System

Abstract

To provide a system capable of monitoring a health condition in real time on the basis of life habit data such as a sleeping time, a food content, an exercise amount, and a stress level, and performing health management proactively.SOLUTION: A system comprising: a terminal for a user to input lifestyle data, which converts the input data into a format and transmits it to a server; means for the server to verify the received data and store it in a database; means for the server to quantify a health condition based on the stored data; means for the server to compare the health condition numerical value with past data and detect an abnormality; means for the server to generate an alert when an abnormality is detected; means for the server to propose a specific countermeasure based on the alert; and means for the terminal to notify the user of the alert and the countermeasure from the server.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] Describe the "problem that the invention aims to solve" and the "means for solving the problem."

[0005] In modern society, many people find it difficult to manage data related to their daily lifestyle habits, making it difficult to properly monitor their health. As a result, they often do not notice a deterioration in their health until they become unwell. There is a need for a system that can solve this problem and provide appropriate health management based on daily lifestyle habits. [Means for solving the problem]

[0006] The present invention solves the above-mentioned problems by providing a system including a terminal for a user to input lifestyle habit data, means for converting the input data into a format and sending it to a server, means for verifying the received data and saving it in a database, means for quantifying health status based on the saved data, means for detecting abnormalities by comparing the health status numerical value with past data, means for generating an alert when an abnormality is detected, means for proposing specific countermeasures based on the alert, and a terminal for notifying the user of the alert and countermeasures from the server. This system monitors health status in real time based on lifestyle habit data such as sleep time, dietary content, amount of exercise, and stress level, enabling proactive health management.

[0007] A "user" is a person who uses this system and inputs lifestyle habit data.

[0008] A "terminal" is a device through which a user inputs lifestyle habit data and transmits the data to a server.

[0009] The "server" is a computer system that receives lifestyle habit data sent from a terminal, stores it, analyzes it, generates alerts, and proposes ways to deal with the data.

[0010] "Lifestyle data" is information about the user's daily health condition and lifestyle, such as sleep time, diet, amount of exercise, and stress level.

[0011] "Converting into a format" means converting the input lifestyle habit data into a format that can be appropriately processed by the server.

[0012] The "database" is a storage device that stores received lifestyle habit data and allows it to be retrieved and analyzed as needed.

[0013] "Quantifying health status" means quantitatively evaluating the user's health status based on lifestyle habit data and expressing it as a numerical value.

[0014] The "health condition value" is a value that indicates the user's health condition, calculated based on lifestyle habit data.

[0015] "Past data" refers to lifestyle habit data and its analysis results that were previously input by the user and stored on the server.

[0016] "Means for detecting abnormalities" refers to technology that compares health status values ​​with past data and detects inconsistencies or abnormalities that exceed a preset threshold.

[0017] An "alert" is a warning message that alerts the user to a detected abnormality.

[0018] "Countermeasures" are specific actions or advice that the user should take in response to the detected abnormality.

[0019] An "action guide" is a set of words or instructions that effectively communicates how to deal with a problem to the user. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] This system allows users to input their daily lifestyle habits, quantifies their health status based on that data, and issues alerts as necessary. Here, we will explain in detail the functions and processing details of each element of this system.

[0042] Entering lifestyle data

[0043] User: Every day, they enter their lifestyle data through a smartphone application, mainly including their sleep time, diet, exercise, and stress level.

[0044] Terminal: The application converts the input data into a dedicated format and sends it to the server.

[0045] Receiving and storing data

[0046] Server: Receives and verifies lifestyle habit data sent from the device. After verification, the data is stored in a database. The stored data includes the user's identification information, details of each data item, and their input timestamp.

[0047] Quantifying health status

[0048] Server: The health status assessment engine starts working based on the saved data. Each lifestyle item (sleep time, diet, exercise, stress level) is weighted and points are calculated. For example, less than 7 hours of sleep is worth -10 points, a high-fat diet is worth -15 points, zero minutes of exercise is worth -20 points, and high stress is worth -10 points.

[0049] Server: The points for each category are added together to calculate an overall health score, which is rated out of 100.

[0050] Generate alerts

[0051] Server: Compares the health score with past data to check for any abnormalities. It compares the score with the score from previous times when the user was unwell, and if the current score is the same or lower, it determines that an abnormality has been detected.

[0052] Server: When an anomaly is detected, the alert generation engine automatically generates an alert message, which includes a warning and instructions for the user to take.

[0053] Suggested Actions

[0054] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as "increase hydration," "take early rest," and "do light exercise."

[0055] Terminal: Notifies the user of action guides and alert messages sent from the server, and prompts the user to take appropriate action through the application interface.

[0056] Processing details with concrete examples

[0057] User data entry and processing

[0058] Example Day 1

[0059] User: Enters the following information into the app: "Sleep: 5 hours, Diet: high fat, high calories, Exercise: 0 minutes, Stress level: high."

[0060] Terminal: Converts the input data into a format and sends it to the server.

[0061] Server: Receives the data, validates it, and stores it in a database.

[0062] Server: The health status assessment engine works and calculates an overall score of 40 points.

[0063] Server: The alert generation engine generates an alert because the score of 40 matches the score of a previous illness.

[0064] On your device: Display an alert saying, "Caution: Rest early and exercise."

[0065] Example Day 2

[0066] User: Enter the following information into the app: "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low."

[0067] Terminal: Converts data into a format and sends it to the server.

[0068] Server: Receives the data, validates it, and stores it in a database.

[0069] Server: The health status assessment engine works and calculates an overall score of 80 points.

[0070] Server: Confirms that there are no abnormalities and does not issue an alert.

[0071] Device: Displays "Your current health condition is good. Keep it up."

[0072] In this way, the system of the present invention can monitor the user's health condition in real time based on the user's daily lifestyle data and proactively manage their health, allowing the user to detect problems before they become unwell and take appropriate measures.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The user launches the smartphone application and inputs daily lifestyle data (sleep time, diet, exercise, stress level).

[0076] Step 2:

[0077] The terminal converts the lifestyle habit data entered by the user into an appropriate format and sends it to the server.

[0078] Step 3:

[0079] The server receives the data sent from the device. The server then verifies the accuracy and format of the data. Inaccurate or invalid data is rejected.

[0080] Step 4:

[0081] The server stores the validated data in a database, including the user's identity, details of each data item, and the date and time the data was entered.

[0082] Step 5:

[0083] The server retrieves the stored data and launches a health status assessment engine, which first adds or subtracts points for each lifestyle habit (sleep time, diet, exercise, stress level).

[0084] Step 6:

[0085] The server adds up the points for each category to calculate an overall health score, which the service rates using, for example, a score out of 100.

[0086] Step 7:

[0087] The server compares the calculated health score with past data, and if the health score is equal to or lower than the score from a previous health condition, it determines that an abnormality has been detected.

[0088] Step 8:

[0089] If the server detects an anomaly, it will launch an alert generation engine to generate a warning message, which will include information that the user should pay attention to and suggested solutions.

[0090] Step 9:

[0091] The server launches the action guide engine to generate specific measures to improve the user's health. The suggested measures include specific actions such as "increase hydration," "take early rest," and "perform light exercise."

[0092] Step 10:

[0093] The server sends the generated alert message and a solution to the terminal.

[0094] Step 11:

[0095] The device receives the message from the server and notifies the user via the notification bar or a pop-up within the app.

[0096] Step 12:

[0097] The user checks the notification and takes necessary measures according to the provided action guide.

[0098] The above is the specific flow of processing by the program in the system of the present invention. This system monitors the user's daily lifestyle habits in real time and proactively supports health management, making it possible to prevent poor health before it occurs.

[0099] Example 1

[0100] 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."

[0101] Conventional health management systems simply record users' lifestyle data, but are inadequate in assessing their health status in real time, detecting abnormalities early, or suggesting specific countermeasures. Furthermore, many assessment methods rely on specific lifestyle items rather than comprehensively assessing multiple lifestyle items, making it difficult for users to comprehensively understand their health status and take appropriate action. Furthermore, they lacked a mechanism for detecting abnormalities using past health data, making preventative health management impossible. To solve this problem, a system was needed that could comprehensively assess a user's health status in real time and suggest appropriate actions.

[0102] 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.

[0103] In this invention, the server includes means for receiving and verifying lifestyle habit data entered by the user, means for storing the data in a database and quantifying it based on each lifestyle habit item using a health status assessment engine, and means for checking the data against a past health score database to determine whether there is an abnormality and, if an abnormality is detected, for suggesting a specific countermeasure using an action guide engine. This makes it possible to evaluate the user's lifestyle habit data from multiple angles, detect abnormalities early, and suggest appropriate countermeasures.

[0104] "Lifestyle data" refers to data that records the user's daily actions and conditions, and specifically includes sleep time, dietary content, amount of exercise, stress level, and the like.

[0105] A "terminal" is a device through which a user inputs lifestyle habit data, and refers to a computing device such as a smartphone or tablet.

[0106] "Server" refers to a central processing unit that receives, verifies, and stores data sent from devices, analyzes the data to assess health status, and generates alerts and corrective actions.

[0107] The "database" is a storage device for holding lifestyle habit data received by the server, and is a system for managing information such as user identification information, details of each data item, and input timestamps.

[0108] The "health status assessment engine" refers to a program that executes an algorithm to weight each item based on stored lifestyle data and calculate an overall health score.

[0109] An "alert generation engine" refers to a program that automatically generates an alert message when an abnormality is detected based on the health score.

[0110] The "action guide engine" refers to a program that suggests specific measures to improve the user's health condition when an abnormality is detected.

[0111] The "past health score database" is a storage device that stores lifestyle habit data previously input by the user and the health score calculated based on that data, and is used as a reference when detecting abnormalities.

[0112] This invention is a system that allows users to input their daily lifestyle habits, quantifies their health status based on that data, and issues alerts as necessary. Here, we will explain how to implement this system, as well as its detailed procedures and configuration.

[0113] Entering lifestyle data

[0114] User: The user uses a device such as a smartphone or tablet to enter lifestyle data through a dedicated application, including items such as sleep time, diet, exercise, and stress level.

[0115] Sending data

[0116] Terminal: The data entered by the user is converted into a dedicated format (for example, JSON format) and sent to the server. HTTPS is used as the transmission protocol, ensuring secure data transfer.

[0117] Data Receipt and Validation

[0118] Server: Receives data sent from the terminal. After receiving, it verifies whether the data format is correct. If the format is invalid, it returns an error message.

[0119] Data storage

[0120] Server: Saves the verified data to the database. The saved data includes the user ID, details of each data item, and the input timestamp.

[0121] Health assessment

[0122] Server: The health assessment engine assigns points to each lifestyle habit based on the stored data and applies weighting logic to calculate an overall health score. For example, if you sleep less than 7 hours, you will receive -10 points.

[0123] Anomaly detection

[0124] Server: The calculated health score is checked against a database of past health scores to see if there are any abnormalities. It is compared with the score from a previous time when health condition worsened, and if it matches or is lower than the previous score, it is determined to be abnormal.

[0125] Generate alerts

[0126] Server: When an anomaly is detected, the alert generation engine automatically generates an alert message, which includes specific actions and warnings that the user should take.

[0127] Suggested Actions

[0128] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as drinking more water, resting early, and doing light exercise.

[0129] User Notification

[0130] Terminal: Notifies the user of the alert message sent from the server and how to deal with it. It displays it to the user through the application interface.

[0131] A specific example of the operation is shown below.

[0132] Example Day 1

[0133] User: Enters the following information into the app: "Sleep: 5 hours, Diet: high fat, high calories, Exercise: 0 minutes, Stress level: high."

[0134] Terminal: Converts the input data into a format and sends it to the server.

[0135] Server: Receives the data, validates it, and stores it in a database.

[0136] Server: The health status assessment engine works and calculates an overall score of 40 points.

[0137] Server: Compare with past data and determine abnormality at "40 points".

[0138] Server: Generate an alert and send a message saying "Caution: Rest early and exercise."

[0139] Terminal: Display an alert message to the user.

[0140] Example Day 2

[0141] User: Enter the following information into the app: "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low."

[0142] Terminal: Converts data into a format and sends it to the server.

[0143] Server: Receives the data, validates it, and stores it in a database.

[0144] Server: The health status assessment engine works and calculates an overall score of 80 points.

[0145] Server: Determines that there is no abnormality and does not issue an alert.

[0146] Device: Displays "Your current health condition is good. Keep it up."

[0147] In this way, the system of the present invention realizes proactive health management by evaluating the health condition of the user based on daily lifestyle habit data, detecting abnormalities early, and proposing appropriate countermeasures.

[0148] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0149] Step 1:

[0150] The user opens the application on their smartphone or tablet and enters their lifestyle habits data for the day.

[0151] The data entered includes sleep duration (e.g., 5 hours), dietary content (e.g., high fat, high calorie), amount of exercise (e.g., 0 minutes), and stress level (e.g., high).

[0152] (Input): Sleep time, diet, exercise, stress level

[0153] (Output): Lifestyle data (JSON format)

[0154] Step 2:

[0155] The terminal converts the input data into a dedicated format (JSON format) and sends it to the server via the HTTPS protocol.

[0156] For example, it generates the following JSON data:

[0157] {"sleep": 5, "food": "high-fat", "exercise": 0, "stress": "high"}

[0158] (Input): Lifestyle data (user input)

[0159] (Output): Formatted lifestyle data (JSON format)

[0160] Step 3:

[0161] The server receives the data sent from the terminal, verifies the validity of the format, and returns an error message if the format is invalid.

[0162] For example, check whether the sleep time is a negative number or whether the meal contents are an unclear string.

[0163] (Input): Formatted lifestyle data (JSON format)

[0164] (Output): Validation result (success or failure), error message (if necessary)

[0165] Step 4:

[0166] The server stores the successfully validated data in a database. The stored information includes the user ID, details of each data item, and the input timestamp.

[0167] For example, execute the following SQL statement:

[0168] INSERT INTO health_data (user_id, sleep, food, exercise, stress, timestamp) VALUES (1, 5, 'high-fat', 0, 'high', '2023-10-10 08:00:00');

[0169] (Input): Successfully verified lifestyle data

[0170] (Output): Save results to the database

[0171] Step 5:

[0172] The server runs a health status assessment engine based on the stored data, weighting each lifestyle item and calculating points.

[0173] For example, if you sleep less than 7 hours, you will receive -10 points.

[0174] (Input): Lifestyle data stored in the database

[0175] (Output): Points for each item, overall health score (e.g., 40 points)

[0176] Step 6:

[0177] The server checks the calculated health score against the past health score database to see if there are any abnormalities. If it is the same as or lower than the past data, it is determined to be abnormal.

[0178] For example, if the past score for a worsening condition was 40 points and the current score is also 40 points, it will be judged as abnormal.

[0179] (Input): Overall health score

[0180] (Output): Result of anomaly detection (normal or abnormal)

[0181] Step 7:

[0182] If an abnormality is detected in the server, an alert generation engine generates an alert message, which includes specific countermeasures that the user should take.

[0183] For example, create a message such as, "Caution: Rest early and do some light exercise."

[0184] (Input): Anomaly detection result (anomaly)

[0185] (Output): Alert message

[0186] Step 8:

[0187] The device notifies the user of the alert message sent from the server and the specific steps to take. The user can then check the alert and be prompted to take action through the application interface.

[0188] For example, a pop-up message might say, "Caution: Rest early and do some light exercise."

[0189] (Input): Alert message and how to deal with it

[0190] (Output):Notification to the user

[0191] This will enable users to monitor their health status in real time and take appropriate measures.

[0192] (Application example 1)

[0193] 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."

[0194] Conventional health management systems only require users to input their own lifestyle data, and are unable to monitor their health status in real time or provide effective feedback. Furthermore, the role of trainers at fitness facilities is limited to face-to-face instruction, making it difficult to comprehensively manage users' health. To address these issues, a system is needed that provides more advanced health management and personalized exercise recommendations for users.

[0195] 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.

[0196] In this invention, the server includes a means for a user to input lifestyle habit data, a means for the terminal to convert the input data into a format and send it to the server, a means for the server to verify the received data and store it in a database, a means for the server to quantify a health condition based on the stored data, a means for the server to compare the health condition numerical value with past data and detect abnormalities, a means for the server to generate an alert when an abnormality is detected, a means for the server to suggest specific countermeasures based on the alert, a means for the terminal to notify the user of the alert from the server and the countermeasures, a means for a fitness facility manager to monitor the user's health data in real time and suggest an optimal exercise menu for the user, and a means for displaying notifications on the user's smart glasses to provide real-time feedback during exercise, thereby enabling comprehensive management of the user's health condition and personalized exercise guidance.

[0197] "User" refers to an individual who inputs lifestyle data and receives health monitoring and measures.

[0198] "Lifestyle data" is information about the user's daily life, and specifically includes sleep time, dietary content, amount of exercise, and stress level.

[0199] A "terminal" is a device through which a user inputs lifestyle habit data, such as a smartphone or smart glasses.

[0200] "Converting to a format" refers to the process of converting lifestyle habit data entered by the user into a format suitable for the database.

[0201] "Server" refers to a central processing unit that stores and analyzes received data and provides relevant feedback to the user.

[0202] "Database" refers to a structured data storage system for storing user lifestyle data and other related data.

[0203] "Quantifying health status" refers to the process of calculating a comprehensive health score based on the user's lifestyle data.

[0204] "Detecting anomalies" refers to the process of identifying cases where current health status values ​​are judged to be abnormal by comparing them with past data.

[0205] An "alert" is a notification message that warns the user when an abnormality is detected and prompts them to take appropriate measures.

[0206] "Solutions" refer to specific actions or advice suggested to improve the user's health.

[0207] A "fitness facility manager" refers to a specialized staff member at a fitness center or gym who monitors users' health data and suggests exercise menus.

[0208] "Real-time monitoring" means that the fitness facility manager can continually check the user's latest health data and take immediate action.

[0209] "Exercise menu" refers to an exercise or fitness plan optimized for the user's health condition.

[0210] "Real-time feedback" refers to information that provides users with immediate advice and instructions during exercise to maximize the effectiveness of their exercise.

[0211] "Smart glasses" are wearable devices that users use while exercising and have the ability to display visual information.

[0212] The system embodying this invention uses various hardware and software components to enhance health management for users at fitness facilities, and is primarily composed of a smartphone, smart glasses, a server, a database, and a fitness facility management system.

[0213] System configuration

[0214] 1. User data entry

[0215] User: Uses a smartphone or smart glasses to input lifestyle data (sleep time, diet, exercise, stress level).

[0216] Terminal: A smartphone or smart glasses converts the input data into the appropriate format and sends it to the server.

[0217] 2. Receipt and storage of data

[0218] Server: Receives lifestyle data sent from the device and verifies the format and content of the data.

[0219] Database: The verified data is stored in a database, recording the user's identity and details of each data item, along with its input timestamp.

[0220] 3. Quantifying health status

[0221] Server: The health status assessment engine runs, weighting each lifestyle item (sleep time, diet, exercise, stress level) and calculating points. For example, if you sleep less than 7 hours, you will receive -10 points, and if you eat a balanced diet, you will receive +20 points.

[0222] Server: The points for each category are added together to calculate an overall health score, which is rated out of 100.

[0223] 4. Generate an alert

[0224] Server: Compares the health score with past data, and determines that an abnormality has been detected if the score is equal to or lower than the score when the user was previously unwell.

[0225] Server: If an anomaly is detected, the alert generation engine generates an alert message, optionally including instructions for remediation.

[0226] 5. Action Suggestions

[0227] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as increasing hydration, resting earlier, and light exercise.

[0228] Terminal: Notifies the user of action guides and alert messages sent from the server.

[0229] 6. Fitness facility management

[0230] Management system: Allows fitness facility managers to monitor users' health data in real time.

[0231] User guidance: Administrators can suggest optimal exercise routines for users and receive real-time feedback using smart glasses while they exercise.

[0232] Examples and prompts

[0233] As a concrete example, consider the case where a user enters the following data into a smartphone app:

[0234] Example 1: Signs of illness

[0235] User input: "Sleep time: 5 hours, Diet: High fat and high calories, Exercise time: 0 minutes, Stress level: High"

[0236] Server process:

[0237] 1. Data reception, verification, and storage.

[0238] 2. Health score calculation: 20 points.

[0239] 3. Generate an alert: "Your health score is declining. Take appropriate action."

[0240] 4. Action Suggestion: "Drink more fluids and get some sleep earlier."

[0241] Example 2: Good condition

[0242] User input: "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low"

[0243] Server process:

[0244] 1. Data reception, verification, and storage.

[0245] 2. Health score calculation: 80 points.

[0246] 3. No alerts.

[0247] 4. Action Suggestion: "Your current health is good. Keep it up."

[0248] Prompt example

[0249] Based on the health data of user ID "12345", calculate a health score and generate an alert message and action suggestions.

[0250] Input data:

[0251] Sleep time: 5 hours

[0252] Diet: High fat, high calorie

[0253] Momentum: 0 minutes

[0254] Stress level: High

[0255] Desired output:

[0256] Health Score

[0257] Alert Messages

[0258] action suggestion

[0259] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0260] Step 1:

[0261] The user enters lifestyle data using a smartphone or smart glasses. Specifically, the user opens the application's input form and enters information such as sleep time, diet, exercise, and stress level. Input: sleep time, diet, exercise, stress level. Output: formatted lifestyle data.

[0262] Step 2:

[0263] The terminal converts the input data into a format and sends it to the server. Specifically, the terminal converts the input data into structured data such as JSON format and sends it to the server's API endpoint. Input: Raw data from the user. Output: Formatted data.

[0264] Step 3:

[0265] The server receives the data and validates its format and content. Specifically, the server verifies that the received data is in the expected format and checks that it does not contain any invalid data. Input: Formatted lifestyle habit data. Output: Validated data.

[0266] Step 4:

[0267] The server stores the verified data in a database. Specifically, it uses a database engine (e.g., MySQL (registered trademark) or PostgreSQL) to store details of each data item and an input timestamp along with the user's identification information. Input: Verified data. Output: Data stored in the database.

[0268] Step 5:

[0269] The server calculates a health score based on the stored data. Specifically, it weights sleep time, diet, exercise, and stress level to calculate an overall health score. For example, if you sleep less than 7 hours, you'll get -10 points, and if you eat a well-balanced diet, you'll get +20 points. Input: Lifestyle data stored in the database. Output: Health score.

[0270] Step 6:

[0271] The server compares the health score with past data to see if any abnormalities are detected. Specifically, it compares the score with the score from a previous time when the user was unwell, and if the score is equal to or lower than that, it is considered to be an abnormality. Input: Health score, past health data. Output: Anomaly detection flag.

[0272] Step 7:

[0273] If the server detects an abnormality, it generates an alert message. Specifically, the alert generation engine runs and generates a message such as "The health score is decreasing. Please take appropriate measures." Input: Anomaly detection flag. Output: Alert message.

[0274] Step 8:

[0275] The server generates an action guide and suggests specific countermeasures, such as "increase hydration and take early rest." Input: health score, anomaly detection flag. Output: countermeasures.

[0276] Step 9:

[0277] The device notifies the user of the alert from the server and how to deal with it. Specifically, a pop-up notification or message is displayed to the user using a smartphone or smart glasses. Input: Alert message, how to deal with it. Output: Notification to the user.

[0278] Step 10:

[0279] The management system provides users' health data to the fitness facility manager in real time. Specifically, the latest health data of users is monitored through an interface such as a dashboard for the manager. Input: Lifestyle data stored in a database. Output: Health data provided to the manager.

[0280] Step 11:

[0281] The administrator proposes the optimal exercise menu for the user and allows the user to receive real-time feedback using smart glasses while exercising. Specifically, the administrator creates a customized exercise menu based on the user's health and fitness data, and the user receives instructions through the smart glasses. Input: Instructions from the administrator dashboard, smart glasses. Output: Customized exercise menu, real-time feedback.

[0282] 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.

[0283] This invention is a system that inputs a user's daily lifestyle data, quantifies their health status based on that data, and issues alerts as needed, and by combining this with an emotion engine that recognizes the user's emotions, it achieves more accurate health management. Here, we will explain in detail the functions and processing content of each element of this system.

[0284] Entering lifestyle data

[0285] User: Every day, they enter their lifestyle data through a smartphone application, mainly including their sleep time, diet, exercise, and stress level.

[0286] Terminal: The application converts the input data into a dedicated format and sends it to the server.

[0287] Receiving and storing data

[0288] Server: Receives and verifies lifestyle habit data sent from the device. After verification, the data is stored in a database. The stored data includes the user's identification information, details of each data item, and their input timestamp.

[0289] Quantifying health status

[0290] Server: The health status assessment engine starts working based on the saved data. Each lifestyle item (sleep time, diet, exercise, stress level) is weighted and points are calculated. For example, less than 7 hours of sleep is worth -10 points, a high-fat diet is worth -15 points, zero minutes of exercise is worth -20 points, and high stress is worth -10 points.

[0291] Server: The points for each category are added together to calculate an overall health score, which is rated out of 100.

[0292] Emotion recognition by emotion engine

[0293] On the device: The emotion engine works to identify the user's emotions through voice analysis, facial recognition, and text analysis. For example, emotion data is collected when the user shows their facial expressions to the smartphone camera or speaks into the microphone.

[0294] Server: Receives emotion data sent from the device and stores it in a database. The stored emotion data includes the user's identification information, the identified emotion, and the date and time when the emotion was recognized.

[0295] Generate alerts

[0296] Server: Compares the health score with past data to check for any abnormalities. It compares the score with the score from previous times when the user was unwell, and if the current score is the same or lower, it determines that an abnormality has been detected.

[0297] Server: When an anomaly is detected, the alert generation engine automatically generates an alert message, which includes a warning and instructions for the user to take.

[0298] Server: The server also takes into account the emotional data identified by the emotion engine and adjusts the alert content as needed. For example, if the user's emotion is identified as "stress" or "anxiety," it generates a more specific and urgent alert.

[0299] Suggested Actions

[0300] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as "increase hydration," "take early rest," and "do light exercise."

[0301] Server: Adjusts the suggested coping strategies based on the emotion data identified by the emotion engine. For example, if the user's emotion is identified as "stress" or "fatigue," the server suggests additional coping strategies such as "relaxation techniques" or "playing relaxation music."

[0302] Terminal: Notifies the user of guidance and alert messages sent from the server, and prompts the user to take appropriate action through the application interface.

[0303] Processing details with concrete examples

[0304] User data entry and processing

[0305] Example Day 1

[0306] User: Enter the following information into the app: "Sleep time: 5 hours, Meals: High fat, High calories, Exercise: 0 minutes, Stress level: High." The app then captures the user's facial expression through the camera and says into the microphone, "I'm tired today."

[0307] Terminal: Converts input data and emotion data into a format and sends it to the server.

[0308] Server: Receives, verifies, and stores data in a database. The health status assessment engine calculates a total score of 40 points. In addition, the emotion engine stores the emotion data identified as "fatigue."

[0309] Server: The alert generation engine generates an alert because the score matches "40 points" from a previous time when the user was feeling unwell. Based on the data from the emotion engine, the alert is adjusted to read, "Current emotion: Fatigue. Caution: Take early rest and try relaxation techniques."

[0310] Terminal: Notify the user of the adjusted alert.

[0311] Example Day 2

[0312] User: Enters "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low" into the app. Also, says "I'm feeling good today" into the microphone.

[0313] Terminal: Converts data into a format and sends it to the server.

[0314] Server: Receives, verifies, and stores data in a database. The health assessment engine calculates an overall score of 80 points. The emotion engine also stores the emotion data identified as "happiness."

[0315] Server: Because the health score is high, no abnormalities are detected. Based on the data from the emotion engine, the display content is adjusted to say, "Current emotion: Happiness. Your current health condition is good. Keep it up."

[0316] Terminal: Notify the user of the adjusted message.

[0317] In this way, by combining the system of the present invention with an emotion engine, it is possible to comprehensively analyze a user's lifestyle data and emotion data, providing more accurate and personalized health management, allowing users to take specific measures to maintain their health while also taking into account their own emotional state.

[0318] The processing flow will be explained below.

[0319] Step 1:

[0320] The user launches the smartphone application and inputs daily lifestyle data (sleep time, dietary habits, exercise amount, stress level), and also inputs emotional data by showing facial expressions to the smartphone camera and speaking into the microphone.

[0321] Step 2:

[0322] The device converts the input lifestyle and emotion data into an appropriate format and sends it to the server. The format conversion is to improve data uniformity and processing efficiency.

[0323] Step 3:

[0324] The server receives the lifestyle and emotion data sent from the device, verifies the accuracy of the received data, and checks for any anomalies.

[0325] Step 4:

[0326] The server stores the validated data in a database, including the user's identity, details of each data item, and a timestamp of the data entry.

[0327] Step 5:

[0328] The server runs a health assessment engine based on the saved lifestyle data, adding or subtracting points for each lifestyle item (sleep time, diet, exercise, stress level) to calculate an overall health score.

[0329] Step 6:

[0330] The server starts the emotion engine and analyzes the transmitted emotion data. It identifies the user's emotion through voice analysis, face recognition, and text analysis, and stores the identified emotion data in a database.

[0331] Step 7:

[0332] The server integrates and analyzes the health score and emotion data, and compares it with past data to check for any abnormalities. If an abnormality is detected, the system proceeds to the next step.

[0333] Step 8:

[0334] When an anomaly is detected, the server activates the alert generation engine to generate a warning message, which includes a warning and action to be taken by the user.The server also adjusts the alert content based on the emotion identified by the emotion engine.

[0335] Step 9:

[0336] The server launches an action guide engine to suggest specific measures, such as "increase hydration," "take early rest," and "try relaxation techniques." Furthermore, the server adjusts the suggestions based on the emotion data recognized by the emotion engine.

[0337] Step 10:

[0338] The server sends the generated alert message and a solution to the terminal.

[0339] Step 11:

[0340] The terminal receives the message from the server and notifies the user through the application interface, prompting the user to take appropriate action.

[0341] Step 12:

[0342] The user checks the notification and takes necessary measures according to the action guide provided, allowing the user to take concrete actions to improve their health condition.

[0343] The above is the processing flow of the system of the present invention, which combines an emotion engine. This system analyzes the user's daily lifestyle data and emotion data in real time, and supports comprehensive health management, making it possible to prevent poor health before it occurs.

[0344] Example 2

[0345] 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."

[0346] In modern society, there are many systems that manage health status based on a user's daily lifestyle data. However, these systems do not take the user's emotional state into account, which can make it difficult to accurately detect changes or abnormalities in physical condition. Another issue is that they are unable to suggest appropriate measures based on the user's emotional state. The present invention aims to achieve more accurate and personalized health management by comprehensively utilizing a user's lifestyle data and emotional data.

[0347] 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 means for the user to input lifestyle habit data and emotional data such as voice and facial expressions; a means for the terminal to convert the input data into a format and send it to the server; a means for the server to verify the received data and store it in a database; a means for the server to quantify the health status based on the stored data; a means for the server to recognize the user's emotions using an emotion engine and store the data; a means for the server to compare the health status numerical value with past data and detect abnormalities; a means for the server to generate an alert when an abnormality is detected and adjust the alert based on the emotional data; a means for the server to propose a specific response based on the alert and adjust the response based on the emotional data; and a means for the terminal to notify the user of the alert from the server and the response. This enables accurate health management that takes the user's emotional state into consideration.

[0348] "User" refers to an individual who uses this system.

[0349] The term "terminal" refers to an electronic device that allows a user to input lifestyle habit data and emotion data and communicate with a server.

[0350] "Server" refers to the central computer system that receives, verifies, and stores input data, and runs various engines to quantify health status, recognize emotions, and generate alerts.

[0351] "Lifestyle data" is information about the user's daily activities, and specifically includes sleep time, dietary content, amount of exercise, and stress level.

[0352] "Emotional Data" refers to a user's emotional state as determined through voice analysis, facial recognition, and / or text analysis.

[0353] "Database" refers to a digital recording system for storing lifestyle and emotional data received from users.

[0354] "Health status assessment engine" refers to the program and algorithm for quantifying a user's health status based on stored lifestyle data.

[0355] "Emotion Engine" refers to programs and algorithms that identify emotions based on a user's voice analysis, facial recognition, and text analysis.

[0356] An "alert generation engine" refers to a program and algorithm that generates a message to notify the user of a warning or countermeasure when an abnormality is detected.

[0357] "Action Guidance Engine" refers to programs and algorithms that suggest specific actions to take based on a user's health and emotional state.

[0358] This system collects a user's daily lifestyle and emotional data, quantifies their health status based on the data, detects abnormalities, and proposes countermeasures. This system is composed of devices such as smartphones, a cloud server, and various engines that perform emotion recognition and data analysis.

[0359] Entering lifestyle data

[0360] Users: Every day, they enter lifestyle data using a dedicated smartphone app. Data entry items include sleep time, dietary habits, exercise, and stress levels. They also use a camera to capture facial expressions and a microphone to record emotional data.

[0361] Sending and Receiving Data

[0362] Terminal: The application converts the data entered by the user into a standard format, which is then sent to the server in real time.

[0363] Server: Receives data sent from the device and verifies the accuracy of the format and content. If invalid data is found, it is corrected or re-requested.

[0364] Data storage

[0365] Server: Saves the verified data in a database, including the user's identity, details of the data item, and the date and time of entry.

[0366] Quantifying health status

[0367] Server: The health status assessment engine starts running and calculates points by weighting each item based on the saved lifestyle data.

[0368] For example: less than 7 hours of sleep is worth -10 points, a high-fat diet is worth -15 points, zero minutes of exercise is worth -20 points, high stress is worth -10 points, etc.

[0369] Server: The points for each item are combined to calculate a health score, which is rated out of 100.

[0370] emotion recognition

[0371] On the device: The emotion engine performs voice analysis, facial recognition, and text analysis of the user. For example, emotion data is collected when the user shows their facial expressions to the smartphone camera or speaks into the microphone.

[0372] Terminal: The collected emotion data is converted into a format and sent to the server.

[0373] Server: Validates the emotion data received from the device and stores it in a database, including the user's identity, the identified emotion, and the date and time of recognition.

[0374] Generate alerts

[0375] Server: Compares the health score with past data to check for abnormalities. If the score matches or is lower than the score from a previous poor health condition, it is determined that an abnormality has been detected.

[0376] Server: When an anomaly is detected, the alert generation engine generates an alert message, which includes countermeasures and warnings. Additionally, it adjusts the alert content based on sentiment data.

[0377] Suggested Actions

[0378] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as drinking more water, resting early, and doing light exercise.

[0379] Server: Based on the emotional data, the server also adjusts coping methods. For example, if the emotion is identified as "stress" or "fatigue," it will suggest additional specific coping methods such as "relaxation techniques" or "playing relaxation music."

[0380] Terminal: Notifies the user of action guides and alert messages sent from the server, and prompts the user to take appropriate action through the application interface.

[0381] Processing details with concrete examples

[0382] Example Day 1

[0383] User: Enters "Sleep time: 5 hours, Meals: High fat, High calories, Exercise: 0 minutes, Stress level: High" into the app, captures facial expression with the camera, and says "I'm tired today" into the microphone.

[0384] Terminal: Converts data and emotion data into a format and sends it to the server.

[0385] Server: Receives, verifies, and stores the data in a database. The health status assessment engine calculates a total score of 40 points, and stores the emotion data identified as "fatigue" by the emotion engine.

[0386] Server: The alert generation engine generates an alert because the score matches "40 points" from a previous experience of illness. Based on the emotion data, an adjusted alert is generated: "Current emotion: Fatigue. Caution: Take early rest and try relaxation techniques."

[0387] Terminal: Notify the user of the adjusted alert.

[0388] Example Day 2

[0389] User: Enters "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low" into the app and says "I feel good today" into the microphone.

[0390] Terminal: Converts data into a format and sends it to the server.

[0391] Server: Receives, verifies, and stores the data in a database. The health assessment engine calculates an overall score of 80 points, and also stores the emotion data identified as "happiness" by the emotion engine.

[0392] Server: No abnormalities are detected because the health score is high. Based on the emotion data, the display is adjusted to say, "Current emotion: Happiness. Your current health condition is good. Keep it up."

[0393] Terminal: Notify the user of the adjusted message.

[0394] Example prompts for generative AI models

[0395] Sample prompt 1: "What alert message would you generate if the user has 5 hours of sleep, eaten a high-fat meal, exercised 0 minutes, and has a high stress level?"

[0396] Sample prompt 2: "When the user has 8 hours of sleep, a balanced diet, 30 minutes of exercise, and low stress, create a message to display based on the health score and sentiment analysis results."

[0397] The system of the present invention allows users to continuously and individually manage their health while taking into account their emotional state. This system combines an emotion engine and a health status assessment engine to comprehensively analyze lifestyle and emotional data, enabling more accurate alerts and suggested actions. This allows users to more accurately understand their daily health status and take appropriate measures.

[0398] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0399] Step 1:

[0400] User: Every day, the user enters lifestyle data using a dedicated smartphone app. The input fields include "sleep time," "meal details," "amount of exercise," and "stress level." The user also captures facial expressions using a camera and speaks into a microphone to input emotional data via voice. At this point, the input consists of lifestyle data and emotional data.

[0401] Step 2:

[0402] Terminal: The application converts the data entered by the user into a standard format. Specifically, it parses text and image data and converts it into structured data. This converted data is output and sent to the server in real time.

[0403] Step 3:

[0404] Server: Receives data sent from the terminal. Validates the accuracy of the content and format of the received data. For example, if a data item is missing or the format is incorrect, it is corrected. The validated data is output and proceeds to the next process.

[0405] Step 4:

[0406] Server: Saves the validated data to the database, including the user's identity, details of the data items, and the date and time of entry. The save process adds a new record to the database.

[0407] Step 5:

[0408] Server: The health status assessment engine begins operation. Based on the saved lifestyle data, points are calculated by weighting each item. For example, if you sleep less than 7 hours, you are assigned -10 points, if you eat a high-fat diet, you are assigned -15 points, if you exercise for 0 minutes, you are assigned -20 points, and if you are highly stressed, you are assigned -10 points. The results of this data processing are output as points.

[0409] Step 6:

[0410] Server: A health score is calculated by adding up the points for each item. The total score is stored in a database. The total score is then integrated with other lifestyle data and output.

[0411] Step 7:

[0412] Device: The emotion engine performs voice analysis, facial recognition, and text analysis of the user. For example, voice analysis identifies emotions from voice, and facial recognition analyzes facial expression data. Emotion data is input, and the identified emotion is output.

[0413] Step 8:

[0414] Terminal: The collected emotion data is converted into a format and sent to the server. This process outputs the emotion data converted into a standard format.

[0415] Step 9:

[0416] Server: Validates the emotion data received from the device and stores it in a database. After validating the emotion data, the data is stored and the identified emotion is added to the database.

[0417] Step 10:

[0418] Server: Compares the health score with past data to check for abnormalities. Compares the past health score with the current score, and if they match or are lower than the current score, an abnormality is detected. The detection result is output.

[0419] Step 11:

[0420] Server: When an anomaly is detected, the alert generation engine generates an alert message. This message includes countermeasures and warnings. Furthermore, the alert content is adjusted based on the emotion data. The adjusted alert message is output.

[0421] Step 12:

[0422] Server: The action guide engine runs and suggests specific measures to improve the user's health. Suggestions include "increase hydration," "take early rest," and "do light exercise." The measures are also adjusted based on emotion data. The suggested and adjusted measures are output.

[0423] Step 13:

[0424] Terminal: Notifies the user of action guides and alert messages sent from the server. Prompts the user to take appropriate action through the application interface. The notification content is output and displayed to the user.

[0425] (Application example 2)

[0426] 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."

[0427] The problem to be solved by the present invention is to combine a user's daily lifestyle data with emotional data to more accurately manage their health condition and provide personalized treatment methods and food suggestions. Conventional health management systems generally evaluate health status based solely on lifestyle data, without taking the user's emotional state into consideration. This has led to the problem of not reflecting emotional fluctuations that affect the user's health condition.

[0428] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0429] In this invention, the server includes means for receiving a user's emotional data and storing it in a database, means for analyzing the stored emotional data and identifying the emotional state that affects the user's health, and means for generating food recommendations based on the emotional state. This enables health management that takes the user's emotional state into consideration. Specific food recommendations and ordering procedures can be supported according to the user's emotional state, allowing for more effective health maintenance.

[0430] Definitions of important words

[0431] A "user" is a person who uses the system to input lifestyle data and emotion data.

[0432] "Lifestyle data" is a general term for data such as a user's sleep time, diet, amount of exercise, and stress level.

[0433] A "terminal" is a device through which a user inputs lifestyle habit data and emotion data and transmits the data to a server.

[0434] "Server" is a centralized management system that receives, verifies, stores, and analyzes data sent from the terminals.

[0435] The "database" is a location in the server where lifestyle habit data and emotion data are stored.

[0436] The "health condition" is a numerical value that represents the degree of health calculated based on the user's daily lifestyle data.

[0437] "Emotional data" refers to data related to emotions collected through voice analysis, facial recognition, and text analysis of users.

[0438] An "emotion engine" is software that analyzes a user's emotional data and identifies a specific emotional state.

[0439] "Food Suggestion" refers to suggesting optimal meals based on the user's health and emotional state.

[0440] An "alert" is a warning message that is sent to the user when an abnormality is detected.

[0441] "Solution" refers to specific actions or measures suggested to the user based on the alert.

[0442] MODE FOR CARRYING OUT THE INVENTION

[0443] The present invention is a system that accurately manages a user's health status based on daily lifestyle and emotional data, and provides personalized treatment methods and food suggestions. Specific implementation methods of this system are described below.

[0444] Hardware and Software Configuration

[0445] User

[0446] Smartphone: A device for inputting lifestyle and emotional data. Users input this data through an application.

[0447] Application: Software that collects lifestyle data (sleep, diet, exercise, stress levels) and emotional data (voice analysis, facial recognition, text analysis). The data is formatted and sent to a server.

[0448] server

[0449] Database: This is where the user's lifestyle and emotional data is stored.

[0450] Data Verification Engine: Validates data sent from the device and confirms its validity.

[0451] Health status assessment engine: Quantifies health status based on saved lifestyle data.

[0452] Emotion Engine: Analyzes user emotional data and identifies emotional states that affect health status.

[0453] Alert generation engine: Compares health status with historical data and generates alerts if anomalies are detected.

[0454] Solution suggestion engine: Based on the alert, it suggests specific solutions to the user.

[0455] Food recommendation engine: Suggests optimal meals based on the user's health and emotional state.

[0456] Receiving and analyzing data

[0457] Data entry and submission

[0458] Users input lifestyle and emotional data using a smartphone application, which is then formatted by the device and sent to a server.

[0459] Data validation and storage

[0460] The server validates the received data and confirms its validity, and once validated, it is stored in a database.

[0461] Quantifying health status and recognizing emotions

[0462] The server runs a health status assessment engine based on the saved lifestyle data to quantify the health status, while an emotion engine runs to analyze the user's emotion data and identify emotions that affect the health status.

[0463] Alert generation and solution suggestions

[0464] Health status values ​​are compared with past data, and if an abnormality is detected, an alert generation engine generates an alert. In addition, a countermeasure suggestion engine proposes specific countermeasures, which are notified to the user via the application.

[0465] Food suggestions

[0466] The server uses a food recommendation engine to suggest optimal meals to users based on their emotional state, allowing users to select and order meals that take into account their health and emotional state.

[0467] Specific examples

[0468] Example of user data entry

[0469] The user enters the following information into the app: "Sleep time: 7 hours, Diet: high protein, low fat, Exercise: 30 minutes, Stress level: low," and the app then detects "happiness" based on their facial expression.

[0470] Server Processing

[0471] The server stores the data in a database, and a health assessment engine calculates an overall score. An emotion engine identifies "happiness" and specific emotions, and a food recommendation engine suggests meals such as "grilled chicken salad."

[0472] Prompt Sentence Examples

[0473] Input prompt for the generative AI model:

[0474] "Build a system that provides personalized health management and food recommendations based on a user's daily lifestyle and emotional data. It receives the following data points and generates recommendations based on them: sleep duration, diet, exercise, stress level, and emotional state (voice analysis, facial recognition, text analysis). Please also clearly state the specific recommendation method and the logic behind it."

[0475] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0476] Program processing steps

[0477] Step 1:

[0478] The user starts the smartphone application and inputs lifestyle data (sleep time, dietary content, exercise amount, stress level) and emotional data (voice, facial expressions, text). This input data is then formatted using the application's formatting function.

[0479] Input: sleep time, diet, exercise, stress level, emotional data

[0480] Output: Formatted lifestyle and emotion data

[0481] Step 2:

[0482] The terminal transmits the formatted data to the server, which also includes the user's identification information.

[0483] Input: Formatted lifestyle data, emotion data, and identification information

[0484] Output: Data sent to the server

[0485] Step 3:

[0486] The server validates the received data and stores it in the database after confirming that it is valid.

[0487] Input: Data sent from the terminal

[0488] Output: Validated data, saved data

[0489] Step 4:

[0490] The server runs a health status evaluation engine based on the saved lifestyle data and quantifies the health status.

[0491] Input: Saved lifestyle data

[0492] Output: Health score

[0493] Step 5:

[0494] The server analyzes the stored emotional data using an emotion engine to recognize specific emotional states.

[0495] Input: Stored emotion data

[0496] Output: Identified emotional state

[0497] Step 6:

[0498] The server compares the health status values ​​with past data to detect any abnormalities, and if an abnormality is detected, the alert generation engine generates an alert.

[0499] Input: Health status, historical data

[0500] Output: Alert (if an anomaly is detected)

[0501] Step 7:

[0502] The server uses a solution suggestion engine to suggest specific solutions based on the alert and stores them in a database.

[0503] Input: alert

[0504] Output: Specific suggestions for how to deal with the problem

[0505] Step 8:

[0506] The server then operates a food suggestion engine to suggest suitable meals to the user, taking into account the identified emotional state, which are also stored in the database.

[0507] Input: Identified emotional state

[0508] Output: Food suggestions

[0509] Step 9:

[0510] The device will notify the user of the alert, along with suggested solutions and food, from the server. The user can then check the suggested solutions through the application and select food delivery.

[0511] Input: Server alerts, actions, food suggestions

[0512] Output: User notification, choice of action

[0513] Through these steps, the present invention comprehensively analyzes the user's lifestyle and emotions, and provides health management and optimal dietary suggestions.

[0514] 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.

[0515] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0516] 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.

[0517] [Second embodiment]

[0518] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0519] 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.

[0520] 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).

[0521] 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.

[0522] 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.

[0523] 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).

[0524] 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. 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.

[0525] 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.

[0526] 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.

[0527] 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.

[0528] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0529] 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."

[0530] This system allows users to input their daily lifestyle habits, quantifies their health status based on that data, and issues alerts as necessary. Here, we will explain in detail the functions and processing details of each element of this system.

[0531] Entering lifestyle data

[0532] User: Every day, they enter their lifestyle data through a smartphone application, mainly including their sleep time, diet, exercise, and stress level.

[0533] Terminal: The application converts the input data into a dedicated format and sends it to the server.

[0534] Receiving and storing data

[0535] Server: Receives and verifies lifestyle habit data sent from the device. After verification, the data is stored in a database. The stored data includes the user's identification information, details of each data item, and their input timestamp.

[0536] Quantifying health status

[0537] Server: The health status assessment engine starts working based on the saved data. Each lifestyle item (sleep time, diet, exercise, stress level) is weighted and points are calculated. For example, less than 7 hours of sleep is worth -10 points, a high-fat diet is worth -15 points, zero minutes of exercise is worth -20 points, and high stress is worth -10 points.

[0538] Server: The points for each category are added together to calculate an overall health score, which is rated out of 100.

[0539] Generate alerts

[0540] Server: Compares the health score with past data to check for any abnormalities. It compares the score with the score from previous times when the user was unwell, and if the current score is the same or lower, it determines that an abnormality has been detected.

[0541] Server: When an anomaly is detected, the alert generation engine automatically generates an alert message, which includes a warning and instructions for the user to take.

[0542] Suggested Actions

[0543] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as "increase hydration," "take early rest," and "do light exercise."

[0544] Terminal: Notifies the user of action guides and alert messages sent from the server, and prompts the user to take appropriate action through the application interface.

[0545] Processing details with concrete examples

[0546] User data entry and processing

[0547] Example Day 1

[0548] User: Enters the following information into the app: "Sleep: 5 hours, Diet: high fat, high calories, Exercise: 0 minutes, Stress level: high."

[0549] Terminal: Converts the input data into a format and sends it to the server.

[0550] Server: Receives the data, validates it, and stores it in a database.

[0551] Server: The health status assessment engine works and calculates an overall score of 40 points.

[0552] Server: The alert generation engine generates an alert because the score of 40 matches the score of a previous illness.

[0553] On your device: Display an alert saying, "Caution: Rest early and exercise."

[0554] Example Day 2

[0555] User: Enter the following information into the app: "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low."

[0556] Terminal: Converts data into a format and sends it to the server.

[0557] Server: Receives the data, validates it, and stores it in a database.

[0558] Server: The health status assessment engine works and calculates an overall score of 80 points.

[0559] Server: Confirms that there are no abnormalities and does not issue an alert.

[0560] Device: Displays "Your current health condition is good. Keep it up."

[0561] In this way, the system of the present invention can monitor the user's health condition in real time based on the user's daily lifestyle data and proactively manage their health, allowing the user to detect problems before they become unwell and take appropriate measures.

[0562] The processing flow will be explained below.

[0563] Step 1:

[0564] The user launches the smartphone application and inputs daily lifestyle data (sleep time, diet, exercise, stress level).

[0565] Step 2:

[0566] The terminal converts the lifestyle habit data entered by the user into an appropriate format and sends it to the server.

[0567] Step 3:

[0568] The server receives the data sent from the device. The server then verifies the accuracy and format of the data. Inaccurate or invalid data is rejected.

[0569] Step 4:

[0570] The server stores the validated data in a database, including the user's identity, details of each data item, and the date and time the data was entered.

[0571] Step 5:

[0572] The server retrieves the stored data and launches a health status assessment engine, which first adds or subtracts points for each lifestyle habit (sleep time, diet, exercise, stress level).

[0573] Step 6:

[0574] The server adds up the points for each category to calculate an overall health score, which the service rates using, for example, a score out of 100.

[0575] Step 7:

[0576] The server compares the calculated health score with past data, and if the health score is equal to or lower than the score from a previous health condition, it determines that an abnormality has been detected.

[0577] Step 8:

[0578] If the server detects an anomaly, it will launch an alert generation engine to generate a warning message, which will include information that the user should pay attention to and suggested solutions.

[0579] Step 9:

[0580] The server launches the action guide engine to generate specific measures to improve the user's health. The suggested measures include specific actions such as "increase hydration," "take early rest," and "perform light exercise."

[0581] Step 10:

[0582] The server sends the generated alert message and a solution to the terminal.

[0583] Step 11:

[0584] The device receives the message from the server and notifies the user via the notification bar or a pop-up within the app.

[0585] Step 12:

[0586] The user checks the notification and takes necessary measures according to the provided action guide.

[0587] The above is the specific flow of processing by the program in the system of the present invention. This system monitors the user's daily lifestyle habits in real time and proactively supports health management, making it possible to prevent poor health before it occurs.

[0588] Example 1

[0589] 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."

[0590] Conventional health management systems simply record users' lifestyle data, but are inadequate in assessing their health status in real time, detecting abnormalities early, or suggesting specific countermeasures. Furthermore, many assessment methods rely on specific lifestyle items rather than comprehensively assessing multiple lifestyle items, making it difficult for users to comprehensively understand their health status and take appropriate action. Furthermore, they lacked a mechanism for detecting abnormalities using past health data, making preventative health management impossible. To solve this problem, a system was needed that could comprehensively assess a user's health status in real time and suggest appropriate actions.

[0591] 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.

[0592] In this invention, the server includes means for receiving and verifying lifestyle habit data entered by the user, means for storing the data in a database and quantifying it based on each lifestyle habit item using a health status assessment engine, and means for checking the data against a past health score database to determine whether there is an abnormality and, if an abnormality is detected, for suggesting a specific countermeasure using an action guide engine. This makes it possible to evaluate the user's lifestyle habit data from multiple angles, detect abnormalities early, and suggest appropriate countermeasures.

[0593] "Lifestyle data" refers to data that records the user's daily actions and conditions, and specifically includes sleep time, dietary content, amount of exercise, stress level, and the like.

[0594] A "terminal" is a device through which a user inputs lifestyle habit data, and refers to a computing device such as a smartphone or tablet.

[0595] "Server" refers to a central processing unit that receives, verifies, and stores data sent from devices, analyzes the data to assess health status, and generates alerts and corrective actions.

[0596] The "database" is a storage device for holding lifestyle habit data received by the server, and is a system for managing information such as user identification information, details of each data item, and input timestamps.

[0597] The "health status assessment engine" refers to a program that executes an algorithm to weight each item based on stored lifestyle data and calculate an overall health score.

[0598] An "alert generation engine" refers to a program that automatically generates an alert message when an abnormality is detected based on the health score.

[0599] The "action guide engine" refers to a program that suggests specific measures to improve the user's health condition when an abnormality is detected.

[0600] The "past health score database" is a storage device that stores lifestyle habit data previously input by the user and the health score calculated based on that data, and is used as a reference when detecting abnormalities.

[0601] This invention is a system that allows users to input their daily lifestyle habits, quantifies their health status based on that data, and issues alerts as necessary. Here, we will explain how to implement this system, as well as its detailed procedures and configuration.

[0602] Entering lifestyle data

[0603] User: The user uses a device such as a smartphone or tablet to enter lifestyle data through a dedicated application, including items such as sleep time, diet, exercise, and stress level.

[0604] Sending data

[0605] Terminal: The data entered by the user is converted into a dedicated format (for example, JSON format) and sent to the server. HTTPS is used as the transmission protocol, ensuring secure data transfer.

[0606] Data Receipt and Validation

[0607] Server: Receives data sent from the terminal. After receiving, it verifies whether the data format is correct. If the format is invalid, it returns an error message.

[0608] Data storage

[0609] Server: Saves the verified data to the database. The saved data includes the user ID, details of each data item, and the input timestamp.

[0610] Health assessment

[0611] Server: The health assessment engine assigns points to each lifestyle habit based on the stored data and applies weighting logic to calculate an overall health score. For example, if you sleep less than 7 hours, you will receive -10 points.

[0612] Anomaly detection

[0613] Server: The calculated health score is checked against a database of past health scores to see if there are any abnormalities. It is compared with the score from a previous time when health condition worsened, and if it matches or is lower than the previous score, it is determined to be abnormal.

[0614] Generate alerts

[0615] Server: When an anomaly is detected, the alert generation engine automatically generates an alert message, which includes specific actions and warnings that the user should take.

[0616] Suggested Actions

[0617] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as drinking more water, resting early, and doing light exercise.

[0618] User Notification

[0619] Terminal: Notifies the user of the alert message sent from the server and how to deal with it. It displays it to the user through the application interface.

[0620] A specific example of the operation is shown below.

[0621] Example Day 1

[0622] User: Enters the following information into the app: "Sleep: 5 hours, Diet: high fat, high calories, Exercise: 0 minutes, Stress level: high."

[0623] Terminal: Converts the input data into a format and sends it to the server.

[0624] Server: Receives the data, validates it, and stores it in a database.

[0625] Server: The health status assessment engine works and calculates an overall score of 40 points.

[0626] Server: Compare with past data and determine abnormality at "40 points".

[0627] Server: Generate an alert and send a message saying "Caution: Rest early and exercise."

[0628] Terminal: Display an alert message to the user.

[0629] Example Day 2

[0630] User: Enter the following information into the app: "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low."

[0631] Terminal: Converts data into a format and sends it to the server.

[0632] Server: Receives the data, validates it, and stores it in a database.

[0633] Server: The health status assessment engine works and calculates an overall score of 80 points.

[0634] Server: Determines that there is no abnormality and does not issue an alert.

[0635] Device: Displays "Your current health condition is good. Keep it up."

[0636] In this way, the system of the present invention realizes proactive health management by evaluating the health condition of the user based on daily lifestyle habit data, detecting abnormalities early, and proposing appropriate countermeasures.

[0637] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0638] Step 1:

[0639] The user opens the application on their smartphone or tablet and enters their lifestyle habits data for the day.

[0640] The data entered includes sleep duration (e.g., 5 hours), dietary content (e.g., high fat, high calorie), amount of exercise (e.g., 0 minutes), and stress level (e.g., high).

[0641] (Input): Sleep time, diet, exercise, stress level

[0642] (Output): Lifestyle data (JSON format)

[0643] Step 2:

[0644] The terminal converts the input data into a dedicated format (JSON format) and sends it to the server via the HTTPS protocol.

[0645] For example, it generates the following JSON data:

[0646] {"sleep": 5, "food": "high-fat", "exercise": 0, "stress": "high"}

[0647] (Input): Lifestyle data (user input)

[0648] (Output): Formatted lifestyle data (JSON format)

[0649] Step 3:

[0650] The server receives the data sent from the terminal, verifies the validity of the format, and returns an error message if the format is invalid.

[0651] For example, check whether the sleep time is a negative number or whether the meal contents are an unclear string.

[0652] (Input): Formatted lifestyle data (JSON format)

[0653] (Output): Validation result (success or failure), error message (if necessary)

[0654] Step 4:

[0655] The server stores the successfully validated data in a database. The stored information includes the user ID, details of each data item, and the input timestamp.

[0656] For example, execute the following SQL statement:

[0657] INSERT INTO health_data (user_id, sleep, food, exercise, stress, timestamp) VALUES (1, 5, 'high-fat', 0, 'high', '2023-10-10 08:00:00');

[0658] (Input): Successfully verified lifestyle data

[0659] (Output): Save results to the database

[0660] Step 5:

[0661] The server runs a health status assessment engine based on the stored data, weighting each lifestyle item and calculating points.

[0662] For example, if you sleep less than 7 hours, you will receive -10 points.

[0663] (Input): Lifestyle data stored in the database

[0664] (Output): Points for each item, overall health score (e.g., 40 points)

[0665] Step 6:

[0666] The server checks the calculated health score against the past health score database to see if there are any abnormalities. If it is the same as or lower than the past data, it is determined to be abnormal.

[0667] For example, if the past score for a worsening condition was 40 points and the current score is also 40 points, it will be judged as abnormal.

[0668] (Input): Overall health score

[0669] (Output): Result of anomaly detection (normal or abnormal)

[0670] Step 7:

[0671] If an abnormality is detected in the server, an alert generation engine generates an alert message, which includes specific countermeasures that the user should take.

[0672] For example, create a message such as, "Caution: Rest early and do some light exercise."

[0673] (Input): Anomaly detection result (anomaly)

[0674] (Output): Alert message

[0675] Step 8:

[0676] The device notifies the user of the alert message sent from the server and the specific steps to take. The user can then check the alert and be prompted to take action through the application interface.

[0677] For example, a pop-up message might say, "Caution: Rest early and do some light exercise."

[0678] (Input): Alert message and how to deal with it

[0679] (Output):Notification to the user

[0680] This will enable users to monitor their health status in real time and take appropriate measures.

[0681] (Application example 1)

[0682] 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."

[0683] Conventional health management systems only require users to input their own lifestyle data, and are unable to monitor their health status in real time or provide effective feedback. Furthermore, the role of trainers at fitness facilities is limited to face-to-face instruction, making it difficult to comprehensively manage users' health. To address these issues, a system is needed that provides more advanced health management and personalized exercise recommendations for users.

[0684] 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.

[0685] In this invention, the server includes a means for a user to input lifestyle habit data, a means for the terminal to convert the input data into a format and send it to the server, a means for the server to verify the received data and store it in a database, a means for the server to quantify a health condition based on the stored data, a means for the server to compare the health condition numerical value with past data and detect abnormalities, a means for the server to generate an alert when an abnormality is detected, a means for the server to suggest specific countermeasures based on the alert, a means for the terminal to notify the user of the alert from the server and the countermeasures, a means for a fitness facility manager to monitor the user's health data in real time and suggest an optimal exercise menu for the user, and a means for displaying notifications on the user's smart glasses to provide real-time feedback during exercise, thereby enabling comprehensive management of the user's health condition and personalized exercise guidance.

[0686] "User" refers to an individual who inputs lifestyle data and receives health monitoring and measures.

[0687] "Lifestyle data" is information about the user's daily life, and specifically includes sleep time, dietary content, amount of exercise, and stress level.

[0688] A "terminal" is a device through which a user inputs lifestyle habit data, such as a smartphone or smart glasses.

[0689] "Converting to a format" refers to the process of converting lifestyle habit data entered by the user into a format suitable for the database.

[0690] "Server" refers to a central processing unit that stores and analyzes received data and provides relevant feedback to the user.

[0691] "Database" refers to a structured data storage system for storing user lifestyle data and other related data.

[0692] "Quantifying health status" refers to the process of calculating a comprehensive health score based on the user's lifestyle data.

[0693] "Detecting anomalies" refers to the process of identifying cases where current health status values ​​are judged to be abnormal by comparing them with past data.

[0694] An "alert" is a notification message that warns the user when an abnormality is detected and prompts them to take appropriate measures.

[0695] "Solutions" refer to specific actions or advice suggested to improve the user's health.

[0696] A "fitness facility manager" refers to a specialized staff member at a fitness center or gym who monitors users' health data and suggests exercise menus.

[0697] "Real-time monitoring" means that the fitness facility manager can continually check the user's latest health data and take immediate action.

[0698] "Exercise menu" refers to an exercise or fitness plan optimized for the user's health condition.

[0699] "Real-time feedback" refers to information that provides users with immediate advice and instructions during exercise to maximize the effectiveness of their exercise.

[0700] "Smart glasses" are wearable devices that users use while exercising and have the ability to display visual information.

[0701] The system embodying this invention uses various hardware and software components to enhance health management for users at fitness facilities, and is primarily composed of a smartphone, smart glasses, a server, a database, and a fitness facility management system.

[0702] System configuration

[0703] 1. User data entry

[0704] User: Uses a smartphone or smart glasses to input lifestyle data (sleep time, diet, exercise, stress level).

[0705] Terminal: A smartphone or smart glasses converts the input data into the appropriate format and sends it to the server.

[0706] 2. Receipt and storage of data

[0707] Server: Receives lifestyle data sent from the device and verifies the format and content of the data.

[0708] Database: The verified data is stored in a database, recording the user's identity and details of each data item, along with its input timestamp.

[0709] 3. Quantifying health status

[0710] Server: The health status assessment engine runs, weighting each lifestyle item (sleep time, diet, exercise, stress level) and calculating points. For example, if you sleep less than 7 hours, you will receive -10 points, and if you eat a balanced diet, you will receive +20 points.

[0711] Server: The points for each category are added together to calculate an overall health score, which is rated out of 100.

[0712] 4. Generate an alert

[0713] Server: Compares the health score with past data, and determines that an abnormality has been detected if the score is equal to or lower than the score when the user was previously unwell.

[0714] Server: If an anomaly is detected, the alert generation engine generates an alert message, optionally including instructions for remediation.

[0715] 5. Action Suggestions

[0716] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as increasing hydration, resting earlier, and light exercise.

[0717] Terminal: Notifies the user of action guides and alert messages sent from the server.

[0718] 6. Fitness facility management

[0719] Management system: Allows fitness facility managers to monitor users' health data in real time.

[0720] User guidance: Administrators can suggest optimal exercise routines for users and receive real-time feedback using smart glasses while they exercise.

[0721] Examples and prompts

[0722] As a concrete example, consider the case where a user enters the following data into a smartphone app:

[0723] Example 1: Signs of illness

[0724] User input: "Sleep time: 5 hours, Diet: High fat and high calories, Exercise time: 0 minutes, Stress level: High"

[0725] Server process:

[0726] 1. Data reception, verification, and storage.

[0727] 2. Health score calculation: 20 points.

[0728] 3. Generate an alert: "Your health score is declining. Take appropriate action."

[0729] 4. Action Suggestion: "Drink more fluids and get some sleep earlier."

[0730] Example 2: Good condition

[0731] User input: "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low"

[0732] Server process:

[0733] 1. Data reception, verification, and storage.

[0734] 2. Health score calculation: 80 points.

[0735] 3. No alerts.

[0736] 4. Action Suggestion: "Your current health is good. Keep it up."

[0737] Prompt example

[0738] Based on the health data of user ID "12345", calculate a health score and generate an alert message and action suggestions.

[0739] Input data:

[0740] Sleep time: 5 hours

[0741] Diet: High fat, high calorie

[0742] Momentum: 0 minutes

[0743] Stress level: High

[0744] Desired output:

[0745] Health Score

[0746] Alert Messages

[0747] action suggestion

[0748] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0749] Step 1:

[0750] The user enters lifestyle data using a smartphone or smart glasses. Specifically, the user opens the application's input form and enters information such as sleep time, diet, exercise, and stress level. Input: sleep time, diet, exercise, stress level. Output: formatted lifestyle data.

[0751] Step 2:

[0752] The terminal converts the input data into a format and sends it to the server. Specifically, the terminal converts the input data into structured data such as JSON format and sends it to the server's API endpoint. Input: Raw data from the user. Output: Formatted data.

[0753] Step 3:

[0754] The server receives the data and validates its format and content. Specifically, the server verifies that the received data is in the expected format and checks that it does not contain any invalid data. Input: Formatted lifestyle habit data. Output: Validated data.

[0755] Step 4:

[0756] The server stores the validated data in a database. Specifically, it uses a database engine (e.g., MySQL or PostgreSQL) to store the details of each data item and the input timestamp along with the user's identity. Input: Validated data. Output: Data stored in the database.

[0757] Step 5:

[0758] The server calculates a health score based on the stored data. Specifically, it weights sleep time, diet, exercise, and stress level to calculate an overall health score. For example, if you sleep less than 7 hours, you'll get -10 points, and if you eat a well-balanced diet, you'll get +20 points. Input: Lifestyle data stored in the database. Output: Health score.

[0759] Step 6:

[0760] The server compares the health score with past data to see if any abnormalities are detected. Specifically, it compares the score with the score from a previous time when the user was unwell, and if the score is equal to or lower than that, it is considered to be an abnormality. Input: Health score, past health data. Output: Anomaly detection flag.

[0761] Step 7:

[0762] If the server detects an abnormality, it generates an alert message. Specifically, the alert generation engine runs and generates a message such as "The health score is decreasing. Please take appropriate measures." Input: Anomaly detection flag. Output: Alert message.

[0763] Step 8:

[0764] The server generates an action guide and suggests specific countermeasures, such as "increase hydration and take early rest." Input: health score, anomaly detection flag. Output: countermeasures.

[0765] Step 9:

[0766] The device notifies the user of the alert from the server and how to deal with it. Specifically, a pop-up notification or message is displayed to the user using a smartphone or smart glasses. Input: Alert message, how to deal with it. Output: Notification to the user.

[0767] Step 10:

[0768] The management system provides users' health data to the fitness facility manager in real time. Specifically, the latest health data of users is monitored through an interface such as a dashboard for the manager. Input: Lifestyle data stored in a database. Output: Health data provided to the manager.

[0769] Step 11:

[0770] The administrator proposes the optimal exercise menu for the user and allows the user to receive real-time feedback using smart glasses while exercising. Specifically, the administrator creates a customized exercise menu based on the user's health and fitness data, and the user receives instructions through the smart glasses. Input: Instructions from the administrator dashboard, smart glasses. Output: Customized exercise menu, real-time feedback.

[0771] 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.

[0772] This invention is a system that inputs a user's daily lifestyle data, quantifies their health status based on that data, and issues alerts as needed, and by combining this with an emotion engine that recognizes the user's emotions, it achieves more accurate health management. Here, we will explain in detail the functions and processing content of each element of this system.

[0773] Entering lifestyle data

[0774] User: Every day, they enter their lifestyle data through a smartphone application, mainly including their sleep time, diet, exercise, and stress level.

[0775] Terminal: The application converts the input data into a dedicated format and sends it to the server.

[0776] Receiving and storing data

[0777] Server: Receives and verifies lifestyle habit data sent from the device. After verification, the data is stored in a database. The stored data includes the user's identification information, details of each data item, and their input timestamp.

[0778] Quantifying health status

[0779] Server: The health status assessment engine starts working based on the saved data. Each lifestyle item (sleep time, diet, exercise, stress level) is weighted and points are calculated. For example, less than 7 hours of sleep is worth -10 points, a high-fat diet is worth -15 points, zero minutes of exercise is worth -20 points, and high stress is worth -10 points.

[0780] Server: The points for each category are added together to calculate an overall health score, which is rated out of 100.

[0781] Emotion recognition by emotion engine

[0782] On the device: The emotion engine works to identify the user's emotions through voice analysis, facial recognition, and text analysis. For example, emotion data is collected when the user shows their facial expressions to the smartphone camera or speaks into the microphone.

[0783] Server: Receives emotion data sent from the device and stores it in a database. The stored emotion data includes the user's identification information, the identified emotion, and the date and time when the emotion was recognized.

[0784] Generate alerts

[0785] Server: Compares the health score with past data to check for any abnormalities. It compares the score with the score from previous times when the user was unwell, and if the current score is the same or lower, it determines that an abnormality has been detected.

[0786] Server: When an anomaly is detected, the alert generation engine automatically generates an alert message, which includes a warning and instructions for the user to take.

[0787] Server: The server also takes into account the emotional data identified by the emotion engine and adjusts the alert content as needed. For example, if the user's emotion is identified as "stress" or "anxiety," it generates a more specific and urgent alert.

[0788] Suggested Actions

[0789] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as "increase hydration," "take early rest," and "do light exercise."

[0790] Server: Adjusts the suggested coping strategies based on the emotion data identified by the emotion engine. For example, if the user's emotion is identified as "stress" or "fatigue," the server suggests additional coping strategies such as "relaxation techniques" or "playing relaxation music."

[0791] Terminal: Notifies the user of guidance and alert messages sent from the server, and prompts the user to take appropriate action through the application interface.

[0792] Processing details with concrete examples

[0793] User data entry and processing

[0794] Example Day 1

[0795] User: Enter the following information into the app: "Sleep time: 5 hours, Meals: High fat, High calories, Exercise: 0 minutes, Stress level: High." The app then captures the user's facial expression through the camera and says into the microphone, "I'm tired today."

[0796] Terminal: Converts input data and emotion data into a format and sends it to the server.

[0797] Server: Receives, verifies, and stores data in a database. The health status assessment engine calculates a total score of 40 points. In addition, the emotion engine stores the emotion data identified as "fatigue."

[0798] Server: The alert generation engine generates an alert because the score matches "40 points" from a previous time when the user was feeling unwell. Based on the data from the emotion engine, the alert is adjusted to read, "Current emotion: Fatigue. Caution: Take early rest and try relaxation techniques."

[0799] Terminal: Notify the user of the adjusted alert.

[0800] Example Day 2

[0801] User: Enters "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low" into the app. Also, says "I'm feeling good today" into the microphone.

[0802] Terminal: Converts data into a format and sends it to the server.

[0803] Server: Receives, verifies, and stores data in a database. The health assessment engine calculates an overall score of 80 points. The emotion engine also stores the emotion data identified as "happiness."

[0804] Server: Because the health score is high, no abnormalities are detected. Based on the data from the emotion engine, the display content is adjusted to say, "Current emotion: Happiness. Your current health condition is good. Keep it up."

[0805] Terminal: Notify the user of the adjusted message.

[0806] In this way, by combining the system of the present invention with an emotion engine, it is possible to comprehensively analyze a user's lifestyle data and emotion data, providing more accurate and personalized health management, allowing users to take specific measures to maintain their health while also taking into account their own emotional state.

[0807] The processing flow will be explained below.

[0808] Step 1:

[0809] The user launches the smartphone application and inputs daily lifestyle data (sleep time, dietary habits, exercise amount, stress level), and also inputs emotional data by showing facial expressions to the smartphone camera and speaking into the microphone.

[0810] Step 2:

[0811] The device converts the input lifestyle and emotion data into an appropriate format and sends it to the server. The format conversion is to improve data uniformity and processing efficiency.

[0812] Step 3:

[0813] The server receives the lifestyle and emotion data sent from the device, verifies the accuracy of the received data, and checks for any anomalies.

[0814] Step 4:

[0815] The server stores the validated data in a database, including the user's identity, details of each data item, and a timestamp of the data entry.

[0816] Step 5:

[0817] The server runs a health assessment engine based on the saved lifestyle data, adding or subtracting points for each lifestyle item (sleep time, diet, exercise, stress level) to calculate an overall health score.

[0818] Step 6:

[0819] The server starts the emotion engine and analyzes the transmitted emotion data. It identifies the user's emotion through voice analysis, face recognition, and text analysis, and stores the identified emotion data in a database.

[0820] Step 7:

[0821] The server integrates and analyzes the health score and emotion data, and compares it with past data to check for any abnormalities. If an abnormality is detected, the system proceeds to the next step.

[0822] Step 8:

[0823] When an anomaly is detected, the server activates the alert generation engine to generate a warning message, which includes a warning and action to be taken by the user.The server also adjusts the alert content based on the emotion identified by the emotion engine.

[0824] Step 9:

[0825] The server launches an action guide engine to suggest specific measures, such as "increase hydration," "take early rest," and "try relaxation techniques." Furthermore, the server adjusts the suggestions based on the emotion data recognized by the emotion engine.

[0826] Step 10:

[0827] The server sends the generated alert message and a solution to the terminal.

[0828] Step 11:

[0829] The terminal receives the message from the server and notifies the user through the application interface, prompting the user to take appropriate action.

[0830] Step 12:

[0831] The user checks the notification and takes necessary measures according to the action guide provided, allowing the user to take concrete actions to improve their health condition.

[0832] The above is the processing flow of the system of the present invention, which combines an emotion engine. This system analyzes the user's daily lifestyle data and emotion data in real time, and supports comprehensive health management, making it possible to prevent poor health before it occurs.

[0833] Example 2

[0834] 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."

[0835] In modern society, there are many systems that manage health status based on a user's daily lifestyle data. However, these systems do not take the user's emotional state into account, which can make it difficult to accurately detect changes or abnormalities in physical condition. Another issue is that they are unable to suggest appropriate measures based on the user's emotional state. The present invention aims to achieve more accurate and personalized health management by comprehensively utilizing a user's lifestyle data and emotional data.

[0836] 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 means for the user to input lifestyle habit data and emotional data such as voice and facial expressions; a means for the terminal to convert the input data into a format and send it to the server; a means for the server to verify the received data and store it in a database; a means for the server to quantify the health status based on the stored data; a means for the server to recognize the user's emotions using an emotion engine and store the data; a means for the server to compare the health status numerical value with past data and detect abnormalities; a means for the server to generate an alert when an abnormality is detected and adjust the alert based on the emotional data; a means for the server to propose a specific response based on the alert and adjust the response based on the emotional data; and a means for the terminal to notify the user of the alert from the server and the response. This enables accurate health management that takes the user's emotional state into consideration.

[0837] "User" refers to an individual who uses this system.

[0838] The term "terminal" refers to an electronic device that allows a user to input lifestyle habit data and emotion data and communicate with a server.

[0839] "Server" refers to the central computer system that receives, verifies, and stores input data, and runs various engines to quantify health status, recognize emotions, and generate alerts.

[0840] "Lifestyle data" is information about the user's daily activities, and specifically includes sleep time, dietary content, amount of exercise, and stress level.

[0841] "Emotional Data" refers to a user's emotional state as determined through voice analysis, facial recognition, and / or text analysis.

[0842] "Database" refers to a digital recording system for storing lifestyle and emotional data received from users.

[0843] "Health status assessment engine" refers to the program and algorithm for quantifying a user's health status based on stored lifestyle data.

[0844] "Emotion Engine" refers to programs and algorithms that identify emotions based on a user's voice analysis, facial recognition, and text analysis.

[0845] An "alert generation engine" refers to a program and algorithm that generates a message to notify the user of a warning or countermeasure when an abnormality is detected.

[0846] "Action Guidance Engine" refers to programs and algorithms that suggest specific actions to take based on a user's health and emotional state.

[0847] This system collects a user's daily lifestyle and emotional data, quantifies their health status based on the data, detects abnormalities, and proposes countermeasures. This system is composed of devices such as smartphones, a cloud server, and various engines that perform emotion recognition and data analysis.

[0848] Entering lifestyle data

[0849] Users: Every day, they enter lifestyle data using a dedicated smartphone app. Data entry items include sleep time, dietary habits, exercise, and stress levels. They also use a camera to capture facial expressions and a microphone to record emotional data.

[0850] Sending and Receiving Data

[0851] Terminal: The application converts the data entered by the user into a standard format, which is then sent to the server in real time.

[0852] Server: Receives data sent from the device and verifies the accuracy of the format and content. If invalid data is found, it is corrected or re-requested.

[0853] Data storage

[0854] Server: Saves the verified data in a database, including the user's identity, details of the data item, and the date and time of entry.

[0855] Quantifying health status

[0856] Server: The health status assessment engine starts running and calculates points by weighting each item based on the saved lifestyle data.

[0857] For example: less than 7 hours of sleep is worth -10 points, a high-fat diet is worth -15 points, zero minutes of exercise is worth -20 points, high stress is worth -10 points, etc.

[0858] Server: The points for each item are combined to calculate a health score, which is rated out of 100.

[0859] emotion recognition

[0860] On the device: The emotion engine performs voice analysis, facial recognition, and text analysis of the user. For example, emotion data is collected when the user shows their facial expressions to the smartphone camera or speaks into the microphone.

[0861] Terminal: The collected emotion data is converted into a format and sent to the server.

[0862] Server: Validates the emotion data received from the device and stores it in a database, including the user's identity, the identified emotion, and the date and time of recognition.

[0863] Generate alerts

[0864] Server: Compares the health score with past data to check for abnormalities. If the score matches or is lower than the score from a previous poor health condition, it is determined that an abnormality has been detected.

[0865] Server: When an anomaly is detected, the alert generation engine generates an alert message, which includes countermeasures and warnings. Additionally, it adjusts the alert content based on sentiment data.

[0866] Suggested Actions

[0867] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as drinking more water, resting early, and doing light exercise.

[0868] Server: Based on the emotional data, the server also adjusts coping methods. For example, if the emotion is identified as "stress" or "fatigue," it will suggest additional specific coping methods such as "relaxation techniques" or "playing relaxation music."

[0869] Terminal: Notifies the user of action guides and alert messages sent from the server, and prompts the user to take appropriate action through the application interface.

[0870] Processing details with concrete examples

[0871] Example Day 1

[0872] User: Enters "Sleep time: 5 hours, Meals: High fat, High calories, Exercise: 0 minutes, Stress level: High" into the app, captures facial expression with the camera, and says "I'm tired today" into the microphone.

[0873] Terminal: Converts data and emotion data into a format and sends it to the server.

[0874] Server: Receives, verifies, and stores the data in a database. The health status assessment engine calculates a total score of 40 points, and stores the emotion data identified as "fatigue" by the emotion engine.

[0875] Server: The alert generation engine generates an alert because the score matches "40 points" from a previous experience of illness. Based on the emotion data, an adjusted alert is generated: "Current emotion: Fatigue. Caution: Take early rest and try relaxation techniques."

[0876] Terminal: Notify the user of the adjusted alert.

[0877] Example Day 2

[0878] User: Enters "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low" into the app and says "I feel good today" into the microphone.

[0879] Terminal: Converts data into a format and sends it to the server.

[0880] Server: Receives, verifies, and stores the data in a database. The health assessment engine calculates an overall score of 80 points, and also stores the emotion data identified as "happiness" by the emotion engine.

[0881] Server: No abnormalities are detected because the health score is high. Based on the emotion data, the display is adjusted to say, "Current emotion: Happiness. Your current health condition is good. Keep it up."

[0882] Terminal: Notify the user of the adjusted message.

[0883] Example prompts for generative AI models

[0884] Sample prompt 1: "What alert message would you generate if the user has 5 hours of sleep, eaten a high-fat meal, exercised 0 minutes, and has a high stress level?"

[0885] Sample prompt 2: "When the user has 8 hours of sleep, a balanced diet, 30 minutes of exercise, and low stress, create a message to display based on the health score and sentiment analysis results."

[0886] The system of the present invention allows users to continuously and individually manage their health while taking into account their emotional state. This system combines an emotion engine and a health status assessment engine to comprehensively analyze lifestyle and emotional data, enabling more accurate alerts and suggested actions. This allows users to more accurately understand their daily health status and take appropriate measures.

[0887] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0888] Step 1:

[0889] User: Every day, the user enters lifestyle data using a dedicated smartphone app. The input fields include "sleep time," "meal details," "amount of exercise," and "stress level." The user also captures facial expressions using a camera and speaks into a microphone to input emotional data via voice. At this point, the input consists of lifestyle data and emotional data.

[0890] Step 2:

[0891] Terminal: The application converts the data entered by the user into a standard format. Specifically, it parses text and image data and converts it into structured data. This converted data is output and sent to the server in real time.

[0892] Step 3:

[0893] Server: Receives data sent from the terminal. Validates the accuracy of the content and format of the received data. For example, if a data item is missing or the format is incorrect, it is corrected. The validated data is output and proceeds to the next process.

[0894] Step 4:

[0895] Server: Saves the validated data to the database, including the user's identity, details of the data items, and the date and time of entry. The save process adds a new record to the database.

[0896] Step 5:

[0897] Server: The health status assessment engine begins operation. Based on the saved lifestyle data, points are calculated by weighting each item. For example, if you sleep less than 7 hours, you are assigned -10 points, if you eat a high-fat diet, you are assigned -15 points, if you exercise for 0 minutes, you are assigned -20 points, and if you are highly stressed, you are assigned -10 points. The results of this data processing are output as points.

[0898] Step 6:

[0899] Server: A health score is calculated by adding up the points for each item. The total score is stored in a database. The total score is then integrated with other lifestyle data and output.

[0900] Step 7:

[0901] Device: The emotion engine performs voice analysis, facial recognition, and text analysis of the user. For example, voice analysis identifies emotions from voice, and facial recognition analyzes facial expression data. Emotion data is input, and the identified emotion is output.

[0902] Step 8:

[0903] Terminal: The collected emotion data is converted into a format and sent to the server. This process outputs the emotion data converted into a standard format.

[0904] Step 9:

[0905] Server: Validates the emotion data received from the device and stores it in a database. After validating the emotion data, the data is stored and the identified emotion is added to the database.

[0906] Step 10:

[0907] Server: Compares the health score with past data to check for abnormalities. Compares the past health score with the current score, and if they match or are lower than the current score, an abnormality is detected. The detection result is output.

[0908] Step 11:

[0909] Server: When an anomaly is detected, the alert generation engine generates an alert message. This message includes countermeasures and warnings. Furthermore, the alert content is adjusted based on the emotion data. The adjusted alert message is output.

[0910] Step 12:

[0911] Server: The action guide engine runs and suggests specific measures to improve the user's health. Suggestions include "increase hydration," "take early rest," and "do light exercise." The measures are also adjusted based on emotion data. The suggested and adjusted measures are output.

[0912] Step 13:

[0913] Terminal: Notifies the user of action guides and alert messages sent from the server. Prompts the user to take appropriate action through the application interface. The notification content is output and displayed to the user.

[0914] (Application example 2)

[0915] 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."

[0916] The problem to be solved by the present invention is to combine a user's daily lifestyle data with emotional data to more accurately manage their health condition and provide personalized treatment methods and food suggestions. Conventional health management systems generally evaluate health status based solely on lifestyle data, without taking the user's emotional state into consideration. This has led to the problem of not reflecting emotional fluctuations that affect the user's health condition.

[0917] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0918] In this invention, the server includes means for receiving a user's emotional data and storing it in a database, means for analyzing the stored emotional data and identifying the emotional state that affects the user's health, and means for generating food recommendations based on the emotional state. This enables health management that takes the user's emotional state into consideration. Specific food recommendations and ordering procedures can be supported according to the user's emotional state, allowing for more effective health maintenance.

[0919] Definitions of important words

[0920] A "user" is a person who uses the system to input lifestyle data and emotion data.

[0921] "Lifestyle data" is a general term for data such as a user's sleep time, diet, amount of exercise, and stress level.

[0922] A "terminal" is a device through which a user inputs lifestyle habit data and emotion data and transmits the data to a server.

[0923] "Server" is a centralized management system that receives, verifies, stores, and analyzes data sent from the terminals.

[0924] The "database" is a location in the server where lifestyle habit data and emotion data are stored.

[0925] The "health condition" is a numerical value that represents the degree of health calculated based on the user's daily lifestyle data.

[0926] "Emotional data" refers to data related to emotions collected through voice analysis, facial recognition, and text analysis of users.

[0927] An "emotion engine" is software that analyzes a user's emotional data and identifies a specific emotional state.

[0928] "Food Suggestion" refers to suggesting optimal meals based on the user's health and emotional state.

[0929] An "alert" is a warning message that is sent to the user when an abnormality is detected.

[0930] "Solution" refers to specific actions or measures suggested to the user based on the alert.

[0931] MODE FOR CARRYING OUT THE INVENTION

[0932] The present invention is a system that accurately manages a user's health status based on daily lifestyle and emotional data, and provides personalized treatment methods and food suggestions. Specific implementation methods of this system are described below.

[0933] Hardware and Software Configuration

[0934] User

[0935] Smartphone: A device for inputting lifestyle and emotional data. Users input this data through an application.

[0936] Application: Software that collects lifestyle data (sleep, diet, exercise, stress levels) and emotional data (voice analysis, facial recognition, text analysis). The data is formatted and sent to a server.

[0937] server

[0938] Database: This is where the user's lifestyle and emotional data is stored.

[0939] Data Verification Engine: Validates data sent from the device and confirms its validity.

[0940] Health status assessment engine: Quantifies health status based on saved lifestyle data.

[0941] Emotion Engine: Analyzes user emotional data and identifies emotional states that affect health status.

[0942] Alert generation engine: Compares health status with historical data and generates alerts if anomalies are detected.

[0943] Solution suggestion engine: Based on the alert, it suggests specific solutions to the user.

[0944] Food recommendation engine: Suggests optimal meals based on the user's health and emotional state.

[0945] Receiving and analyzing data

[0946] Data entry and submission

[0947] Users input lifestyle and emotional data using a smartphone application, which is then formatted by the device and sent to a server.

[0948] Data validation and storage

[0949] The server validates the received data and confirms its validity, and once validated, it is stored in a database.

[0950] Quantifying health status and recognizing emotions

[0951] The server runs a health status assessment engine based on the saved lifestyle data to quantify the health status, while an emotion engine runs to analyze the user's emotion data and identify emotions that affect the health status.

[0952] Alert generation and solution suggestions

[0953] Health status values ​​are compared with past data, and if an abnormality is detected, an alert generation engine generates an alert. In addition, a countermeasure suggestion engine proposes specific countermeasures, which are notified to the user via the application.

[0954] Food suggestions

[0955] The server uses a food recommendation engine to suggest optimal meals to users based on their emotional state, allowing users to select and order meals that take into account their health and emotional state.

[0956] Specific examples

[0957] Example of user data entry

[0958] The user enters the following information into the app: "Sleep time: 7 hours, Diet: high protein, low fat, Exercise: 30 minutes, Stress level: low," and the app then detects "happiness" based on their facial expression.

[0959] Server Processing

[0960] The server stores the data in a database, and a health assessment engine calculates an overall score. An emotion engine identifies "happiness" and specific emotions, and a food recommendation engine suggests meals such as "grilled chicken salad."

[0961] Prompt Sentence Examples

[0962] Input prompt for the generative AI model:

[0963] "Build a system that provides personalized health management and food recommendations based on a user's daily lifestyle and emotional data. It receives the following data points and generates recommendations based on them: sleep duration, diet, exercise, stress level, and emotional state (voice analysis, facial recognition, text analysis). Please also clearly state the specific recommendation method and the logic behind it."

[0964] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0965] Program processing steps

[0966] Step 1:

[0967] The user starts the smartphone application and inputs lifestyle data (sleep time, dietary content, exercise amount, stress level) and emotional data (voice, facial expressions, text). This input data is then formatted using the application's formatting function.

[0968] Input: sleep time, diet, exercise, stress level, emotional data

[0969] Output: Formatted lifestyle and emotion data

[0970] Step 2:

[0971] The terminal transmits the formatted data to the server, which also includes the user's identification information.

[0972] Input: Formatted lifestyle data, emotion data, and identification information

[0973] Output: Data sent to the server

[0974] Step 3:

[0975] The server validates the received data and stores it in the database after confirming that it is valid.

[0976] Input: Data sent from the terminal

[0977] Output: Validated data, saved data

[0978] Step 4:

[0979] The server runs a health status evaluation engine based on the saved lifestyle data and quantifies the health status.

[0980] Input: Saved lifestyle data

[0981] Output: Health score

[0982] Step 5:

[0983] The server analyzes the stored emotional data using an emotion engine to recognize specific emotional states.

[0984] Input: Stored emotion data

[0985] Output: Identified emotional state

[0986] Step 6:

[0987] The server compares the health status values ​​with past data to detect any abnormalities, and if an abnormality is detected, the alert generation engine generates an alert.

[0988] Input: Health status, historical data

[0989] Output: Alert (if an anomaly is detected)

[0990] Step 7:

[0991] The server uses a solution suggestion engine to suggest specific solutions based on the alert and stores them in a database.

[0992] Input: alert

[0993] Output: Specific suggestions for how to deal with the problem

[0994] Step 8:

[0995] The server then operates a food suggestion engine to suggest suitable meals to the user, taking into account the identified emotional state, which are also stored in the database.

[0996] Input: Identified emotional state

[0997] Output: Food suggestions

[0998] Step 9:

[0999] The device will notify the user of the alert, along with suggested solutions and food, from the server. The user can then check the suggested solutions through the application and select food delivery.

[1000] Input: Server alerts, actions, food suggestions

[1001] Output: User notification, choice of action

[1002] Through these steps, the present invention comprehensively analyzes the user's lifestyle and emotions, and provides health management and optimal dietary suggestions.

[1003] 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.

[1004] 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.

[1005] 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.

[1006] [Third embodiment]

[1007] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1008] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1009] 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).

[1010] 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.

[1011] 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.

[1012] 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).

[1013] 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. 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.

[1014] 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.

[1015] 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.

[1016] 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.

[1017] 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.

[1018] 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."

[1019] This system allows users to input their daily lifestyle habits, quantifies their health status based on that data, and issues alerts as necessary. Here, we will explain in detail the functions and processing details of each element of this system.

[1020] Entering lifestyle data

[1021] User: Every day, they enter their lifestyle data through a smartphone application, mainly including their sleep time, diet, exercise, and stress level.

[1022] Terminal: The application converts the input data into a dedicated format and sends it to the server.

[1023] Receiving and storing data

[1024] Server: Receives and verifies lifestyle habit data sent from the device. After verification, the data is stored in a database. The stored data includes the user's identification information, details of each data item, and their input timestamp.

[1025] Quantifying health status

[1026] Server: The health status assessment engine starts working based on the saved data. Each lifestyle item (sleep time, diet, exercise, stress level) is weighted and points are calculated. For example, less than 7 hours of sleep is worth -10 points, a high-fat diet is worth -15 points, zero minutes of exercise is worth -20 points, and high stress is worth -10 points.

[1027] Server: The points for each category are added together to calculate an overall health score, which is rated out of 100.

[1028] Generate alerts

[1029] Server: Compares the health score with past data to check for any abnormalities. It compares the score with the score from previous times when the user was unwell, and if the current score is the same or lower, it determines that an abnormality has been detected.

[1030] Server: When an anomaly is detected, the alert generation engine automatically generates an alert message, which includes a warning and instructions for the user to take.

[1031] Suggested Actions

[1032] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as "increase hydration," "take early rest," and "do light exercise."

[1033] Terminal: Notifies the user of action guides and alert messages sent from the server, and prompts the user to take appropriate action through the application interface.

[1034] Processing details with concrete examples

[1035] User data entry and processing

[1036] Example Day 1

[1037] User: Enters the following information into the app: "Sleep: 5 hours, Diet: high fat, high calories, Exercise: 0 minutes, Stress level: high."

[1038] Terminal: Converts the input data into a format and sends it to the server.

[1039] Server: Receives the data, validates it, and stores it in a database.

[1040] Server: The health status assessment engine works and calculates an overall score of 40 points.

[1041] Server: The alert generation engine generates an alert because the score of 40 matches the score of a previous illness.

[1042] On your device: Display an alert saying, "Caution: Rest early and exercise."

[1043] Example Day 2

[1044] User: Enter the following information into the app: "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low."

[1045] Terminal: Converts data into a format and sends it to the server.

[1046] Server: Receives the data, validates it, and stores it in a database.

[1047] Server: The health status assessment engine works and calculates an overall score of 80 points.

[1048] Server: Confirms that there are no abnormalities and does not issue an alert.

[1049] Device: Displays "Your current health condition is good. Keep it up."

[1050] In this way, the system of the present invention can monitor the user's health condition in real time based on the user's daily lifestyle data and proactively manage their health, allowing the user to detect problems before they become unwell and take appropriate measures.

[1051] The processing flow will be explained below.

[1052] Step 1:

[1053] The user launches the smartphone application and inputs daily lifestyle data (sleep time, diet, exercise, stress level).

[1054] Step 2:

[1055] The terminal converts the lifestyle habit data entered by the user into an appropriate format and sends it to the server.

[1056] Step 3:

[1057] The server receives the data sent from the device. The server then verifies the accuracy and format of the data. Inaccurate or invalid data is rejected.

[1058] Step 4:

[1059] The server stores the validated data in a database, including the user's identity, details of each data item, and the date and time the data was entered.

[1060] Step 5:

[1061] The server retrieves the stored data and launches a health status assessment engine, which first adds or subtracts points for each lifestyle habit (sleep time, diet, exercise, stress level).

[1062] Step 6:

[1063] The server adds up the points for each category to calculate an overall health score, which the service rates using, for example, a score out of 100.

[1064] Step 7:

[1065] The server compares the calculated health score with past data, and if the health score is equal to or lower than the score from a previous health condition, it determines that an abnormality has been detected.

[1066] Step 8:

[1067] If the server detects an anomaly, it will launch an alert generation engine to generate a warning message, which will include information that the user should pay attention to and suggested solutions.

[1068] Step 9:

[1069] The server launches the action guide engine to generate specific measures to improve the user's health. The suggested measures include specific actions such as "increase hydration," "take early rest," and "perform light exercise."

[1070] Step 10:

[1071] The server sends the generated alert message and a solution to the terminal.

[1072] Step 11:

[1073] The device receives the message from the server and notifies the user via the notification bar or a pop-up within the app.

[1074] Step 12:

[1075] The user checks the notification and takes necessary measures according to the provided action guide.

[1076] The above is the specific flow of processing by the program in the system of the present invention. This system monitors the user's daily lifestyle habits in real time and proactively supports health management, making it possible to prevent poor health before it occurs.

[1077] Example 1

[1078] 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."

[1079] Conventional health management systems simply record users' lifestyle data, but are inadequate in assessing their health status in real time, detecting abnormalities early, or suggesting specific countermeasures. Furthermore, many assessment methods rely on specific lifestyle items rather than comprehensively assessing multiple lifestyle items, making it difficult for users to comprehensively understand their health status and take appropriate action. Furthermore, they lacked a mechanism for detecting abnormalities using past health data, making preventative health management impossible. To solve this problem, a system was needed that could comprehensively assess a user's health status in real time and suggest appropriate actions.

[1080] 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.

[1081] In this invention, the server includes means for receiving and verifying lifestyle habit data entered by the user, means for storing the data in a database and quantifying it based on each lifestyle habit item using a health status assessment engine, and means for checking the data against a past health score database to determine whether there is an abnormality and, if an abnormality is detected, for suggesting a specific countermeasure using an action guide engine. This makes it possible to evaluate the user's lifestyle habit data from multiple angles, detect abnormalities early, and suggest appropriate countermeasures.

[1082] "Lifestyle data" refers to data that records the user's daily actions and conditions, and specifically includes sleep time, dietary content, amount of exercise, stress level, and the like.

[1083] A "terminal" is a device through which a user inputs lifestyle habit data, and refers to a computing device such as a smartphone or tablet.

[1084] "Server" refers to a central processing unit that receives, verifies, and stores data sent from devices, analyzes the data to assess health status, and generates alerts and corrective actions.

[1085] The "database" is a storage device for holding lifestyle habit data received by the server, and is a system for managing information such as user identification information, details of each data item, and input timestamps.

[1086] The "health status assessment engine" refers to a program that executes an algorithm to weight each item based on stored lifestyle data and calculate an overall health score.

[1087] An "alert generation engine" refers to a program that automatically generates an alert message when an abnormality is detected based on the health score.

[1088] The "action guide engine" refers to a program that suggests specific measures to improve the user's health condition when an abnormality is detected.

[1089] The "past health score database" is a storage device that stores lifestyle habit data previously input by the user and the health score calculated based on that data, and is used as a reference when detecting abnormalities.

[1090] This invention is a system that allows users to input their daily lifestyle habits, quantifies their health status based on that data, and issues alerts as necessary. Here, we will explain how to implement this system, as well as its detailed procedures and configuration.

[1091] Entering lifestyle data

[1092] User: The user uses a device such as a smartphone or tablet to enter lifestyle data through a dedicated application, including items such as sleep time, diet, exercise, and stress level.

[1093] Sending data

[1094] Terminal: The data entered by the user is converted into a dedicated format (for example, JSON format) and sent to the server. HTTPS is used as the transmission protocol, ensuring secure data transfer.

[1095] Data Receipt and Validation

[1096] Server: Receives data sent from the terminal. After receiving, it verifies whether the data format is correct. If the format is invalid, it returns an error message.

[1097] Data storage

[1098] Server: Saves the verified data to the database. The saved data includes the user ID, details of each data item, and the input timestamp.

[1099] Health assessment

[1100] Server: The health assessment engine assigns points to each lifestyle habit based on the stored data and applies weighting logic to calculate an overall health score. For example, if you sleep less than 7 hours, you will receive -10 points.

[1101] Anomaly detection

[1102] Server: The calculated health score is checked against a database of past health scores to see if there are any abnormalities. It is compared with the score from a previous time when health condition worsened, and if it matches or is lower than the previous score, it is determined to be abnormal.

[1103] Generate alerts

[1104] Server: When an anomaly is detected, the alert generation engine automatically generates an alert message, which includes specific actions and warnings that the user should take.

[1105] Suggested Actions

[1106] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as drinking more water, resting early, and doing light exercise.

[1107] User Notification

[1108] Terminal: Notifies the user of the alert message sent from the server and how to deal with it. It displays it to the user through the application interface.

[1109] A specific example of the operation is shown below.

[1110] Example Day 1

[1111] User: Enters the following information into the app: "Sleep: 5 hours, Diet: high fat, high calories, Exercise: 0 minutes, Stress level: high."

[1112] Terminal: Converts the input data into a format and sends it to the server.

[1113] Server: Receives the data, validates it, and stores it in a database.

[1114] Server: The health status assessment engine works and calculates an overall score of 40 points.

[1115] Server: Compare with past data and determine abnormality at "40 points".

[1116] Server: Generate an alert and send a message saying "Caution: Rest early and exercise."

[1117] Terminal: Display an alert message to the user.

[1118] Example Day 2

[1119] User: Enter the following information into the app: "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low."

[1120] Terminal: Converts data into a format and sends it to the server.

[1121] Server: Receives the data, validates it, and stores it in a database.

[1122] Server: The health status assessment engine works and calculates an overall score of 80 points.

[1123] Server: Determines that there is no abnormality and does not issue an alert.

[1124] Device: Displays "Your current health condition is good. Keep it up."

[1125] In this way, the system of the present invention realizes proactive health management by evaluating the health condition of the user based on daily lifestyle habit data, detecting abnormalities early, and proposing appropriate countermeasures.

[1126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1127] Step 1:

[1128] The user opens the application on their smartphone or tablet and enters their lifestyle habits data for the day.

[1129] The data entered includes sleep duration (e.g., 5 hours), dietary content (e.g., high fat, high calorie), amount of exercise (e.g., 0 minutes), and stress level (e.g., high).

[1130] (Input): Sleep time, diet, exercise, stress level

[1131] (Output): Lifestyle data (JSON format)

[1132] Step 2:

[1133] The terminal converts the input data into a dedicated format (JSON format) and sends it to the server via the HTTPS protocol.

[1134] For example, it generates the following JSON data:

[1135] {"sleep": 5, "food": "high-fat", "exercise": 0, "stress": "high"}

[1136] (Input): Lifestyle data (user input)

[1137] (Output): Formatted lifestyle data (JSON format)

[1138] Step 3:

[1139] The server receives the data sent from the terminal, verifies the validity of the format, and returns an error message if the format is invalid.

[1140] For example, check whether the sleep time is a negative number or whether the meal contents are an unclear string.

[1141] (Input): Formatted lifestyle data (JSON format)

[1142] (Output): Validation result (success or failure), error message (if necessary)

[1143] Step 4:

[1144] The server stores the successfully validated data in a database. The stored information includes the user ID, details of each data item, and the input timestamp.

[1145] For example, execute the following SQL statement:

[1146] INSERT INTO health_data (user_id, sleep, food, exercise, stress, timestamp) VALUES (1, 5, 'high-fat', 0, 'high', '2023-10-10 08:00:00');

[1147] (Input): Successfully verified lifestyle data

[1148] (Output): Save results to the database

[1149] Step 5:

[1150] The server runs a health status assessment engine based on the stored data, weighting each lifestyle item and calculating points.

[1151] For example, if you sleep less than 7 hours, you will receive -10 points.

[1152] (Input): Lifestyle data stored in the database

[1153] (Output): Points for each item, overall health score (e.g., 40 points)

[1154] Step 6:

[1155] The server checks the calculated health score against the past health score database to see if there are any abnormalities. If it is the same as or lower than the past data, it is determined to be abnormal.

[1156] For example, if the past score for a worsening condition was 40 points and the current score is also 40 points, it will be judged as abnormal.

[1157] (Input): Overall health score

[1158] (Output): Result of anomaly detection (normal or abnormal)

[1159] Step 7:

[1160] If an abnormality is detected in the server, an alert generation engine generates an alert message, which includes specific countermeasures that the user should take.

[1161] For example, create a message such as, "Caution: Rest early and do some light exercise."

[1162] (Input): Anomaly detection result (anomaly)

[1163] (Output): Alert message

[1164] Step 8:

[1165] The device notifies the user of the alert message sent from the server and the specific steps to take. The user can then check the alert and be prompted to take action through the application interface.

[1166] For example, a pop-up message might say, "Caution: Rest early and do some light exercise."

[1167] (Input): Alert message and how to deal with it

[1168] (Output):Notification to the user

[1169] This will enable users to monitor their health status in real time and take appropriate measures.

[1170] (Application example 1)

[1171] 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."

[1172] Conventional health management systems only require users to input their own lifestyle data, and are unable to monitor their health status in real time or provide effective feedback. Furthermore, the role of trainers at fitness facilities is limited to face-to-face instruction, making it difficult to comprehensively manage users' health. To address these issues, a system is needed that provides more advanced health management and personalized exercise recommendations for users.

[1173] 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.

[1174] In this invention, the server includes a means for a user to input lifestyle habit data, a means for the terminal to convert the input data into a format and send it to the server, a means for the server to verify the received data and store it in a database, a means for the server to quantify a health condition based on the stored data, a means for the server to compare the health condition numerical value with past data and detect abnormalities, a means for the server to generate an alert when an abnormality is detected, a means for the server to suggest specific countermeasures based on the alert, a means for the terminal to notify the user of the alert from the server and the countermeasures, a means for a fitness facility manager to monitor the user's health data in real time and suggest an optimal exercise menu for the user, and a means for displaying notifications on the user's smart glasses to provide real-time feedback during exercise, thereby enabling comprehensive management of the user's health condition and personalized exercise guidance.

[1175] "User" refers to an individual who inputs lifestyle data and receives health monitoring and measures.

[1176] "Lifestyle data" is information about the user's daily life, and specifically includes sleep time, dietary content, amount of exercise, and stress level.

[1177] A "terminal" is a device through which a user inputs lifestyle habit data, such as a smartphone or smart glasses.

[1178] "Converting to a format" refers to the process of converting lifestyle habit data entered by the user into a format suitable for the database.

[1179] "Server" refers to a central processing unit that stores and analyzes received data and provides relevant feedback to the user.

[1180] "Database" refers to a structured data storage system for storing user lifestyle data and other related data.

[1181] "Quantifying health status" refers to the process of calculating a comprehensive health score based on the user's lifestyle data.

[1182] "Detecting anomalies" refers to the process of identifying cases where current health status values ​​are judged to be abnormal by comparing them with past data.

[1183] An "alert" is a notification message that warns the user when an abnormality is detected and prompts them to take appropriate measures.

[1184] "Solutions" refer to specific actions or advice suggested to improve the user's health.

[1185] A "fitness facility manager" refers to a specialized staff member at a fitness center or gym who monitors users' health data and suggests exercise menus.

[1186] "Real-time monitoring" means that the fitness facility manager can continually check the user's latest health data and take immediate action.

[1187] "Exercise menu" refers to an exercise or fitness plan optimized for the user's health condition.

[1188] "Real-time feedback" refers to information that provides users with immediate advice and instructions during exercise to maximize the effectiveness of their exercise.

[1189] "Smart glasses" are wearable devices that users use while exercising and have the ability to display visual information.

[1190] The system embodying this invention uses various hardware and software components to enhance health management for users at fitness facilities, and is primarily composed of a smartphone, smart glasses, a server, a database, and a fitness facility management system.

[1191] System configuration

[1192] 1. User data entry

[1193] User: Uses a smartphone or smart glasses to input lifestyle data (sleep time, diet, exercise, stress level).

[1194] Terminal: A smartphone or smart glasses converts the input data into the appropriate format and sends it to the server.

[1195] 2. Receipt and storage of data

[1196] Server: Receives lifestyle data sent from the device and verifies the format and content of the data.

[1197] Database: The verified data is stored in a database, recording the user's identity and details of each data item, along with its input timestamp.

[1198] 3. Quantifying health status

[1199] Server: The health status assessment engine runs, weighting each lifestyle item (sleep time, diet, exercise, stress level) and calculating points. For example, if you sleep less than 7 hours, you will receive -10 points, and if you eat a balanced diet, you will receive +20 points.

[1200] Server: The points for each category are added together to calculate an overall health score, which is rated out of 100.

[1201] 4. Generate an alert

[1202] Server: Compares the health score with past data, and determines that an abnormality has been detected if the score is equal to or lower than the score when the user was previously unwell.

[1203] Server: If an anomaly is detected, the alert generation engine generates an alert message, optionally including instructions for remediation.

[1204] 5. Action Suggestions

[1205] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as increasing hydration, resting earlier, and light exercise.

[1206] Terminal: Notifies the user of action guides and alert messages sent from the server.

[1207] 6. Fitness facility management

[1208] Management system: Allows fitness facility managers to monitor users' health data in real time.

[1209] User guidance: Administrators can suggest optimal exercise routines for users and receive real-time feedback using smart glasses while they exercise.

[1210] Examples and prompts

[1211] As a concrete example, consider the case where a user enters the following data into a smartphone app:

[1212] Example 1: Signs of illness

[1213] User input: "Sleep time: 5 hours, Diet: High fat and high calories, Exercise time: 0 minutes, Stress level: High"

[1214] Server process:

[1215] 1. Data reception, verification, and storage.

[1216] 2. Health score calculation: 20 points.

[1217] 3. Generate an alert: "Your health score is declining. Take appropriate action."

[1218] 4. Action Suggestion: "Drink more fluids and get some sleep earlier."

[1219] Example 2: Good condition

[1220] User input: "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low"

[1221] Server process:

[1222] 1. Data reception, verification, and storage.

[1223] 2. Health score calculation: 80 points.

[1224] 3. No alerts.

[1225] 4. Action Suggestion: "Your current health is good. Keep it up."

[1226] Prompt example

[1227] Based on the health data of user ID "12345", calculate a health score and generate an alert message and action suggestions.

[1228] Input data:

[1229] Sleep time: 5 hours

[1230] Diet: High fat, high calorie

[1231] Momentum: 0 minutes

[1232] Stress level: High

[1233] Desired output:

[1234] Health Score

[1235] Alert Messages

[1236] action suggestion

[1237] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1238] Step 1:

[1239] The user enters lifestyle data using a smartphone or smart glasses. Specifically, the user opens the application's input form and enters information such as sleep time, diet, exercise, and stress level. Input: sleep time, diet, exercise, stress level. Output: formatted lifestyle data.

[1240] Step 2:

[1241] The terminal converts the input data into a format and sends it to the server. Specifically, the terminal converts the input data into structured data such as JSON format and sends it to the server's API endpoint. Input: Raw data from the user. Output: Formatted data.

[1242] Step 3:

[1243] The server receives the data and validates its format and content. Specifically, the server verifies that the received data is in the expected format and checks that it does not contain any invalid data. Input: Formatted lifestyle habit data. Output: Validated data.

[1244] Step 4:

[1245] The server stores the validated data in a database. Specifically, it uses a database engine (e.g., MySQL or PostgreSQL) to store the details of each data item and the input timestamp along with the user's identity. Input: Validated data. Output: Data stored in the database.

[1246] Step 5:

[1247] The server calculates a health score based on the stored data. Specifically, it weights sleep time, diet, exercise, and stress level to calculate an overall health score. For example, if you sleep less than 7 hours, you'll get -10 points, and if you eat a well-balanced diet, you'll get +20 points. Input: Lifestyle data stored in the database. Output: Health score.

[1248] Step 6:

[1249] The server compares the health score with past data to see if any abnormalities are detected. Specifically, it compares the score with the score from a previous time when the user was unwell, and if the score is equal to or lower than that, it is considered to be an abnormality. Input: Health score, past health data. Output: Anomaly detection flag.

[1250] Step 7:

[1251] If the server detects an abnormality, it generates an alert message. Specifically, the alert generation engine runs and generates a message such as "The health score is decreasing. Please take appropriate measures." Input: Anomaly detection flag. Output: Alert message.

[1252] Step 8:

[1253] The server generates an action guide and suggests specific countermeasures, such as "increase hydration and take early rest." Input: health score, anomaly detection flag. Output: countermeasures.

[1254] Step 9:

[1255] The device notifies the user of the alert from the server and how to deal with it. Specifically, a pop-up notification or message is displayed to the user using a smartphone or smart glasses. Input: Alert message, how to deal with it. Output: Notification to the user.

[1256] Step 10:

[1257] The management system provides users' health data to the fitness facility manager in real time. Specifically, the latest health data of users is monitored through an interface such as a dashboard for the manager. Input: Lifestyle data stored in a database. Output: Health data provided to the manager.

[1258] Step 11:

[1259] The administrator proposes the optimal exercise menu for the user and allows the user to receive real-time feedback using smart glasses while exercising. Specifically, the administrator creates a customized exercise menu based on the user's health and fitness data, and the user receives instructions through the smart glasses. Input: Instructions from the administrator dashboard, smart glasses. Output: Customized exercise menu, real-time feedback.

[1260] 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.

[1261] This invention is a system that inputs a user's daily lifestyle data, quantifies their health status based on that data, and issues alerts as needed, and by combining this with an emotion engine that recognizes the user's emotions, it achieves more accurate health management. Here, we will explain in detail the functions and processing content of each element of this system.

[1262] Entering lifestyle data

[1263] User: Every day, they enter their lifestyle data through a smartphone application, mainly including their sleep time, diet, exercise, and stress level.

[1264] Terminal: The application converts the input data into a dedicated format and sends it to the server.

[1265] Receiving and storing data

[1266] Server: Receives and verifies lifestyle habit data sent from the device. After verification, the data is stored in a database. The stored data includes the user's identification information, details of each data item, and their input timestamp.

[1267] Quantifying health status

[1268] Server: The health status assessment engine starts working based on the saved data. Each lifestyle item (sleep time, diet, exercise, stress level) is weighted and points are calculated. For example, less than 7 hours of sleep is worth -10 points, a high-fat diet is worth -15 points, zero minutes of exercise is worth -20 points, and high stress is worth -10 points.

[1269] Server: The points for each category are added together to calculate an overall health score, which is rated out of 100.

[1270] Emotion recognition by emotion engine

[1271] On the device: The emotion engine works to identify the user's emotions through voice analysis, facial recognition, and text analysis. For example, emotion data is collected when the user shows their facial expressions to the smartphone camera or speaks into the microphone.

[1272] Server: Receives emotion data sent from the device and stores it in a database. The stored emotion data includes the user's identification information, the identified emotion, and the date and time when the emotion was recognized.

[1273] Generate alerts

[1274] Server: Compares the health score with past data to check for any abnormalities. It compares the score with the score from previous times when the user was unwell, and if the current score is the same or lower, it determines that an abnormality has been detected.

[1275] Server: When an anomaly is detected, the alert generation engine automatically generates an alert message, which includes a warning and instructions for the user to take.

[1276] Server: The server also takes into account the emotional data identified by the emotion engine and adjusts the alert content as needed. For example, if the user's emotion is identified as "stress" or "anxiety," it generates a more specific and urgent alert.

[1277] Suggested Actions

[1278] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as "increase hydration," "take early rest," and "do light exercise."

[1279] Server: Adjusts the suggested coping strategies based on the emotion data identified by the emotion engine. For example, if the user's emotion is identified as "stress" or "fatigue," the server suggests additional coping strategies such as "relaxation techniques" or "playing relaxation music."

[1280] Terminal: Notifies the user of guidance and alert messages sent from the server, and prompts the user to take appropriate action through the application interface.

[1281] Processing details with concrete examples

[1282] User data entry and processing

[1283] Example Day 1

[1284] User: Enter the following information into the app: "Sleep time: 5 hours, Meals: High fat, High calories, Exercise: 0 minutes, Stress level: High." The app then captures the user's facial expression through the camera and says into the microphone, "I'm tired today."

[1285] Terminal: Converts input data and emotion data into a format and sends it to the server.

[1286] Server: Receives, verifies, and stores data in a database. The health status assessment engine calculates a total score of 40 points. In addition, the emotion engine stores the emotion data identified as "fatigue."

[1287] Server: The alert generation engine generates an alert because the score matches "40 points" from a previous time when the user was feeling unwell. Based on the data from the emotion engine, the alert is adjusted to read, "Current emotion: Fatigue. Caution: Take early rest and try relaxation techniques."

[1288] Terminal: Notify the user of the adjusted alert.

[1289] Example Day 2

[1290] User: Enters "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low" into the app. Also, says "I'm feeling good today" into the microphone.

[1291] Terminal: Converts data into a format and sends it to the server.

[1292] Server: Receives, verifies, and stores data in a database. The health assessment engine calculates an overall score of 80 points. The emotion engine also stores the emotion data identified as "happiness."

[1293] Server: Because the health score is high, no abnormalities are detected. Based on the data from the emotion engine, the display content is adjusted to say, "Current emotion: Happiness. Your current health condition is good. Keep it up."

[1294] Terminal: Notify the user of the adjusted message.

[1295] In this way, by combining the system of the present invention with an emotion engine, it is possible to comprehensively analyze a user's lifestyle data and emotion data, providing more accurate and personalized health management, allowing users to take specific measures to maintain their health while also taking into account their own emotional state.

[1296] The processing flow will be explained below.

[1297] Step 1:

[1298] The user launches the smartphone application and inputs daily lifestyle data (sleep time, dietary habits, exercise amount, stress level), and also inputs emotional data by showing facial expressions to the smartphone camera and speaking into the microphone.

[1299] Step 2:

[1300] The device converts the input lifestyle and emotion data into an appropriate format and sends it to the server. The format conversion is to improve data uniformity and processing efficiency.

[1301] Step 3:

[1302] The server receives the lifestyle and emotion data sent from the device, verifies the accuracy of the received data, and checks for any anomalies.

[1303] Step 4:

[1304] The server stores the validated data in a database, including the user's identity, details of each data item, and a timestamp of the data entry.

[1305] Step 5:

[1306] The server runs a health assessment engine based on the saved lifestyle data, adding or subtracting points for each lifestyle item (sleep time, diet, exercise, stress level) to calculate an overall health score.

[1307] Step 6:

[1308] The server starts the emotion engine and analyzes the transmitted emotion data. It identifies the user's emotion through voice analysis, face recognition, and text analysis, and stores the identified emotion data in a database.

[1309] Step 7:

[1310] The server integrates and analyzes the health score and emotion data, and compares it with past data to check for any abnormalities. If an abnormality is detected, the system proceeds to the next step.

[1311] Step 8:

[1312] When an anomaly is detected, the server activates the alert generation engine to generate a warning message, which includes a warning and action to be taken by the user.The server also adjusts the alert content based on the emotion identified by the emotion engine.

[1313] Step 9:

[1314] The server launches an action guide engine to suggest specific measures, such as "increase hydration," "take early rest," and "try relaxation techniques." Furthermore, the server adjusts the suggestions based on the emotion data recognized by the emotion engine.

[1315] Step 10:

[1316] The server sends the generated alert message and a solution to the terminal.

[1317] Step 11:

[1318] The terminal receives the message from the server and notifies the user through the application interface, prompting the user to take appropriate action.

[1319] Step 12:

[1320] The user checks the notification and takes necessary measures according to the action guide provided, allowing the user to take concrete actions to improve their health condition.

[1321] The above is the processing flow of the system of the present invention, which combines an emotion engine. This system analyzes the user's daily lifestyle data and emotion data in real time, and supports comprehensive health management, making it possible to prevent poor health before it occurs.

[1322] Example 2

[1323] 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."

[1324] In modern society, there are many systems that manage health status based on a user's daily lifestyle data. However, these systems do not take the user's emotional state into account, which can make it difficult to accurately detect changes or abnormalities in physical condition. Another issue is that they are unable to suggest appropriate measures based on the user's emotional state. The present invention aims to achieve more accurate and personalized health management by comprehensively utilizing a user's lifestyle data and emotional data.

[1325] 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 means for the user to input lifestyle habit data and emotional data such as voice and facial expressions; a means for the terminal to convert the input data into a format and send it to the server; a means for the server to verify the received data and store it in a database; a means for the server to quantify the health status based on the stored data; a means for the server to recognize the user's emotions using an emotion engine and store the data; a means for the server to compare the health status numerical value with past data and detect abnormalities; a means for the server to generate an alert when an abnormality is detected and adjust the alert based on the emotional data; a means for the server to propose a specific response based on the alert and adjust the response based on the emotional data; and a means for the terminal to notify the user of the alert from the server and the response. This enables accurate health management that takes the user's emotional state into consideration.

[1326] "User" refers to an individual who uses this system.

[1327] The term "terminal" refers to an electronic device that allows a user to input lifestyle habit data and emotion data and communicate with a server.

[1328] "Server" refers to the central computer system that receives, verifies, and stores input data, and runs various engines to quantify health status, recognize emotions, and generate alerts.

[1329] "Lifestyle data" is information about the user's daily activities, and specifically includes sleep time, dietary content, amount of exercise, and stress level.

[1330] "Emotional Data" refers to a user's emotional state as determined through voice analysis, facial recognition, and / or text analysis.

[1331] "Database" refers to a digital recording system for storing lifestyle and emotional data received from users.

[1332] "Health status assessment engine" refers to the program and algorithm for quantifying a user's health status based on stored lifestyle data.

[1333] "Emotion Engine" refers to programs and algorithms that identify emotions based on a user's voice analysis, facial recognition, and text analysis.

[1334] An "alert generation engine" refers to a program and algorithm that generates a message to notify the user of a warning or countermeasure when an abnormality is detected.

[1335] "Action Guidance Engine" refers to programs and algorithms that suggest specific actions to take based on a user's health and emotional state.

[1336] This system collects a user's daily lifestyle and emotional data, quantifies their health status based on the data, detects abnormalities, and proposes countermeasures. This system is composed of devices such as smartphones, a cloud server, and various engines that perform emotion recognition and data analysis.

[1337] Entering lifestyle data

[1338] Users: Every day, they enter lifestyle data using a dedicated smartphone app. Data entry items include sleep time, dietary habits, exercise, and stress levels. They also use a camera to capture facial expressions and a microphone to record emotional data.

[1339] Sending and Receiving Data

[1340] Terminal: The application converts the data entered by the user into a standard format, which is then sent to the server in real time.

[1341] Server: Receives data sent from the device and verifies the accuracy of the format and content. If invalid data is found, it is corrected or re-requested.

[1342] Data storage

[1343] Server: Saves the verified data in a database, including the user's identity, details of the data item, and the date and time of entry.

[1344] Quantifying health status

[1345] Server: The health status assessment engine starts running and calculates points by weighting each item based on the saved lifestyle data.

[1346] For example: less than 7 hours of sleep is worth -10 points, a high-fat diet is worth -15 points, zero minutes of exercise is worth -20 points, high stress is worth -10 points, etc.

[1347] Server: The points for each item are combined to calculate a health score, which is rated out of 100.

[1348] emotion recognition

[1349] On the device: The emotion engine performs voice analysis, facial recognition, and text analysis of the user. For example, emotion data is collected when the user shows their facial expressions to the smartphone camera or speaks into the microphone.

[1350] Terminal: The collected emotion data is converted into a format and sent to the server.

[1351] Server: Validates the emotion data received from the device and stores it in a database, including the user's identity, the identified emotion, and the date and time of recognition.

[1352] Generate alerts

[1353] Server: Compares the health score with past data to check for abnormalities. If the score matches or is lower than the score from a previous poor health condition, it is determined that an abnormality has been detected.

[1354] Server: When an anomaly is detected, the alert generation engine generates an alert message, which includes countermeasures and warnings. Additionally, it adjusts the alert content based on sentiment data.

[1355] Suggested Actions

[1356] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as drinking more water, resting early, and doing light exercise.

[1357] Server: Based on the emotional data, the server also adjusts coping methods. For example, if the emotion is identified as "stress" or "fatigue," it will suggest additional specific coping methods such as "relaxation techniques" or "playing relaxation music."

[1358] Terminal: Notifies the user of action guides and alert messages sent from the server, and prompts the user to take appropriate action through the application interface.

[1359] Processing details with concrete examples

[1360] Example Day 1

[1361] User: Enters "Sleep time: 5 hours, Meals: High fat, High calories, Exercise: 0 minutes, Stress level: High" into the app, captures facial expression with the camera, and says "I'm tired today" into the microphone.

[1362] Terminal: Converts data and emotion data into a format and sends it to the server.

[1363] Server: Receives, verifies, and stores the data in a database. The health status assessment engine calculates a total score of 40 points, and stores the emotion data identified as "fatigue" by the emotion engine.

[1364] Server: The alert generation engine generates an alert because the score matches "40 points" from a previous experience of illness. Based on the emotion data, an adjusted alert is generated: "Current emotion: Fatigue. Caution: Take early rest and try relaxation techniques."

[1365] Terminal: Notify the user of the adjusted alert.

[1366] Example Day 2

[1367] User: Enters "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low" into the app and says "I feel good today" into the microphone.

[1368] Terminal: Converts data into a format and sends it to the server.

[1369] Server: Receives, verifies, and stores the data in a database. The health assessment engine calculates an overall score of 80 points, and also stores the emotion data identified as "happiness" by the emotion engine.

[1370] Server: No abnormalities are detected because the health score is high. Based on the emotion data, the display is adjusted to say, "Current emotion: Happiness. Your current health condition is good. Keep it up."

[1371] Terminal: Notify the user of the adjusted message.

[1372] Example prompts for generative AI models

[1373] Sample prompt 1: "What alert message would you generate if the user has 5 hours of sleep, eaten a high-fat meal, exercised 0 minutes, and has a high stress level?"

[1374] Sample prompt 2: "When the user has 8 hours of sleep, a balanced diet, 30 minutes of exercise, and low stress, create a message to display based on the health score and sentiment analysis results."

[1375] The system of the present invention allows users to continuously and individually manage their health while taking into account their emotional state. This system combines an emotion engine and a health status assessment engine to comprehensively analyze lifestyle and emotional data, enabling more accurate alerts and suggested actions. This allows users to more accurately understand their daily health status and take appropriate measures.

[1376] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1377] Step 1:

[1378] User: Every day, the user enters lifestyle data using a dedicated smartphone app. The input fields include "sleep time," "meal details," "amount of exercise," and "stress level." The user also captures facial expressions using a camera and speaks into a microphone to input emotional data via voice. At this point, the input consists of lifestyle data and emotional data.

[1379] Step 2:

[1380] Terminal: The application converts the data entered by the user into a standard format. Specifically, it parses text and image data and converts it into structured data. This converted data is output and sent to the server in real time.

[1381] Step 3:

[1382] Server: Receives data sent from the terminal. Validates the accuracy of the content and format of the received data. For example, if a data item is missing or the format is incorrect, it is corrected. The validated data is output and proceeds to the next process.

[1383] Step 4:

[1384] Server: Saves the validated data to the database, including the user's identity, details of the data items, and the date and time of entry. The save process adds a new record to the database.

[1385] Step 5:

[1386] Server: The health status assessment engine begins operation. Based on the saved lifestyle data, points are calculated by weighting each item. For example, if you sleep less than 7 hours, you are assigned -10 points, if you eat a high-fat diet, you are assigned -15 points, if you exercise for 0 minutes, you are assigned -20 points, and if you are highly stressed, you are assigned -10 points. The results of this data processing are output as points.

[1387] Step 6:

[1388] Server: A health score is calculated by adding up the points for each item. The total score is stored in a database. The total score is then integrated with other lifestyle data and output.

[1389] Step 7:

[1390] Device: The emotion engine performs voice analysis, facial recognition, and text analysis of the user. For example, voice analysis identifies emotions from voice, and facial recognition analyzes facial expression data. Emotion data is input, and the identified emotion is output.

[1391] Step 8:

[1392] Terminal: The collected emotion data is converted into a format and sent to the server. This process outputs the emotion data converted into a standard format.

[1393] Step 9:

[1394] Server: Validates the emotion data received from the device and stores it in a database. After validating the emotion data, the data is stored and the identified emotion is added to the database.

[1395] Step 10:

[1396] Server: Compares the health score with past data to check for abnormalities. Compares the past health score with the current score, and if they match or are lower than the current score, an abnormality is detected. The detection result is output.

[1397] Step 11:

[1398] Server: When an anomaly is detected, the alert generation engine generates an alert message. This message includes countermeasures and warnings. Furthermore, the alert content is adjusted based on the emotion data. The adjusted alert message is output.

[1399] Step 12:

[1400] Server: The action guide engine runs and suggests specific measures to improve the user's health. Suggestions include "increase hydration," "take early rest," and "do light exercise." The measures are also adjusted based on emotion data. The suggested and adjusted measures are output.

[1401] Step 13:

[1402] Terminal: Notifies the user of action guides and alert messages sent from the server. Prompts the user to take appropriate action through the application interface. The notification content is output and displayed to the user.

[1403] (Application example 2)

[1404] 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."

[1405] The problem to be solved by the present invention is to combine a user's daily lifestyle data with emotional data to more accurately manage their health condition and provide personalized treatment methods and food suggestions. Conventional health management systems generally evaluate health status based solely on lifestyle data, without taking the user's emotional state into consideration. This has led to the problem of not reflecting emotional fluctuations that affect the user's health condition.

[1406] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1407] In this invention, the server includes means for receiving a user's emotional data and storing it in a database, means for analyzing the stored emotional data and identifying the emotional state that affects the user's health, and means for generating food recommendations based on the emotional state. This enables health management that takes the user's emotional state into consideration. Specific food recommendations and ordering procedures can be supported according to the user's emotional state, allowing for more effective health maintenance.

[1408] Definitions of important words

[1409] A "user" is a person who uses the system to input lifestyle data and emotion data.

[1410] "Lifestyle data" is a general term for data such as a user's sleep time, diet, amount of exercise, and stress level.

[1411] A "terminal" is a device through which a user inputs lifestyle habit data and emotion data and transmits the data to a server.

[1412] "Server" is a centralized management system that receives, verifies, stores, and analyzes data sent from the terminals.

[1413] The "database" is a location in the server where lifestyle habit data and emotion data are stored.

[1414] The "health condition" is a numerical value that represents the degree of health calculated based on the user's daily lifestyle data.

[1415] "Emotional data" refers to data related to emotions collected through voice analysis, facial recognition, and text analysis of users.

[1416] An "emotion engine" is software that analyzes a user's emotional data and identifies a specific emotional state.

[1417] "Food Suggestion" refers to suggesting optimal meals based on the user's health and emotional state.

[1418] An "alert" is a warning message that is sent to the user when an abnormality is detected.

[1419] "Solution" refers to specific actions or measures suggested to the user based on the alert.

[1420] MODE FOR CARRYING OUT THE INVENTION

[1421] The present invention is a system that accurately manages a user's health status based on daily lifestyle and emotional data, and provides personalized treatment methods and food suggestions. Specific implementation methods of this system are described below.

[1422] Hardware and Software Configuration

[1423] User

[1424] Smartphone: A device for inputting lifestyle and emotional data. Users input this data through an application.

[1425] Application: Software that collects lifestyle data (sleep, diet, exercise, stress levels) and emotional data (voice analysis, facial recognition, text analysis). The data is formatted and sent to a server.

[1426] server

[1427] Database: This is where the user's lifestyle and emotional data is stored.

[1428] Data Verification Engine: Validates data sent from the device and confirms its validity.

[1429] Health status assessment engine: Quantifies health status based on saved lifestyle data.

[1430] Emotion Engine: Analyzes user emotional data and identifies emotional states that affect health status.

[1431] Alert generation engine: Compares health status with historical data and generates alerts if anomalies are detected.

[1432] Solution suggestion engine: Based on the alert, it suggests specific solutions to the user.

[1433] Food recommendation engine: Suggests optimal meals based on the user's health and emotional state.

[1434] Receiving and analyzing data

[1435] Data entry and submission

[1436] Users input lifestyle and emotional data using a smartphone application, which is then formatted by the device and sent to a server.

[1437] Data validation and storage

[1438] The server validates the received data and confirms its validity, and once validated, it is stored in a database.

[1439] Quantifying health status and recognizing emotions

[1440] The server runs a health status assessment engine based on the saved lifestyle data to quantify the health status, while an emotion engine runs to analyze the user's emotion data and identify emotions that affect the health status.

[1441] Alert generation and solution suggestions

[1442] Health status values ​​are compared with past data, and if an abnormality is detected, an alert generation engine generates an alert. In addition, a countermeasure suggestion engine proposes specific countermeasures, which are notified to the user via the application.

[1443] Food suggestions

[1444] The server uses a food recommendation engine to suggest optimal meals to users based on their emotional state, allowing users to select and order meals that take into account their health and emotional state.

[1445] Specific examples

[1446] Example of user data entry

[1447] The user enters the following information into the app: "Sleep time: 7 hours, Diet: high protein, low fat, Exercise: 30 minutes, Stress level: low," and the app then detects "happiness" based on their facial expression.

[1448] Server Processing

[1449] The server stores the data in a database, and a health assessment engine calculates an overall score. An emotion engine identifies "happiness" and specific emotions, and a food recommendation engine suggests meals such as "grilled chicken salad."

[1450] Prompt Sentence Examples

[1451] Input prompt for the generative AI model:

[1452] "Build a system that provides personalized health management and food recommendations based on a user's daily lifestyle and emotional data. It receives the following data points and generates recommendations based on them: sleep duration, diet, exercise, stress level, and emotional state (voice analysis, facial recognition, text analysis). Please also clearly state the specific recommendation method and the logic behind it."

[1453] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1454] Program processing steps

[1455] Step 1:

[1456] The user starts the smartphone application and inputs lifestyle data (sleep time, dietary content, exercise amount, stress level) and emotional data (voice, facial expressions, text). This input data is then formatted using the application's formatting function.

[1457] Input: sleep time, diet, exercise, stress level, emotional data

[1458] Output: Formatted lifestyle and emotion data

[1459] Step 2:

[1460] The terminal transmits the formatted data to the server, which also includes the user's identification information.

[1461] Input: Formatted lifestyle data, emotion data, and identification information

[1462] Output: Data sent to the server

[1463] Step 3:

[1464] The server validates the received data and stores it in the database after confirming that it is valid.

[1465] Input: Data sent from the terminal

[1466] Output: Validated data, saved data

[1467] Step 4:

[1468] The server runs a health status evaluation engine based on the saved lifestyle data and quantifies the health status.

[1469] Input: Saved lifestyle data

[1470] Output: Health score

[1471] Step 5:

[1472] The server analyzes the stored emotional data using an emotion engine to recognize specific emotional states.

[1473] Input: Stored emotion data

[1474] Output: Identified emotional state

[1475] Step 6:

[1476] The server compares the health status values ​​with past data to detect any abnormalities, and if an abnormality is detected, the alert generation engine generates an alert.

[1477] Input: Health status, historical data

[1478] Output: Alert (if an anomaly is detected)

[1479] Step 7:

[1480] The server uses a solution suggestion engine to suggest specific solutions based on the alert and stores them in a database.

[1481] Input: alert

[1482] Output: Specific suggestions for how to deal with the problem

[1483] Step 8:

[1484] The server then operates a food suggestion engine to suggest suitable meals to the user, taking into account the identified emotional state, which are also stored in the database.

[1485] Input: Identified emotional state

[1486] Output: Food suggestions

[1487] Step 9:

[1488] The device will notify the user of the alert, along with suggested solutions and food, from the server. The user can then check the suggested solutions through the application and select food delivery.

[1489] Input: Server alerts, actions, food suggestions

[1490] Output: User notification, choice of action

[1491] Through these steps, the present invention comprehensively analyzes the user's lifestyle and emotions, and provides health management and optimal dietary suggestions.

[1492] 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.

[1493] 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.

[1494] 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.

[1495] [Fourth embodiment]

[1496] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1497] 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.

[1498] 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).

[1499] 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.

[1500] 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.

[1501] 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).

[1502] 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. 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.

[1503] 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.

[1504] 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.

[1505] 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.

[1506] 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.

[1507] 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.

[1508] 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."

[1509] This system allows users to input their daily lifestyle habits, quantifies their health status based on that data, and issues alerts as necessary. Here, we will explain in detail the functions and processing details of each element of this system.

[1510] Entering lifestyle data

[1511] User: Every day, they enter their lifestyle data through a smartphone application, mainly including their sleep time, diet, exercise, and stress level.

[1512] Terminal: The application converts the input data into a dedicated format and sends it to the server.

[1513] Receiving and storing data

[1514] Server: Receives and verifies lifestyle habit data sent from the device. After verification, the data is stored in a database. The stored data includes the user's identification information, details of each data item, and their input timestamp.

[1515] Quantifying health status

[1516] Server: The health status assessment engine starts working based on the saved data. Each lifestyle item (sleep time, diet, exercise, stress level) is weighted and points are calculated. For example, less than 7 hours of sleep is worth -10 points, a high-fat diet is worth -15 points, zero minutes of exercise is worth -20 points, and high stress is worth -10 points.

[1517] Server: The points for each category are added together to calculate an overall health score, which is rated out of 100.

[1518] Generate alerts

[1519] Server: Compares the health score with past data to check for any abnormalities. It compares the score with the score from previous times when the user was unwell, and if the current score is the same or lower, it determines that an abnormality has been detected.

[1520] Server: When an anomaly is detected, the alert generation engine automatically generates an alert message, which includes a warning and instructions for the user to take.

[1521] Suggested Actions

[1522] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as "increase hydration," "take early rest," and "do light exercise."

[1523] Terminal: Notifies the user of action guides and alert messages sent from the server, and prompts the user to take appropriate action through the application interface.

[1524] Processing details with concrete examples

[1525] User data entry and processing

[1526] Example Day 1

[1527] User: Enters the following information into the app: "Sleep: 5 hours, Diet: high fat, high calories, Exercise: 0 minutes, Stress level: high."

[1528] Terminal: Converts the input data into a format and sends it to the server.

[1529] Server: Receives the data, validates it, and stores it in a database.

[1530] Server: The health status assessment engine works and calculates an overall score of 40 points.

[1531] Server: The alert generation engine generates an alert because the score of 40 matches the score of a previous illness.

[1532] On your device: Display an alert saying, "Caution: Rest early and exercise."

[1533] Example Day 2

[1534] User: Enter the following information into the app: "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low."

[1535] Terminal: Converts data into a format and sends it to the server.

[1536] Server: Receives the data, validates it, and stores it in a database.

[1537] Server: The health status assessment engine works and calculates an overall score of 80 points.

[1538] Server: Confirms that there are no abnormalities and does not issue an alert.

[1539] Device: Displays "Your current health condition is good. Keep it up."

[1540] In this way, the system of the present invention can monitor the user's health condition in real time based on the user's daily lifestyle data and proactively manage their health, allowing the user to detect problems before they become unwell and take appropriate measures.

[1541] The processing flow will be explained below.

[1542] Step 1:

[1543] The user launches the smartphone application and inputs daily lifestyle data (sleep time, diet, exercise, stress level).

[1544] Step 2:

[1545] The terminal converts the lifestyle habit data entered by the user into an appropriate format and sends it to the server.

[1546] Step 3:

[1547] The server receives the data sent from the device. The server then verifies the accuracy and format of the data. Inaccurate or invalid data is rejected.

[1548] Step 4:

[1549] The server stores the validated data in a database, including the user's identity, details of each data item, and the date and time the data was entered.

[1550] Step 5:

[1551] The server retrieves the stored data and launches a health status assessment engine, which first adds or subtracts points for each lifestyle habit (sleep time, diet, exercise, stress level).

[1552] Step 6:

[1553] The server adds up the points for each category to calculate an overall health score, which the service rates using, for example, a score out of 100.

[1554] Step 7:

[1555] The server compares the calculated health score with past data, and if the health score is equal to or lower than the score from a previous health condition, it determines that an abnormality has been detected.

[1556] Step 8:

[1557] If the server detects an anomaly, it will launch an alert generation engine to generate a warning message, which will include information that the user should pay attention to and suggested solutions.

[1558] Step 9:

[1559] The server launches the action guide engine to generate specific measures to improve the user's health. The suggested measures include specific actions such as "increase hydration," "take early rest," and "perform light exercise."

[1560] Step 10:

[1561] The server sends the generated alert message and a solution to the terminal.

[1562] Step 11:

[1563] The device receives the message from the server and notifies the user via the notification bar or a pop-up within the app.

[1564] Step 12:

[1565] The user checks the notification and takes necessary measures according to the provided action guide.

[1566] The above is the specific flow of processing by the program in the system of the present invention. This system monitors the user's daily lifestyle habits in real time and proactively supports health management, making it possible to prevent poor health before it occurs.

[1567] Example 1

[1568] 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."

[1569] Conventional health management systems simply record users' lifestyle data, but are inadequate in assessing their health status in real time, detecting abnormalities early, or suggesting specific countermeasures. Furthermore, many assessment methods rely on specific lifestyle items rather than comprehensively assessing multiple lifestyle items, making it difficult for users to comprehensively understand their health status and take appropriate action. Furthermore, they lacked a mechanism for detecting abnormalities using past health data, making preventative health management impossible. To solve this problem, a system was needed that could comprehensively assess a user's health status in real time and suggest appropriate actions.

[1570] 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.

[1571] In this invention, the server includes means for receiving and verifying lifestyle habit data entered by the user, means for storing the data in a database and quantifying it based on each lifestyle habit item using a health status assessment engine, and means for checking the data against a past health score database to determine whether there is an abnormality and, if an abnormality is detected, for suggesting a specific countermeasure using an action guide engine. This makes it possible to evaluate the user's lifestyle habit data from multiple angles, detect abnormalities early, and suggest appropriate countermeasures.

[1572] "Lifestyle data" refers to data that records the user's daily actions and conditions, and specifically includes sleep time, dietary content, amount of exercise, stress level, and the like.

[1573] A "terminal" is a device through which a user inputs lifestyle habit data, and refers to a computing device such as a smartphone or tablet.

[1574] "Server" refers to a central processing unit that receives, verifies, and stores data sent from devices, analyzes the data to assess health status, and generates alerts and corrective actions.

[1575] The "database" is a storage device for holding lifestyle habit data received by the server, and is a system for managing information such as user identification information, details of each data item, and input timestamps.

[1576] The "health status assessment engine" refers to a program that executes an algorithm to weight each item based on stored lifestyle data and calculate an overall health score.

[1577] An "alert generation engine" refers to a program that automatically generates an alert message when an abnormality is detected based on the health score.

[1578] The "action guide engine" refers to a program that suggests specific measures to improve the user's health condition when an abnormality is detected.

[1579] The "past health score database" is a storage device that stores lifestyle habit data previously input by the user and the health score calculated based on that data, and is used as a reference when detecting abnormalities.

[1580] This invention is a system that allows users to input their daily lifestyle habits, quantifies their health status based on that data, and issues alerts as necessary. Here, we will explain how to implement this system, as well as its detailed procedures and configuration.

[1581] Entering lifestyle data

[1582] User: The user uses a device such as a smartphone or tablet to enter lifestyle data through a dedicated application, including items such as sleep time, diet, exercise, and stress level.

[1583] Sending data

[1584] Terminal: The data entered by the user is converted into a dedicated format (for example, JSON format) and sent to the server. HTTPS is used as the transmission protocol, ensuring secure data transfer.

[1585] Data Receipt and Validation

[1586] Server: Receives data sent from the terminal. After receiving, it verifies whether the data format is correct. If the format is invalid, it returns an error message.

[1587] Data storage

[1588] Server: Saves the verified data to the database. The saved data includes the user ID, details of each data item, and the input timestamp.

[1589] Health assessment

[1590] Server: The health assessment engine assigns points to each lifestyle habit based on the stored data and applies weighting logic to calculate an overall health score. For example, if you sleep less than 7 hours, you will receive -10 points.

[1591] Anomaly detection

[1592] Server: The calculated health score is checked against a database of past health scores to see if there are any abnormalities. It is compared with the score from a previous time when health condition worsened, and if it matches or is lower than the previous score, it is determined to be abnormal.

[1593] Generate alerts

[1594] Server: When an anomaly is detected, the alert generation engine automatically generates an alert message, which includes specific actions and warnings that the user should take.

[1595] Suggested Actions

[1596] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as drinking more water, resting early, and doing light exercise.

[1597] User Notification

[1598] Terminal: Notifies the user of the alert message sent from the server and how to deal with it. It displays it to the user through the application interface.

[1599] A specific example of the operation is shown below.

[1600] Example Day 1

[1601] User: Enters the following information into the app: "Sleep: 5 hours, Diet: high fat, high calories, Exercise: 0 minutes, Stress level: high."

[1602] Terminal: Converts the input data into a format and sends it to the server.

[1603] Server: Receives the data, validates it, and stores it in a database.

[1604] Server: The health status assessment engine works and calculates an overall score of 40 points.

[1605] Server: Compare with past data and determine abnormality at "40 points".

[1606] Server: Generate an alert and send a message saying "Caution: Rest early and exercise."

[1607] Terminal: Display an alert message to the user.

[1608] Example Day 2

[1609] User: Enter the following information into the app: "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low."

[1610] Terminal: Converts data into a format and sends it to the server.

[1611] Server: Receives the data, validates it, and stores it in a database.

[1612] Server: The health status assessment engine works and calculates an overall score of 80 points.

[1613] Server: Determines that there is no abnormality and does not issue an alert.

[1614] Device: Displays "Your current health condition is good. Keep it up."

[1615] In this way, the system of the present invention realizes proactive health management by evaluating the health condition of the user based on daily lifestyle habit data, detecting abnormalities early, and proposing appropriate countermeasures.

[1616] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1617] Step 1:

[1618] The user opens the application on their smartphone or tablet and enters their lifestyle habits data for the day.

[1619] The data entered includes sleep duration (e.g., 5 hours), dietary content (e.g., high fat, high calorie), amount of exercise (e.g., 0 minutes), and stress level (e.g., high).

[1620] (Input): Sleep time, diet, exercise, stress level

[1621] (Output): Lifestyle data (JSON format)

[1622] Step 2:

[1623] The terminal converts the input data into a dedicated format (JSON format) and sends it to the server via the HTTPS protocol.

[1624] For example, it generates the following JSON data:

[1625] {"sleep": 5, "food": "high-fat", "exercise": 0, "stress": "high"}

[1626] (Input): Lifestyle data (user input)

[1627] (Output): Formatted lifestyle data (JSON format)

[1628] Step 3:

[1629] The server receives the data sent from the terminal, verifies the validity of the format, and returns an error message if the format is invalid.

[1630] For example, check whether the sleep time is a negative number or whether the meal contents are an unclear string.

[1631] (Input): Formatted lifestyle data (JSON format)

[1632] (Output): Validation result (success or failure), error message (if necessary)

[1633] Step 4:

[1634] The server stores the successfully validated data in a database. The stored information includes the user ID, details of each data item, and the input timestamp.

[1635] For example, execute the following SQL statement:

[1636] INSERT INTO health_data (user_id, sleep, food, exercise, stress, timestamp) VALUES (1, 5, 'high-fat', 0, 'high', '2023-10-10 08:00:00');

[1637] (Input): Successfully verified lifestyle data

[1638] (Output): Save results to the database

[1639] Step 5:

[1640] The server runs a health status assessment engine based on the stored data, weighting each lifestyle item and calculating points.

[1641] For example, if you sleep less than 7 hours, you will receive -10 points.

[1642] (Input): Lifestyle data stored in the database

[1643] (Output): Points for each item, overall health score (e.g., 40 points)

[1644] Step 6:

[1645] The server checks the calculated health score against the past health score database to see if there are any abnormalities. If it is the same as or lower than the past data, it is determined to be abnormal.

[1646] For example, if the past score for a worsening condition was 40 points and the current score is also 40 points, it will be judged as abnormal.

[1647] (Input): Overall health score

[1648] (Output): Result of anomaly detection (normal or abnormal)

[1649] Step 7:

[1650] If an abnormality is detected in the server, an alert generation engine generates an alert message, which includes specific countermeasures that the user should take.

[1651] For example, create a message such as, "Caution: Rest early and do some light exercise."

[1652] (Input): Anomaly detection result (anomaly)

[1653] (Output): Alert message

[1654] Step 8:

[1655] The device notifies the user of the alert message sent from the server and the specific steps to take. The user can then check the alert and be prompted to take action through the application interface.

[1656] For example, a pop-up message might say, "Caution: Rest early and do some light exercise."

[1657] (Input): Alert message and how to deal with it

[1658] (Output):Notification to the user

[1659] This will enable users to monitor their health status in real time and take appropriate measures.

[1660] (Application example 1)

[1661] 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."

[1662] Conventional health management systems only require users to input their own lifestyle data, and are unable to monitor their health status in real time or provide effective feedback. Furthermore, the role of trainers at fitness facilities is limited to face-to-face instruction, making it difficult to comprehensively manage users' health. To address these issues, a system is needed that provides more advanced health management and personalized exercise recommendations for users.

[1663] 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.

[1664] In this invention, the server includes a means for a user to input lifestyle habit data, a means for the terminal to convert the input data into a format and send it to the server, a means for the server to verify the received data and store it in a database, a means for the server to quantify a health condition based on the stored data, a means for the server to compare the health condition numerical value with past data and detect abnormalities, a means for the server to generate an alert when an abnormality is detected, a means for the server to suggest specific countermeasures based on the alert, a means for the terminal to notify the user of the alert from the server and the countermeasures, a means for a fitness facility manager to monitor the user's health data in real time and suggest an optimal exercise menu for the user, and a means for displaying notifications on the user's smart glasses to provide real-time feedback during exercise, thereby enabling comprehensive management of the user's health condition and personalized exercise guidance.

[1665] "User" refers to an individual who inputs lifestyle data and receives health monitoring and measures.

[1666] "Lifestyle data" is information about the user's daily life, and specifically includes sleep time, dietary content, amount of exercise, and stress level.

[1667] A "terminal" is a device through which a user inputs lifestyle habit data, such as a smartphone or smart glasses.

[1668] "Converting to a format" refers to the process of converting lifestyle habit data entered by the user into a format suitable for the database.

[1669] "Server" refers to a central processing unit that stores and analyzes received data and provides relevant feedback to the user.

[1670] "Database" refers to a structured data storage system for storing user lifestyle data and other related data.

[1671] "Quantifying health status" refers to the process of calculating a comprehensive health score based on the user's lifestyle data.

[1672] "Detecting anomalies" refers to the process of identifying cases where current health status values ​​are judged to be abnormal by comparing them with past data.

[1673] An "alert" is a notification message that warns the user when an abnormality is detected and prompts them to take appropriate measures.

[1674] "Solutions" refer to specific actions or advice suggested to improve the user's health.

[1675] A "fitness facility manager" refers to a specialized staff member at a fitness center or gym who monitors users' health data and suggests exercise menus.

[1676] "Real-time monitoring" means that the fitness facility manager can continually check the user's latest health data and take immediate action.

[1677] "Exercise menu" refers to an exercise or fitness plan optimized for the user's health condition.

[1678] "Real-time feedback" refers to information that provides users with immediate advice and instructions during exercise to maximize the effectiveness of their exercise.

[1679] "Smart glasses" are wearable devices that users use while exercising and have the ability to display visual information.

[1680] The system embodying this invention uses various hardware and software components to enhance health management for users at fitness facilities, and is primarily composed of a smartphone, smart glasses, a server, a database, and a fitness facility management system.

[1681] System configuration

[1682] 1. User data entry

[1683] User: Uses a smartphone or smart glasses to input lifestyle data (sleep time, diet, exercise, stress level).

[1684] Terminal: A smartphone or smart glasses converts the input data into the appropriate format and sends it to the server.

[1685] 2. Receipt and storage of data

[1686] Server: Receives lifestyle data sent from the device and verifies the format and content of the data.

[1687] Database: The verified data is stored in a database, recording the user's identity and details of each data item, along with its input timestamp.

[1688] 3. Quantifying health status

[1689] Server: The health status assessment engine runs, weighting each lifestyle item (sleep time, diet, exercise, stress level) and calculating points. For example, if you sleep less than 7 hours, you will receive -10 points, and if you eat a balanced diet, you will receive +20 points.

[1690] Server: The points for each category are added together to calculate an overall health score, which is rated out of 100.

[1691] 4. Generate an alert

[1692] Server: Compares the health score with past data, and determines that an abnormality has been detected if the score is equal to or lower than the score when the user was previously unwell.

[1693] Server: If an anomaly is detected, the alert generation engine generates an alert message, optionally including instructions for remediation.

[1694] 5. Action Suggestions

[1695] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as increasing hydration, resting earlier, and light exercise.

[1696] Terminal: Notifies the user of action guides and alert messages sent from the server.

[1697] 6. Fitness facility management

[1698] Management system: Allows fitness facility managers to monitor users' health data in real time.

[1699] User guidance: Administrators can suggest optimal exercise routines for users and receive real-time feedback using smart glasses while they exercise.

[1700] Examples and prompts

[1701] As a concrete example, consider the case where a user enters the following data into a smartphone app:

[1702] Example 1: Signs of illness

[1703] User input: "Sleep time: 5 hours, Diet: High fat and high calories, Exercise time: 0 minutes, Stress level: High"

[1704] Server process:

[1705] 1. Data reception, verification, and storage.

[1706] 2. Health score calculation: 20 points.

[1707] 3. Generate an alert: "Your health score is declining. Take appropriate action."

[1708] 4. Action Suggestion: "Drink more fluids and get some sleep earlier."

[1709] Example 2: Good condition

[1710] User input: "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low"

[1711] Server process:

[1712] 1. Data reception, verification, and storage.

[1713] 2. Health score calculation: 80 points.

[1714] 3. No alerts.

[1715] 4. Action Suggestion: "Your current health is good. Keep it up."

[1716] Prompt example

[1717] Based on the health data of user ID "12345", calculate a health score and generate an alert message and action suggestions.

[1718] Input data:

[1719] Sleep time: 5 hours

[1720] Diet: High fat, high calorie

[1721] Momentum: 0 minutes

[1722] Stress level: High

[1723] Desired output:

[1724] Health Score

[1725] Alert Messages

[1726] action suggestion

[1727] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1728] Step 1:

[1729] The user enters lifestyle data using a smartphone or smart glasses. Specifically, the user opens the application's input form and enters information such as sleep time, diet, exercise, and stress level. Input: sleep time, diet, exercise, stress level. Output: formatted lifestyle data.

[1730] Step 2:

[1731] The terminal converts the input data into a format and sends it to the server. Specifically, the terminal converts the input data into structured data such as JSON format and sends it to the server's API endpoint. Input: Raw data from the user. Output: Formatted data.

[1732] Step 3:

[1733] The server receives the data and validates its format and content. Specifically, the server verifies that the received data is in the expected format and checks that it does not contain any invalid data. Input: Formatted lifestyle habit data. Output: Validated data.

[1734] Step 4:

[1735] The server stores the validated data in a database. Specifically, it uses a database engine (e.g., MySQL or PostgreSQL) to store the details of each data item and the input timestamp along with the user's identity. Input: Validated data. Output: Data stored in the database.

[1736] Step 5:

[1737] The server calculates a health score based on the stored data. Specifically, it weights sleep time, diet, exercise, and stress level to calculate an overall health score. For example, if you sleep less than 7 hours, you'll get -10 points, and if you eat a well-balanced diet, you'll get +20 points. Input: Lifestyle data stored in the database. Output: Health score.

[1738] Step 6:

[1739] The server compares the health score with past data to see if any abnormalities are detected. Specifically, it compares the score with the score from a previous time when the user was unwell, and if the score is equal to or lower than that, it is considered to be an abnormality. Input: Health score, past health data. Output: Anomaly detection flag.

[1740] Step 7:

[1741] If the server detects an abnormality, it generates an alert message. Specifically, the alert generation engine runs and generates a message such as "The health score is decreasing. Please take appropriate measures." Input: Anomaly detection flag. Output: Alert message.

[1742] Step 8:

[1743] The server generates an action guide and suggests specific countermeasures, such as "increase hydration and take early rest." Input: health score, anomaly detection flag. Output: countermeasures.

[1744] Step 9:

[1745] The device notifies the user of the alert from the server and how to deal with it. Specifically, a pop-up notification or message is displayed to the user using a smartphone or smart glasses. Input: Alert message, how to deal with it. Output: Notification to the user.

[1746] Step 10:

[1747] The management system provides users' health data to the fitness facility manager in real time. Specifically, the latest health data of users is monitored through an interface such as a dashboard for the manager. Input: Lifestyle data stored in a database. Output: Health data provided to the manager.

[1748] Step 11:

[1749] The administrator proposes the optimal exercise menu for the user and allows the user to receive real-time feedback using smart glasses while exercising. Specifically, the administrator creates a customized exercise menu based on the user's health and fitness data, and the user receives instructions through the smart glasses. Input: Instructions from the administrator dashboard, smart glasses. Output: Customized exercise menu, real-time feedback.

[1750] 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.

[1751] This invention is a system that inputs a user's daily lifestyle data, quantifies their health status based on that data, and issues alerts as needed, and by combining this with an emotion engine that recognizes the user's emotions, it achieves more accurate health management. Here, we will explain in detail the functions and processing content of each element of this system.

[1752] Entering lifestyle data

[1753] User: Every day, they enter their lifestyle data through a smartphone application, mainly including their sleep time, diet, exercise, and stress level.

[1754] Terminal: The application converts the input data into a dedicated format and sends it to the server.

[1755] Receiving and storing data

[1756] Server: Receives and verifies lifestyle habit data sent from the device. After verification, the data is stored in a database. The stored data includes the user's identification information, details of each data item, and their input timestamp.

[1757] Quantifying health status

[1758] Server: The health status assessment engine starts working based on the saved data. Each lifestyle item (sleep time, diet, exercise, stress level) is weighted and points are calculated. For example, less than 7 hours of sleep is worth -10 points, a high-fat diet is worth -15 points, zero minutes of exercise is worth -20 points, and high stress is worth -10 points.

[1759] Server: The points for each category are added together to calculate an overall health score, which is rated out of 100.

[1760] Emotion recognition by emotion engine

[1761] On the device: The emotion engine works to identify the user's emotions through voice analysis, facial recognition, and text analysis. For example, emotion data is collected when the user shows their facial expressions to the smartphone camera or speaks into the microphone.

[1762] Server: Receives emotion data sent from the device and stores it in a database. The stored emotion data includes the user's identification information, the identified emotion, and the date and time when the emotion was recognized.

[1763] Generate alerts

[1764] Server: Compares the health score with past data to check for any abnormalities. It compares the score with the score from previous times when the user was unwell, and if the current score is the same or lower, it determines that an abnormality has been detected.

[1765] Server: When an anomaly is detected, the alert generation engine automatically generates an alert message, which includes a warning and instructions for the user to take.

[1766] Server: The server also takes into account the emotional data identified by the emotion engine and adjusts the alert content as needed. For example, if the user's emotion is identified as "stress" or "anxiety," it generates a more specific and urgent alert.

[1767] Suggested Actions

[1768] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as "increase hydration," "take early rest," and "do light exercise."

[1769] Server: Adjusts the suggested coping strategies based on the emotion data identified by the emotion engine. For example, if the user's emotion is identified as "stress" or "fatigue," the server suggests additional coping strategies such as "relaxation techniques" or "playing relaxation music."

[1770] Terminal: Notifies the user of guidance and alert messages sent from the server, and prompts the user to take appropriate action through the application interface.

[1771] Processing details with concrete examples

[1772] User data entry and processing

[1773] Example Day 1

[1774] User: Enter the following information into the app: "Sleep time: 5 hours, Meals: High fat, High calories, Exercise: 0 minutes, Stress level: High." The app then captures the user's facial expression through the camera and says into the microphone, "I'm tired today."

[1775] Terminal: Converts input data and emotion data into a format and sends it to the server.

[1776] Server: Receives, verifies, and stores data in a database. The health status assessment engine calculates a total score of 40 points. In addition, the emotion engine stores the emotion data identified as "fatigue."

[1777] Server: The alert generation engine generates an alert because the score matches "40 points" from a previous time when the user was feeling unwell. Based on the data from the emotion engine, the alert is adjusted to read, "Current emotion: Fatigue. Caution: Take early rest and try relaxation techniques."

[1778] Terminal: Notify the user of the adjusted alert.

[1779] Example Day 2

[1780] User: Enters "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low" into the app. Also, says "I'm feeling good today" into the microphone.

[1781] Terminal: Converts data into a format and sends it to the server.

[1782] Server: Receives, verifies, and stores data in a database. The health assessment engine calculates an overall score of 80 points. The emotion engine also stores the emotion data identified as "happiness."

[1783] Server: Because the health score is high, no abnormalities are detected. Based on the data from the emotion engine, the display content is adjusted to say, "Current emotion: Happiness. Your current health condition is good. Keep it up."

[1784] Terminal: Notify the user of the adjusted message.

[1785] In this way, by combining the system of the present invention with an emotion engine, it is possible to comprehensively analyze a user's lifestyle data and emotion data, providing more accurate and personalized health management, allowing users to take specific measures to maintain their health while also taking into account their own emotional state.

[1786] The processing flow will be explained below.

[1787] Step 1:

[1788] The user launches the smartphone application and inputs daily lifestyle data (sleep time, dietary habits, exercise amount, stress level), and also inputs emotional data by showing facial expressions to the smartphone camera and speaking into the microphone.

[1789] Step 2:

[1790] The device converts the input lifestyle and emotion data into an appropriate format and sends it to the server. The format conversion is to improve data uniformity and processing efficiency.

[1791] Step 3:

[1792] The server receives the lifestyle and emotion data sent from the device, verifies the accuracy of the received data, and checks for any anomalies.

[1793] Step 4:

[1794] The server stores the validated data in a database, including the user's identity, details of each data item, and a timestamp of the data entry.

[1795] Step 5:

[1796] The server runs a health assessment engine based on the saved lifestyle data, adding or subtracting points for each lifestyle item (sleep time, diet, exercise, stress level) to calculate an overall health score.

[1797] Step 6:

[1798] The server starts the emotion engine and analyzes the transmitted emotion data. It identifies the user's emotion through voice analysis, face recognition, and text analysis, and stores the identified emotion data in a database.

[1799] Step 7:

[1800] The server integrates and analyzes the health score and emotion data, and compares it with past data to check for any abnormalities. If an abnormality is detected, the system proceeds to the next step.

[1801] Step 8:

[1802] When an anomaly is detected, the server activates the alert generation engine to generate a warning message, which includes a warning and action to be taken by the user.The server also adjusts the alert content based on the emotion identified by the emotion engine.

[1803] Step 9:

[1804] The server launches an action guide engine to suggest specific measures, such as "increase hydration," "take early rest," and "try relaxation techniques." Furthermore, the server adjusts the suggestions based on the emotion data recognized by the emotion engine.

[1805] Step 10:

[1806] The server sends the generated alert message and a solution to the terminal.

[1807] Step 11:

[1808] The terminal receives the message from the server and notifies the user through the application interface, prompting the user to take appropriate action.

[1809] Step 12:

[1810] The user checks the notification and takes necessary measures according to the action guide provided, allowing the user to take concrete actions to improve their health condition.

[1811] The above is the processing flow of the system of the present invention, which combines an emotion engine. This system analyzes the user's daily lifestyle data and emotion data in real time, and supports comprehensive health management, making it possible to prevent poor health before it occurs.

[1812] Example 2

[1813] 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."

[1814] In modern society, there are many systems that manage health status based on a user's daily lifestyle data. However, these systems do not take the user's emotional state into account, which can make it difficult to accurately detect changes or abnormalities in physical condition. Another issue is that they are unable to suggest appropriate measures based on the user's emotional state. The present invention aims to achieve more accurate and personalized health management by comprehensively utilizing a user's lifestyle data and emotional data.

[1815] 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 means for the user to input lifestyle habit data and emotional data such as voice and facial expressions; a means for the terminal to convert the input data into a format and send it to the server; a means for the server to verify the received data and store it in a database; a means for the server to quantify the health status based on the stored data; a means for the server to recognize the user's emotions using an emotion engine and store the data; a means for the server to compare the health status numerical value with past data and detect abnormalities; a means for the server to generate an alert when an abnormality is detected and adjust the alert based on the emotional data; a means for the server to propose a specific response based on the alert and adjust the response based on the emotional data; and a means for the terminal to notify the user of the alert from the server and the response. This enables accurate health management that takes the user's emotional state into consideration.

[1816] "User" refers to an individual who uses this system.

[1817] The term "terminal" refers to an electronic device that allows a user to input lifestyle habit data and emotion data and communicate with a server.

[1818] "Server" refers to the central computer system that receives, verifies, and stores input data, and runs various engines to quantify health status, recognize emotions, and generate alerts.

[1819] "Lifestyle data" is information about the user's daily activities, and specifically includes sleep time, dietary content, amount of exercise, and stress level.

[1820] "Emotional Data" refers to a user's emotional state as determined through voice analysis, facial recognition, and / or text analysis.

[1821] "Database" refers to a digital recording system for storing lifestyle and emotional data received from users.

[1822] "Health status assessment engine" refers to the program and algorithm for quantifying a user's health status based on stored lifestyle data.

[1823] "Emotion Engine" refers to programs and algorithms that identify emotions based on a user's voice analysis, facial recognition, and text analysis.

[1824] An "alert generation engine" refers to a program and algorithm that generates a message to notify the user of a warning or countermeasure when an abnormality is detected.

[1825] "Action Guidance Engine" refers to programs and algorithms that suggest specific actions to take based on a user's health and emotional state.

[1826] This system collects a user's daily lifestyle and emotional data, quantifies their health status based on the data, detects abnormalities, and proposes countermeasures. This system is composed of devices such as smartphones, a cloud server, and various engines that perform emotion recognition and data analysis.

[1827] Entering lifestyle data

[1828] Users: Every day, they enter lifestyle data using a dedicated smartphone app. Data entry items include sleep time, dietary habits, exercise, and stress levels. They also use a camera to capture facial expressions and a microphone to record emotional data.

[1829] Sending and Receiving Data

[1830] Terminal: The application converts the data entered by the user into a standard format, which is then sent to the server in real time.

[1831] Server: Receives data sent from the device and verifies the accuracy of the format and content. If invalid data is found, it is corrected or re-requested.

[1832] Data storage

[1833] Server: Saves the verified data in a database, including the user's identity, details of the data item, and the date and time of entry.

[1834] Quantifying health status

[1835] Server: The health status assessment engine starts running and calculates points by weighting each item based on the saved lifestyle data.

[1836] For example: less than 7 hours of sleep is worth -10 points, a high-fat diet is worth -15 points, zero minutes of exercise is worth -20 points, high stress is worth -10 points, etc.

[1837] Server: The points for each item are combined to calculate a health score, which is rated out of 100.

[1838] emotion recognition

[1839] On the device: The emotion engine performs voice analysis, facial recognition, and text analysis of the user. For example, emotion data is collected when the user shows their facial expressions to the smartphone camera or speaks into the microphone.

[1840] Terminal: The collected emotion data is converted into a format and sent to the server.

[1841] Server: Validates the emotion data received from the device and stores it in a database, including the user's identity, the identified emotion, and the date and time of recognition.

[1842] Generate alerts

[1843] Server: Compares the health score with past data to check for abnormalities. If the score matches or is lower than the score from a previous poor health condition, it is determined that an abnormality has been detected.

[1844] Server: When an anomaly is detected, the alert generation engine generates an alert message, which includes countermeasures and warnings. Additionally, it adjusts the alert content based on sentiment data.

[1845] Suggested Actions

[1846] Server: The action guide engine runs and suggests specific actions to improve the user's health, such as drinking more water, resting early, and doing light exercise.

[1847] Server: Based on the emotional data, the server also adjusts coping methods. For example, if the emotion is identified as "stress" or "fatigue," it will suggest additional specific coping methods such as "relaxation techniques" or "playing relaxation music."

[1848] Terminal: Notifies the user of action guides and alert messages sent from the server, and prompts the user to take appropriate action through the application interface.

[1849] Processing details with concrete examples

[1850] Example Day 1

[1851] User: Enters "Sleep time: 5 hours, Meals: High fat, High calories, Exercise: 0 minutes, Stress level: High" into the app, captures facial expression with the camera, and says "I'm tired today" into the microphone.

[1852] Terminal: Converts data and emotion data into a format and sends it to the server.

[1853] Server: Receives, verifies, and stores the data in a database. The health status assessment engine calculates a total score of 40 points, and stores the emotion data identified as "fatigue" by the emotion engine.

[1854] Server: The alert generation engine generates an alert because the score matches "40 points" from a previous experience of illness. Based on the emotion data, an adjusted alert is generated: "Current emotion: Fatigue. Caution: Take early rest and try relaxation techniques."

[1855] Terminal: Notify the user of the adjusted alert.

[1856] Example Day 2

[1857] User: Enters "Sleep: 8 hours, Diet: Well-balanced, Exercise: 30 minutes, Stress level: Low" into the app and says "I feel good today" into the microphone.

[1858] Terminal: Converts data into a format and sends it to the server.

[1859] Server: Receives, verifies, and stores the data in a database. The health assessment engine calculates an overall score of 80 points, and also stores the emotion data identified as "happiness" by the emotion engine.

[1860] Server: No abnormalities are detected because the health score is high. Based on the emotion data, the display is adjusted to say, "Current emotion: Happiness. Your current health condition is good. Keep it up."

[1861] Terminal: Notify the user of the adjusted message.

[1862] Example prompts for generative AI models

[1863] Sample prompt 1: "What alert message would you generate if the user has 5 hours of sleep, eaten a high-fat meal, exercised 0 minutes, and has a high stress level?"

[1864] Sample prompt 2: "When the user has 8 hours of sleep, a balanced diet, 30 minutes of exercise, and low stress, create a message to display based on the health score and sentiment analysis results."

[1865] The system of the present invention allows users to continuously and individually manage their health while taking into account their emotional state. This system combines an emotion engine and a health status assessment engine to comprehensively analyze lifestyle and emotional data, enabling more accurate alerts and suggested actions. This allows users to more accurately understand their daily health status and take appropriate measures.

[1866] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1867] Step 1:

[1868] User: Every day, the user enters lifestyle data using a dedicated smartphone app. The input fields include "sleep time," "meal details," "amount of exercise," and "stress level." The user also captures facial expressions using a camera and speaks into a microphone to input emotional data via voice. At this point, the input consists of lifestyle data and emotional data.

[1869] Step 2:

[1870] Terminal: The application converts the data entered by the user into a standard format. Specifically, it parses text and image data and converts it into structured data. This converted data is output and sent to the server in real time.

[1871] Step 3:

[1872] Server: Receives data sent from the terminal. Validates the accuracy of the content and format of the received data. For example, if a data item is missing or the format is incorrect, it is corrected. The validated data is output and proceeds to the next process.

[1873] Step 4:

[1874] Server: Saves the validated data to the database, including the user's identity, details of the data items, and the date and time of entry. The save process adds a new record to the database.

[1875] Step 5:

[1876] Server: The health status assessment engine begins operation. Based on the saved lifestyle data, points are calculated by weighting each item. For example, if you sleep less than 7 hours, you are assigned -10 points, if you eat a high-fat diet, you are assigned -15 points, if you exercise for 0 minutes, you are assigned -20 points, and if you are highly stressed, you are assigned -10 points. The results of this data processing are output as points.

[1877] Step 6:

[1878] Server: A health score is calculated by adding up the points for each item. The total score is stored in a database. The total score is then integrated with other lifestyle data and output.

[1879] Step 7:

[1880] Device: The emotion engine performs voice analysis, facial recognition, and text analysis of the user. For example, voice analysis identifies emotions from voice, and facial recognition analyzes facial expression data. Emotion data is input, and the identified emotion is output.

[1881] Step 8:

[1882] Terminal: The collected emotion data is converted into a format and sent to the server. This process outputs the emotion data converted into a standard format.

[1883] Step 9:

[1884] Server: Validates the emotion data received from the device and stores it in a database. After validating the emotion data, the data is stored and the identified emotion is added to the database.

[1885] Step 10:

[1886] Server: Compares the health score with past data to check for abnormalities. Compares the past health score with the current score, and if they match or are lower than the current score, an abnormality is detected. The detection result is output.

[1887] Step 11:

[1888] Server: When an anomaly is detected, the alert generation engine generates an alert message. This message includes countermeasures and warnings. Furthermore, the alert content is adjusted based on the emotion data. The adjusted alert message is output.

[1889] Step 12:

[1890] Server: The action guide engine runs and suggests specific measures to improve the user's health. Suggestions include "increase hydration," "take early rest," and "do light exercise." The measures are also adjusted based on emotion data. The suggested and adjusted measures are output.

[1891] Step 13:

[1892] Terminal: Notifies the user of action guides and alert messages sent from the server. Prompts the user to take appropriate action through the application interface. The notification content is output and displayed to the user.

[1893] (Application example 2)

[1894] 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."

[1895] The problem to be solved by the present invention is to combine a user's daily lifestyle data with emotional data to more accurately manage their health condition and provide personalized treatment methods and food suggestions. Conventional health management systems generally evaluate health status based solely on lifestyle data, without taking the user's emotional state into consideration. This has led to the problem of not reflecting emotional fluctuations that affect the user's health condition.

[1896] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1897] In this invention, the server includes means for receiving a user's emotional data and storing it in a database, means for analyzing the stored emotional data and identifying the emotional state that affects the user's health, and means for generating food recommendations based on the emotional state. This enables health management that takes the user's emotional state into consideration. Specific food recommendations and ordering procedures can be supported according to the user's emotional state, allowing for more effective health maintenance.

[1898] Definitions of important words

[1899] A "user" is a person who uses the system to input lifestyle data and emotion data.

[1900] "Lifestyle data" is a general term for data such as a user's sleep time, diet, amount of exercise, and stress level.

[1901] A "terminal" is a device through which a user inputs lifestyle habit data and emotion data and transmits the data to a server.

[1902] "Server" is a centralized management system that receives, verifies, stores, and analyzes data sent from the terminals.

[1903] The "database" is a location in the server where lifestyle habit data and emotion data are stored.

[1904] The "health condition" is a numerical value that represents the degree of health calculated based on the user's daily lifestyle data.

[1905] "Emotional data" refers to data related to emotions collected through voice analysis, facial recognition, and text analysis of users.

[1906] An "emotion engine" is software that analyzes a user's emotional data and identifies a specific emotional state.

[1907] "Food Suggestion" refers to suggesting optimal meals based on the user's health and emotional state.

[1908] An "alert" is a warning message that is sent to the user when an abnormality is detected.

[1909] "Solution" refers to specific actions or measures suggested to the user based on the alert.

[1910] MODE FOR CARRYING OUT THE INVENTION

[1911] The present invention is a system that accurately manages a user's health status based on daily lifestyle and emotional data, and provides personalized treatment methods and food suggestions. Specific implementation methods of this system are described below.

[1912] Hardware and Software Configuration

[1913] User

[1914] Smartphone: A device for inputting lifestyle and emotional data. Users input this data through an application.

[1915] Application: Software that collects lifestyle data (sleep, diet, exercise, stress levels) and emotional data (voice analysis, facial recognition, text analysis). The data is formatted and sent to a server.

[1916] server

[1917] Database: This is where the user's lifestyle and emotional data is stored.

[1918] Data Verification Engine: Validates data sent from the device and confirms its validity.

[1919] Health status assessment engine: Quantifies health status based on saved lifestyle data.

[1920] Emotion Engine: Analyzes user emotional data and identifies emotional states that affect health status.

[1921] Alert generation engine: Compares health status with historical data and generates alerts if anomalies are detected.

[1922] Solution suggestion engine: Based on the alert, it suggests specific solutions to the user.

[1923] Food recommendation engine: Suggests optimal meals based on the user's health and emotional state.

[1924] Receiving and analyzing data

[1925] Data entry and submission

[1926] Users input lifestyle and emotional data using a smartphone application, which is then formatted by the device and sent to a server.

[1927] Data validation and storage

[1928] The server validates the received data and confirms its validity, and once validated, it is stored in a database.

[1929] Quantifying health status and recognizing emotions

[1930] The server runs a health status assessment engine based on the saved lifestyle data to quantify the health status, while an emotion engine runs to analyze the user's emotion data and identify emotions that affect the health status.

[1931] Alert generation and solution suggestions

[1932] Health status values ​​are compared with past data, and if an abnormality is detected, an alert generation engine generates an alert. In addition, a countermeasure suggestion engine proposes specific countermeasures, which are notified to the user via the application.

[1933] Food suggestions

[1934] The server uses a food recommendation engine to suggest optimal meals to users based on their emotional state, allowing users to select and order meals that take into account their health and emotional state.

[1935] Specific examples

[1936] Example of user data entry

[1937] The user enters the following information into the app: "Sleep time: 7 hours, Diet: high protein, low fat, Exercise: 30 minutes, Stress level: low," and the app then detects "happiness" based on their facial expression.

[1938] Server Processing

[1939] The server stores the data in a database, and a health assessment engine calculates an overall score. An emotion engine identifies "happiness" and specific emotions, and a food recommendation engine suggests meals such as "grilled chicken salad."

[1940] Prompt Sentence Examples

[1941] Input prompt for the generative AI model:

[1942] "Build a system that provides personalized health management and food recommendations based on a user's daily lifestyle and emotional data. It receives the following data points and generates recommendations based on them: sleep duration, diet, exercise, stress level, and emotional state (voice analysis, facial recognition, text analysis). Please also clearly state the specific recommendation method and the logic behind it."

[1943] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1944] Program processing steps

[1945] Step 1:

[1946] The user starts the smartphone application and inputs lifestyle data (sleep time, dietary content, exercise amount, stress level) and emotional data (voice, facial expressions, text). This input data is then formatted using the application's formatting function.

[1947] Input: sleep time, diet, exercise, stress level, emotional data

[1948] Output: Formatted lifestyle and emotion data

[1949] Step 2:

[1950] The terminal transmits the formatted data to the server, which also includes the user's identification information.

[1951] Input: Formatted lifestyle data, emotion data, and identification information

[1952] Output: Data sent to the server

[1953] Step 3:

[1954] The server validates the received data and stores it in the database after confirming that it is valid.

[1955] Input: Data sent from the terminal

[1956] Output: Validated data, saved data

[1957] Step 4:

[1958] The server runs a health status evaluation engine based on the saved lifestyle data and quantifies the health status.

[1959] Input: Saved lifestyle data

[1960] Output: Health score

[1961] Step 5:

[1962] The server analyzes the stored emotional data using an emotion engine to recognize specific emotional states.

[1963] Input: Stored emotion data

[1964] Output: Identified emotional state

[1965] Step 6:

[1966] The server compares the health status values ​​with past data to detect any abnormalities, and if an abnormality is detected, the alert generation engine generates an alert.

[1967] Input: Health status, historical data

[1968] Output: Alert (if an anomaly is detected)

[1969] Step 7:

[1970] The server uses a solution suggestion engine to suggest specific solutions based on the alert and stores them in a database.

[1971] Input: alert

[1972] Output: Specific suggestions for how to deal with the problem

[1973] Step 8:

[1974] The server then operates a food suggestion engine to suggest suitable meals to the user, taking into account the identified emotional state, which are also stored in the database.

[1975] Input: Identified emotional state

[1976] Output: Food suggestions

[1977] Step 9:

[1978] The device will notify the user of the alert, along with suggested solutions and food, from the server. The user can then check the suggested solutions through the application and select food delivery.

[1979] Input: Server alerts, actions, food suggestions

[1980] Output: User notification, choice of action

[1981] Through these steps, the present invention comprehensively analyzes the user's lifestyle and emotions, and provides health management and optimal dietary suggestions.

[1982] 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.

[1983] 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.

[1984] 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.

[1985] 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.

[1986] 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.

[1987] 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.

[1988] 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).

[1989] 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.

[1990] 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."

[1991] 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.

[1992] 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).

[1993] 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.

[1994] 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.

[1995] 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.

[1996] 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.

[1997] 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.

[1998] 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.

[1999] 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.

[2000] 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.

[2001] 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.

[2002] 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.

[2003] The following is further disclosed regarding the above embodiment.

[2004] (Claim 1)

[2005] The user inputs lifestyle data

[2006] A means for converting input data into a format and transmitting the format to a server;

[2007] A means for the server to validate the data received and store it in a database;

[2008] A means for the server to quantify health status based on the stored data;

[2009] The server compares the health status values ​​with past data to detect abnormalities,

[2010] A means for the server to generate an alert when an anomaly is detected;

[2011] A means for the server to suggest specific countermeasures based on the alert;

[2012] A means for the device to notify the user of alerts from the server and how to deal with them

[2013] A system including:

[2014] (Claim 2)

[2015] Lifestyle data includes sleep time, diet, exercise, and stress level

[2016] 10. The system of claim 1.

[2017] (Claim 3)

[2018] Include a method for weighting each lifestyle item when quantifying health status.

[2019] 10. The system of claim 1.

[2020] "Example 1"

[2021] (Claim 1)

[2022] a means for a user to input lifestyle habit data;

[2023] A means for converting input data into a format and transmitting the format to a server;

[2024] A means for the server to validate the data received and store it in a database;

[2025] A means for the server to quantify health status based on the stored data;

[2026] The server compares the health status values ​​with past data to detect abnormalities,

[2027] A means for the server to generate an alert when an anomaly is detected;

[2028] A means for the server to suggest specific countermeasures based on the alert;

[2029] A means for the terminal to notify the user of the alert from the server and how to deal with it;

[2030] A means including a health status assessment engine that assesses health status by applying weighting logic based on various lifestyle data;

[2031] A means of referencing the past health score database to determine abnormalities, and

[2032] A means by which the action guide engine generates advice to improve the user's health when an anomaly is detected

[2033] A system including:

[2034] (Claim 2)

[2035] 2. The system according to claim 1, wherein the lifestyle data includes sleep time, dietary content, amount of exercise, and stress level.

[2036] (Claim 3)

[2037] 2. The system according to claim 1, further comprising a means for weighting each lifestyle item when quantifying the health condition.

[2038] "Application Example 1"

[2039] (Claim 1)

[2040] a means for a user to input lifestyle habit data;

[2041] A means for converting input data into a format and transmitting the format to a server;

[2042] A means for the server to validate the data received and store it in a database;

[2043] A means for the server to quantify health status based on the stored data;

[2044] The server compares the health status values ​​with past data to detect abnormalities,

[2045] A means for the server to generate an alert when an anomaly is detected;

[2046] A means for the server to suggest specific countermeasures based on the alert;

[2047] A means for the terminal to notify the user of the alert from the server and how to deal with it;

[2048] A means for fitness facility managers to monitor users' health data in real time and suggest optimal exercise menus for users;

[2049] A means for displaying notifications on a user's smart glasses to provide real-time feedback during exercise

[2050] A system including:

[2051] (Claim 2)

[2052] 2. The system according to claim 1, wherein the lifestyle data includes sleep time, dietary content, amount of exercise, and stress level.

[2053] (Claim 3)

[2054] 2. The system according to claim 1, further comprising a means for weighting each lifestyle item when quantifying the health condition.

[2055] "Example 2: Combining Emotion Engines"

[2056] (Claim 1)

[2057] A means for a user to input lifestyle habit data and emotional data such as voice and facial expression;

[2058] A means for converting input data into a format and transmitting the format to a server;

[2059] A means for the server to validate the data received and store it in a database;

[2060] A means for the server to quantify health status based on the st...

Claims

1. The user inputs lifestyle data A means for converting input data into a format and transmitting the format to a server; A means for the server to validate the data received and store it in a database; A means for the server to quantify health status based on the stored data; The server compares the health status values ​​with past data to detect abnormalities, A means for the server to generate an alert when an anomaly is detected; A means for the server to suggest specific countermeasures based on the alert; A means for the device to notify the user of alerts from the server and how to deal with them A system including:

2. Lifestyle data includes sleep time, diet, exercise, and stress level The system of claim 1 .

3. Include a method for weighting each lifestyle item when quantifying health status. The system of claim 1 .

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