

[0627] The server proposes the optimal meal menu to the user based on the personalized advice. Input data: personalized advice. Output data: proposed meal menu.

[0628] Step 8:

[0629] The server sends the generated advice and suggested meal menu to the user's terminal. Input data: suggested meal menu and advice. Output data: advice and meal menu sent to the user's terminal.

[0630] Step 9:

[0631] The advice and suggested meal menu received by the terminal are displayed on the user interface. Information is presented in a form that is easy for the user to understand. Input data: Received advice and meal menu. Output data: Advice and meal menu displayed on the user interface.

[0632] Example prompt sentence:

[0633] "Please suggest a meal menu that will have a relaxing effect for a user who is experiencing high heart rate and feeling stressed."

[0634] 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.

[0635] 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.

[0636] 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.

[0637] [Third embodiment]

[0638] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0639] 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.

[0640] 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).

[0641] 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.

[0642] 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.

[0643] 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).

[0644] 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.

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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."

[0650] The present invention provides a system that allows users to manage their own health data in real time and receive personalized advice. This system includes a terminal, a server, and an AI engine for analyzing the user's health data. Specific embodiments of the present invention will be described below.

[0651] First, a user inputs their health data using a device such as a smartphone or wearable device. This health data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through the input means on the device.

[0652] Next, the device sends the collected health data to the server. The data to be sent is converted into a data format such as JSON. The device then sends an HTTP POST request over the Internet to send the data to the server. The data is sent using common communication methods such as Wi-Fi or mobile data communication.

[0653] The server receives this data at a specified endpoint, where it is parsed and stored in a database by the receiving means, allowing the server to centralize the health data of all users.

[0654] The stored data is then analyzed by an AI engine on the server, which uses machine learning algorithms to analyze the user's health data and generate specific insights, such as how a user's heart rate changes during exercise or how their dietary habits affect their health.

[0655] Once the analysis is complete, the AI ​​engine generates personalized advice, including specific behavioral suggestions and health management recommendations, such as "drink more water" or "walk 30 minutes every day." This advice is then sent to the user's device via the server's provisioning means. The advice is then displayed on the device's user interface.

[0656] As a concrete example, after a user finishes their morning jog, they send their heart rate and step count via smartphone to a server, which receives and analyzes the data. The AI ​​engine then evaluates whether the user's heart rate is within an appropriate range and whether the number of steps has reached the target. Based on the analysis results, advice such as "Always drink water after jogging" is displayed on the user's smartphone.

[0657] This allows users to understand their health status in real time and receive personalized, accurate advice, making health management easier, while healthcare providers can use this data to provide more accurate diagnosis and treatment.

[0658] The processing flow will be explained below.

[0659] Step 1:

[0660] The user opens their smartphone and launches a dedicated application, which uses the wearable device to collect health data such as heart rate and number of steps.

[0661] Step 2:

[0662] The device captures the health data entered by the user within the application, which organizes the data in a standard format such as JSON.

[0663] Step 3:

[0664] The device sends the captured health data to a specified API endpoint on the server using an HTTP POST request.

[0665] Step 4:

[0666] The server receives the data at the API endpoint, which is parsed from the request body in JSON format.

[0667] Step 5:

[0668] The server stores the parsed data in an internal database. Specifically, data is managed for each user ID, and past data is also accumulated.

[0669] Step 6:

[0670] The server calls the AI ​​engine to analyze the stored data, and the AI ​​engine then begins analysis based on the health data.

[0671] Step 7:

[0672] The server-based AI engine uses machine learning algorithms to analyze health data, generating health insights and risk assessments.

[0673] Step 8:

[0674] The AI ​​engine generates personalized advice based on the analysis results, such as "Try to relax" if your heart rate is high.

[0675] Step 9:

[0676] The server prepares the generated advice for transmission to the user's terminal, and converts the data to be transmitted into a format that is easy for the user to understand.

[0677] Step 10:

[0678] The server sends the generated advice to the terminal as an HTTP response, using data personalized based on the user ID.

[0679] Step 11:

[0680] The device displays the advice received from the server within the application, and the specific advice is presented visually on the user interface.

[0681] Step 12:

[0682] The user can then review the displayed advice and adjust their behavior based on it, for example by taking specific actions such as drinking water or doing stretches.

[0683] In this way, the system can assist users in managing their health in real time and provide appropriate advice to improve their health status.

[0684] Example 1

[0685] 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."

[0686] In recent years, there has been an increasing demand for healthcare systems that allow users to monitor their health status in real time and receive personalized advice. However, current systems lack consistent and efficient collection, transmission, analysis, and provision of health data, and there are particular issues with the accuracy and immediacy of personalized advice. The present invention aims to solve these problems and provide a system that allows users to manage their health status in real time and receive appropriate advice.

[0687] 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.

[0688] In this invention, the server includes an input means for inputting a user's health data, a transmission means for transmitting the health data to the server, a receiving means for receiving and storing the health data in the server, an analysis means including an AI engine for analyzing the health data in the server, a provision means for providing the user with personalized advice generated by the analysis means, a means for converting data collected by the analysis means into JSON format and transmitting it, and a display means for displaying the personalized advice provided by the provision means. This enables efficient collection, transmission, and analysis of health data and immediate provision of personalized advice.

[0689] "Input means" means a device or method by which a user inputs their health data into the system.

[0690] "Transmitting means" refers to a device or method for transmitting the input health data to the server.

[0691] "Receiving means" refers to a device or method by which the server receives and stores health data sent from the user.

[0692] "Analysis means" refers to a device or method for analyzing health data stored on the server, and includes an AI engine.

[0693] The "providing means" is a device or method for providing the user with the personalized advice generated by the analyzing means.

[0694] "Means for converting to JSON format" refers to a device or method for converting collected health data into JSON format and transmitting it.

[0695] "Display means" refers to a device or method for displaying the provided personalized advice on the user's terminal.

[0696] "Health data" refers to data that indicates the user's health condition, such as heart rate, number of steps, blood pressure, and body temperature.

[0697] "Server" means a computer system that receives, stores, and analyzes health data submitted by a user and generates and provides personalized advice.

[0698] A "user" is an individual who utilizes the system to input their health data and receive personalized advice.

[0699] The present invention provides a system for users to manage their own health data in real time and receive personalized advice. The system includes a terminal, a server, and an AI engine for analyzing the user's health data.

[0700] First, a user inputs their own health data using a device such as a smartphone or wearable device. This health data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through the input means on the device.

[0701] Next, the device sends the collected health data to the server. The data to be sent is converted into a data format such as JSON. The device then sends an HTTP POST request over the Internet to send the data to the server. The data is sent using common communication methods such as Wi-Fi or mobile data communication.

[0702] The server receives this data at a specified endpoint, where it is parsed and stored in a database by the receiving means, allowing the server to centralize the health data of all users.

[0703] The stored data is analyzed by an AI engine on the server. This analysis method uses machine learning algorithms to analyze the user's health data and generate specific insights. For example, the AI ​​engine is implemented using Python and analyzes the collected data using frameworks such as TensorFlow and PyTorch. For example, it extracts information such as how a user's heart rate changes during exercise and how their dietary habits affect their health.

[0704] Once the analysis is complete, the AI ​​engine generates personalized advice, including specific behavioral suggestions and health management recommendations, such as "drink more water" or "walk 30 minutes every day." This advice is sent to the user's device by the server's provisioning means, and is displayed on the device's user interface.

[0705] Examples:

[0706] After a user finishes their morning jog, they send their heart rate and step count to a server via their smartphone. The server receives and analyzes the data. The AI ​​engine then analyzes the data and evaluates whether the user's heart rate is within an appropriate range and whether the number of steps has reached the target. Based on the analysis results, advice such as "Always drink water after jogging" is displayed on the user's smartphone.

[0707] Example prompt for generative AI model:

[0708] "When a user finishes jogging and enters their heart rate and number of steps, please explain the specific steps for sending that data to the server, analyzing the data on the server, and generating advice."

[0709] This allows users to easily manage their health by understanding their health status in real time and receiving personalized and accurate advice, while healthcare providers can also use this data to provide more accurate diagnosis and treatment.

[0710] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0711] Step 1:

[0712] The user enters their own health data. The user opens the smartphone app and manually enters data such as heart rate, steps, blood pressure, and body temperature, or checks data automatically transferred from the wearable device. This provides the health data collected by the input method.

[0713] Input: Data entered by the user, such as heart rate, steps, blood pressure, and temperature

[0714] Output: Health data collected by input means

[0715] Step 2:

[0716] The device sends the collected health data to a server. The app on the device converts the collected data into JSON format and sends it to a specified endpoint on the server via the Internet using an HTTP POST request. Wi-Fi or mobile data communication is used as the transmission method.

[0717] Input: Health data converted to JSON format

[0718] Output: Health data sent to the server via HTTP POST request

[0719] Step 3:

[0720] The server receives and stores health data. The data received at the specified endpoint is parsed in JSON format and stored in a database. Specifically, the server's API receives an HTTP request, parses it, and stores it in a database (such as MongoDB or MySQL).

[0721] Input: Health data sent in an HTTP POST request

[0722] Output: Parsed health data stored in a database

[0723] Step 4:

[0724] An AI engine on the server analyzes the health data, using machine learning algorithms to extract insights about the user's health. The AI ​​engine is implemented using Python and performs analysis using frameworks such as TensorFlow and PyTorch.

[0725] Input: Health data stored in a database

[0726] Output: User health insights

[0727] Step 5:

[0728] The server generates personalized advice. Based on the analysis results, specific behavioral suggestions and health management recommendations are generated. For example, advice such as "drink more water" or "walk 30 minutes every day" is generated.

[0729] Input: User health insights

[0730] Output: personalized advice

[0731] Step 6:

[0732] The server sends the generated advice to the user's terminal using an HTTP POST request, and the terminal displays the received advice on its user interface.

[0733] Input: personalized advice

[0734] Output: Advice displayed on the user's terminal

[0735] Examples:

[0736] For example, when a user finishes jogging and enters their heart rate and number of steps, the device converts this data into JSON format and sends it to the server. The server receives and stores the data, and the AI ​​engine analyzes it. Based on the analysis results, advice such as "Always drink water after jogging" is generated and displayed on the user's smartphone.

[0737] Example prompt for generative AI model:

[0738] "When a user finishes jogging and enters their heart rate and number of steps, please explain the specific steps for sending that data to the server, analyzing the data on the server, and generating advice."

[0739] (Application example 1)

[0740] 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."

[0741] Conventional health management systems can collect and analyze users' health data, but often only provide personalized advice based on the results. This creates the challenge of making it difficult for users to find the health-related products and services they need. In particular, there is a need for a system that can automatically recommend products that are optimal for a user's health condition.

[0742] 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.

[0743] In this invention, the server includes an input means for inputting a user's health data, a transmission means for transmitting the health data to the server, a reception means for receiving and storing the health data, an analysis means including an AI engine for analyzing the health data, and a provision means for providing the user with personalized advice and product recommendations generated by the analysis means, thereby enabling the user to manage their own health and efficiently find products that are optimal for their health condition.

[0744] "Input means" refers to a mechanism by which a user inputs their own health data, and is implemented using a terminal such as a smartphone or wearable device.

[0745] The "transmission means" is a function that transmits health data from the user's device to the server, and uses Wi-Fi or mobile data communication.

[0746] The "receiving means" is a function by which the server receives and stores health data sent from the user's terminal.

[0747] The "analysis means" is a function that analyzes the received health data using an AI engine installed on the server.

[0748] The "provision means" is a mechanism for providing the user with personalized advice and product recommendations generated by the analysis means.

[0749] The "display means" is a function that displays the personalized advice and product recommendations on the user's terminal.

[0750] "Health insights" are insightful health information generated based on a user's past health data.

[0751] "Product recommendations" are health-related products recommended by the analysis means based on the user's health data.

[0752] The present invention is a system for managing a user's health data in real time and providing personalized advice and product recommendations. Specific embodiments for carrying out the present invention will be described below.

[0753] First, a user inputs their health data using a device such as a smartphone or wearable device. This health data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through the input means on the device.

[0754] Next, the device sends the collected health data to the server. The data to be sent is converted into a data format such as JSON. The device then sends an HTTP POST request over the Internet to send the data to the server. The data is sent using common communication methods such as Wi-Fi or mobile data communication.

[0755] The server receives this data at a specified endpoint, where it is parsed and stored in a database by the receiving means, allowing the server to centralize the health data of all users.

[0756] The stored data is analyzed by an AI engine on the server. This analysis method uses machine learning algorithms to analyze the user's health data and generate specific insights and product recommendations. For example, it extracts information such as how a user's heart rate changes during exercise and how their dietary habits affect their health. Based on this information, it then recommends related products such as vitamin supplements, fitness equipment, and health foods that are best suited to the user.

[0757] Once the analysis is complete, the AI ​​engine generates personalized advice and product recommendations. This advice includes specific behavioral suggestions and health management recommendations. For example, in addition to advice such as "drink more water" and "walk 30 minutes daily," product recommendations such as "take vitamin supplement A" are also made. This advice and product recommendations are sent to the user's device by the server's provisioning means. The sent advice and product recommendations are then displayed on the device's user interface.

[0758] As a concrete example, after a user finishes their morning jog, they send their heart rate and step count via smartphone to a server, which receives and analyzes the data. The AI ​​engine then analyzes the data and evaluates whether the user's heart rate is within an appropriate range and whether the number of steps has reached the target. Based on the analysis results, product recommendations such as "We recommend taking Vitamin Supplement A" are displayed on the user's smartphone, along with advice such as "Always drink water after jogging."

[0759] Example prompt sentence:

[0760] Input data:

[0761] {

[0762] "user_id": "user123",

[0763] "heart_rate": 70,

[0764] "steps": 5000,

[0765] "blood pressure": "120 / 80",

[0766] "body_temperature": 36.5

[0767] }

[0768] Generated advice:

[0769] "Your heart rate is within the normal range. Walk a few more steps to reach your goal for today. I also recommend taking Vitamin Supplement A."

[0770] Recommended products:

[0771] ["Vitamin Supplement A", "Fitness Equipment B", "Health Food C"]

[0772] This will allow users to understand their health status in real time and receive personalized health management advice and related product recommendations, making daily health management easier. Healthcare providers can also use this data to provide more accurate diagnosis and treatment.

[0773] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0774] Step 1:

[0775] Users input their own health data using a device such as a smartphone or wearable device. The input data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through the device's input means. The input data is temporarily stored in the device's memory.

[0776] Step 2:

[0777] The device sends the collected health data to the server. The data to be sent is converted into a data format such as JSON. The device then sends an HTTP POST request over the Internet to send the data to the server. Wi-Fi or mobile data communication is used as the transmission method. Specifically, the device sets the header and body of the HTTP request and performs the transmission procedure.

[0778] Step 3:

[0779] The server receives this data at the specified endpoint. The received data is parsed by the receiving means and stored in a database. Specifically, the data in JSON format is parsed, mapped to each field in the database, and stored. This allows the server to centrally manage the health data of all users.

[0780] Step 4:

[0781] An AI engine on the server analyzes the stored health data. The analysis method uses machine learning algorithms to analyze the user's health data and generate specific insights and product recommendations. Specific calculations include extracting insights based on heart rate trends, progress toward step goal achievement, and past trends, and recommending products based on these. For example, evaluations include whether the heart rate is within an appropriate range and whether the step goal has been achieved.

[0782] Step 5:

[0783] The server's provision means sends the personalized advice and product recommendations generated by the analysis means to the user's device. Specific data includes health advice and a product list in JSON format. This is then sent back to the user's device as an HTTP response. The reception means receives the response, parses it as necessary, and displays it on the user interface.

[0784] Step 6:

[0785] The device displays the received advice and product recommendations on the user interface. Specifically, the application displays personalized advice and product lists in a format that is easy for users to understand. Through this display, users can check suggested actions and products tailored to their health condition.

[0786] 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.

[0787] The present invention provides a system for users to manage their own health data and emotional state in real time and receive personalized advice. This system includes a terminal, a server, an AI engine, and an emotion engine. Specific embodiments for implementing the present invention will be described below.

[0788] First, a user inputs their health data using a device such as a smartphone or wearable device. This health data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through input means on the device. Users can also express their emotional state using voice input or facial expressions on the device.

[0789] The device then transmits the collected health and emotion data to a server. The data is converted into a standard format, such as JSON. The device then sends an HTTP POST request over the Internet to transmit the data to the server. The transmission method uses common communication methods, such as Wi-Fi or mobile data.

[0790] The server receives this data at the specified endpoint. The received data is parsed from the request body in JSON format. The parsed data is saved in an internal database and managed for each user ID.

[0791] The stored data is analyzed by an AI engine and an emotion engine on the server. The AI ​​engine analyzes the health data and generates specific insights. The emotion engine detects the user's emotional state from their voice, facial expressions, text input, etc. and combines this data to evaluate the user's overall condition.

[0792] Once the analysis is complete, the AI ​​engine and emotion engine generate personalized advice, including suggested actions based on health status and relaxation techniques corresponding to emotional states. For example, if a person's heart rate is high, the engine will generate advice such as "Try to relax." If the person's emotional state indicates stress, the engine will add a suggestion such as "stretch."

[0793] The generated advice is sent to the user's terminal by the server's providing means, and the sent advice is displayed on the terminal's user interface in a format that is easy for the user to understand.

[0794] As a specific example, after a user finishes their morning jog, they send their heart rate and step count to a server via their smartphone, which then receives and analyzes the data. At the same time, if the user records their emotional state through voice input, this data is also analyzed. The AI ​​engine and emotion engine work together to evaluate whether the user's heart rate is within an appropriate range and whether the number of steps has reached the target. In addition, because the user's emotional state is taken into consideration, specific and comprehensive advice such as "Always drink water after jogging, and take deep breaths if you feel stressed" is displayed on the user's smartphone.

[0795] In this way, users can understand their own health and emotional state in real time and receive personalized and accurate advice, enabling them to take comprehensive health management in their daily lives.

[0796] The processing flow will be explained below.

[0797] Step 1:

[0798] The user opens their smartphone and launches a dedicated application. The wearable device collects health data such as heart rate and number of steps taken. In addition, the user uses voice input to record their current emotional state (e.g., stressed, relaxed, etc.).

[0799] Step 2:

[0800] The device captures user-entered health data such as heart rate and step count, as well as emotional data obtained from voice input, and converts this data into standard formats such as JSON.

[0801] Step 3:

[0802] The device sends the captured health and emotion data to the server's designated API endpoint using an HTTP POST request.

[0803] Step 4:

[0804] The server receives health and emotion data via the API endpoint. The received data is parsed in JSON format and stored in an internal database. Data is managed by user ID, and past data is also accumulated.

[0805] Step 5:

[0806] The server calls the AI ​​engine and emotion engine to analyze the stored data. The AI ​​engine analyzes the health data and generates specific health insights, such as assessing whether your heart rate is high or low, or whether your step count is reaching your target.

[0807] Step 6:

[0808] The emotion engine on the server analyzes emotion data obtained through voice input or other means, and uses machine learning algorithms to identify the user's emotional state, such as "stressed" or "relaxed."

[0809] Step 7:

[0810] The AI ​​engine and emotion engine combine their respective analysis results. Based on the combined data, personalized advice is generated for the user. For example, if the heart rate is high and the emotional state indicates stress, advice such as "take deep breaths to relax" is generated.

[0811] Step 8:

[0812] The server prepares the generated personalized advice for sending to the user's device. The advice is formatted in a way that is easy for the user to understand, such as JSON format.

[0813] Step 9:

[0814] The server sends the generated advice to the device as an HTTP response, with personalized data based on the user ID.

[0815] Step 10:

[0816] The device displays the advice received from the server within the application, and the specific advice is presented visually on the user interface.

[0817] Step 11:

[0818] The user can then review the displayed advice and adjust their behavior accordingly, such as taking deep breaths to relax, drinking water, or stretching.

[0819] In this way, the system comprehensively analyzes the user's health and emotional data and provides personalized advice in real time, helping the user manage their overall health.

[0820] Example 2

[0821] 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."

[0822] Conventional health management systems collect and analyze users' health data using only a limited range of information, making it difficult to provide advice that takes into account their emotional state. Furthermore, the generation of personalized advice is insufficient, limiting their effectiveness in comprehensive health management. This makes it difficult for users to appropriately address specific health issues or stress states, and often results in only general advice being provided.

[0823] 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 a receiving means for receiving and storing the user's health data and emotion data, an analyzing means including an AI engine and an emotion engine for analyzing the health data and emotion data, and a providing means for providing the user with personalized advice generated by the analyzing means. This makes it possible to grasp not only the user's health state but also their emotional state in real time and provide comprehensive and personalized advice.

[0824] "Health data" refers to information including physiological indicators such as the user's heart rate, number of steps, blood pressure, and body temperature.

[0825] "Emotion data" is information indicating the emotional state of the user obtained from voice, facial expression, text input, and the like.

[0826] "Input means" refers to devices or software that allow users to input health data and emotional data. Specifically, this refers to smartphones, wearable devices, etc.

[0827] "Transmission means" refers to the functions and devices that allow the device to transmit health data and emotion data to the server. This includes Wi-Fi and mobile data communications.

[0828] "Receiving means" refers to a function or device that allows the server to receive and store health data and emotion data transmitted from the terminal.

[0829] "Analysis means" refers to the functions and devices used to analyze the health data and emotion data received by the server. Specifically, it includes an AI engine and an emotion engine.

[0830] An "AI engine" is an algorithm or program that analyzes health data and generates specific insights.

[0831] An "emotion engine" is an algorithm or program that analyzes emotional states from voice, facial expressions, text input, etc.

[0832] The "provision means" refers to a function or device for providing the user with the personalized advice generated by the analysis means.

[0833] The "display means" is a function or device for displaying the advice generated by the providing means on the user's terminal.

[0834] "Health insights" are various insights and advice obtained by analyzing a user's health data.

[0835] "Emotional insights" are various insights and advice obtained by analyzing a user's emotional data.

[0836] "Real-time" means that data is collected, analyzed, and advice is provided without delay.

[0837] MODE FOR CARRYING OUT THE INVENTION

[0838] The present invention provides a system for users to manage their own health data and emotional state in real time and receive personalized advice. This system includes a terminal, a server, an AI engine, and an emotion engine. Specific embodiments are described below.

[0839] Terminal

[0840] Users input their own health data using devices such as smartphones or wearable devices, including heart rate, number of steps, blood pressure, and body temperature. Users can also record their emotional state through voice input and facial expression recognition using the microphone and camera on their devices.

[0841] server

[0842] The health and emotion data sent from the device is sent to a server via the Internet. Specifically, the device converts the data into JSON format and sends it to the server via an HTTP POST request. Wi-Fi or mobile data communication is used as the communication method.

[0843] The server receives the data at the specified endpoint and parses it in JSON format. The parsed data is stored in a relational database system such as MySQL or PostgreSQL and managed by user ID.

[0844] AI engine and emotion engine

[0845] The data stored on the server is analyzed by an AI engine and an emotion engine. The AI ​​engine analyzes the health data and generates specific health insights. For example, it analyzes heart rate and step count data to evaluate the amount of exercise and fatigue level for the day. The AI ​​engine uses Python libraries such as TensorFlow and PyTorch to build and run models.

[0846] The emotion engine uses voice and facial recognition technology to detect the user's emotional state. Voice input is analyzed based on the user's tone of voice and the way they speak, while facial recognition is based on the user's facial expressions captured by the camera. This allows the engine to assess the user's stress level and mood.

[0847] Means of provision and display

[0848] Once the analysis is complete, the AI ​​and emotion engines generate personalized advice, including suggested actions based on the user's health and emotional state. For example, if your heart rate is high, they might suggest "try to relax," or if your emotional state indicates stress, they might suggest "take deep breaths."

[0849] The generated advice is sent from the server to the device and displayed on the device's user interface, which can be in the form of a notification, an app dashboard, a widget, or the like.

[0850] Specific examples

[0851] For example, after a user finishes jogging in the morning, they can use their smartphone to input their heart rate and step count data. At the same time, they can record their emotional state by voice input, such as "I feel a little tired from jogging today." The device then sends this data to the server.

[0852] The server receives the data and begins analysis. The AI ​​engine analyzes the heart rate data, and the emotion engine detects "fatigue" and "stress." The server generates advice such as "Don't forget to hydrate after jogging. If you feel tired, take a deep breath and relax," and sends it to the device. The device displays the received advice to the user as a notification.

[0853] Prompt Sentence Examples

[0854] "When a user sends heart rate and step count data from their smartphone and records their emotional state through voice input, the AI ​​and emotion engine will analyze it and generate optimal advice."

[0855] In this way, users can understand their own health and emotional state in real time and receive personalized advice, enabling comprehensive health management in daily life.

[0856] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0857] Step 1:

[0858] The user inputs health and emotional data.

[0859] Input: Users use smartphones or wearable devices to input health data such as heart rate, steps, blood pressure, and temperature, as well as record emotional state through voice input and facial expressions.

[0860] Data processing: Health and emotional data is converted into digital form within the app.

[0861] Output: The converted digital data is stored in a temporary database on the smartphone.

[0862] Step 2:

[0863] The device sends the data to the server.

[0864] Input: Health and emotion data collected in step 1.

[0865] Data processing: The device converts the collected data into JSON format and sends it to the server using an HTTP POST request. The device connects to the Internet via Wi-Fi or mobile data.

[0866] Output: JSON formatted data sent to the server.

[0867] Step 3:

[0868] The server receives the data and stores it in a database.

[0869] Input: Health and emotion data sent from the device in JSON format.

[0870] Data processing: The server receives the data, parses it from JSON format, and stores it in a relational database (e.g., MySQL or PostgreSQL) along with the user ID.

[0871] Output: Structured data stored in a database.

[0872] Step 4:

[0873] An AI engine and emotion engine on the server analyze the data.

[0874] Input: Health and emotion data stored in a database.

[0875] Data Computation: The AI ​​engine analyzes health data and generates health insights based on, for example, heart rate and step count, while the emotion engine analyzes emotional states using voice and facial recognition technologies, specifically using Python libraries such as TensorFlow and PyTorch.

[0876] Output: Generated health and sentiment insights.

[0877] Step 5:

[0878] The server generates personalized advice.

[0879] Input: Health and sentiment insights generated in step 4.

[0880] Data Computation: Based on the analysis results, personalized advice is generated based on the user's situation, including specific suggested actions that take into account both their health and emotional state.

[0881] Output: personalized advice.

[0882] Step 6:

[0883] The server sends the advice to the terminal.

[0884] Input: The personalized advice generated in step 5.

[0885] Data processing: The server converts the personalized advice into JSON format and sends it to the device as an HTTP response using the secure HTTPS protocol.

[0886] Output: Advice data sent to the terminal.

[0887] Step 7:

[0888] The terminal displays the advice to the user.

[0889] Input: Advice data sent by the server.

[0890] Data processing: The device parses the received advice and displays it in the user interface, which could be a notification, an in-app dashboard, or a widget.

[0891] Output: The advice that is displayed to the user.

[0892] This process allows users to manage their health and emotional state in real time and receive accurate, personalized advice.

[0893] (Application example 2)

[0894] 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."

[0895] The main purpose of conventional health management systems was to provide consistent advice based on the user's health data, but they had limitations in providing personalized advice that took into account the user's emotional state, or in suggesting meal plans that adapted to the user's health and emotional state. This resulted in a lack of motivation for users to actually follow the advice, and the health management system was not as effective as it could be.

[0896] The specific processing by the specific 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 input means for inputting the user's health data and emotional state, transmission means for transmitting the health data and emotional state to the server, reception means for receiving and storing the health data and emotional state, analysis means including an AI engine and an emotion engine for analyzing the health data and emotional state, provision means for providing the user with personalized advice generated by the analysis means, and suggestion means for proposing an optimal meal menu based on the advice. As a result, appropriate action suggestions and meal menus are provided in real time based on the user's health state and emotional state, making it possible to improve the effectiveness of health management.

[0897] "User's health data" refers to various data related to the user's health condition, such as the user's heart rate, number of steps, blood pressure, and body temperature.

[0898] "Emotional state" is data that indicates the user's mental and psychological state, and is collected through voice input and facial expression recognition.

[0899] "Input means" refers to a means by which a user inputs health data and emotional state using a smartphone, wearable device, or the like.

[0900] The "transmission means" is a means for transmitting the input health data and emotional state to the server via a network.

[0901] The "receiving means" is a means for receiving, on the server, the health data and emotional state sent via the transmitting means.

[0902] The "AI Engine" is an artificial intelligence engine that analyzes received health data and generates specific insights and suggestions.

[0903] The "emotion engine" is an artificial intelligence engine that analyzes the received emotional state and evaluates the user's psychological state.

[0904] "Analysis means" refers to means for analyzing health data and emotional states, including an AI engine and an emotion engine.

[0905] The "provision means" is a means for providing the user with the personalized advice or suggestions generated by the analysis means.

[0906] The "suggestion means" is a means for proposing an optimal meal menu based on the advice generated by the analysis means.

[0907] The "display means" is a means for displaying the advice or suggestion generated by the providing means on the user's terminal.

[0908] "Health Insights" are useful information and advice derived from a user's health data and emotional state.

[0909] A "meal menu" is a balanced meal option suggested based on the user's health data and emotional state.

[0910] The present invention provides a system for managing a user's health data and emotional state in real time and providing advice, including personalized meal menu suggestions. The system includes a terminal, a server, an AI engine, and an emotion engine.

[0911] First, a user inputs their health data and emotional state using a device such as a smartphone or wearable device. Health data includes heart rate, number of steps, body temperature, blood pressure, etc. This data is collected through input means on the device. Users can also input their emotional state using voice input or facial recognition.

[0912] The collected health and emotion data is converted into a standard format such as JSON and sent from the device to the server. Transmission is via common communication methods such as Wi-Fi or mobile data. The server receives the data at a specified endpoint and receives parsed data in JSON format from the request body. The received data is stored in an internal database and managed by user ID.

[0913] Next, the AI ​​engine and emotion engine running on the server analyze the received health and emotion data. The AI ​​engine analyzes the health data and generates specific insights, such as evaluating calorie consumption and nutritional balance. The emotion engine detects the user's emotional state from their voice and facial expressions and combines this data to generate a comprehensive evaluation. This generates personalized advice that takes into account the user's health and emotional state.

[0914] The generated advice is transmitted from the server to the user's terminal. The providing means also includes a suggesting means for suggesting meal menus. The optimal meal menu is suggested based on the user's health data and emotional state. For example, a meal with a relaxing effect is suggested to a user who has a high heart rate and is feeling stressed.

[0915] As a concrete example, consider the case where a user sends their heart rate and step count to a server via their device after jogging. If the user reports "I feel stressed" through voice input, the server analyzes the received data and comprehensively evaluates the user's health and emotional state. Specific advice such as "Your heart rate is high, so we recommend you drink some herbal tea to relax. Also, consider choosing a grilled chicken and salad set for a balanced diet" is generated and displayed on the user's device.

[0916] Examples of prompts that may be used include:

[0917] "Please suggest a meal menu that will have a relaxing effect for a user who is experiencing high heart rate and feeling stressed."

[0918] This invention allows users to understand their own health data and emotional state in real time, receive advice including personalized meal menu suggestions, and enable more appropriate health management.

[0919] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0920] Step 1:

[0921] Users use smartphones or wearable devices to input health data (heart rate, number of steps, body temperature, blood pressure, etc.) and emotional state data (voice input and facial expression recognition). The input data is collected by the device's application. Input data: heart rate, number of steps, body temperature, blood pressure, voice input, facial expression data. Output data: health data and emotional data formatted within the device.

[0922] Step 2:

[0923] The health and emotion data collected by the device is converted into JSON format and sent to the server. The data is sent via Wi-Fi or mobile data. Input data: Formatted health and emotion data. Output data: Transmitted data in JSON format.

[0924] Step 3:

[0925] The server receives the data at the specified endpoint and parses it from JSON format. The received data is stored in an internal database and managed for each user ID. Input data: Transmitted data in JSON format. Output data: Parsed health data and emotion data, stored in the internal database.

[0926] Step 4:

[0927] The AI ​​engine on the server analyzes the health data and generates specific insights such as calorie consumption and nutritional balance. Input data: Parsed health data. Output data: Health insights (calorie consumption, nutritional balance, etc.).

[0928] Step 5:

[0929] The emotion engine on the server analyzes the emotion data and evaluates the emotional state from the voice and facial expressions. Input data: Parsed emotion data. Output data: Evaluation result of the emotional state.

[0930] Step 6:

[0931] The server integrates the analysis results of the AI ​​engine and the emotion engine to generate personalized advice based on the user's health and emotional state. Input data: health insights, emotional state assessment results. Output data: personalized advice.

[0932] Step 7:

[0933] The server proposes the optimal meal menu to the user based on the personalized advice. Input data: personalized advice. Output data: proposed meal menu.

[0934] Step 8:

[0935] The server sends the generated advice and suggested meal menu to the user's terminal. Input data: suggested meal menu and advice. Output data: advice and meal menu sent to the user's terminal.

[0936] Step 9:

[0937] The advice and suggested meal menu received by the terminal are displayed on the user interface. Information is presented in a form that is easy for the user to understand. Input data: Received advice and meal menu. Output data: Advice and meal menu displayed on the user interface.

[0938] Example prompt sentence:

[0939] "Please suggest a meal menu that will have a relaxing effect for a user who is experiencing high heart rate and feeling stressed."

[0940] 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.

[0941] 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.

[0942] 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.

[0943] [Fourth embodiment]

[0944] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0945] 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.

[0946] 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).

[0947] 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.

[0948] 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.

[0949] 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).

[0950] 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.

[0951] 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.

[0952] 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.

[0953] 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.

[0954] 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.

[0955] 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.

[0956] 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."

[0957] The present invention provides a system that allows users to manage their own health data in real time and receive personalized advice. This system includes a terminal, a server, and an AI engine for analyzing the user's health data. Specific embodiments of the present invention will be described below.

[0958] First, a user inputs their health data using a device such as a smartphone or wearable device. This health data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through the input means on the device.

[0959] Next, the device sends the collected health data to the server. The data to be sent is converted into a data format such as JSON. The device then sends an HTTP POST request over the Internet to send the data to the server. The data is sent using common communication methods such as Wi-Fi or mobile data communication.

[0960] The server receives this data at a specified endpoint, where it is parsed and stored in a database by the receiving means, allowing the server to centralize the health data of all users.

[0961] The stored data is then analyzed by an AI engine on the server, which uses machine learning algorithms to analyze the user's health data and generate specific insights, such as how a user's heart rate changes during exercise or how their dietary habits affect their health.

[0962] Once the analysis is complete, the AI ​​engine generates personalized advice, including specific behavioral suggestions and health management recommendations, such as "drink more water" or "walk 30 minutes every day." This advice is then sent to the user's device via the server's provisioning means. The advice is then displayed on the device's user interface.

[0963] As a concrete example, after a user finishes their morning jog, they send their heart rate and step count via smartphone to a server, which receives and analyzes the data. The AI ​​engine then evaluates whether the user's heart rate is within an appropriate range and whether the number of steps has reached the target. Based on the analysis results, advice such as "Always drink water after jogging" is displayed on the user's smartphone.

[0964] This allows users to understand their health status in real time and receive personalized, accurate advice, making health management easier, while healthcare providers can use this data to provide more accurate diagnosis and treatment.

[0965] The processing flow will be explained below.

[0966] Step 1:

[0967] The user opens their smartphone and launches a dedicated application, which uses the wearable device to collect health data such as heart rate and number of steps.

[0968] Step 2:

[0969] The device captures the health data entered by the user within the application, which organizes the data in a standard format such as JSON.

[0970] Step 3:

[0971] The device sends the captured health data to a specified API endpoint on the server using an HTTP POST request.

[0972] Step 4:

[0973] The server receives the data at the API endpoint, which is parsed from the request body in JSON format.

[0974] Step 5:

[0975] The server stores the parsed data in an internal database. Specifically, data is managed for each user ID, and past data is also accumulated.

[0976] Step 6:

[0977] The server calls the AI ​​engine to analyze the stored data, and the AI ​​engine then begins analysis based on the health data.

[0978] Step 7:

[0979] The server-based AI engine uses machine learning algorithms to analyze health data, generating health insights and risk assessments.

[0980] Step 8:

[0981] The AI ​​engine generates personalized advice based on the analysis results, such as "Try to relax" if your heart rate is high.

[0982] Step 9:

[0983] The server prepares the generated advice for transmission to the user's terminal, and converts the data to be transmitted into a format that is easy for the user to understand.

[0984] Step 10:

[0985] The server sends the generated advice to the terminal as an HTTP response, using data personalized based on the user ID.

[0986] Step 11:

[0987] The device displays the advice received from the server within the application, and the specific advice is presented visually on the user interface.

[0988] Step 12:

[0989] The user can then review the displayed advice and adjust their behavior based on it, for example by taking specific actions such as drinking water or doing stretches.

[0990] In this way, the system can assist users in managing their health in real time and provide appropriate advice to improve their health status.

[0991] Example 1

[0992] 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."

[0993] In recent years, there has been an increasing demand for healthcare systems that allow users to monitor their health status in real time and receive personalized advice. However, current systems lack consistent and efficient collection, transmission, analysis, and provision of health data, and there are particular issues with the accuracy and immediacy of personalized advice. The present invention aims to solve these problems and provide a system that allows users to manage their health status in real time and receive appropriate advice.

[0994] 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.

[0995] In this invention, the server includes an input means for inputting a user's health data, a transmission means for transmitting the health data to the server, a receiving means for receiving and storing the health data in the server, an analysis means including an AI engine for analyzing the health data in the server, a provision means for providing the user with personalized advice generated by the analysis means, a means for converting data collected by the analysis means into JSON format and transmitting it, and a display means for displaying the personalized advice provided by the provision means. This enables efficient collection, transmission, and analysis of health data and immediate provision of personalized advice.

[0996] "Input means" means a device or method by which a user inputs their health data into the system.

[0997] "Transmitting means" refers to a device or method for transmitting the input health data to the server.

[0998] "Receiving means" refers to a device or method by which the server receives and stores health data sent from the user.

[0999] "Analysis means" refers to a device or method for analyzing health data stored on the server, and includes an AI engine.

[1000] The "providing means" is a device or method for providing the user with the personalized advice generated by the analyzing means.

[1001] "Means for converting to JSON format" refers to a device or method for converting collected health data into JSON format and transmitting it.

[1002] "Display means" refers to a device or method for displaying the provided personalized advice on the user's terminal.

[1003] "Health data" refers to data that indicates the user's health condition, such as heart rate, number of steps, blood pressure, and body temperature.

[1004] "Server" means a computer system that receives, stores, and analyzes health data submitted by a user and generates and provides personalized advice.

[1005] A "user" is an individual who utilizes the system to input their health data and receive personalized advice.

[1006] The present invention provides a system for users to manage their own health data in real time and receive personalized advice. The system includes a terminal, a server, and an AI engine for analyzing the user's health data.

[1007] First, a user inputs their own health data using a device such as a smartphone or wearable device. This health data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through the input means on the device.

[1008] Next, the device sends the collected health data to the server. The data to be sent is converted into a data format such as JSON. The device then sends an HTTP POST request over the Internet to send the data to the server. The data is sent using common communication methods such as Wi-Fi or mobile data communication.

[1009] The server receives this data at a specified endpoint, where it is parsed and stored in a database by the receiving means, allowing the server to centralize the health data of all users.

[1010] The stored data is analyzed by an AI engine on the server. This analysis method uses machine learning algorithms to analyze the user's health data and generate specific insights. For example, the AI ​​engine is implemented using Python and analyzes the collected data using frameworks such as TensorFlow and PyTorch. For example, it extracts information such as how a user's heart rate changes during exercise and how their dietary habits affect their health.

[1011] Once the analysis is complete, the AI ​​engine generates personalized advice, including specific behavioral suggestions and health management recommendations, such as "drink more water" or "walk 30 minutes every day." This advice is sent to the user's device by the server's provisioning means, and is displayed on the device's user interface.

[1012] Examples:

[1013] After a user finishes their morning jog, they send their heart rate and step count to a server via their smartphone. The server receives and analyzes the data. The AI ​​engine then analyzes the data and evaluates whether the user's heart rate is within an appropriate range and whether the number of steps has reached the target. Based on the analysis results, advice such as "Always drink water after jogging" is displayed on the user's smartphone.

[1014] Example prompt for generative AI model:

[1015] "When a user finishes jogging and enters their heart rate and number of steps, please explain the specific steps for sending that data to the server, analyzing the data on the server, and generating advice."

[1016] This allows users to easily manage their health by understanding their health status in real time and receiving personalized and accurate advice, while healthcare providers can also use this data to provide more accurate diagnosis and treatment.

[1017] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1018] Step 1:

[1019] The user enters their own health data. The user opens the smartphone app and manually enters data such as heart rate, steps, blood pressure, and body temperature, or checks data automatically transferred from the wearable device. This provides the health data collected by the input method.

[1020] Input: Data entered by the user, such as heart rate, steps, blood pressure, and temperature

[1021] Output: Health data collected by input means

[1022] Step 2:

[1023] The device sends the collected health data to a server. The app on the device converts the collected data into JSON format and sends it to a specified endpoint on the server via the Internet using an HTTP POST request. Wi-Fi or mobile data communication is used as the transmission method.

[1024] Input: Health data converted to JSON format

[1025] Output: Health data sent to the server via HTTP POST request

[1026] Step 3:

[1027] The server receives and stores health data. The data received at the specified endpoint is parsed in JSON format and stored in a database. Specifically, the server's API receives an HTTP request, parses it, and stores it in a database (such as MongoDB or MySQL).

[1028] Input: Health data sent in an HTTP POST request

[1029] Output: Parsed health data stored in a database

[1030] Step 4:

[1031] An AI engine on the server analyzes the health data, using machine learning algorithms to extract insights about the user's health. The AI ​​engine is implemented using Python and performs analysis using frameworks such as TensorFlow and PyTorch.

[1032] Input: Health data stored in a database

[1033] Output: User health insights

[1034] Step 5:

[1035] The server generates personalized advice. Based on the analysis results, specific behavioral suggestions and health management recommendations are generated. For example, advice such as "drink more water" or "walk 30 minutes every day" is generated.

[1036] Input: User health insights

[1037] Output: personalized advice

[1038] Step 6:

[1039] The server sends the generated advice to the user's terminal using an HTTP POST request, and the terminal displays the received advice on its user interface.

[1040] Input: personalized advice

[1041] Output: Advice displayed on the user's terminal

[1042] Examples:

[1043] For example, when a user finishes jogging and enters their heart rate and number of steps, the device converts this data into JSON format and sends it to the server. The server receives and stores the data, and the AI ​​engine analyzes it. Based on the analysis results, advice such as "Always drink water after jogging" is generated and displayed on the user's smartphone.

[1044] Example prompt for generative AI model:

[1045] "When a user finishes jogging and enters their heart rate and number of steps, please explain the specific steps for sending that data to the server, analyzing the data on the server, and generating advice."

[1046] (Application example 1)

[1047] 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."

[1048] Conventional health management systems can collect and analyze users' health data, but often only provide personalized advice based on the results. This creates the challenge of making it difficult for users to find the health-related products and services they need. In particular, there is a need for a system that can automatically recommend products that are optimal for a user's health condition.

[1049] 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.

[1050] In this invention, the server includes an input means for inputting a user's health data, a transmission means for transmitting the health data to the server, a reception means for receiving and storing the health data, an analysis means including an AI engine for analyzing the health data, and a provision means for providing the user with personalized advice and product recommendations generated by the analysis means, thereby enabling the user to manage their own health and efficiently find products that are optimal for their health condition.

[1051] "Input means" refers to a mechanism by which a user inputs their own health data, and is implemented using a terminal such as a smartphone or wearable device.

[1052] The "transmission means" is a function that transmits health data from the user's device to the server, and uses Wi-Fi or mobile data communication.

[1053] The "receiving means" is a function by which the server receives and stores health data sent from the user's terminal.

[1054] The "analysis means" is a function that analyzes the received health data using an AI engine installed on the server.

[1055] The "provision means" is a mechanism for providing the user with personalized advice and product recommendations generated by the analysis means.

[1056] The "display means" is a function that displays the personalized advice and product recommendations on the user's terminal.

[1057] "Health insights" are insightful health information generated based on a user's past health data.

[1058] "Product recommendations" are health-related products recommended by the analysis means based on the user's health data.

[1059] The present invention is a system for managing a user's health data in real time and providing personalized advice and product recommendations. Specific embodiments for carrying out the present invention will be described below.

[1060] First, a user inputs their health data using a device such as a smartphone or wearable device. This health data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through the input means on the device.

[1061] Next, the device sends the collected health data to the server. The data to be sent is converted into a data format such as JSON. The device then sends an HTTP POST request over the Internet to send the data to the server. The data is sent using common communication methods such as Wi-Fi or mobile data communication.

[1062] The server receives this data at a specified endpoint, where it is parsed and stored in a database by the receiving means, allowing the server to centralize the health data of all users.

[1063] The stored data is analyzed by an AI engine on the server. This analysis method uses machine learning algorithms to analyze the user's health data and generate specific insights and product recommendations. For example, it extracts information such as how a user's heart rate changes during exercise and how their dietary habits affect their health. Based on this information, it then recommends related products such as vitamin supplements, fitness equipment, and health foods that are best suited to the user.

[1064] Once the analysis is complete, the AI ​​engine generates personalized advice and product recommendations. This advice includes specific behavioral suggestions and health management recommendations. For example, in addition to advice such as "drink more water" and "walk 30 minutes daily," product recommendations such as "take vitamin supplement A" are also made. This advice and product recommendations are sent to the user's device by the server's provisioning means. The sent advice and product recommendations are then displayed on the device's user interface.

[1065] As a concrete example, after a user finishes their morning jog, they send their heart rate and step count via smartphone to a server, which receives and analyzes the data. The AI ​​engine then analyzes the data and evaluates whether the user's heart rate is within an appropriate range and whether the number of steps has reached the target. Based on the analysis results, product recommendations such as "We recommend taking Vitamin Supplement A" are displayed on the user's smartphone, along with advice such as "Always drink water after jogging."

[1066] Example prompt sentence:

[1067] Input data:

[1068] {

[1069] "user_id": "user123",

[1070] "heart_rate": 70,

[1071] "steps": 5000,

[1072] "blood pressure": "120 / 80",

[1073] "body_temperature": 36.5

[1074] }

[1075] Generated advice:

[1076] "Your heart rate is within the normal range. Walk a few more steps to reach your goal for today. I also recommend taking Vitamin Supplement A."

[1077] Recommended products:

[1078] ["Vitamin Supplement A", "Fitness Equipment B", "Health Food C"]

[1079] This will allow users to understand their health status in real time and receive personalized health management advice and related product recommendations, making daily health management easier. Healthcare providers can also use this data to provide more accurate diagnosis and treatment.

[1080] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1081] Step 1:

[1082] Users input their own health data using a device such as a smartphone or wearable device. The input data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through the device's input means. The input data is temporarily stored in the device's memory.

[1083] Step 2:

[1084] The device sends the collected health data to the server. The data to be sent is converted into a data format such as JSON. The device then sends an HTTP POST request over the Internet to send the data to the server. Wi-Fi or mobile data communication is used as the transmission method. Specifically, the device sets the header and body of the HTTP request and performs the transmission procedure.

[1085] Step 3:

[1086] The server receives this data at the specified endpoint. The received data is parsed by the receiving means and stored in a database. Specifically, the data in JSON format is parsed, mapped to each field in the database, and stored. This allows the server to centrally manage the health data of all users.

[1087] Step 4:

[1088] An AI engine on the server analyzes the stored health data. The analysis method uses machine learning algorithms to analyze the user's health data and generate specific insights and product recommendations. Specific calculations include extracting insights based on heart rate trends, progress toward step goal achievement, and past trends, and recommending products based on these. For example, evaluations include whether the heart rate is within an appropriate range and whether the step goal has been achieved.

[1089] Step 5:

[1090] The server's provision means sends the personalized advice and product recommendations generated by the analysis means to the user's device. Specific data includes health advice and a product list in JSON format. This is then sent back to the user's device as an HTTP response. The reception means receives the response, parses it as necessary, and displays it on the user interface.

[1091] Step 6:

[1092] The device displays the received advice and product recommendations on the user interface. Specifically, the application displays personalized advice and product lists in a format that is easy for users to understand. Through this display, users can check suggested actions and products tailored to their health condition.

[1093] 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.

[1094] The present invention provides a system for users to manage their own health data and emotional state in real time and receive personalized advice. This system includes a terminal, a server, an AI engine, and an emotion engine. Specific embodiments for implementing the present invention will be described below.

[1095] First, a user inputs their health data using a device such as a smartphone or wearable device. This health data includes heart rate, number of steps, blood pressure, body temperature, etc. This data is collected through input means on the device. Users can also express their emotional state using voice input or facial expressions on the device.

[1096] The device then transmits the collected health and emotion data to a server. The data is converted into a standard format, such as JSON. The device then sends an HTTP POST request over the Internet to transmit the data to the server. The transmission method uses common communication methods, such as Wi-Fi or mobile data.

[1097] The server receives this data at the specified endpoint. The received data is parsed from the request body in JSON format. The parsed data is saved in an internal database and managed for each user ID.

[1098] The stored data is analyzed by an AI engine and an emotion engine on the server. The AI ​​engine analyzes the health data and generates specific insights. The emotion engine detects the user's emotional state from their voice, facial expressions, text input, etc. and combines this data to evaluate the user's overall condition.

[1099] Once the analysis is complete, the AI ​​engine and emotion engine generate personalized advice, including suggested actions based on health status and relaxation techniques corresponding to emotional states. For example, if a person's heart rate is high, the engine will generate advice such as "Try to relax." If the person's emotional state indicates stress, the engine will add a suggestion such as "stretch."

[1100] The generated advice is sent to the user's terminal by the server's providing means, and the sent advice is displayed on the terminal's user interface in a format that is easy for the user to understand.

[1101] As a specific example, after a user finishes their morning jog, they send their heart rate and step count to a server via their smartphone, which then receives and analyzes the data. At the same time, if the user records their emotional state through voice input, this data is also analyzed. The AI ​​engine and emotion engine work together to evaluate whether the user's heart rate is within an appropriate range and whether the number of steps has reached the target. In addition, because the user's emotional state is taken into consideration, specific and comprehensive advice such as "Always drink water after jogging, and take deep breaths if you feel stressed" is displayed on the user's smartphone.

[1102] In this way, users can understand their own health and emotional state in real time and receive personalized and accurate advice, enabling them to take comprehensive health management in their daily lives.

[1103] The processing flow will be explained below.

[1104] Step 1:

[1105] The user opens their smartphone and launches a dedicated application. The wearable device collects health data such as heart rate and number of steps taken. In addition, the user uses voice input to record their current emotional state (e.g., stressed, relaxed, etc.).

[1106] Step 2:

[1107] The device captures user-entered health data such as heart rate and step count, as well as emotional data obtained from voice input, and converts this data into standard formats such as JSON.

[1108] Step 3:

[1109] The device sends the captured health and emotion data to the server's designated API endpoint using an HTTP POST request.

[1110] Step 4:

[1111] The server receives health and emotion data via the API endpoint. The received data is parsed in JSON format and stored in an internal database. Data is managed by user ID, and past data is also accumulated.

[1112] Step 5:

[1113] The server calls the AI ​​engine and emotion engine to analyze the stored data. The AI ​​engine analyzes the health data and generates specific health insights, such as assessing whether your heart rate is high or low, or whether your step count is reaching your target.

[1114] Step 6:

[1115] The emotion engine on the server analyzes emotion data obtained through voice input or other means, and uses machine learning algorithms to identify the user's emotional state, such as "stressed" or "relaxed."

[1116] Step 7:

[1117] The AI ​​engine and emotion engine combine their respective analysis results. Based on the combined data, personalized advice is generated for the user. For example, if the heart rate is high and the emotional state indicates stress, advice such as "take deep breaths to relax" is generated.

[1118] Step 8:

[1119] The server prepares the generated personalized advice for sending to the user's device. The advice is formatted in a way that is easy for the user to understand, such as JSON format.

[1120] Step 9:

[1121] The server sends the generated advice to the device as an HTTP response, with personalized data based on the user ID.

[1122] Step 10:

[1123] The device displays the advice received from the server within the application, and the specific advice is presented visually on the user interface.

[1124] Step 11:

[1125] The user can then review the displayed advice and adjust their behavior accordingly, such as taking deep breaths to relax, drinking water, or stretching.

[1126] In this way, the system comprehensively analyzes the user's health and emotional data and provides personalized advice in real time, helping the user manage their overall health.

[1127] Example 2

[1128] 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."

[1129] Conventional health management systems collect and analyze users' health data using only a limited range of information, making it difficult to provide advice that takes into account their emotional state. Furthermore, the generation of personalized advice is insufficient, limiting their effectiveness in comprehensive health management. This makes it difficult for users to appropriately address specific health issues or stress states, and often results in only general advice being provided.

[1130] 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 a receiving means for receiving and storing the user's health data and emotion data, an analyzing means including an AI engine and an emotion engine for analyzing the health data and emotion data, and a providing means for providing the user with personalized advice generated by the analyzing means. This makes it possible to grasp not only the user's health state but also their emotional state in real time and provide comprehensive and personalized advice.

[1131] "Health data" refers to information including physiological indicators such as the user's heart rate, number of steps, blood pressure, and body temperature.

[1132] "Emotion data" is information indicating the emotional state of the user obtained from voice, facial expression, text input, and the like.

[1133] "Input means" refers to devices or software that allow users to input health data and emotional data. Specifically, this refers to smartphones, wearable devices, etc.

[1134] "Transmission means" refers to the functions and devices that allow the device to transmit health data and emotion data to the server. This includes Wi-Fi and mobile data communications.

[1135] "Receiving means" refers to a function or device that allows the server to receive and store health data and emotion data transmitted from the terminal.

[1136] "Analysis means" refers to the functions and devices used to analyze the health data and emotion data received by the server. Specifically, it includes an AI engine and an emotion engine.

[1137] An "AI engine" is an algorithm or program that analyzes health data and generates specific insights.

[1138] An "emotion engine" is an algorithm or program that analyzes emotional states from voice, facial expressions, text input, etc.

[1139] The "provision means" refers to a function or device for providing the user with the personalized advice generated by the analysis means.

[1140] The "display means" is a function or device for displaying the advice generated by the providing means on the user's terminal.

[1141] "Health insights" are various insights and advice obtained by analyzing a user's health data.

[1142] "Emotional insights" are various insights and advice obtained by analyzing a user's emotional data.

[1143] "Real-time" means that data is collected, analyzed, and advice is provided without delay.

[1144] MODE FOR CARRYING OUT THE INVENTION

[1145] The present invention provides a system for users to manage their own health data and emotional state in real time and receive personalized advice. This system includes a terminal, a server, an AI engine, and an emotion engine. Specific embodiments are described below.

[1146] Terminal

[1147] Users input their own health data using devices such as smartphones or wearable devices, including heart rate, number of steps, blood pressure, and body temperature. Users can also record their emotional state through voice input and facial expression recognition using the microphone and camera on their devices.

[1148] server

[1149] The health and emotion data sent from the device is sent to a server via the Internet. Specifically, the device converts the data into JSON format and sends it to the server via an HTTP POST request. Wi-Fi or mobile data communication is used as the communication method.

[1150] The server receives the data at the specified endpoint and parses it in JSON format. The parsed data is stored in a relational database system such as MySQL or PostgreSQL and managed by user ID.

[1151] AI engine and emotion engine

[1152] The data stored on the server is analyzed by an AI engine and an emotion engine. The AI ​​engine analyzes the health data and generates specific health insights. For example, it analyzes heart rate and step count data to evaluate the amount of exercise and fatigue level for the day. The AI ​​engine uses Python libraries such as TensorFlow and PyTorch to build and run models.

[1153] The emotion engine uses voice and facial recognition technology to detect the user's emotional state. Voice input is analyzed based on the user's tone of voice and the way they speak, while facial recognition is based on the user's facial expressions captured by the camera. This allows the engine to assess the user's stress level and mood.

[1154] Means of provision and display

[1155] Once the analysis is complete, the AI ​​and emotion engines generate personalized advice, including suggested actions based on the user's health and emotional state. For example, if your heart rate is high, they might suggest "try to relax," or if your emotional state indicates stress, they might suggest "take deep breaths."

[1156] The generated advice is sent from the server to the device and displayed on the device's user interface, which can be in the form of a notification, an app dashboard, a widget, or the like.

[1157] Specific examples

[1158] For example, after a user finishes jogging in the morning, they can use their smartphone to input their heart rate and step count data. At the same time, they can record their emotional state by voice input, such as "I feel a little tired from jogging today." The device then sends this data to the server.

[1159] The server receives the data and begins analysis. The AI ​​engine analyzes the heart rate data, and the emotion engine detects "fatigue" and "stress." The server generates advice such as "Don't forget to hydrate after jogging. If you feel tired, take a deep breath and relax," and sends it to the device. The device displays the received advice to the user as a notification.

[1160] Prompt Sentence Examples

[1161] "When a user sends heart rate and step count data from their smartphone and records their emotional state through voice input, the AI ​​and emotion engine will analyze it and generate optimal advice."

[1162] In this way, users can understand their own health and emotional state in real time and receive personalized advice, enabling comprehensive health management in daily life.

[1163] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1164] Step 1:

[1165] The user inputs health and emotional data.

[1166] Input: Users use smartphones or wearable devices to input health data such as heart rate, steps, blood pressure, and temperature, as well as record emotional state through voice input and facial expressions.

[1167] Data processing: Health and emotional data is converted into digital form within the app.

[1168] Output: The converted digital data is stored in a temporary database on the smartphone.

[1169] Step 2:

[1170] The device sends the data to the server.

[1171] Input: Health and emotion data collected in step 1.

[1172] Data processing: The device converts the collected data into JSON format and sends it to the server using an HTTP POST request. The device connects to the Internet via Wi-Fi or mobile data.

[1173] Output: JSON formatted data sent to the server.

[1174] Step 3:

[1175] The server receives the data and stores it in a database.

[1176] Input: Health and emotion data sent from the device in JSON format.

[1177] Data processing: The server receives the data, parses it from JSON format, and stores it in a relational database (e.g., MySQL or PostgreSQL) along with the user ID.

[1178] Output: Structured data stored in a database.

[1179] Step 4:

[1180] An AI engine and emotion engine on the server analyze the data.

[1181] Input: Health and emotion data stored in a database.

[1182] Data Computation: The AI ​​engine analyzes health data and generates health insights based on, for example, heart rate and step count, while the emotion engine analyzes emotional states using voice and facial recognition technologies, specifically using Python libraries such as TensorFlow and PyTorch.

[1183] Output: Generated health and sentiment insights.

[1184] Step 5:

[1185] The server generates personalized advice.

[1186] Input: Health and sentiment insights generated in step 4.

[1187] Data Computation: Based on the analysis results, personalized advice is generated based on the user's situation, including specific suggested actions that take into account both their health and emotional state.

[1188] Output: personalized advice.

[1189] Step 6:

[1190] The server sends the advice to the terminal.

[1191] Input: The personalized advice generated in step 5.

[1192] Data processing: The server converts the personalized advice into JSON format and sends it to the device as an HTTP response using the secure HTTPS protocol.

[1193] Output: Advice data sent to the terminal.

[1194] Step 7:

[1195] The terminal displays the advice to the user.

[1196] Input: Advice data sent by the server.

[1197] Data processing: The device parses the received advice and displays it in the user interface, which could be a notification, an in-app dashboard, or a widget.

[1198] Output: The advice that is displayed to the user.

[1199] This process allows users to manage their health and emotional state in real time and receive accurate, personalized advice.

[1200] (Application example 2)

[1201] 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."

[1202] The main purpose of conventional health management systems was to provide consistent advice based on the user's health data, but they had limitations in providing personalized advice that took into account the user's emotional state, or in suggesting meal plans that adapted to the user's health and emotional state. This resulted in a lack of motivation for users to actually follow the advice, and the health management system was not as effective as it could be.

[1203] The specific processing by the specific 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 input means for inputting the user's health data and emotional state, transmission means for transmitting the health data and emotional state to the server, reception means for receiving and storing the health data and emotional state, analysis means including an AI engine and an emotion engine for analyzing the health data and emotional state, provision means for providing the user with personalized advice generated by the analysis means, and suggestion means for proposing an optimal meal menu based on the advice. As a result, appropriate action suggestions and meal menus are provided in real time based on the user's health state and emotional state, making it possible to improve the effectiveness of health management.

[1204] "User's health data" refers to various data related to the user's health condition, such as the user's heart rate, number of steps, blood pressure, and body temperature.

[1205] "Emotional state" is data that indicates the user's mental and psychological state, and is collected through voice input and facial expression recognition.

[1206] "Input means" refers to a means by which a user inputs health data and emotional state using a smartphone, wearable device, or the like.

[1207] The "transmission means" is a means for transmitting the input health data and emotional state to the server via a network.

[1208] The "receiving means" is a means for receiving, on the server, the health data and emotional state sent via the transmitting means.

[1209] The "AI Engine" is an artificial intelligence engine that analyzes received health data and generates specific insights and suggestions.

[1210] The "emotion engine" is an artificial intelligence engine that analyzes the received emotional state and evaluates the user's psychological state.

[1211] "Analysis means" refers to means for analyzing health data and emotional states, including an AI engine and an emotion engine.

[1212] The "provision means" is a means for providing the user with the personalized advice or suggestions generated by the analysis means.

[1213] The "suggestion means" is a means for proposing an optimal meal menu based on the advice generated by the analysis means.

[1214] The "display means" is a means for displaying the advice or suggestion generated by the providing means on the user's terminal.

[1215] "Health Insights" are useful information and advice derived from a user's health data and emotional state.

[1216] A "meal menu" is a balanced meal option suggested based on the user's health data and emotional state.

[1217] The present invention provides a system for managing a user's health data and emotional state in real time and providing advice, including personalized meal menu suggestions. The system includes a terminal, a server, an AI engine, and an emotion engine.

[1218] First, a user inputs their health data and emotional state using a device such as a smartphone or wearable device. Health data includes heart rate, number of steps, body temperature, blood pressure, etc. This data is collected through input means on the device. Users can also input their emotional state using voice input or facial recognition.

[1219] The collected health and emotion data is converted into a standard format such as JSON and sent from the device to the server. Transmission is via common communication methods such as Wi-Fi or mobile data. The server receives the data at a specified endpoint and receives parsed data in JSON format from the request body. The received data is stored in an internal database and managed by user ID.

[1220] Next, the AI ​​engine and emotion engine running on the server analyze the received health and emotion data. The AI ​​engine analyzes the health data and generates specific insights, such as evaluating calorie consumption and nutritional balance. The emotion engine detects the user's emotional state from their voice and facial expressions and combines this data to generate a comprehensive evaluation. This generates personalized advice that takes into account the user's health and emotional state.

[1221] The generated advice is transmitted from the server to the user's terminal. The providing means also includes a suggesting means for suggesting meal menus. The optimal meal menu is suggested based on the user's health data and emotional state. For example, a meal with a relaxing effect is suggested to a user who has a high heart rate and is feeling stressed.

[1222] As a concrete example, consider the case where a user sends their heart rate and step count to a server via their device after jogging. If the user reports "I feel stressed" through voice input, the server analyzes the received data and comprehensively evaluates the user's health and emotional state. Specific advice such as "Your heart rate is high, so we recommend you drink some herbal tea to relax. Also, consider choosing a grilled chicken and salad set for a balanced diet" is generated and displayed on the user's device.

[1223] Examples of prompts that may be used include:

[1224] "Please suggest a meal menu that will have a relaxing effect for a user who is experiencing high heart rate and feeling stressed."

[1225] This invention allows users to understand their own health data and emotional state in real time, receive advice including personalized meal menu suggestions, and enable more appropriate health management.

[1226] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1227] Step 1:

[1228] Users use smartphones or wearable devices to input health data (heart rate, number of steps, body temperature, blood pressure, etc.) and emotional state data (voice input and facial expression recognition). The input data is collected by the device's application. Input data: heart rate, number of steps, body temperature, blood pressure, voice input, facial expression data. Output data: health data and emotional data formatted within the device.

[1229] Step 2:

[1230] The health and emotion data collected by the device is converted into JSON format and sent to the server. The data is sent via Wi-Fi or mobile data. Input data: Formatted health and emotion data. Output data: Transmitted data in JSON format.

[1231] Step 3:

[1232] The server receives the data at the specified endpoint and parses it from JSON format. The received data is stored in an internal database and managed for each user ID. Input data: Transmitted data in JSON format. Output data: Parsed health data and emotion data, stored in the internal database.

[1233] Step 4:

[1234] The AI ​​engine on the server analyzes the health data and generates specific insights such as calorie consumption and nutritional balance. Input data: Parsed health data. Output data: Health insights (calorie consumption, nutritional balance, etc.).

[1235] Step 5:

[1236] The emotion engine on the server analyzes the emotion data and evaluates the emotional state from the voice and facial expressions. Input data: Parsed emotion data. Output data: Evaluation result of the emotional state.

[1237] Step 6:

[1238] The server integrates the analysis results of the AI ​​engine and the emotion engine to generate personalized advice based on the user's health and emotional state. Input data: health insights, emotional state assessment results. Output data: personalized advice.

[1239] Step 7:

[1240] The server proposes the optimal meal menu to the user based on the personalized advice. Input data: personalized advice. Output data: proposed meal menu.

[1241] Step 8:

[1242] The server sends the generated advice and suggested meal menu to the user's terminal. Input data: suggested meal menu and advice. Output data: advice and meal menu sent to the user's terminal.

[1243] Step 9:

[1244] The advice and suggested meal menu received by the terminal are displayed on the user interface. Information is presented in a form that is easy for the user to understand. Input data: Received advice and meal menu. Output data: Advice and meal menu displayed on the user interface.

[1245] Example prompt sentence:

[1246] "Please suggest a meal menu that will have a relaxing effect for a user who is experiencing high heart rate and feeling stressed."

[1247] 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.

[1248] 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.

[1249] 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.

[1250] 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.

[1251] 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.

[1252] 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.

[1253] 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).

[1254] 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.

[1255] 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."

[1256] 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.

[1257] 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).

[1258] 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.

[1259] 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.

[1260] 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.

[1261] 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.

[1262] 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.

[1263] 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.

[1264] 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.

[1265] 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.

[1266] 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.

[1267] 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.

[1268] The following is further disclosed regarding the above embodiment.

[1269] (Claim 1)

[1270] an input means for inputting health data of a user;

[1271] a transmitting means for transmitting the health data to a server;

[1272] a receiving means in the server for receiving and storing the health data;

[1273] an analysis means including an AI engine that analyzes the health data in the server;

[1274] providing means for providing the user with the personalized advice generated by the analysis means;

[1275] A system including:

[1276] (Claim 2)

[1277] 2. The system according to claim 1, wherein the providing means includes a display means for displaying the personalized advice on a terminal of the user.

[1278] (Claim 3)

[1279] 10. The system of claim 1, wherein the analyzing means includes means for generating health insights based on the user's past health data.

[1280] "Example 1"

[1281] (Claim 1)

[1282] an input means for inputting health data of a user;

[1283] a transmitting means for transmitting the health data to a server;

[1284] a receiving means in the server for receiving and storing the health data;

[1285] an analysis means including an AI engine that analyzes the health data in the server;

[1286] providing means for providing the user with the personalized advice generated by the analysis means;

[1287] means for converting the data collected by the analysis means into a JSON format and transmitting the converted data;

[1288] a display means for displaying the personalized advice provided by the providing means;

[1289] A system including:

[1290] (Claim 2)

[1291] 2. The system according to claim 1, wherein the providing means includes a display means for displaying the personalized advice on a terminal of the user.

[1292] (Claim 3)

[1293] 10. The system of claim 1, wherein the analyzing means includes means for generating health insights based on the user's past health data.

[1294] "Application Example 1"

[1295] (Claim 1)

[1296] an input means for inputting health data of a user;

[1297] a transmitting means for transmitting the health data to a server;

[1298] a receiving means in the server for receiving and storing the health data;

[1299] an analysis means including an AI engine that analyzes the health data in the server;

[1300] providing means for providing the user with the personalized advice and product recommendations generated by the analysis means;

[1301] A system including:

[1302] (Claim 2)

[1303] 2. The system of claim 1, wherein the providing means includes display means for displaying the personalized advice and product recommendations on a user's terminal.

[1304] (Claim 3)

[1305] 10. The system of claim 1, wherein the analyzing means includes means for generating health insights and associated product recommendations based on the user's historical health data.

[1306] "Example 2: Combining Emotion Engines"

[1307] (Claim 1)

[1308] input means for inputting health data and emotion data of a user;

[1309] a transmitting means for transmitting the health data and emotion data to a server;

[1310] a receiving means for receiving and storing the health data and emotion data in the server;

[1311] analysis means including an AI engine and an emotion engine that analyze the health data and emotion data in the server;

[1312] providing means for providing the user with the personalized advice generated by the analysis means;

[1313] A system including:

[1314] (Claim 2)

[1315] 2. The system according to claim 1, wherein the providing means includes a display means for displaying the personalized advice on a terminal of the user.

[1316] (Claim 3)

[1317] 10. The system of claim 1, wherein the analyzing means includes means for generating health insights and emotional insights based on the user's past health data and emotional data.

[1318] "Application example 2 when combining emotion engines"

[1319] (Claim 1)

[1320] input means for inputting health data and emotional state of the user;

[1321] a transmitting means for transmitting the health data and emotional state to a server;

[1322] receiving means for receiving and storing the health data and emotional state in the server;

[1323] an analysis means including an AI engine and an emotion engine that analyzes the health data and the emotional state in the server;

[1324] providing means for providing the user with the personalized advice generated by the analysis means;

[1325] a suggestion means for suggesting an optimal meal menu based on the advice;

[1326] A system including:

[1327] (Claim 2)

[1328] 2. The system according to claim 1, wherein the providing means includes a display means for displaying the personalized advice and meal menu on a user's terminal.

[1329] (Claim 3)

[1330] 10. The system of claim 1, wherein the analyzing means includes means for generating health insights based on the user's past health data and emotional state. [Explanation of symbols]

[1331] 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. an input means for inputting health data of a user; a transmitting means for transmitting the health data to a server; a receiving means in the server for receiving and storing the health data; an analysis means including an AI engine that analyzes the health data in the server; providing means for providing the user with the personalized advice generated by the analysis means; A system including:

2. 2. The system according to claim 1, wherein the providing means includes display means for displaying the personalized advice on a terminal of the user.

3. The system of claim 1 , wherein the analyzing means includes means for generating health insights based on the user's past health data.

Images