System

Abstract

A system is provided.SOLUTION: The system includes means for acquiring physical data, means for transmitting the physical data to a server, means for calculating a metabolic rate, means for calculating a required calorie intake and an amount of exercise, means for generating voice advice, means for managing an exercise menu, and means for generating statistical data and utilizing the statistical data in a medical site.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] In recent years, there has been a demand for providing optimal health management for individual users in the fields of health management, fitness, medicine, and healthcare. However, conventional systems have had difficulty in integrating individual physical data and dietary content, and automating metabolic calculations, calorie intake requirements, and exercise volume calculations. Furthermore, consistent data management for generating statistical data and utilizing it in clinical settings has been insufficient, and customized reports and analysis data for corporate use have not been provided. The present invention aims to solve these problems and provide a more comprehensive and personalized health management system. [Means for solving the problem]

[0005] The present invention solves the above problems by the following means. First, a system is provided that includes a means for acquiring physical data, a means for transmitting the physical data to a server, a means for calculating metabolic rate, a means for calculating required calorie intake and exercise volume, a means for generating audio advice, a means for managing exercise menus, and a means for generating statistical data for use in medical settings. Second, a system is provided that further includes a means for inputting dietary data, a means for transmitting the dietary data to a server, and a means for calculating calories and nutritional balance. Third, a system is provided that further includes a means for generating reports and analysis data customized for corporate clients, and a means for providing the reports and analysis data. This makes it possible to provide advanced support based on the user's individual health condition and enhance health management for medical settings and corporations.

[0006] "Physical data" refers to physiological values ​​and information related to a user's health and fitness, such as weight, body fat, heart rate, and number of steps.

[0007] "Server" refers to a computer system that stores, calculates, and processes data over a network and provides services in response to requests from clients.

[0008] "Metabolic rate" refers to an index that represents the user's basal metabolic rate and is used to calculate the amount of energy consumed by the user in a resting state.

[0009] "Caloric intake requirement" refers to the amount of energy intake required each day based on the user's current weight and goal weight.

[0010] "Momentum" refers to the level and frequency of physical activity required by a user to achieve their daily routine and fitness goals.

[0011] "Audio advice" refers to audio instructions or advice based on a user's health and fitness goals.

[0012] "Exercise menu" refers to a specific exercise program or plan suggested based on the user's health goals and fitness level.

[0013] "Statistical data" refers to information that shows certain trends or patterns, obtained by aggregating and analyzing physical data collected from multiple users.

[0014] "Healthcare setting" refers to hospitals, clinics, and other healthcare facilities where patient diagnosis, treatment, and healthcare are provided.

[0015] "Dietary Data" refers to information about the contents, quantity, calories, and nutritional content of food and beverages consumed by a User.

[0016] A "corporate customer" refers to an entity, such as a company or organization, that purchases services or products as a unit other than an individual.

[0017] "Report" refers to a document or digital data that compiles analytical results, statistical data, and other information.

[0018] "Analytical data" refers to information and figures obtained as a result of analysis based on collected data. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] DETAILED DESCRIPTION OF THE INVENTION The present invention relates to a wearable tool and a related system that comprehensively supports a user's health management. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail.

[0041] Data collection

[0042] When a user wears a wearable device, the device collects real-time physical data such as weight, body fat, heart rate, and number of steps taken. This data is temporarily stored on the device and sent to a server at regular intervals.

[0043] Metabolic calculation and calculation of required calorie intake and exercise amount

[0044] The server calculates the user's basal metabolic rate (BMR) based on the received physical data. The BMR is calculated using, for example, the Harris-Benedict equation. The server then calculates the user's calorie intake and exercise needs based on the user's target weight and activity level.

[0045] Providing audio advice

[0046] The server generates an appropriate weight loss plan based on the calculation results and creates voice advice using a text-to-speech engine. The voice advice is then sent to the device and provided to the user.

[0047] Enter your meal details and calculate nutritional information

[0048] When a user enters their meal information through the app, the device sends that information to a server, which analyzes the received meal data and calculates calories and nutritional balance. This allows the server to provide feedback on whether the user's meal is in line with their health goals.

[0049] Exercise menu management

[0050] The server generates an individually customized exercise menu based on the user's data, which is then sent to the device, where it is provided to the user and progress is tracked.

[0051] Generating statistical data and utilizing it in medical settings

[0052] The server aggregates data collected from many users and generates statistical data, such as the average body fat percentage by age and gender. This statistical data is useful in medical settings for individual diagnosis and optimization of treatment.

[0053] Customization services for corporations

[0054] The server generates customized reports and analytical data for corporate clients, allowing them to efficiently manage the health of their employees. The server provides these reports to corporate clients on a regular basis and also provides consulting services as needed.

[0055] Specific examples

[0056] For example, suppose a user has data on their weight: 70 kg, body fat percentage: 25%, and heart rate: 72 bpm. The device collects this data and sends it to the server. The server calculates their basal metabolic rate, for example, 1500 kcal / day. The server then considers the user's target weight and suggests a required calorie intake of 1800 kcal / day and exercise three times a week.

[0057] The generated voice advice is provided to the user via the device in the form of "Today's target calorie intake is 1800 kcal. We recommend a 30-minute walk." Furthermore, if the user inputs "200 g of chicken breast, salad, and one bowl of brown rice" for lunch, the server calculates the calories to 500 kcal and also analyzes the balance of the total calorie intake.

[0058] In this way, by using the system of the present invention, users can perform efficient health management, and medical facilities and companies can also manage and utilize data appropriately.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The user puts on a wearable tool, which is equipped with sensors such as a weight scale, a body fat scale, a heart rate monitor, and a pedometer.

[0062] Step 2:

[0063] The device collects data from the wearable device, specifically physical data such as weight, body fat percentage, heart rate, and number of steps taken, in real time.

[0064] Step 3:

[0065] The device temporarily stores the acquired physiological data in memory, ready to be sent to a server later.

[0066] Step 4:

[0067] The device sends the collected data to the server at regular intervals, using Bluetooth or Wi-Fi as the communication method.

[0068] Step 5:

[0069] The server receives the data sent from the device and checks the integrity of the data, for example, checking whether the data is missing or contains errors.

[0070] Step 6:

[0071] The server calculates the user's basal metabolic rate based on the received data, for example, using the Harris-Benedict equation.

[0072] Step 7:

[0073] The server calculates the required calorie intake and recommended exercise amount based on the user's basal metabolic rate and target weight. The required calorie intake is then displayed as a specific number, such as 1800 kcal / day.

[0074] Step 8:

[0075] The server generates textual audio advice based on the calculation results. For example, it might say, "Today's target calorie intake is 1,800 kcal. We recommend walking for 30 minutes."

[0076] Step 9:

[0077] The server uses a text-to-speech engine to convert the generated text into an audio file, which is then sent to the device.

[0078] Step 10:

[0079] The device plays the audio file received from the server, based on the timing or request specified by the user.

[0080] Step 11:

[0081] The user inputs the details of their meal into the app. For example, they input a specific meal such as "200g of chicken breast, salad, and one bowl of brown rice."

[0082] Step 12:

[0083] The device sends the inputted meal data to the server, which uses the data to evaluate calorie and nutritional balance.

[0084] Step 13:

[0085] The server analyzes the received dietary data, calculates calories and nutritional balance, and generates feedback on the user's total calorie intake and nutritional balance based on this data.

[0086] Step 14:

[0087] The server sends feedback data to the device, such as "Today's calorie intake is 90% of your goal. I recommend having a light meal for dinner."

[0088] Step 15:

[0089] The server generates a personalized exercise menu based on the user's data, including specific exercises and their frequency.

[0090] Step 16:

[0091] The server sends the generated exercise menu to the terminal, which displays the menu to the user and tracks the exercise progress.

[0092] Step 17:

[0093] The server aggregates data collected from many users and generates statistical data, such as calculating the average body fat percentage by age and gender.

[0094] Step 18:

[0095] The server provides the generated statistical data to medical facilities, providing doctors with data that can help them make individual diagnoses and optimize treatment.

[0096] Step 19:

[0097] The server generates customized reports and analytical data for corporate clients, such as reports summarizing employee health management status.

[0098] Step 20:

[0099] The server provides regular reports and analytical data to corporate clients, and also provides health management consulting services as needed.

[0100] Example 1

[0101] 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."

[0102] Current systems for efficient health management lack the means to comprehensively understand a user's physical condition and provide appropriate advice. Furthermore, it is difficult to provide customized feedback and exercise menus for individual users. Furthermore, there is a lack of statistical data available for use in businesses and medical settings. To address these issues, a more comprehensive and customized healthcare system is needed.

[0103] 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.

[0104] In this invention, the server includes means for the user to acquire physical data, means for the terminal to transmit the physical data to the server, means for the server to calculate metabolic rate, means for the server to calculate required calorie intake and exercise amount, means for the server to generate audio advice, means for the server to manage exercise menus, and means for the server to generate statistical data and use it in medical settings, thereby enabling efficient health management for users and appropriate data management and use in medical settings and companies.

[0105] "User" refers to an individual who uses the system to manage their own health.

[0106] "Terminal" refers to a wearable tool worn by a user or a device for transmitting data.

[0107] A "server" refers to a computer system that receives data sent by users and performs analysis and calculations.

[0108] "Physical data" refers to measurement data about the user's body, such as weight, body fat, heart rate, and number of steps.

[0109] "Metabolic rate" is a numerical representation of the user's basal metabolic rate, and is an index for calculating energy expenditure.

[0110] "Required calorie intake" refers to the amount of calories a user should consume per day.

[0111] "Movement" refers to the recommended target amount of physical activity a user is recommended to achieve within a particular period of time.

[0112] "Voice advice" refers to instructions or suggestions in the form of voice that are generated based on the results of calculations.

[0113] An "exercise menu" refers to a specific exercise plan proposed to maintain or improve the user's health.

[0114] "Statistical data" refers to statistical information obtained by comprehensively analyzing data collected from multiple users.

[0115] "Healthcare setting" refers to a location where health care services are provided, such as a hospital or clinic.

[0116] "Dietary data" refers to information about the contents of meals consumed by a user.

[0117] "Calories" refers to a unit of measurement that indicates the amount of energy in food.

[0118] "Nutritional balance" refers to the appropriate proportions of nutrients such as protein, fat, and carbohydrates.

[0119] "Corporate Customer" refers to a company, organization, or other entity that uses this system for business purposes.

[0120] "Customized Report" means a report of analytical data tailored to the needs of a particular Corporate Client.

[0121] MODE FOR CARRYING OUT THE INVENTION

[0122] The present invention is a system that comprehensively supports a user's health management, and specific embodiments thereof will be described in detail below.

[0123] overview

[0124] This system collects and manages physical data such as weight, body fat, heart rate, and number of steps taken through a wearable device worn by the user in real time. The data is sent via the device to a server, which performs various calculations and analyses based on the collected data. It also provides users with customized health advice and exercise menus, and supports use in businesses and medical settings.

[0125] Data collection

[0126] When a user wears a wearable device, the device collects physical data in real time. The device used for this is a wearable tool equipped with multiple sensors. An example is a smartwatch with a built-in scale and heart rate monitor. The collected data is temporarily stored on the device and encrypted and sent to a server at regular intervals.

[0127] Data analysis and feedback

[0128] The server analyzes the received data and calculates the user's basal metabolic rate. The Harris-Benedict equation is used to calculate the basal metabolic rate: 88.362 + (13.397 × weight) + (4.799 × height) - (5.677 × age). The necessary calculations are performed based on the user's input data. The server then calculates the required calorie intake and exercise volume based on the user's target weight and activity level.

[0129] The generated results are provided to the user as voice advice. The voice advice generated using a text-to-speech engine is sent to the terminal, which then notifies the user directly.

[0130] Dietary data and nutritional analysis

[0131] When a user enters their meal details through the app, the device sends the data to a server. The server analyzes the received meal data and calculates calories and nutritional balance. This allows the device to provide feedback on how well the user's meal matches their health goals. For example, if a user enters "200g chicken breast, salad, and one bowl of brown rice" for lunch, the device calculates the calories and analyzes the balance based on the total calorie intake.

[0132] Providing individual exercise menus

[0133] The server generates a personalized exercise menu based on the user's health data, taking into account information such as the user's weight, basal metabolic rate, and target weight. The generated exercise menu is sent to the device and provided to the user. The device also has the function of tracking exercise progress.

[0134] Generate statistical data

[0135] The server aggregates data collected from many users and generates statistical data, including average body fat percentage by age and gender, which is used in medical settings and companies to optimize diagnosis and treatment.

[0136] Corporate Services

[0137] It also generates customized reports and analytical data for corporate clients. The server periodically generates these reports and provides them to corporate clients. Consulting services are also provided as needed.

[0138] Specific examples

[0139] For example, suppose a user has data showing a weight of 70 kg, a body fat percentage of 25%, and a heart rate of 72 bpm. The device collects this data and sends it to the server. The server calculates the user's basal metabolic rate, for example, 1500 kcal / day. The server then takes the user's target weight into account and suggests a required calorie intake of 1800 kcal / day and three exercise sessions per week. The generated audio advice is provided to the user via the device in the form of, "Today's target calorie intake is 1800 kcal. I recommend a 30-minute walk." If the user also inputs "200 g of chicken breast, salad, and one bowl of brown rice" for lunch, the server calculates the calories and analyzes the balance of the total calorie intake.

[0140] Example prompts to input to the generative AI model

[0141] "Calculate the user's basal metabolic rate based on their weight, body fat percentage, and heart rate data, and create voice advice on the amount of calories they need to eat and exercise. Use the following data:

[0142] Weight: 70kg

[0143] Body fat percentage: 25%

[0144] Heart rate: 72 bpm

[0145] Also, please calculate the calories of the 200g chicken breast, salad, and bowl of brown rice that the user ate for lunch, and provide feedback on the balance of the total calorie intake.

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] Step 1:

[0148] The user puts on a wearable device, which measures real-time data such as weight, body fat, heart rate, and steps taken.

[0149] Step 2:

[0150] The terminal receives measurement data from the wearable device, which is temporarily stored in the terminal.

[0151] Input: Body data from wearable devices

[0152] Output: Data temporarily stored in the device

[0153] Step 3:

[0154] The device sends the collected data to the server at regular intervals. The data is encrypted before transmission.

[0155] Input: Temporary data in the terminal

[0156] Output: Data sent to the server

[0157] Step 4:

[0158] The server receives the data sent from the terminal and stores it in a database.

[0159] Input: Data sent from the terminal

[0160] Output: Data stored in the database on the server

[0161] Step 5:

[0162] The server calculates the user's basal metabolic rate based on the received data. The calculation is performed using the Harris-Benedict equation.

[0163] Input: Physical data in the database

[0164] Output: Calculated basal metabolic rate

[0165] Step 6:

[0166] The server calculates the required calorie intake and amount of exercise, taking into account the user's target weight and activity level.

[0167] Input: Basal metabolic rate, target weight, activity level

[0168] Output: Required calorie intake and exercise amount

[0169] Step 7:

[0170] The server generates the audio advice, using a text-to-speech engine to create audio advice based on the calculation results.

[0171] Input: Required calorie intake and exercise amount

[0172] Output: Voice advice

[0173] Step 8:

[0174] The server transmits the generated voice advice to the terminal, which notifies the user of the advice.

[0175] Input: Generated voice advice

[0176] Output: Audio advice notification to the user

[0177] Step 9:

[0178] The user inputs the details of their meal through a dedicated app. For example, they input "200g of chicken breast, salad, and one bowl of brown rice for lunch."

[0179] Input: Meal details

[0180] Output: Meal data via a dedicated app

[0181] Step 10:

[0182] The device sends the meal data to the server, and the server receives the meal data.

[0183] Input: Meal data

[0184] Output: Data sent to the server

[0185] Step 11:

[0186] The server analyzes the received dietary data and calculates calories and nutritional balance.

[0187] Input: Meal data

[0188] Output: Calorie and nutritional balance results

[0189] Step 12:

[0190] The server generates feedback based on the meal content, which is sent to the device and can be viewed by the user.

[0191] Input: Calories and nutritional balance results

[0192] Output: Feedback

[0193] Step 13:

[0194] The server generates a customized exercise menu based on the user's data.

[0195] Input: User's physical data, health goals

[0196] Output: Customized exercise menu

[0197] Step 14:

[0198] The server sends the generated exercise menu to the device, which displays the menu to the user and tracks their progress.

[0199] Enter: Customized Exercise Menu

[0200] Output: Data sent to the device, progress data

[0201] Step 15:

[0202] The server aggregates the data collected from multiple users and generates statistical data, such as calculating the average body fat percentage by age and gender.

[0203] Input: A lot of user data

[0204] Output:Statistical data

[0205] Step 16:

[0206] The server provides the generated statistical data to medical facilities, which helps optimize diagnosis and treatment.

[0207] Input:Statistical data

[0208] Output: Data provided to medical facilities

[0209] Step 17:

[0210] The server generates and provides customized reports and analytical data for companies, periodically sending reports to corporate clients and providing consulting services as needed.

[0211] Input: Corporate customer data

[0212] Output: Customized reports and analytical data

[0213] (Application example 1)

[0214] 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."

[0215] In modern society, users are expected to manage their health efficiently and comprehensively. However, while current systems manage individual health data, they lack functionality such as proposing optimal products in a virtual store or offering virtual experiences with exercise equipment. Furthermore, conventional systems face challenges in providing sufficient accuracy in terms of dietary feedback and voice advice.

[0216] 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.

[0217] In this invention, the server includes means for proposing optimal products based on the user's health data within the virtual store, means for virtually experiencing exercise equipment, means for tracking progress, means for providing health advice using a generative AI model, means for providing dietary feedback using a generative AI model, and means for managing statistical data on health-related products within the virtual store, thereby enabling users to comprehensively manage their health and optimally purchase and experience health-related products within the virtual store.

[0218] "Physical data" refers to physiological data such as the user's weight, body fat percentage, heart rate, and number of steps.

[0219] A "server" is a computer that receives, processes, and stores data via a network.

[0220] "Metabolic rate" is an index of energy consumption calculated based on the user's basal metabolic rate.

[0221] "Caloric intake requirement" refers to the daily calorie intake required by a user to maintain health or achieve a goal.

[0222] "Exercise volume" refers to the intensity and amount of exercise a user should do per day.

[0223] "Voice Advice" means audio health advice provided to a user that is created using a generative AI model.

[0224] An "exercise menu" is an exercise plan that is individually customized based on the user's goals.

[0225] "Statistical data" refers to statistical information obtained by aggregating data collected from many users.

[0226] A "virtual store" is a virtual environment where users can purchase products in a digital space, rather than in a real store.

[0227] "Means for suggesting optimal products" refers to algorithms or systems that suggest optimal health-related products to users based on their health data.

[0228] The "means for virtually experiencing exercise equipment" refers to a simulation system that allows users to try out exercise equipment in a digital space.

[0229] A "progress tracking means" is a system for recording and managing a user's exercise and health management progress.

[0230] A "generative AI model" is a model that uses artificial intelligence to analyze data, make predictions, and generate advice.

[0231] A "means for providing health advice" is a system that uses a generative AI model to provide health advice to a user.

[0232] "Means for providing dietary feedback" refers to a system that calculates calories and nutritional balance based on dietary data entered by the user and provides feedback.

[0233] The "means for managing statistical data on health-related products" refers to a system that collects, analyzes, and manages data such as sales and popularity of health-related products sold in the virtual store.

[0234] In order to implement the present invention, the system uses various hardware and software. The following describes the system program that realizes this application example.

[0235] System Configuration

[0236] Hardware:

[0237] 1. Wearable tools: Devices worn by the user that collect physical data such as weight, body fat percentage, heart rate, and number of steps.

[0238] 2. Smartphone or tablet: A device where users input health and dietary data, which receives data from the wearable tool and transmits it to the server.

[0239] 3. Server: Analyzes and stores data, and provides voice advice and feedback using generative AI models.

[0240] software:

[0241] 1. Google® Text-to-Speech (gTTS): Software for providing voice advice to users.

[0242] 2. Flask Web Framework: A web framework that handles server-side processing.

[0243] 3. Generative AI model: An AI model that generates advice and feedback based on health and dietary data.

[0244] Data processing and calculation

[0245] Physical data acquisition and transmission

[0246] When a user wears the wearable tool, it collects real-time physical data and transmits it to a smartphone.

[0247] The smartphone sends this data to a server at regular intervals.

[0248] Calculating metabolic rate and calorie intake targets

[0249] The server uses the received physical data to calculate the user's basal metabolic rate using the Harris-Benedict equation or similar.

[0250] Next, the target calorie intake is calculated based on the basal metabolic rate, the user's target weight, and activity level.

[0251] Generating health advice

[0252] The server uses a generative AI model to generate appropriate health advice based on the calculation results, which is then generated as audio advice using Google Text-to-Speech and sent to the smartphone.

[0253] Meal data entry and feedback

[0254] When a user enters their meal details into their smartphone, the information is sent to the server.

[0255] The server analyzes the submitted meal data, calculates calories and nutritional balance, and provides feedback using a generative AI model.

[0256] Exercise menu management

[0257] The server generates an individually customized exercise menu based on the user's data and sends it to the smartphone.

[0258] Users can track their progress through their smartphones.

[0259] Applications in virtual stores

[0260] The server suggests optimal products in the virtual store based on the user's health data.

[0261] We provide a simulation system that allows users to virtually experience exercise equipment and tracks their progress.

[0262] Specific examples

[0263] For example, let's consider the case where a user sends data showing a weight of 70 kg, a body fat percentage of 25%, a heart rate of 72 bpm, and a step count of 5,000. The server that receives this data calculates the basal metabolic rate and calculates the target calorie intake. It also uses an AI model to generate voice advice such as, "Today's target calorie intake is 1,800 kcal. We recommend walking for 30 minutes."

[0264] If you enter your meal data for lunch, such as "200g chicken breast, salad, and one bowl of brown rice," the server will calculate the calories and provide you with feedback on your meal.

[0265] Prompt Sentence Examples

[0266] "Calculate today's target calorie intake based on weight: 70 kg, body fat percentage: 25%, heart rate: 72 bpm, and steps: 5000."

[0267] "Please analyze the dietary data of a lunch consisting of 200g of chicken breast, salad, and one bowl of brown rice, and provide feedback on the calories and nutritional balance."

[0268] This system enables users to manage their health efficiently and allows them to optimally select and experience health-related products in a virtual store.

[0269] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0270] Step 1:

[0271] The user wears a wearable tool to collect physiological data such as weight, body fat percentage, heart rate, and number of steps. The input is physiological data obtained from the wearable tool worn by the user, which transmits this data in real time to a smartphone. The output is physiological data stored on the smartphone.

[0272] Step 2:

[0273] The smartphone collects and transmits the collected physiological data at regular intervals to a server. The input is the physiological data collected by the wearable tool, and the smartphone transmits the data to the server. The output is the user's physiological data, which is stored on the server. The specific operation is to transmit the data to the server using a communication protocol.

[0274] Step 3:

[0275] The server calculates the basal metabolic rate (BMR) based on the received physical data. The input is the user's physiological data sent to the server, and the data calculation is performed based on the Harris-Benedict equation. The output is the user's basal metabolic rate (BMR).

[0276] Step 4:

[0277] The server calculates the required calorie intake and amount of exercise based on the user's basal metabolic rate, taking into account the user's target weight and activity level. The input is the basal metabolic rate, target weight, and activity level, and based on these, it performs data calculations to calculate the required calorie intake and amount of exercise. The output is the required calorie intake and amount of exercise.

[0278] Step 5:

[0279] The server uses the calculation results to generate voice advice using a generative AI model. The input is data on required calorie intake and exercise amount, and the generative AI model is used to generate advice. Specifically, it uses Google Text-to-Speech to generate an audio file and send it back to the smartphone. The output is an audio file.

[0280] Step 6:

[0281] The user inputs the details of their meal into their smartphone and sends the data to the server. The input is the meal data entered by the user into their smartphone and sent to the server. The output is the meal data stored on the server.

[0282] Step 7:

[0283] The server analyzes the received meal data and calculates calories and nutritional balance. The input is the meal data sent to the server, and the server's data processing algorithm (generative AI model) is used to calculate calories and nutritional balance. The output is feedback data.

[0284] Step 8:

[0285] The server generates an individually customized exercise menu based on the user's data and sends it to the smartphone. The input is the user's physiological data and goal information stored on the server, and the exercise menu is generated using an algorithm within the server. The output is the customized exercise menu.

[0286] Step 9:

[0287] The user checks the exercise menu through their smartphone and tracks their progress. The input is the exercise menu sent from the server and displayed on the smartphone. Progress data is obtained by inputting the exercise content the user will perform. The output is the user's progress data.

[0288] Step 10:

[0289] The server proposes optimal products in a virtual store based on the user's health data. The input is the user's health data and the metadata of the products in the virtual store, and the optimal products are determined based on data analysis. The output is a list of suggested products.

[0290] Step 11:

[0291] The server provides a virtual simulation of the exercise equipment experience and tracks the user's progress. The input is data about the exercise equipment in the virtual store and the user's exercise data, and the simulation system provides the exercise experience. The output is progress tracking data.

[0292] This system enables users to manage their health comprehensively and to optimally select and experience health-related products in a virtual store.

[0293] 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.

[0294] The present invention provides a wearable tool and related system that comprehensively supports a user's health management, and aims to provide more personalized services by combining it with an emotion engine that recognizes the user's emotions. Hereinafter, embodiments of the present invention will be described in detail.

[0295] Data collection

[0296] When a user wears a wearable tool, the device collects real-time physical data such as weight, body fat, heart rate, and number of steps. This data is temporarily stored on the device and sent to a server at regular intervals. In addition, an emotion engine is used to obtain emotional data from the user's facial expressions and voice.

[0297] Metabolic calculation and calculation of required calorie intake and exercise amount

[0298] The server calculates the user's basal metabolic rate (BMR) based on the received physical data. The BMR is calculated using, for example, the Harris-Benedict equation. The server then calculates the user's calorie intake and exercise needs based on the user's target weight and activity level.

[0299] Providing audio advice

[0300] The server generates an appropriate weight loss plan based on the calculation results and creates voice advice. This voice advice is generated using a text-to-speech engine. Furthermore, the content and tone of the voice advice are adjusted based on the user's emotional data recognized by the emotion engine. The generated voice advice is sent to the device and provided to the user.

[0301] Enter your meal details and calculate nutritional information

[0302] When a user enters their meal information through the app, the device sends that information to a server, which analyzes the received meal data and calculates calories and nutritional balance. This allows the server to provide feedback on whether the user's meal is in line with their health goals.

[0303] Exercise menu management

[0304] The server generates an individually customized exercise menu based on the user's data. In addition, the exercise menu can be adjusted taking into account the user's emotional data recognized by the emotion engine. The generated exercise menu is sent from the server to the device, which provides it to the user and tracks their progress.

[0305] Generating statistical data and utilizing it in medical settings

[0306] The server aggregates data collected from many users and generates statistical data, such as the average body fat percentage by age and gender. This statistical data is useful in medical settings for individual diagnosis and optimization of treatment.

[0307] Customization services for corporations

[0308] The server generates customized reports and analytical data for corporate clients, allowing them to efficiently manage the health of their employees. The server provides these reports to corporate clients on a regular basis and also provides consulting services as needed.

[0309] Specific examples

[0310] For example, suppose a user has data on their weight: 70 kg, body fat percentage: 25%, and heart rate: 72 bpm. The device collects this data and sends it to the server. The server calculates their basal metabolic rate, for example, 1500 kcal / day. The server then considers the user's target weight and suggests a required calorie intake of 1800 kcal / day and exercise three times a week.

[0311] At the same time, if the emotion engine detects "stress" from the user's facial expressions or voice, the corresponding voice advice will change. Advice will be provided in the form of "Today's target calorie intake is 1800 kcal. We recommend a 30-minute walk to relax."

[0312] Furthermore, if a user inputs "200g chicken breast, salad, and one bowl of brown rice" for lunch, the server calculates the calories to 500kcal and analyzes the balance of the total calorie intake. This provides feedback such as "Today's calorie intake is 90% of your goal. I recommend something light for dinner."

[0313] In this way, by using the system of the present invention, users can efficiently manage their health, and by using the emotion engine, support that takes into account psychological state can be provided. This will enable appropriate data management and utilization in medical settings and companies.

[0314] The processing flow will be explained below.

[0315] Step 1:

[0316] The user puts on a wearable tool, which is equipped with sensors such as a weight scale, a body fat scale, a heart rate monitor, and a pedometer.

[0317] Step 2:

[0318] The device collects data from the wearable device, specifically physical data such as weight, body fat percentage, heart rate, and number of steps taken, in real time.

[0319] Step 3:

[0320] The device temporarily stores the acquired physiological data in memory, ready to be sent to a server later.

[0321] Step 4:

[0322] The device sends the collected data to the server at regular intervals, using Bluetooth or Wi-Fi as the communication method.

[0323] Step 5:

[0324] The server receives the data sent from the device and checks the integrity of the data, for example, checking whether the data is missing or contains errors.

[0325] Step 6:

[0326] The server calculates the user's basal metabolic rate based on the received data, for example, using the Harris-Benedict equation.

[0327] Step 7:

[0328] The server calculates the required calorie intake and recommended exercise amount based on the user's basal metabolic rate and target weight. The required calorie intake is then displayed as a specific number, such as 1800 kcal / day.

[0329] Step 8:

[0330] The device collects the user's facial expressions and voice data and sends it to the emotion engine, which analyzes the user's emotions and generates emotion data such as "stress" or "relaxation."

[0331] Step 9:

[0332] The server generates the most appropriate voice advice text for the user based on the emotion data and metabolic data received from the emotion engine. For example, it might say, "Today's target calorie intake is 1800 kcal. We recommend a 30-minute walk to relax."

[0333] Step 10:

[0334] The server uses a text-to-speech engine to convert the generated text into an audio file, which is then sent to the device.

[0335] Step 11:

[0336] The device plays the audio file received from the server, based on the timing or request specified by the user.

[0337] Step 12:

[0338] The user inputs the details of their meal into the app. For example, they input a specific meal such as "200g of chicken breast, salad, and one bowl of brown rice."

[0339] Step 13:

[0340] The device sends the inputted meal data to the server, which uses the data to evaluate calorie and nutritional balance.

[0341] Step 14:

[0342] The server analyzes the received dietary data, calculates calories and nutritional balance, and generates feedback on the user's total calorie intake and nutritional balance based on this data.

[0343] Step 15:

[0344] The server sends feedback data to the device, such as "Today's calorie intake is 90% of your goal. I recommend having a light meal for dinner."

[0345] Step 16:

[0346] The server uses the user's data to generate a personalized workout routine, including specific exercises and their frequency.

[0347] Step 17:

[0348] The server sends the generated exercise menu to the terminal, which displays the exercise menu to the English user and tracks the exercise progress.

[0349] Step 18:

[0350] The emotion engine analyzes the user's emotional data and adjusts the exercise menu based on the user's psychological state. For example, it recommends relaxing exercises for users with high stress levels.

[0351] Step 19:

[0352] The server aggregates data collected from many users and generates statistical data, such as calculating the average body fat percentage by age and gender.

[0353] Step 20:

[0354] The server provides the generated statistical data to medical facilities, providing doctors with data that can help them make individual diagnoses and optimize treatment.

[0355] Step 21:

[0356] The server generates customized reports and analytical data for corporate clients, such as reports summarizing employee health management status.

[0357] Step 22:

[0358] The server provides regular reports and analytical data to corporate clients, and also provides health management consulting services as needed.

[0359] The above are the specific steps for implementing the present invention. Through this system, comprehensive health management based on the user's physical data and emotional data can be realized.

[0360] Example 2

[0361] 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."

[0362] Conventional health management systems provide management and advice based solely on the user's physical data, but do not take into account the user's emotional state, making efficient and personalized health management difficult. They also lack the functionality to provide detailed feedback on dietary content and nutritional balance, or to offer customized services for corporate clients. This poses a challenge, preventing advice tailored to the user's psychological state and making it difficult for companies to efficiently manage the health of their employees.

[0363] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0364] In this invention, the server includes a means for acquiring physical data, a means for transmitting the physical data to the server, a means for calculating metabolic rate, a means for calculating required calorie intake and exercise volume, a means for generating audio advice, a means for collecting and analyzing emotional data, a means for adjusting the content and tone of the audio advice based on the emotional data, a means for generating and adjusting an exercise menu, and a means for generating statistical data for use in clinical settings. This enables personalized health management that takes into account not only the user's physical data but also their emotional data. Furthermore, since the system also includes the input and analysis of dietary information and the provision of customization services for corporations, it can function as a comprehensive health management system.

[0365] "Physical data" refers to various data that indicate the user's physical condition, such as the user's weight, body fat, heart rate, and number of steps.

[0366] A "server" is a computer system that receives, stores, analyzes various data, and provides the results.

[0367] "Metabolic rate" represents the user's basal metabolic rate, and is a value calculated based on the Harris-Benedict equation or the like.

[0368] "Required calorie intake" is the amount of calories that a user should take in per day, and is a value calculated based on a target weight and activity level.

[0369] The "amount of exercise" refers to the amount of exercise that the user should do in a day, and indicates the specific scale of the exercise, including the time and intensity.

[0370] "Voice advice" refers to voice instructions or advice that is generated based on the required calorie intake and amount of exercise and provided to the user.

[0371] "Emotion data" is data that indicates the user's mental and emotional state, obtained from the user's facial expressions and voice.

[0372] An "exercise menu" refers to an exercise plan or training schedule for each user that is created based on the user's individual data.

[0373] "Statistical data" is data collected from many users and compiled and analyzed according to attributes such as age and gender.

[0374] The present invention is a system that comprehensively supports a user's health management, and aims to provide more personalized services by incorporating emotion data in particular. Hereinafter, embodiments of the present invention will be described in detail.

[0375] Data collection

[0376] When a user wears a wearable tool, the device collects real-time physical data such as weight, body fat, heart rate, and number of steps. This data is temporarily stored on the device and sent to a server at regular intervals. At the same time, an emotion engine is used to obtain emotional data from the user's facial expressions and voice.

[0377] Sending data

[0378] The device sends the collected physical and emotional data to a server using Wi-Fi, Bluetooth, or other communication methods, and the data is sent securely.

[0379] Metabolic calculation and calorie / exercise calculation

[0380] The server calculates the user's basal metabolic rate using the Harris-Benedict equation based on the received physical data, and then calculates the user's daily calorie intake and exercise requirements based on the user's target weight and activity level.

[0381] Providing audio advice

[0382] The server uses a text-to-speech engine (e.g., Google Text-to-Speech API) to generate audio advice based on required calorie intake and exercise volume. The content and tone of the advice are also adjusted based on the emotional data. The generated audio advice is sent to the device and provided to the user.

[0383] Input and analysis of meal contents

[0384] Users input their meal plans through a dedicated app, and the device sends the information to a server, which analyzes the received meal data, calculates calories and nutritional balance, and provides feedback on whether the user's meal plans are in line with their set health goals.

[0385] Creation and management of exercise menus

[0386] The server generates a personalized exercise menu based on the user's physical and emotional data. The exercise menu can also be adjusted based on the emotional data. The generated exercise menu is sent to the device, which provides it to the user and tracks their progress.

[0387] Generating statistical data and utilizing it in medical settings

[0388] The server aggregates data collected from many users and generates statistical data by age and gender, which is used in medical settings to optimize diagnosis and treatment.

[0389] Customization services for corporations

[0390] The server generates customized reports and analytical data for corporate clients, allowing them to efficiently manage the health of their employees. The server provides these reports to corporate clients on a regular basis and also provides consulting services as needed.

[0391] Specific examples

[0392] For example, if a user has data on their weight (70 kg), body fat percentage (25%), and heart rate (72 bpm), the device that collected this data sends it to the server. The server calculates their basal metabolic rate, for example, 1500 kcal / day. The server then takes the user's target weight into account and sets the required calorie intake at 1800 kcal / day, suggesting three times a week of exercise.

[0393] At the same time, if the emotion engine detects "stress" from the user's facial expressions and voice, it will provide voice advice such as, "We recommend a 30-minute walk to relax." Furthermore, if the user inputs "200g of chicken breast, salad, and one bowl of brown rice" for lunch, the server will calculate the calories to 500kcal and can also analyze the balance of total calorie intake. Feedback will be provided in the form of, "Today's calorie intake is 90% of your goal. We recommend something light for dinner."

[0394] Prompt Sentence Examples

[0395] "Write pseudocode in natural language for a system that creates a personalized health management plan based on a user's weight, body fat percentage, heart rate, number of steps, diet, etc., along with an explanation of each step."

[0396] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0397] Step 1:

[0398] The user puts on a wearable tool. This tool uses built-in sensors to collect real-time physical data such as weight, body fat, heart rate, and number of steps. The collected data is temporarily stored on the device. The input is the user's physical data, and the output is the data stored on the device.

[0399] Step 2:

[0400] At regular intervals, the device sends the collected physiological data to a server. The communication method is Wi-Fi or Bluetooth. Specifically, the data stored in the device is sent to the server via the network. The input is the physiological data in the device, and the output is the physiological data sent to the server.

[0401] Step 3:

[0402] The device also collects the user's facial expressions and voice to generate emotion data. The emotion engine analyzes this data and identifies the user's emotional state (e.g., stress, joy, fatigue, etc.). This emotion data is also periodically sent to the server. The input is the user's facial expressions and voice, and the output is the emotion data sent to the server.

[0403] Step 4:

[0404] The server calculates the basal metabolic rate (BMR) using the Harris-Benedict equation based on the received physical data. Specifically, it calculates the BMR using the user's weight, height, age, and gender. The input is the received physical data, and the output is the calculated basal metabolic rate.

[0405] Step 5:

[0406] The server calculates the required calorie intake and exercise amount based on the user's basal metabolic rate, taking into account the user's target weight and activity level. Specifically, it calculates the total daily energy expenditure (TDEE) by applying a correction factor for each activity level, and then sets the calorie intake and exercise goals based on the user's goals. The inputs are the basal metabolic rate, target weight, and activity level, and the output is the required calorie intake and exercise amount.

[0407] Step 6:

[0408] The server uses a text-to-speech engine to generate audio advice based on the required calorie intake and amount of exercise. Furthermore, it adjusts the content and tone of the advice using emotional data. The generated audio advice is sent to the device. The input is the calculated required calorie intake and emotional data, and the output is the generated audio advice.

[0409] Step 7:

[0410] The terminal provides the generated voice advice to the user. The terminal uses a voice playback function to convey the advice to the user. The input is the generated voice advice, and the output is the voice advice provided to the user.

[0411] Step 8:

[0412] The user inputs the details of their meal through a dedicated app. The device sends this data to the server. The input is the user's meal data, and the output is the meal data sent to the server.

[0413] Step 9:

[0414] The server analyzes the received meal data, calculates calories and nutritional balance, and generates feedback based on the analysis results and sends it to the device. The input is the received meal data, and the output is the calculated calories and feedback.

[0415] Step 10:

[0416] The server generates a customized exercise menu based on the user's data. It can also adjust the exercise menu taking into account emotional data. The generated exercise menu is sent to the terminal. The input is the user's physical data and emotional data, and the output is the generated exercise menu.

[0417] Step 11:

[0418] The terminal provides the user with an exercise menu and tracks the progress. The input is the generated exercise menu, and the output is the exercise menu provided to the user and tracking data.

[0419] (Application example 2)

[0420] 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."

[0421] In recent years, interest in health management has grown, creating a demand for systems that can provide advice and recommendations tailored to individual users. However, existing systems tend to be limited to providing information based on physical data and lack personalized recommendations that take into account the user's emotional state. Furthermore, it has been difficult to recommend optimal products and services based on the user's health condition and emotions during the shopping experience in a physical store. Therefore, there is a demand for systems that can provide more comprehensive and personalized health management services.

[0422] 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.

[0423] In this invention, the server includes means for acquiring physical data, means for calculating the required calorie intake and amount of exercise, means for acquiring user emotion data, means for recommending products or services based on the emotion data, and means for displaying and providing audio guidance on the smart device with the recommended content. This enables personalized recommendations based on the user's health condition and emotions, and makes it possible to provide optimal products and services even in a shopping experience at a physical store.

[0424] "Physical data" refers to data related to the user's physical condition, such as the user's weight, body fat percentage, heart rate, and number of steps.

[0425] The "server" is a computer system that receives and analyzes the physical data and emotional data, calculates the required calorie intake and amount of exercise, and recommends products and services.

[0426] "Metabolic rate" is a calculated value used to calculate the user's basal metabolic rate, and indicates the basic amount of energy the user consumes in a day.

[0427] "Required calorie intake" indicates the amount of energy a user should take in per day, and is calculated based on the user's target weight and activity level.

[0428] "Voice advice" means spoken instructions or recommendations provided to the user by the system and generated using text-to-speech technology.

[0429] An "exercise menu" is a plan that indicates the exercise content that a user should do based on the user's health goals.

[0430] "Statistical data" refers to data obtained by aggregating and analyzing data collected from a large number of users, and is used as a reference for diagnosis and treatment in medical settings.

[0431] "Emotion data" is data that indicates the emotional state of the user, obtained from the user's facial expressions and voice.

[0432] "Product or service recommendation" means proposing optimal products or services to a user based on the user's physical data and emotional data.

[0433] "Smart devices" are devices that have the function of displaying information and providing voice guidance, such as smartphones, smart glasses, and head-mounted displays.

[0434] The present invention is a system that comprehensively supports users' health management, and in particular provides personalized recommendations by utilizing emotional data. The system includes physical and emotional data collection, data analysis, recommendation generation, display, and voice guidance.

[0435] System configuration

[0436] The system consists of the following main hardware and software:

[0437] Wearable tool: A device that collects health data (weight, body fat percentage, heart rate, number of steps, etc.) in real time and transmits it to a device.

[0438] Smart Device: Smart glasses are used as a device to display health and emotional data and provide voice guidance.

[0439] Server: The computer system that receives and analyzes the collected data.

[0440] Emotion recognition engine: Software that recognizes emotions from the user's facial expressions and voice.

[0441] Text-to-Speech Engine: Software that generates spoken recommendations and advice.

[0442] Recommendation engine: Software that recommends the most suitable products and services to users based on their physical and emotional data.

[0443] System Operation

[0444] Data collection

[0445] By wearing the wearable tool, the user can collect real-time health data such as weight, body fat percentage, heart rate, and number of steps. This data is transmitted to a device via smart glasses. In addition, an emotion recognition engine uses the smart glasses' camera and microphone to analyze the user's facial expressions and voice to obtain emotional data.

[0446] Data analysis and recommendation generation

[0447] The server calculates the user's metabolic rate based on the received health data, and calculates the required calorie intake and amount of exercise.The recommendation engine also considers the emotional data acquired by the emotion recognition engine and selects the most suitable products and services for the user.

[0448] Display and audio guidance of recommendations

[0449] The generated recommendation is displayed on the smart device and audibly guided using a text-to-speech engine. For example, if the user is feeling stressed, the recommendation may be displayed and spoken, such as "We recommend herbal tea for its relaxing effects."

[0450] Specific examples

[0451] For example, if a user has data showing a weight of 70 kg, a body fat percentage of 25%, and a heart rate of 80 bpm, and the emotion recognition engine detects "stress," the system operates as follows: The server receives the health data and calculates the metabolic rate. Next, the recommendation engine selects "relaxing herbal teas and aromatic oils" based on the emotion data. The smart glasses will display a message saying, "We recommend relaxing herbal teas," and the text-to-speech engine will provide a similar message via voice.

[0452] Also, when a user inputs a health care question into the generative AI model via a smart device, the following prompt can be used: "If the user is feeling stressed, please recommend products that have a relaxation effect, such as herbal tea or aromatic oil."

[0453] In this way, the system of the present invention provides personalized recommendations based on the user's health and emotional state, enhancing the shopping experience in brick-and-mortar stores.

[0454] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0455] Step 1:

[0456] Users wear a wearable tool that collects health data (weight, body fat percentage, heart rate, number of steps, etc.) in real time.

[0457] Input: Health data from wearable tools

[0458] Output: Health data sent to smart glasses

[0459] How it works: The wearable tool is worn by the user, and the built-in sensors detect data such as weight, body fat percentage, heart rate, and number of steps, and send this information to the device in real time.

[0460] Step 2:

[0461] Using the camera and microphone of the smart glasses, the emotion recognition engine analyzes the user's facial expressions and voice to obtain emotional data.

[0462] Input: User's facial expression, voice

[0463] Output: Emotion data

[0464] How it works: The smart glasses' camera captures the user's facial expressions and microphone records their voice, which are then sent to an emotion recognition engine to analyze the user's emotional state (e.g., stress, joy, anxiety, etc.).

[0465] Step 3:

[0466] The smart glasses send the collected health and emotional data to a server.

[0467] Input: Health data, emotion data

[0468] Output: Health and emotion data sent to the server

[0469] How it works: The smart glasses send health and emotion data to the server, which then receives it. The data is encrypted before transmission.

[0470] Step 4:

[0471] The server calculates the metabolic rate based on the health data received, and calculates the required calorie intake and amount of exercise.

[0472] Input: Health data

[0473] Output: metabolic rate, calorie needs, and exercise volume

[0474] How it works: The server analyzes health data and calculates metabolic rate using the Harris-Benedict equation, etc. It then calculates the required calorie intake and amount of exercise based on the user's target weight and activity level.

[0475] Step 5:

[0476] The server takes into account the emotional data, and the recommendation engine generates the most suitable products and services for the user.

[0477] Input: Emotion data, calculation results from health data

[0478] Output: Recommendation (product or service)

[0479] How it works: The server analyzes the emotional data to understand the user's current emotional state. The recommendation engine then selects products and services that will have a relaxing effect for the user based on the health and emotional data.

[0480] Step 6:

[0481] The generated recommendation content is sent to the smart device, where it is displayed and provided as audio guidance.

[0482] Input: Recommendation from the server

[0483] Output: Recommendations displayed on smart devices and audio guidance

[0484] Specific operation: The recommendation content is sent to the smart glasses and displayed on the screen. At the same time, the text-to-speech engine generates a voice prompt and delivers it to the user through the smart glasses' speaker.

[0485] In this way, the system supports more personalized health management by understanding the user's health and emotional state in detail at each step and recommending optimal products and services.

[0486] 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.

[0487] 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.

[0488] 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.

[0489] [Second embodiment]

[0490] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0491] 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.

[0492] 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).

[0493] 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.

[0494] 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.

[0495] 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).

[0496] 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.

[0497] 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.

[0498] 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.

[0499] 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.

[0500] 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.

[0501] 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."

[0502] DETAILED DESCRIPTION OF THE INVENTION The present invention relates to a wearable tool and a related system that comprehensively supports a user's health management. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail.

[0503] Data collection

[0504] When a user wears a wearable device, the device collects real-time physical data such as weight, body fat, heart rate, and number of steps taken. This data is temporarily stored on the device and sent to a server at regular intervals.

[0505] Metabolic calculation and calculation of required calorie intake and exercise amount

[0506] The server calculates the user's basal metabolic rate (BMR) based on the received physical data. The BMR is calculated using, for example, the Harris-Benedict equation. The server then calculates the user's calorie intake and exercise needs based on the user's target weight and activity level.

[0507] Providing audio advice

[0508] The server generates an appropriate weight loss plan based on the calculation results and creates voice advice using a text-to-speech engine. The voice advice is then sent to the device and provided to the user.

[0509] Enter your meal details and calculate nutritional information

[0510] When a user enters their meal information through the app, the device sends that information to a server, which analyzes the received meal data and calculates calories and nutritional balance. This allows the server to provide feedback on whether the user's meal is in line with their health goals.

[0511] Exercise menu management

[0512] The server generates an individually customized exercise menu based on the user's data, which is then sent to the device, where it is provided to the user and progress is tracked.

[0513] Generating statistical data and utilizing it in medical settings

[0514] The server aggregates data collected from many users and generates statistical data, such as the average body fat percentage by age and gender. This statistical data is useful in medical settings for individual diagnosis and optimization of treatment.

[0515] Customization services for corporations

[0516] The server generates customized reports and analytical data for corporate clients, allowing them to efficiently manage the health of their employees. The server provides these reports to corporate clients on a regular basis and also provides consulting services as needed.

[0517] Specific examples

[0518] For example, suppose a user has data on their weight: 70 kg, body fat percentage: 25%, and heart rate: 72 bpm. The device collects this data and sends it to the server. The server calculates their basal metabolic rate, for example, 1500 kcal / day. The server then considers the user's target weight and suggests a required calorie intake of 1800 kcal / day and exercise three times a week.

[0519] The generated voice advice is provided to the user via the device in the form of "Today's target calorie intake is 1800 kcal. We recommend a 30-minute walk." Furthermore, if the user inputs "200 g of chicken breast, salad, and one bowl of brown rice" for lunch, the server calculates the calories to 500 kcal and also analyzes the balance of the total calorie intake.

[0520] In this way, by using the system of the present invention, users can perform efficient health management, and medical facilities and companies can also manage and utilize data appropriately.

[0521] The processing flow will be explained below.

[0522] Step 1:

[0523] The user puts on a wearable tool, which is equipped with sensors such as a weight scale, a body fat scale, a heart rate monitor, and a pedometer.

[0524] Step 2:

[0525] The device collects data from the wearable device, specifically physical data such as weight, body fat percentage, heart rate, and number of steps taken, in real time.

[0526] Step 3:

[0527] The device temporarily stores the acquired physiological data in memory, ready to be sent to a server later.

[0528] Step 4:

[0529] The device sends the collected data to the server at regular intervals, using Bluetooth or Wi-Fi as the communication method.

[0530] Step 5:

[0531] The server receives the data sent from the device and checks the integrity of the data, for example, checking whether the data is missing or contains errors.

[0532] Step 6:

[0533] The server calculates the user's basal metabolic rate based on the received data, for example, using the Harris-Benedict equation.

[0534] Step 7:

[0535] The server calculates the required calorie intake and recommended exercise amount based on the user's basal metabolic rate and target weight. The required calorie intake is then displayed as a specific number, such as 1800 kcal / day.

[0536] Step 8:

[0537] The server generates textual audio advice based on the calculation results. For example, it might say, "Today's target calorie intake is 1,800 kcal. We recommend walking for 30 minutes."

[0538] Step 9:

[0539] The server uses a text-to-speech engine to convert the generated text into an audio file, which is then sent to the device.

[0540] Step 10:

[0541] The device plays the audio file received from the server, based on the timing or request specified by the user.

[0542] Step 11:

[0543] The user inputs the details of their meal into the app. For example, they input a specific meal such as "200g of chicken breast, salad, and one bowl of brown rice."

[0544] Step 12:

[0545] The device sends the inputted meal data to the server, which uses the data to evaluate calorie and nutritional balance.

[0546] Step 13:

[0547] The server analyzes the received dietary data, calculates calories and nutritional balance, and generates feedback on the user's total calorie intake and nutritional balance based on this data.

[0548] Step 14:

[0549] The server sends feedback data to the device, such as "Today's calorie intake is 90% of your goal. I recommend having a light meal for dinner."

[0550] Step 15:

[0551] The server generates a personalized exercise menu based on the user's data, including specific exercises and their frequency.

[0552] Step 16:

[0553] The server sends the generated exercise menu to the terminal, which displays the menu to the user and tracks the exercise progress.

[0554] Step 17:

[0555] The server aggregates data collected from many users and generates statistical data, such as calculating the average body fat percentage by age and gender.

[0556] Step 18:

[0557] The server provides the generated statistical data to medical facilities, providing doctors with data that can help them make individual diagnoses and optimize treatment.

[0558] Step 19:

[0559] The server generates customized reports and analytical data for corporate clients, such as reports summarizing employee health management status.

[0560] Step 20:

[0561] The server provides regular reports and analytical data to corporate clients, and also provides health management consulting services as needed.

[0562] Example 1

[0563] 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."

[0564] Current systems for efficient health management lack the means to comprehensively understand a user's physical condition and provide appropriate advice. Furthermore, it is difficult to provide customized feedback and exercise menus for individual users. Furthermore, there is a lack of statistical data available for use in businesses and medical settings. To address these issues, a more comprehensive and customized healthcare system is needed.

[0565] 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.

[0566] In this invention, the server includes means for the user to acquire physical data, means for the terminal to transmit the physical data to the server, means for the server to calculate metabolic rate, means for the server to calculate required calorie intake and exercise amount, means for the server to generate audio advice, means for the server to manage exercise menus, and means for the server to generate statistical data and use it in medical settings, thereby enabling efficient health management for users and appropriate data management and use in medical settings and companies.

[0567] "User" refers to an individual who uses the system to manage their own health.

[0568] "Terminal" refers to a wearable tool worn by a user or a device for transmitting data.

[0569] A "server" refers to a computer system that receives data sent by users and performs analysis and calculations.

[0570] "Physical data" refers to measurement data about the user's body, such as weight, body fat, heart rate, and number of steps.

[0571] "Metabolic rate" is a numerical representation of the user's basal metabolic rate, and is an index for calculating energy expenditure.

[0572] "Required calorie intake" refers to the amount of calories a user should consume per day.

[0573] "Movement" refers to the recommended target amount of physical activity a user is recommended to achieve within a particular period of time.

[0574] "Voice advice" refers to instructions or suggestions in the form of voice that are generated based on the results of calculations.

[0575] An "exercise menu" refers to a specific exercise plan proposed to maintain or improve the user's health.

[0576] "Statistical data" refers to statistical information obtained by comprehensively analyzing data collected from multiple users.

[0577] "Healthcare setting" refers to a location where health care services are provided, such as a hospital or clinic.

[0578] "Dietary data" refers to information about the contents of meals consumed by a user.

[0579] "Calories" refers to a unit of measurement that indicates the amount of energy in food.

[0580] "Nutritional balance" refers to the appropriate proportions of nutrients such as protein, fat, and carbohydrates.

[0581] "Corporate Customer" refers to a company, organization, or other entity that uses this system for business purposes.

[0582] "Customized Report" means a report of analytical data tailored to the needs of a particular Corporate Client.

[0583] MODE FOR CARRYING OUT THE INVENTION

[0584] The present invention is a system that comprehensively supports a user's health management, and specific embodiments thereof will be described in detail below.

[0585] overview

[0586] This system collects and manages physical data such as weight, body fat, heart rate, and number of steps taken through a wearable device worn by the user in real time. The data is sent via the device to a server, which performs various calculations and analyses based on the collected data. It also provides users with customized health advice and exercise menus, and supports use in businesses and medical settings.

[0587] Data collection

[0588] When a user wears a wearable device, the device collects physical data in real time. The device used for this is a wearable tool equipped with multiple sensors. An example is a smartwatch with a built-in scale and heart rate monitor. The collected data is temporarily stored on the device and encrypted and sent to a server at regular intervals.

[0589] Data analysis and feedback

[0590] The server analyzes the received data and calculates the user's basal metabolic rate. The Harris-Benedict equation is used to calculate the basal metabolic rate: 88.362 + (13.397 × weight) + (4.799 × height) - (5.677 × age). The necessary calculations are performed based on the user's input data. The server then calculates the required calorie intake and exercise volume based on the user's target weight and activity level.

[0591] The generated results are provided to the user as voice advice. The voice advice generated using a text-to-speech engine is sent to the terminal, which then notifies the user directly.

[0592] Dietary data and nutritional analysis

[0593] When a user enters their meal details through the app, the device sends the data to a server. The server analyzes the received meal data and calculates calories and nutritional balance. This allows the device to provide feedback on how well the user's meal matches their health goals. For example, if a user enters "200g chicken breast, salad, and one bowl of brown rice" for lunch, the device calculates the calories and analyzes the balance based on the total calorie intake.

[0594] Providing individual exercise menus

[0595] The server generates a personalized exercise menu based on the user's health data, taking into account information such as the user's weight, basal metabolic rate, and target weight. The generated exercise menu is sent to the device and provided to the user. The device also has the function of tracking exercise progress.

[0596] Generate statistical data

[0597] The server aggregates data collected from many users and generates statistical data, including average body fat percentage by age and gender, which is used in medical settings and companies to optimize diagnosis and treatment.

[0598] Corporate Services

[0599] It also generates customized reports and analytical data for corporate clients. The server periodically generates these reports and provides them to corporate clients. Consulting services are also provided as needed.

[0600] Specific examples

[0601] For example, suppose a user has data showing a weight of 70 kg, a body fat percentage of 25%, and a heart rate of 72 bpm. The device collects this data and sends it to the server. The server calculates the user's basal metabolic rate, for example, 1500 kcal / day. The server then takes the user's target weight into account and suggests a required calorie intake of 1800 kcal / day and three exercise sessions per week. The generated audio advice is provided to the user via the device in the form of, "Today's target calorie intake is 1800 kcal. I recommend a 30-minute walk." If the user also inputs "200 g of chicken breast, salad, and one bowl of brown rice" for lunch, the server calculates the calories and analyzes the balance of the total calorie intake.

[0602] Example prompts to input to the generative AI model

[0603] "Calculate the user's basal metabolic rate based on their weight, body fat percentage, and heart rate data, and create voice advice on the amount of calories they need to eat and exercise. Use the following data:

[0604] Weight: 70kg

[0605] Body fat percentage: 25%

[0606] Heart rate: 72 bpm

[0607] Also, please calculate the calories of the 200g chicken breast, salad, and bowl of brown rice that the user ate for lunch, and provide feedback on the balance of the total calorie intake.

[0608] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0609] Step 1:

[0610] The user puts on a wearable device, which measures real-time data such as weight, body fat, heart rate, and steps taken.

[0611] Step 2:

[0612] The terminal receives measurement data from the wearable device, which is temporarily stored in the terminal.

[0613] Input: Body data from wearable devices

[0614] Output: Data temporarily stored in the device

[0615] Step 3:

[0616] The device sends the collected data to the server at regular intervals. The data is encrypted before transmission.

[0617] Input: Temporary data in the terminal

[0618] Output: Data sent to the server

[0619] Step 4:

[0620] The server receives the data sent from the terminal and stores it in a database.

[0621] Input: Data sent from the terminal

[0622] Output: Data stored in the database on the server

[0623] Step 5:

[0624] The server calculates the user's basal metabolic rate based on the received data. The calculation is performed using the Harris-Benedict equation.

[0625] Input: Physical data in the database

[0626] Output: Calculated basal metabolic rate

[0627] Step 6:

[0628] The server calculates the required calorie intake and amount of exercise, taking into account the user's target weight and activity level.

[0629] Input: Basal metabolic rate, target weight, activity level

[0630] Output: Required calorie intake and exercise amount

[0631] Step 7:

[0632] The server generates the audio advice, using a text-to-speech engine to create audio advice based on the calculation results.

[0633] Input: Required calorie intake and exercise amount

[0634] Output: Voice advice

[0635] Step 8:

[0636] The server transmits the generated voice advice to the terminal, which notifies the user of the advice.

[0637] Input: Generated voice advice

[0638] Output: Audio advice notification to the user

[0639] Step 9:

[0640] The user inputs the details of their meal through a dedicated app. For example, they input "200g of chicken breast, salad, and one bowl of brown rice for lunch."

[0641] Input: Meal details

[0642] Output: Meal data via a dedicated app

[0643] Step 10:

[0644] The device sends the meal data to the server, and the server receives the meal data.

[0645] Input: Meal data

[0646] Output: Data sent to the server

[0647] Step 11:

[0648] The server analyzes the received dietary data and calculates calories and nutritional balance.

[0649] Input: Meal data

[0650] Output: Calorie and nutritional balance results

[0651] Step 12:

[0652] The server generates feedback based on the meal content, which is sent to the device and can be viewed by the user.

[0653] Input: Calories and nutritional balance results

[0654] Output: Feedback

[0655] Step 13:

[0656] The server generates a customized exercise menu based on the user's data.

[0657] Input: User's physical data, health goals

[0658] Output: Customized exercise menu

[0659] Step 14:

[0660] The server sends the generated exercise menu to the device, which displays the menu to the user and tracks their progress.

[0661] Enter: Customized Exercise Menu

[0662] Output: Data sent to the device, progress data

[0663] Step 15:

[0664] The server aggregates the data collected from multiple users and generates statistical data, such as calculating the average body fat percentage by age and gender.

[0665] Input: A lot of user data

[0666] Output:Statistical data

[0667] Step 16:

[0668] The server provides the generated statistical data to medical facilities, which helps optimize diagnosis and treatment.

[0669] Input:Statistical data

[0670] Output: Data provided to medical facilities

[0671] Step 17:

[0672] The server generates and provides customized reports and analytical data for companies, periodically sending reports to corporate clients and providing consulting services as needed.

[0673] Input: Corporate customer data

[0674] Output: Customized reports and analytical data

[0675] (Application example 1)

[0676] 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."

[0677] In modern society, users are expected to manage their health efficiently and comprehensively. However, while current systems manage individual health data, they lack functionality such as proposing optimal products in a virtual store or offering virtual experiences with exercise equipment. Furthermore, conventional systems face challenges in providing sufficient accuracy in terms of dietary feedback and voice advice.

[0678] 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.

[0679] In this invention, the server includes means for proposing optimal products based on the user's health data within the virtual store, means for virtually experiencing exercise equipment, means for tracking progress, means for providing health advice using a generative AI model, means for providing dietary feedback using a generative AI model, and means for managing statistical data on health-related products within the virtual store, thereby enabling users to comprehensively manage their health and optimally purchase and experience health-related products within the virtual store.

[0680] "Physical data" refers to physiological data such as the user's weight, body fat percentage, heart rate, and number of steps.

[0681] A "server" is a computer that receives, processes, and stores data via a network.

[0682] "Metabolic rate" is an index of energy consumption calculated based on the user's basal metabolic rate.

[0683] "Caloric intake requirement" refers to the daily calorie intake required by a user to maintain health or achieve a goal.

[0684] "Exercise volume" refers to the intensity and amount of exercise a user should do per day.

[0685] "Voice Advice" means audio health advice provided to a user that is created using a generative AI model.

[0686] An "exercise menu" is an exercise plan that is individually customized based on the user's goals.

[0687] "Statistical data" refers to statistical information obtained by aggregating data collected from many users.

[0688] A "virtual store" is a virtual environment where users can purchase products in a digital space, rather than in a real store.

[0689] "Means for suggesting optimal products" refers to algorithms or systems that suggest optimal health-related products to users based on their health data.

[0690] The "means for virtually experiencing exercise equipment" refers to a simulation system that allows users to try out exercise equipment in a digital space.

[0691] A "progress tracking means" is a system for recording and managing a user's exercise and health management progress.

[0692] A "generative AI model" is a model that uses artificial intelligence to analyze data, make predictions, and generate advice.

[0693] A "means for providing health advice" is a system that uses a generative AI model to provide health advice to a user.

[0694] "Means for providing dietary feedback" refers to a system that calculates calories and nutritional balance based on dietary data entered by the user and provides feedback.

[0695] The "means for managing statistical data on health-related products" refers to a system that collects, analyzes, and manages data such as sales and popularity of health-related products sold in the virtual store.

[0696] In order to implement the present invention, the system uses various hardware and software. The following describes the system program that realizes this application example.

[0697] System Configuration

[0698] Hardware:

[0699] 1. Wearable tools: Devices worn by the user that collect physical data such as weight, body fat percentage, heart rate, and number of steps.

[0700] 2. Smartphone or tablet: A device where users input health and dietary data, which receives data from the wearable tool and transmits it to the server.

[0701] 3. Server: Analyzes and stores data, and provides voice advice and feedback using generative AI models.

[0702] software:

[0703] 1. Google Text-to-Speech (gTTS): Software for providing voice advice to users.

[0704] 2. Flask Web Framework: A web framework that handles server-side processing.

[0705] 3. Generative AI model: An AI model that generates advice and feedback based on health and dietary data.

[0706] Data processing and calculation

[0707] Physical data acquisition and transmission

[0708] When a user wears the wearable tool, it collects real-time physical data and transmits it to a smartphone.

[0709] The smartphone sends this data to a server at regular intervals.

[0710] Calculating metabolic rate and calorie intake targets

[0711] The server uses the received physical data to calculate the user's basal metabolic rate using the Harris-Benedict equation or similar.

[0712] Next, the target calorie intake is calculated based on the basal metabolic rate, the user's target weight, and activity level.

[0713] Generating health advice

[0714] The server uses a generative AI model to generate appropriate health advice based on the calculation results, which is then generated as audio advice using Google Text-to-Speech and sent to the smartphone.

[0715] Meal data entry and feedback

[0716] When a user enters their meal details into their smartphone, the information is sent to the server.

[0717] The server analyzes the submitted meal data, calculates calories and nutritional balance, and provides feedback using a generative AI model.

[0718] Exercise menu management

[0719] The server generates an individually customized exercise menu based on the user's data and sends it to the smartphone.

[0720] Users can track their progress through their smartphones.

[0721] Applications in virtual stores

[0722] The server suggests optimal products in the virtual store based on the user's health data.

[0723] We provide a simulation system that allows users to virtually experience exercise equipment and tracks their progress.

[0724] Specific examples

[0725] For example, let's consider the case where a user sends data showing a weight of 70 kg, a body fat percentage of 25%, a heart rate of 72 bpm, and a step count of 5,000. The server that receives this data calculates the basal metabolic rate and calculates the target calorie intake. It also uses an AI model to generate voice advice such as, "Today's target calorie intake is 1,800 kcal. We recommend walking for 30 minutes."

[0726] If you enter your meal data for lunch, such as "200g chicken breast, salad, and one bowl of brown rice," the server will calculate the calories and provide you with feedback on your meal.

[0727] Prompt Sentence Examples

[0728] "Calculate today's target calorie intake based on weight: 70 kg, body fat percentage: 25%, heart rate: 72 bpm, and steps: 5000."

[0729] "Please analyze the dietary data of a lunch consisting of 200g of chicken breast, salad, and one bowl of brown rice, and provide feedback on the calories and nutritional balance."

[0730] This system enables users to manage their health efficiently and allows them to optimally select and experience health-related products in a virtual store.

[0731] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0732] Step 1:

[0733] The user wears a wearable tool to collect physiological data such as weight, body fat percentage, heart rate, and number of steps. The input is physiological data obtained from the wearable tool worn by the user, which transmits this data in real time to a smartphone. The output is physiological data stored on the smartphone.

[0734] Step 2:

[0735] The smartphone collects and transmits the collected physiological data at regular intervals to a server. The input is the physiological data collected by the wearable tool, and the smartphone transmits the data to the server. The output is the user's physiological data, which is stored on the server. The specific operation is to transmit the data to the server using a communication protocol.

[0736] Step 3:

[0737] The server calculates the basal metabolic rate (BMR) based on the received physical data. The input is the user's physiological data sent to the server, and the data calculation is performed based on the Harris-Benedict equation. The output is the user's basal metabolic rate (BMR).

[0738] Step 4:

[0739] The server calculates the required calorie intake and amount of exercise based on the user's basal metabolic rate, taking into account the user's target weight and activity level. The input is the basal metabolic rate, target weight, and activity level, and based on these, it performs data calculations to calculate the required calorie intake and amount of exercise. The output is the required calorie intake and amount of exercise.

[0740] Step 5:

[0741] The server uses the calculation results to generate voice advice using a generative AI model. The input is data on required calorie intake and exercise amount, and the generative AI model is used to generate advice. Specifically, it uses Google Text-to-Speech to generate an audio file and send it back to the smartphone. The output is an audio file.

[0742] Step 6:

[0743] The user inputs the details of their meal into their smartphone and sends the data to the server. The input is the meal data entered by the user into their smartphone and sent to the server. The output is the meal data stored on the server.

[0744] Step 7:

[0745] The server analyzes the received meal data and calculates calories and nutritional balance. The input is the meal data sent to the server, and the server's data processing algorithm (generative AI model) is used to calculate calories and nutritional balance. The output is feedback data.

[0746] Step 8:

[0747] The server generates an individually customized exercise menu based on the user's data and sends it to the smartphone. The input is the user's physiological data and goal information stored on the server, and the exercise menu is generated using an algorithm within the server. The output is the customized exercise menu.

[0748] Step 9:

[0749] The user checks the exercise menu through their smartphone and tracks their progress. The input is the exercise menu sent from the server and displayed on the smartphone. Progress data is obtained by inputting the exercise content the user will perform. The output is the user's progress data.

[0750] Step 10:

[0751] The server proposes optimal products in a virtual store based on the user's health data. The input is the user's health data and the metadata of the products in the virtual store, and the optimal products are determined based on data analysis. The output is a list of suggested products.

[0752] Step 11:

[0753] The server provides a virtual simulation of the exercise equipment experience and tracks the user's progress. The input is data about the exercise equipment in the virtual store and the user's exercise data, and the simulation system provides the exercise experience. The output is progress tracking data.

[0754] This system enables users to manage their health comprehensively and to optimally select and experience health-related products in a virtual store.

[0755] 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.

[0756] The present invention provides a wearable tool and related system that comprehensively supports a user's health management, and aims to provide more personalized services by combining it with an emotion engine that recognizes the user's emotions. Hereinafter, embodiments of the present invention will be described in detail.

[0757] Data collection

[0758] When a user wears a wearable tool, the device collects real-time physical data such as weight, body fat, heart rate, and number of steps. This data is temporarily stored on the device and sent to a server at regular intervals. In addition, an emotion engine is used to obtain emotional data from the user's facial expressions and voice.

[0759] Metabolic calculation and calculation of required calorie intake and exercise amount

[0760] The server calculates the user's basal metabolic rate (BMR) based on the received physical data. The BMR is calculated using, for example, the Harris-Benedict equation. The server then calculates the user's calorie intake and exercise needs based on the user's target weight and activity level.

[0761] Providing audio advice

[0762] The server generates an appropriate weight loss plan based on the calculation results and creates voice advice. This voice advice is generated using a text-to-speech engine. Furthermore, the content and tone of the voice advice are adjusted based on the user's emotional data recognized by the emotion engine. The generated voice advice is sent to the device and provided to the user.

[0763] Enter your meal details and calculate nutritional information

[0764] When a user enters their meal information through the app, the device sends that information to a server, which analyzes the received meal data and calculates calories and nutritional balance. This allows the server to provide feedback on whether the user's meal is in line with their health goals.

[0765] Exercise menu management

[0766] The server generates an individually customized exercise menu based on the user's data. In addition, the exercise menu can be adjusted taking into account the user's emotional data recognized by the emotion engine. The generated exercise menu is sent from the server to the device, which provides it to the user and tracks their progress.

[0767] Generating statistical data and utilizing it in medical settings

[0768] The server aggregates data collected from many users and generates statistical data, such as the average body fat percentage by age and gender. This statistical data is useful in medical settings for individual diagnosis and optimization of treatment.

[0769] Customization services for corporations

[0770] The server generates customized reports and analytical data for corporate clients, allowing them to efficiently manage the health of their employees. The server provides these reports to corporate clients on a regular basis and also provides consulting services as needed.

[0771] Specific examples

[0772] For example, suppose a user has data on their weight: 70 kg, body fat percentage: 25%, and heart rate: 72 bpm. The device collects this data and sends it to the server. The server calculates their basal metabolic rate, for example, 1500 kcal / day. The server then considers the user's target weight and suggests a required calorie intake of 1800 kcal / day and exercise three times a week.

[0773] At the same time, if the emotion engine detects "stress" from the user's facial expressions or voice, the corresponding voice advice will change. Advice will be provided in the form of "Today's target calorie intake is 1800 kcal. We recommend a 30-minute walk to relax."

[0774] Furthermore, if a user inputs "200g chicken breast, salad, and one bowl of brown rice" for lunch, the server calculates the calories to 500kcal and analyzes the balance of the total calorie intake. This provides feedback such as "Today's calorie intake is 90% of your goal. I recommend something light for dinner."

[0775] In this way, by using the system of the present invention, users can efficiently manage their health, and by using the emotion engine, support that takes into account psychological state can be provided. This will enable appropriate data management and utilization in medical settings and companies.

[0776] The processing flow will be explained below.

[0777] Step 1:

[0778] The user puts on a wearable tool, which is equipped with sensors such as a weight scale, a body fat scale, a heart rate monitor, and a pedometer.

[0779] Step 2:

[0780] The device collects data from the wearable device, specifically physical data such as weight, body fat percentage, heart rate, and number of steps taken, in real time.

[0781] Step 3:

[0782] The device temporarily stores the acquired physiological data in memory, ready to be sent to a server later.

[0783] Step 4:

[0784] The device sends the collected data to the server at regular intervals, using Bluetooth or Wi-Fi as the communication method.

[0785] Step 5:

[0786] The server receives the data sent from the device and checks the integrity of the data, for example, checking whether the data is missing or contains errors.

[0787] Step 6:

[0788] The server calculates the user's basal metabolic rate based on the received data, for example, using the Harris-Benedict equation.

[0789] Step 7:

[0790] The server calculates the required calorie intake and recommended exercise amount based on the user's basal metabolic rate and target weight. The required calorie intake is then displayed as a specific number, such as 1800 kcal / day.

[0791] Step 8:

[0792] The device collects the user's facial expressions and voice data and sends it to the emotion engine, which analyzes the user's emotions and generates emotion data such as "stress" or "relaxation."

[0793] Step 9:

[0794] The server generates the most appropriate voice advice text for the user based on the emotion data and metabolic data received from the emotion engine. For example, it might say, "Today's target calorie intake is 1800 kcal. We recommend a 30-minute walk to relax."

[0795] Step 10:

[0796] The server uses a text-to-speech engine to convert the generated text into an audio file, which is then sent to the device.

[0797] Step 11:

[0798] The device plays the audio file received from the server, based on the timing or request specified by the user.

[0799] Step 12:

[0800] The user inputs the details of their meal into the app. For example, they input a specific meal such as "200g of chicken breast, salad, and one bowl of brown rice."

[0801] Step 13:

[0802] The device sends the inputted meal data to the server, which uses the data to evaluate calorie and nutritional balance.

[0803] Step 14:

[0804] The server analyzes the received dietary data, calculates calories and nutritional balance, and generates feedback on the user's total calorie intake and nutritional balance based on this data.

[0805] Step 15:

[0806] The server sends feedback data to the device, such as "Today's calorie intake is 90% of your goal. I recommend having a light meal for dinner."

[0807] Step 16:

[0808] The server uses the user's data to generate a personalized workout routine, including specific exercises and their frequency.

[0809] Step 17:

[0810] The server sends the generated exercise menu to the terminal, which displays the exercise menu to the English user and tracks the exercise progress.

[0811] Step 18:

[0812] The emotion engine analyzes the user's emotional data and adjusts the exercise menu based on the user's psychological state. For example, it recommends relaxing exercises for users with high stress levels.

[0813] Step 19:

[0814] The server aggregates data collected from many users and generates statistical data, such as calculating the average body fat percentage by age and gender.

[0815] Step 20:

[0816] The server provides the generated statistical data to medical facilities, providing doctors with data that can help them make individual diagnoses and optimize treatment.

[0817] Step 21:

[0818] The server generates customized reports and analytical data for corporate clients, such as reports summarizing employee health management status.

[0819] Step 22:

[0820] The server provides regular reports and analytical data to corporate clients, and also provides health management consulting services as needed.

[0821] The above are the specific steps for implementing the present invention. Through this system, comprehensive health management based on the user's physical data and emotional data can be realized.

[0822] Example 2

[0823] 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."

[0824] Conventional health management systems provide management and advice based solely on the user's physical data, but do not take into account the user's emotional state, making efficient and personalized health management difficult. They also lack the functionality to provide detailed feedback on dietary content and nutritional balance, or to offer customized services for corporate clients. This poses a challenge, preventing advice tailored to the user's psychological state and making it difficult for companies to efficiently manage the health of their employees.

[0825] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0826] In this invention, the server includes a means for acquiring physical data, a means for transmitting the physical data to the server, a means for calculating metabolic rate, a means for calculating required calorie intake and exercise volume, a means for generating audio advice, a means for collecting and analyzing emotional data, a means for adjusting the content and tone of the audio advice based on the emotional data, a means for generating and adjusting an exercise menu, and a means for generating statistical data for use in clinical settings. This enables personalized health management that takes into account not only the user's physical data but also their emotional data. Furthermore, since the system also includes the input and analysis of dietary information and the provision of customization services for corporations, it can function as a comprehensive health management system.

[0827] "Physical data" refers to various data that indicate the user's physical condition, such as the user's weight, body fat, heart rate, and number of steps.

[0828] A "server" is a computer system that receives, stores, analyzes various data, and provides the results.

[0829] "Metabolic rate" represents the user's basal metabolic rate, and is a value calculated based on the Harris-Benedict equation or the like.

[0830] "Required calorie intake" is the amount of calories that a user should take in per day, and is a value calculated based on a target weight and activity level.

[0831] The "amount of exercise" refers to the amount of exercise that the user should do in a day, and indicates the specific scale of the exercise, including the time and intensity.

[0832] "Voice advice" refers to voice instructions or advice that is generated based on the required calorie intake and amount of exercise and provided to the user.

[0833] "Emotion data" is data that indicates the user's mental and emotional state, obtained from the user's facial expressions and voice.

[0834] An "exercise menu" refers to an exercise plan or training schedule for each user that is created based on the user's individual data.

[0835] "Statistical data" is data collected from many users and compiled and analyzed according to attributes such as age and gender.

[0836] The present invention is a system that comprehensively supports a user's health management, and aims to provide more personalized services by incorporating emotion data in particular. Hereinafter, embodiments of the present invention will be described in detail.

[0837] Data collection

[0838] When a user wears a wearable tool, the device collects real-time physical data such as weight, body fat, heart rate, and number of steps. This data is temporarily stored on the device and sent to a server at regular intervals. At the same time, an emotion engine is used to obtain emotional data from the user's facial expressions and voice.

[0839] Sending data

[0840] The device sends the collected physical and emotional data to a server using Wi-Fi, Bluetooth, or other communication methods, and the data is sent securely.

[0841] Metabolic calculation and calorie / exercise calculation

[0842] The server calculates the user's basal metabolic rate using the Harris-Benedict equation based on the received physical data, and then calculates the user's daily calorie intake and exercise requirements based on the user's target weight and activity level.

[0843] Providing audio advice

[0844] The server uses a text-to-speech engine (e.g., Google Text-to-Speech API) to generate audio advice based on required calorie intake and exercise volume. The content and tone of the advice are also adjusted based on the emotional data. The generated audio advice is sent to the device and provided to the user.

[0845] Input and analysis of meal contents

[0846] Users input their meal plans through a dedicated app, and the device sends the information to a server, which analyzes the received meal data, calculates calories and nutritional balance, and provides feedback on whether the user's meal plans are in line with their set health goals.

[0847] Creation and management of exercise menus

[0848] The server generates a personalized exercise menu based on the user's physical and emotional data. The exercise menu can also be adjusted based on the emotional data. The generated exercise menu is sent to the device, which provides it to the user and tracks their progress.

[0849] Generating statistical data and utilizing it in medical settings

[0850] The server aggregates data collected from many users and generates statistical data by age and gender, which is used in medical settings to optimize diagnosis and treatment.

[0851] Customization services for corporations

[0852] The server generates customized reports and analytical data for corporate clients, allowing them to efficiently manage the health of their employees. The server provides these reports to corporate clients on a regular basis and also provides consulting services as needed.

[0853] Specific examples

[0854] For example, if a user has data on their weight (70 kg), body fat percentage (25%), and heart rate (72 bpm), the device that collected this data sends it to the server. The server calculates their basal metabolic rate, for example, 1500 kcal / day. The server then takes the user's target weight into account and sets the required calorie intake at 1800 kcal / day, suggesting three times a week of exercise.

[0855] At the same time, if the emotion engine detects "stress" from the user's facial expressions and voice, it will provide voice advice such as, "We recommend a 30-minute walk to relax." Furthermore, if the user inputs "200g of chicken breast, salad, and one bowl of brown rice" for lunch, the server will calculate the calories to 500kcal and can also analyze the balance of total calorie intake. Feedback will be provided in the form of, "Today's calorie intake is 90% of your goal. We recommend something light for dinner."

[0856] Prompt Sentence Examples

[0857] "Write pseudocode in natural language for a system that creates a personalized health management plan based on a user's weight, body fat percentage, heart rate, number of steps, diet, etc., along with an explanation of each step."

[0858] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0859] Step 1:

[0860] The user puts on a wearable tool. This tool uses built-in sensors to collect real-time physical data such as weight, body fat, heart rate, and number of steps. The collected data is temporarily stored on the device. The input is the user's physical data, and the output is the data stored on the device.

[0861] Step 2:

[0862] At regular intervals, the device sends the collected physiological data to a server. The communication method is Wi-Fi or Bluetooth. Specifically, the data stored in the device is sent to the server via the network. The input is the physiological data in the device, and the output is the physiological data sent to the server.

[0863] Step 3:

[0864] The device also collects the user's facial expressions and voice to generate emotion data. The emotion engine analyzes this data and identifies the user's emotional state (e.g., stress, joy, fatigue, etc.). This emotion data is also periodically sent to the server. The input is the user's facial expressions and voice, and the output is the emotion data sent to the server.

[0865] Step 4:

[0866] The server calculates the basal metabolic rate (BMR) using the Harris-Benedict equation based on the received physical data. Specifically, it calculates the BMR using the user's weight, height, age, and gender. The input is the received physical data, and the output is the calculated basal metabolic rate.

[0867] Step 5:

[0868] The server calculates the required calorie intake and exercise amount based on the user's basal metabolic rate, taking into account the user's target weight and activity level. Specifically, it calculates the total daily energy expenditure (TDEE) by applying a correction factor for each activity level, and then sets the calorie intake and exercise goals based on the user's goals. The inputs are the basal metabolic rate, target weight, and activity level, and the output is the required calorie intake and exercise amount.

[0869] Step 6:

[0870] The server uses a text-to-speech engine to generate audio advice based on the required calorie intake and amount of exercise. Furthermore, it adjusts the content and tone of the advice using emotional data. The generated audio advice is sent to the device. The input is the calculated required calorie intake and emotional data, and the output is the generated audio advice.

[0871] Step 7:

[0872] The terminal provides the generated voice advice to the user. The terminal uses a voice playback function to convey the advice to the user. The input is the generated voice advice, and the output is the voice advice provided to the user.

[0873] Step 8:

[0874] The user inputs the details of their meal through a dedicated app. The device sends this data to the server. The input is the user's meal data, and the output is the meal data sent to the server.

[0875] Step 9:

[0876] The server analyzes the received meal data, calculates calories and nutritional balance, and generates feedback based on the analysis results and sends it to the device. The input is the received meal data, and the output is the calculated calories and feedback.

[0877] Step 10:

[0878] The server generates a customized exercise menu based on the user's data. It can also adjust the exercise menu taking into account emotional data. The generated exercise menu is sent to the terminal. The input is the user's physical data and emotional data, and the output is the generated exercise menu.

[0879] Step 11:

[0880] The terminal provides the user with an exercise menu and tracks the progress. The input is the generated exercise menu, and the output is the exercise menu provided to the user and tracking data.

[0881] (Application example 2)

[0882] 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."

[0883] In recent years, interest in health management has grown, creating a demand for systems that can provide advice and recommendations tailored to individual users. However, existing systems tend to be limited to providing information based on physical data and lack personalized recommendations that take into account the user's emotional state. Furthermore, it has been difficult to recommend optimal products and services based on the user's health condition and emotions during the shopping experience in a physical store. Therefore, there is a demand for systems that can provide more comprehensive and personalized health management services.

[0884] 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.

[0885] In this invention, the server includes means for acquiring physical data, means for calculating the required calorie intake and amount of exercise, means for acquiring user emotion data, means for recommending products or services based on the emotion data, and means for displaying and providing audio guidance on the smart device with the recommended content. This enables personalized recommendations based on the user's health condition and emotions, and makes it possible to provide optimal products and services even in a shopping experience at a physical store.

[0886] "Physical data" refers to data related to the user's physical condition, such as the user's weight, body fat percentage, heart rate, and number of steps.

[0887] The "server" is a computer system that receives and analyzes the physical data and emotional data, calculates the required calorie intake and amount of exercise, and recommends products and services.

[0888] "Metabolic rate" is a calculated value used to calculate the user's basal metabolic rate, and indicates the basic amount of energy the user consumes in a day.

[0889] "Required calorie intake" indicates the amount of energy a user should take in per day, and is calculated based on the user's target weight and activity level.

[0890] "Voice advice" means spoken instructions or recommendations provided to the user by the system and generated using text-to-speech technology.

[0891] An "exercise menu" is a plan that indicates the exercise content that a user should do based on the user's health goals.

[0892] "Statistical data" refers to data obtained by aggregating and analyzing data collected from a large number of users, and is used as a reference for diagnosis and treatment in medical settings.

[0893] "Emotion data" is data that indicates the emotional state of the user, obtained from the user's facial expressions and voice.

[0894] "Product or service recommendation" means proposing optimal products or services to a user based on the user's physical data and emotional data.

[0895] "Smart devices" are devices that have the function of displaying information and providing voice guidance, such as smartphones, smart glasses, and head-mounted displays.

[0896] The present invention is a system that comprehensively supports users' health management, and in particular provides personalized recommendations by utilizing emotional data. The system includes physical and emotional data collection, data analysis, recommendation generation, display, and voice guidance.

[0897] System configuration

[0898] The system consists of the following main hardware and software:

[0899] Wearable tool: A device that collects health data (weight, body fat percentage, heart rate, number of steps, etc.) in real time and transmits it to a device.

[0900] Smart Device: Smart glasses are used as a device to display health and emotional data and provide voice guidance.

[0901] Server: The computer system that receives and analyzes the collected data.

[0902] Emotion recognition engine: Software that recognizes emotions from the user's facial expressions and voice.

[0903] Text-to-Speech Engine: Software that generates spoken recommendations and advice.

[0904] Recommendation engine: Software that recommends the most suitable products and services to users based on their physical and emotional data.

[0905] System Operation

[0906] Data collection

[0907] By wearing the wearable tool, the user can collect real-time health data such as weight, body fat percentage, heart rate, and number of steps. This data is transmitted to a device via smart glasses. In addition, an emotion recognition engine uses the smart glasses' camera and microphone to analyze the user's facial expressions and voice to obtain emotional data.

[0908] Data analysis and recommendation generation

[0909] The server calculates the user's metabolic rate based on the received health data, and calculates the required calorie intake and amount of exercise.The recommendation engine also considers the emotional data acquired by the emotion recognition engine and selects the most suitable products and services for the user.

[0910] Display and audio guidance of recommendations

[0911] The generated recommendation is displayed on the smart device and audibly guided using a text-to-speech engine. For example, if the user is feeling stressed, the recommendation may be displayed and spoken, such as "We recommend herbal tea for its relaxing effects."

[0912] Specific examples

[0913] For example, if a user has data showing a weight of 70 kg, a body fat percentage of 25%, and a heart rate of 80 bpm, and the emotion recognition engine detects "stress," the system operates as follows: The server receives the health data and calculates the metabolic rate. Next, the recommendation engine selects "relaxing herbal teas and aromatic oils" based on the emotion data. The smart glasses will display a message saying, "We recommend relaxing herbal teas," and the text-to-speech engine will provide a similar message via voice.

[0914] Also, when a user inputs a health care question into the generative AI model via a smart device, the following prompt can be used: "If the user is feeling stressed, please recommend products that have a relaxation effect, such as herbal tea or aromatic oil."

[0915] In this way, the system of the present invention provides personalized recommendations based on the user's health and emotional state, enhancing the shopping experience in brick-and-mortar stores.

[0916] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0917] Step 1:

[0918] Users wear a wearable tool that collects health data (weight, body fat percentage, heart rate, number of steps, etc.) in real time.

[0919] Input: Health data from wearable tools

[0920] Output: Health data sent to smart glasses

[0921] How it works: The wearable tool is worn by the user, and the built-in sensors detect data such as weight, body fat percentage, heart rate, and number of steps, and send this information to the device in real time.

[0922] Step 2:

[0923] Using the camera and microphone of the smart glasses, the emotion recognition engine analyzes the user's facial expressions and voice to obtain emotional data.

[0924] Input: User's facial expression, voice

[0925] Output: Emotion data

[0926] How it works: The smart glasses' camera captures the user's facial expressions and microphone records their voice, which are then sent to an emotion recognition engine to analyze the user's emotional state (e.g., stress, joy, anxiety, etc.).

[0927] Step 3:

[0928] The smart glasses send the collected health and emotional data to a server.

[0929] Input: Health data, emotion data

[0930] Output: Health and emotion data sent to the server

[0931] How it works: The smart glasses send health and emotion data to the server, which then receives it. The data is encrypted before transmission.

[0932] Step 4:

[0933] The server calculates the metabolic rate based on the health data received, and calculates the required calorie intake and amount of exercise.

[0934] Input: Health data

[0935] Output: metabolic rate, calorie needs, and exercise volume

[0936] How it works: The server analyzes health data and calculates metabolic rate using the Harris-Benedict equation, etc. It then calculates the required calorie intake and amount of exercise based on the user's target weight and activity level.

[0937] Step 5:

[0938] The server takes into account the emotional data, and the recommendation engine generates the most suitable products and services for the user.

[0939] Input: Emotion data, calculation results from health data

[0940] Output: Recommendation (product or service)

[0941] How it works: The server analyzes the emotional data to understand the user's current emotional state. The recommendation engine then selects products and services that will have a relaxing effect for the user based on the health and emotional data.

[0942] Step 6:

[0943] The generated recommendation content is sent to the smart device, where it is displayed and provided as audio guidance.

[0944] Input: Recommendation from the server

[0945] Output: Recommendations displayed on smart devices and audio guidance

[0946] Specific operation: The recommendation content is sent to the smart glasses and displayed on the screen. At the same time, the text-to-speech engine generates a voice prompt and delivers it to the user through the smart glasses' speaker.

[0947] In this way, the system supports more personalized health management by understanding the user's health and emotional state in detail at each step and recommending optimal products and services.

[0948] 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.

[0949] 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.

[0950] 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.

[0951] [Third embodiment]

[0952] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0953] 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.

[0954] 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).

[0955] 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.

[0956] 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.

[0957] 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).

[0958] 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.

[0959] 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.

[0960] 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.

[0961] 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.

[0962] 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.

[0963] 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."

[0964] DETAILED DESCRIPTION OF THE INVENTION The present invention relates to a wearable tool and a related system that comprehensively supports a user's health management. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail.

[0965] Data collection

[0966] When a user wears a wearable device, the device collects real-time physical data such as weight, body fat, heart rate, and number of steps taken. This data is temporarily stored on the device and sent to a server at regular intervals.

[0967] Metabolic calculation and calculation of required calorie intake and exercise amount

[0968] The server calculates the user's basal metabolic rate (BMR) based on the received physical data. The BMR is calculated using, for example, the Harris-Benedict equation. The server then calculates the user's calorie intake and exercise needs based on the user's target weight and activity level.

[0969] Providing audio advice

[0970] The server generates an appropriate weight loss plan based on the calculation results and creates voice advice using a text-to-speech engine. The voice advice is then sent to the device and provided to the user.

[0971] Enter your meal details and calculate nutritional information

[0972] When a user enters their meal information through the app, the device sends that information to a server, which analyzes the received meal data and calculates calories and nutritional balance. This allows the server to provide feedback on whether the user's meal is in line with their health goals.

[0973] Exercise menu management

[0974] The server generates an individually customized exercise menu based on the user's data, which is then sent to the device, where it is provided to the user and progress is tracked.

[0975] Generating statistical data and utilizing it in medical settings

[0976] The server aggregates data collected from many users and generates statistical data, such as the average body fat percentage by age and gender. This statistical data is useful in medical settings for individual diagnosis and optimization of treatment.

[0977] Customization services for corporations

[0978] The server generates customized reports and analytical data for corporate clients, allowing them to efficiently manage the health of their employees. The server provides these reports to corporate clients on a regular basis and also provides consulting services as needed.

[0979] Specific examples

[0980] For example, suppose a user has data on their weight: 70 kg, body fat percentage: 25%, and heart rate: 72 bpm. The device collects this data and sends it to the server. The server calculates their basal metabolic rate, for example, 1500 kcal / day. The server then considers the user's target weight and suggests a required calorie intake of 1800 kcal / day and exercise three times a week.

[0981] The generated voice advice is provided to the user via the device in the form of "Today's target calorie intake is 1800 kcal. We recommend a 30-minute walk." Furthermore, if the user inputs "200 g of chicken breast, salad, and one bowl of brown rice" for lunch, the server calculates the calories to 500 kcal and also analyzes the balance of the total calorie intake.

[0982] In this way, by using the system of the present invention, users can perform efficient health management, and medical facilities and companies can also manage and utilize data appropriately.

[0983] The processing flow will be explained below.

[0984] Step 1:

[0985] The user puts on a wearable tool, which is equipped with sensors such as a weight scale, a body fat scale, a heart rate monitor, and a pedometer.

[0986] Step 2:

[0987] The device collects data from the wearable device, specifically physical data such as weight, body fat percentage, heart rate, and number of steps taken, in real time.

[0988] Step 3:

[0989] The device temporarily stores the acquired physiological data in memory, ready to be sent to a server later.

[0990] Step 4:

[0991] The device sends the collected data to the server at regular intervals, using Bluetooth or Wi-Fi as the communication method.

[0992] Step 5:

[0993] The server receives the data sent from the device and checks the integrity of the data, for example, checking whether the data is missing or contains errors.

[0994] Step 6:

[0995] The server calculates the user's basal metabolic rate based on the received data, for example, using the Harris-Benedict equation.

[0996] Step 7:

[0997] The server calculates the required calorie intake and recommended exercise amount based on the user's basal metabolic rate and target weight. The required calorie intake is then displayed as a specific number, such as 1800 kcal / day.

[0998] Step 8:

[0999] The server generates textual audio advice based on the calculation results. For example, it might say, "Today's target calorie intake is 1,800 kcal. We recommend walking for 30 minutes."

[1000] Step 9:

[1001] The server uses a text-to-speech engine to convert the generated text into an audio file, which is then sent to the device.

[1002] Step 10:

[1003] The device plays the audio file received from the server, based on the timing or request specified by the user.

[1004] Step 11:

[1005] The user inputs the details of their meal into the app. For example, they input a specific meal such as "200g of chicken breast, salad, and one bowl of brown rice."

[1006] Step 12:

[1007] The device sends the inputted meal data to the server, which uses the data to evaluate calorie and nutritional balance.

[1008] Step 13:

[1009] The server analyzes the received dietary data, calculates calories and nutritional balance, and generates feedback on the user's total calorie intake and nutritional balance based on this data.

[1010] Step 14:

[1011] The server sends feedback data to the device, such as "Today's calorie intake is 90% of your goal. I recommend having a light meal for dinner."

[1012] Step 15:

[1013] The server generates a personalized exercise menu based on the user's data, including specific exercises and their frequency.

[1014] Step 16:

[1015] The server sends the generated exercise menu to the terminal, which displays the menu to the user and tracks the exercise progress.

[1016] Step 17:

[1017] The server aggregates data collected from many users and generates statistical data, such as calculating the average body fat percentage by age and gender.

[1018] Step 18:

[1019] The server provides the generated statistical data to medical facilities, providing doctors with data that can help them make individual diagnoses and optimize treatment.

[1020] Step 19:

[1021] The server generates customized reports and analytical data for corporate clients, such as reports summarizing employee health management status.

[1022] Step 20:

[1023] The server provides regular reports and analytical data to corporate clients, and also provides health management consulting services as needed.

[1024] Example 1

[1025] 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."

[1026] Current systems for efficient health management lack the means to comprehensively understand a user's physical condition and provide appropriate advice. Furthermore, it is difficult to provide customized feedback and exercise menus for individual users. Furthermore, there is a lack of statistical data available for use in businesses and medical settings. To address these issues, a more comprehensive and customized healthcare system is needed.

[1027] 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.

[1028] In this invention, the server includes means for the user to acquire physical data, means for the terminal to transmit the physical data to the server, means for the server to calculate metabolic rate, means for the server to calculate required calorie intake and exercise amount, means for the server to generate audio advice, means for the server to manage exercise menus, and means for the server to generate statistical data and use it in medical settings, thereby enabling efficient health management for users and appropriate data management and use in medical settings and companies.

[1029] "User" refers to an individual who uses the system to manage their own health.

[1030] "Terminal" refers to a wearable tool worn by a user or a device for transmitting data.

[1031] A "server" refers to a computer system that receives data sent by users and performs analysis and calculations.

[1032] "Physical data" refers to measurement data about the user's body, such as weight, body fat, heart rate, and number of steps.

[1033] "Metabolic rate" is a numerical representation of the user's basal metabolic rate, and is an index for calculating energy expenditure.

[1034] "Required calorie intake" refers to the amount of calories a user should consume per day.

[1035] "Movement" refers to the recommended target amount of physical activity a user is recommended to achieve within a particular period of time.

[1036] "Voice advice" refers to instructions or suggestions in the form of voice that are generated based on the results of calculations.

[1037] An "exercise menu" refers to a specific exercise plan proposed to maintain or improve the user's health.

[1038] "Statistical data" refers to statistical information obtained by comprehensively analyzing data collected from multiple users.

[1039] "Healthcare setting" refers to a location where health care services are provided, such as a hospital or clinic.

[1040] "Dietary data" refers to information about the contents of meals consumed by a user.

[1041] "Calories" refers to a unit of measurement that indicates the amount of energy in food.

[1042] "Nutritional balance" refers to the appropriate proportions of nutrients such as protein, fat, and carbohydrates.

[1043] "Corporate Customer" refers to a company, organization, or other entity that uses this system for business purposes.

[1044] "Customized Report" means a report of analytical data tailored to the needs of a particular Corporate Client.

[1045] MODE FOR CARRYING OUT THE INVENTION

[1046] The present invention is a system that comprehensively supports a user's health management, and specific embodiments thereof will be described in detail below.

[1047] overview

[1048] This system collects and manages physical data such as weight, body fat, heart rate, and number of steps taken through a wearable device worn by the user in real time. The data is sent via the device to a server, which performs various calculations and analyses based on the collected data. It also provides users with customized health advice and exercise menus, and supports use in businesses and medical settings.

[1049] Data collection

[1050] When a user wears a wearable device, the device collects physical data in real time. The device used for this is a wearable tool equipped with multiple sensors. An example is a smartwatch with a built-in scale and heart rate monitor. The collected data is temporarily stored on the device and encrypted and sent to a server at regular intervals.

[1051] Data analysis and feedback

[1052] The server analyzes the received data and calculates the user's basal metabolic rate. The Harris-Benedict equation is used to calculate the basal metabolic rate: 88.362 + (13.397 × weight) + (4.799 × height) - (5.677 × age). The necessary calculations are performed based on the user's input data. The server then calculates the required calorie intake and exercise volume based on the user's target weight and activity level.

[1053] The generated results are provided to the user as voice advice. The voice advice generated using a text-to-speech engine is sent to the terminal, which then notifies the user directly.

[1054] Dietary data and nutritional analysis

[1055] When a user enters their meal details through the app, the device sends the data to a server. The server analyzes the received meal data and calculates calories and nutritional balance. This allows the device to provide feedback on how well the user's meal matches their health goals. For example, if a user enters "200g chicken breast, salad, and one bowl of brown rice" for lunch, the device calculates the calories and analyzes the balance based on the total calorie intake.

[1056] Providing individual exercise menus

[1057] The server generates a personalized exercise menu based on the user's health data, taking into account information such as the user's weight, basal metabolic rate, and target weight. The generated exercise menu is sent to the device and provided to the user. The device also has the function of tracking exercise progress.

[1058] Generate statistical data

[1059] The server aggregates data collected from many users and generates statistical data, including average body fat percentage by age and gender, which is used in medical settings and companies to optimize diagnosis and treatment.

[1060] Corporate Services

[1061] It also generates customized reports and analytical data for corporate clients. The server periodically generates these reports and provides them to corporate clients. Consulting services are also provided as needed.

[1062] Specific examples

[1063] For example, suppose a user has data showing a weight of 70 kg, a body fat percentage of 25%, and a heart rate of 72 bpm. The device collects this data and sends it to the server. The server calculates the user's basal metabolic rate, for example, 1500 kcal / day. The server then takes the user's target weight into account and suggests a required calorie intake of 1800 kcal / day and three exercise sessions per week. The generated audio advice is provided to the user via the device in the form of, "Today's target calorie intake is 1800 kcal. I recommend a 30-minute walk." If the user also inputs "200 g of chicken breast, salad, and one bowl of brown rice" for lunch, the server calculates the calories and analyzes the balance of the total calorie intake.

[1064] Example prompts to input to the generative AI model

[1065] "Calculate the user's basal metabolic rate based on their weight, body fat percentage, and heart rate data, and create voice advice on the amount of calories they need to eat and exercise. Use the following data:

[1066] Weight: 70kg

[1067] Body fat percentage: 25%

[1068] Heart rate: 72 bpm

[1069] Also, please calculate the calories of the 200g chicken breast, salad, and bowl of brown rice that the user ate for lunch, and provide feedback on the balance of the total calorie intake.

[1070] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1071] Step 1:

[1072] The user puts on a wearable device, which measures real-time data such as weight, body fat, heart rate, and steps taken.

[1073] Step 2:

[1074] The terminal receives measurement data from the wearable device, which is temporarily stored in the terminal.

[1075] Input: Body data from wearable devices

[1076] Output: Data temporarily stored in the device

[1077] Step 3:

[1078] The device sends the collected data to the server at regular intervals. The data is encrypted before transmission.

[1079] Input: Temporary data in the terminal

[1080] Output: Data sent to the server

[1081] Step 4:

[1082] The server receives the data sent from the terminal and stores it in a database.

[1083] Input: Data sent from the terminal

[1084] Output: Data stored in the database on the server

[1085] Step 5:

[1086] The server calculates the user's basal metabolic rate based on the received data. The calculation is performed using the Harris-Benedict equation.

[1087] Input: Physical data in the database

[1088] Output: Calculated basal metabolic rate

[1089] Step 6:

[1090] The server calculates the required calorie intake and amount of exercise, taking into account the user's target weight and activity level.

[1091] Input: Basal metabolic rate, target weight, activity level

[1092] Output: Required calorie intake and exercise amount

[1093] Step 7:

[1094] The server generates the audio advice, using a text-to-speech engine to create audio advice based on the calculation results.

[1095] Input: Required calorie intake and exercise amount

[1096] Output: Voice advice

[1097] Step 8:

[1098] The server transmits the generated voice advice to the terminal, which notifies the user of the advice.

[1099] Input: Generated voice advice

[1100] Output: Audio advice notification to the user

[1101] Step 9:

[1102] The user inputs the details of their meal through a dedicated app. For example, they input "200g of chicken breast, salad, and one bowl of brown rice for lunch."

[1103] Input: Meal details

[1104] Output: Meal data via a dedicated app

[1105] Step 10:

[1106] The device sends the meal data to the server, and the server receives the meal data.

[1107] Input: Meal data

[1108] Output: Data sent to the server

[1109] Step 11:

[1110] The server analyzes the received dietary data and calculates calories and nutritional balance.

[1111] Input: Meal data

[1112] Output: Calorie and nutritional balance results

[1113] Step 12:

[1114] The server generates feedback based on the meal content, which is sent to the device and can be viewed by the user.

[1115] Input: Calories and nutritional balance results

[1116] Output: Feedback

[1117] Step 13:

[1118] The server generates a customized exercise menu based on the user's data.

[1119] Input: User's physical data, health goals

[1120] Output: Customized exercise menu

[1121] Step 14:

[1122] The server sends the generated exercise menu to the device, which displays the menu to the user and tracks their progress.

[1123] Enter: Customized Exercise Menu

[1124] Output: Data sent to the device, progress data

[1125] Step 15:

[1126] The server aggregates the data collected from multiple users and generates statistical data, such as calculating the average body fat percentage by age and gender.

[1127] Input: A lot of user data

[1128] Output:Statistical data

[1129] Step 16:

[1130] The server provides the generated statistical data to medical facilities, which helps optimize diagnosis and treatment.

[1131] Input:Statistical data

[1132] Output: Data provided to medical facilities

[1133] Step 17:

[1134] The server generates and provides customized reports and analytical data for companies, periodically sending reports to corporate clients and providing consulting services as needed.

[1135] Input: Corporate customer data

[1136] Output: Customized reports and analytical data

[1137] (Application example 1)

[1138] 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."

[1139] In modern society, users are expected to manage their health efficiently and comprehensively. However, while current systems manage individual health data, they lack functionality such as proposing optimal products in a virtual store or offering virtual experiences with exercise equipment. Furthermore, conventional systems face challenges in providing sufficient accuracy in terms of dietary feedback and voice advice.

[1140] 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.

[1141] In this invention, the server includes means for proposing optimal products based on the user's health data within the virtual store, means for virtually experiencing exercise equipment, means for tracking progress, means for providing health advice using a generative AI model, means for providing dietary feedback using a generative AI model, and means for managing statistical data on health-related products within the virtual store, thereby enabling users to comprehensively manage their health and optimally purchase and experience health-related products within the virtual store.

[1142] "Physical data" refers to physiological data such as the user's weight, body fat percentage, heart rate, and number of steps.

[1143] A "server" is a computer that receives, processes, and stores data via a network.

[1144] "Metabolic rate" is an index of energy consumption calculated based on the user's basal metabolic rate.

[1145] "Caloric intake requirement" refers to the daily calorie intake required by a user to maintain health or achieve a goal.

[1146] "Exercise volume" refers to the intensity and amount of exercise a user should do per day.

[1147] "Voice Advice" means audio health advice provided to a user that is created using a generative AI model.

[1148] An "exercise menu" is an exercise plan that is individually customized based on the user's goals.

[1149] "Statistical data" refers to statistical information obtained by aggregating data collected from many users.

[1150] A "virtual store" is a virtual environment where users can purchase products in a digital space, rather than in a real store.

[1151] "Means for suggesting optimal products" refers to algorithms or systems that suggest optimal health-related products to users based on their health data.

[1152] The "means for virtually experiencing exercise equipment" refers to a simulation system that allows users to try out exercise equipment in a digital space.

[1153] A "progress tracking means" is a system for recording and managing a user's exercise and health management progress.

[1154] A "generative AI model" is a model that uses artificial intelligence to analyze data, make predictions, and generate advice.

[1155] A "means for providing health advice" is a system that uses a generative AI model to provide health advice to a user.

[1156] "Means for providing dietary feedback" refers to a system that calculates calories and nutritional balance based on dietary data entered by the user and provides feedback.

[1157] The "means for managing statistical data on health-related products" refers to a system that collects, analyzes, and manages data such as sales and popularity of health-related products sold in the virtual store.

[1158] In order to implement the present invention, the system uses various hardware and software. The following describes the system program that realizes this application example.

[1159] System Configuration

[1160] Hardware:

[1161] 1. Wearable tools: Devices worn by the user that collect physical data such as weight, body fat percentage, heart rate, and number of steps.

[1162] 2. Smartphone or tablet: A device where users input health and dietary data, which receives data from the wearable tool and transmits it to the server.

[1163] 3. Server: Analyzes and stores data, and provides voice advice and feedback using generative AI models.

[1164] software:

[1165] 1. Google Text-to-Speech (gTTS): Software for providing voice advice to users.

[1166] 2. Flask Web Framework: A web framework that handles server-side processing.

[1167] 3. Generative AI model: An AI model that generates advice and feedback based on health and dietary data.

[1168] Data processing and calculation

[1169] Physical data acquisition and transmission

[1170] When a user wears the wearable tool, it collects real-time physical data and transmits it to a smartphone.

[1171] The smartphone sends this data to a server at regular intervals.

[1172] Calculating metabolic rate and calorie intake targets

[1173] The server uses the received physical data to calculate the user's basal metabolic rate using the Harris-Benedict equation or similar.

[1174] Next, the target calorie intake is calculated based on the basal metabolic rate, the user's target weight, and activity level.

[1175] Generating health advice

[1176] The server uses a generative AI model to generate appropriate health advice based on the calculation results, which is then generated as audio advice using Google Text-to-Speech and sent to the smartphone.

[1177] Meal data entry and feedback

[1178] When a user enters their meal details into their smartphone, the information is sent to the server.

[1179] The server analyzes the submitted meal data, calculates calories and nutritional balance, and provides feedback using a generative AI model.

[1180] Exercise menu management

[1181] The server generates an individually customized exercise menu based on the user's data and sends it to the smartphone.

[1182] Users can track their progress through their smartphones.

[1183] Applications in virtual stores

[1184] The server suggests optimal products in the virtual store based on the user's health data.

[1185] We provide a simulation system that allows users to virtually experience exercise equipment and tracks their progress.

[1186] Specific examples

[1187] For example, let's consider the case where a user sends data showing a weight of 70 kg, a body fat percentage of 25%, a heart rate of 72 bpm, and a step count of 5,000. The server that receives this data calculates the basal metabolic rate and calculates the target calorie intake. It also uses an AI model to generate voice advice such as, "Today's target calorie intake is 1,800 kcal. We recommend walking for 30 minutes."

[1188] If you enter your meal data for lunch, such as "200g chicken breast, salad, and one bowl of brown rice," the server will calculate the calories and provide you with feedback on your meal.

[1189] Prompt Sentence Examples

[1190] "Calculate today's target calorie intake based on weight: 70 kg, body fat percentage: 25%, heart rate: 72 bpm, and steps: 5000."

[1191] "Please analyze the dietary data of a lunch consisting of 200g of chicken breast, salad, and one bowl of brown rice, and provide feedback on the calories and nutritional balance."

[1192] This system enables users to manage their health efficiently and allows them to optimally select and experience health-related products in a virtual store.

[1193] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1194] Step 1:

[1195] The user wears a wearable tool to collect physiological data such as weight, body fat percentage, heart rate, and number of steps. The input is physiological data obtained from the wearable tool worn by the user, which transmits this data in real time to a smartphone. The output is physiological data stored on the smartphone.

[1196] Step 2:

[1197] The smartphone collects and transmits the collected physiological data at regular intervals to a server. The input is the physiological data collected by the wearable tool, and the smartphone transmits the data to the server. The output is the user's physiological data, which is stored on the server. The specific operation is to transmit the data to the server using a communication protocol.

[1198] Step 3:

[1199] The server calculates the basal metabolic rate (BMR) based on the received physical data. The input is the user's physiological data sent to the server, and the data calculation is performed based on the Harris-Benedict equation. The output is the user's basal metabolic rate (BMR).

[1200] Step 4:

[1201] The server calculates the required calorie intake and amount of exercise based on the user's basal metabolic rate, taking into account the user's target weight and activity level. The input is the basal metabolic rate, target weight, and activity level, and based on these, it performs data calculations to calculate the required calorie intake and amount of exercise. The output is the required calorie intake and amount of exercise.

[1202] Step 5:

[1203] The server uses the calculation results to generate voice advice using a generative AI model. The input is data on required calorie intake and exercise amount, and the generative AI model is used to generate advice. Specifically, it uses Google Text-to-Speech to generate an audio file and send it back to the smartphone. The output is an audio file.

[1204] Step 6:

[1205] The user inputs the details of their meal into their smartphone and sends the data to the server. The input is the meal data entered by the user into their smartphone and sent to the server. The output is the meal data stored on the server.

[1206] Step 7:

[1207] The server analyzes the received meal data and calculates calories and nutritional balance. The input is the meal data sent to the server, and the server's data processing algorithm (generative AI model) is used to calculate calories and nutritional balance. The output is feedback data.

[1208] Step 8:

[1209] The server generates an individually customized exercise menu based on the user's data and sends it to the smartphone. The input is the user's physiological data and goal information stored on the server, and the exercise menu is generated using an algorithm within the server. The output is the customized exercise menu.

[1210] Step 9:

[1211] The user checks the exercise menu through their smartphone and tracks their progress. The input is the exercise menu sent from the server and displayed on the smartphone. Progress data is obtained by inputting the exercise content the user will perform. The output is the user's progress data.

[1212] Step 10:

[1213] The server proposes optimal products in a virtual store based on the user's health data. The input is the user's health data and the metadata of the products in the virtual store, and the optimal products are determined based on data analysis. The output is a list of suggested products.

[1214] Step 11:

[1215] The server provides a virtual simulation of the exercise equipment experience and tracks the user's progress. The input is data about the exercise equipment in the virtual store and the user's exercise data, and the simulation system provides the exercise experience. The output is progress tracking data.

[1216] This system enables users to manage their health comprehensively and to optimally select and experience health-related products in a virtual store.

[1217] 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.

[1218] The present invention provides a wearable tool and related system that comprehensively supports a user's health management, and aims to provide more personalized services by combining it with an emotion engine that recognizes the user's emotions. Hereinafter, embodiments of the present invention will be described in detail.

[1219] Data collection

[1220] When a user wears a wearable tool, the device collects real-time physical data such as weight, body fat, heart rate, and number of steps. This data is temporarily stored on the device and sent to a server at regular intervals. In addition, an emotion engine is used to obtain emotional data from the user's facial expressions and voice.

[1221] Metabolic calculation and calculation of required calorie intake and exercise amount

[1222] The server calculates the user's basal metabolic rate (BMR) based on the received physical data. The BMR is calculated using, for example, the Harris-Benedict equation. The server then calculates the user's calorie intake and exercise needs based on the user's target weight and activity level.

[1223] Providing audio advice

[1224] The server generates an appropriate weight loss plan based on the calculation results and creates voice advice. This voice advice is generated using a text-to-speech engine. Furthermore, the content and tone of the voice advice are adjusted based on the user's emotional data recognized by the emotion engine. The generated voice advice is sent to the device and provided to the user.

[1225] Enter your meal details and calculate nutritional information

[1226] When a user enters their meal information through the app, the device sends that information to a server, which analyzes the received meal data and calculates calories and nutritional balance. This allows the server to provide feedback on whether the user's meal is in line with their health goals.

[1227] Exercise menu management

[1228] The server generates an individually customized exercise menu based on the user's data. In addition, the exercise menu can be adjusted taking into account the user's emotional data recognized by the emotion engine. The generated exercise menu is sent from the server to the device, which provides it to the user and tracks their progress.

[1229] Generating statistical data and utilizing it in medical settings

[1230] The server aggregates data collected from many users and generates statistical data, such as the average body fat percentage by age and gender. This statistical data is useful in medical settings for individual diagnosis and optimization of treatment.

[1231] Customization services for corporations

[1232] The server generates customized reports and analytical data for corporate clients, allowing them to efficiently manage the health of their employees. The server provides these reports to corporate clients on a regular basis and also provides consulting services as needed.

[1233] Specific examples

[1234] For example, suppose a user has data on their weight: 70 kg, body fat percentage: 25%, and heart rate: 72 bpm. The device collects this data and sends it to the server. The server calculates their basal metabolic rate, for example, 1500 kcal / day. The server then considers the user's target weight and suggests a required calorie intake of 1800 kcal / day and exercise three times a week.

[1235] At the same time, if the emotion engine detects "stress" from the user's facial expressions or voice, the corresponding voice advice will change. Advice will be provided in the form of "Today's target calorie intake is 1800 kcal. We recommend a 30-minute walk to relax."

[1236] Furthermore, if a user inputs "200g chicken breast, salad, and one bowl of brown rice" for lunch, the server calculates the calories to 500kcal and analyzes the balance of the total calorie intake. This provides feedback such as "Today's calorie intake is 90% of your goal. I recommend something light for dinner."

[1237] In this way, by using the system of the present invention, users can efficiently manage their health, and by using the emotion engine, support that takes into account psychological state can be provided. This will enable appropriate data management and utilization in medical settings and companies.

[1238] The processing flow will be explained below.

[1239] Step 1:

[1240] The user puts on a wearable tool, which is equipped with sensors such as a weight scale, a body fat scale, a heart rate monitor, and a pedometer.

[1241] Step 2:

[1242] The device collects data from the wearable device, specifically physical data such as weight, body fat percentage, heart rate, and number of steps taken, in real time.

[1243] Step 3:

[1244] The device temporarily stores the acquired physiological data in memory, ready to be sent to a server later.

[1245] Step 4:

[1246] The device sends the collected data to the server at regular intervals, using Bluetooth or Wi-Fi as the communication method.

[1247] Step 5:

[1248] The server receives the data sent from the device and checks the integrity of the data, for example, checking whether the data is missing or contains errors.

[1249] Step 6:

[1250] The server calculates the user's basal metabolic rate based on the received data, for example, using the Harris-Benedict equation.

[1251] Step 7:

[1252] The server calculates the required calorie intake and recommended exercise amount based on the user's basal metabolic rate and target weight. The required calorie intake is then displayed as a specific number, such as 1800 kcal / day.

[1253] Step 8:

[1254] The device collects the user's facial expressions and voice data and sends it to the emotion engine, which analyzes the user's emotions and generates emotion data such as "stress" or "relaxation."

[1255] Step 9:

[1256] The server generates the most appropriate voice advice text for the user based on the emotion data and metabolic data received from the emotion engine. For example, it might say, "Today's target calorie intake is 1800 kcal. We recommend a 30-minute walk to relax."

[1257] Step 10:

[1258] The server uses a text-to-speech engine to convert the generated text into an audio file, which is then sent to the device.

[1259] Step 11:

[1260] The device plays the audio file received from the server, based on the timing or request specified by the user.

[1261] Step 12:

[1262] The user inputs the details of their meal into the app. For example, they input a specific meal such as "200g of chicken breast, salad, and one bowl of brown rice."

[1263] Step 13:

[1264] The device sends the inputted meal data to the server, which uses the data to evaluate calorie and nutritional balance.

[1265] Step 14:

[1266] The server analyzes the received dietary data, calculates calories and nutritional balance, and generates feedback on the user's total calorie intake and nutritional balance based on this data.

[1267] Step 15:

[1268] The server sends feedback data to the device, such as "Today's calorie intake is 90% of your goal. I recommend having a light meal for dinner."

[1269] Step 16:

[1270] The server uses the user's data to generate a personalized workout routine, including specific exercises and their frequency.

[1271] Step 17:

[1272] The server sends the generated exercise menu to the terminal, which displays the exercise menu to the English user and tracks the exercise progress.

[1273] Step 18:

[1274] The emotion engine analyzes the user's emotional data and adjusts the exercise menu based on the user's psychological state. For example, it recommends relaxing exercises for users with high stress levels.

[1275] Step 19:

[1276] The server aggregates data collected from many users and generates statistical data, such as calculating the average body fat percentage by age and gender.

[1277] Step 20:

[1278] The server provides the generated statistical data to medical facilities, providing doctors with data that can help them make individual diagnoses and optimize treatment.

[1279] Step 21:

[1280] The server generates customized reports and analytical data for corporate clients, such as reports summarizing employee health management status.

[1281] Step 22:

[1282] The server provides regular reports and analytical data to corporate clients, and also provides health management consulting services as needed.

[1283] The above are the specific steps for implementing the present invention. Through this system, comprehensive health management based on the user's physical data and emotional data can be realized.

[1284] Example 2

[1285] 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."

[1286] Conventional health management systems provide management and advice based solely on the user's physical data, but do not take into account the user's emotional state, making efficient and personalized health management difficult. They also lack the functionality to provide detailed feedback on dietary content and nutritional balance, or to offer customized services for corporate clients. This poses a challenge, preventing advice tailored to the user's psychological state and making it difficult for companies to efficiently manage the health of their employees.

[1287] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1288] In this invention, the server includes a means for acquiring physical data, a means for transmitting the physical data to the server, a means for calculating metabolic rate, a means for calculating required calorie intake and exercise volume, a means for generating audio advice, a means for collecting and analyzing emotional data, a means for adjusting the content and tone of the audio advice based on the emotional data, a means for generating and adjusting an exercise menu, and a means for generating statistical data for use in clinical settings. This enables personalized health management that takes into account not only the user's physical data but also their emotional data. Furthermore, since the system also includes the input and analysis of dietary information and the provision of customization services for corporations, it can function as a comprehensive health management system.

[1289] "Physical data" refers to various data that indicate the user's physical condition, such as the user's weight, body fat, heart rate, and number of steps.

[1290] A "server" is a computer system that receives, stores, analyzes various data, and provides the results.

[1291] "Metabolic rate" represents the user's basal metabolic rate, and is a value calculated based on the Harris-Benedict equation or the like.

[1292] "Required calorie intake" is the amount of calories that a user should take in per day, and is a value calculated based on a target weight and activity level.

[1293] The "amount of exercise" refers to the amount of exercise that the user should do in a day, and indicates the specific scale of the exercise, including the time and intensity.

[1294] "Voice advice" refers to voice instructions or advice that is generated based on the required calorie intake and amount of exercise and provided to the user.

[1295] "Emotion data" is data that indicates the user's mental and emotional state, obtained from the user's facial expressions and voice.

[1296] An "exercise menu" refers to an exercise plan or training schedule for each user that is created based on the user's individual data.

[1297] "Statistical data" is data collected from many users and compiled and analyzed according to attributes such as age and gender.

[1298] The present invention is a system that comprehensively supports a user's health management, and aims to provide more personalized services by incorporating emotion data in particular. Hereinafter, embodiments of the present invention will be described in detail.

[1299] Data collection

[1300] When a user wears a wearable tool, the device collects real-time physical data such as weight, body fat, heart rate, and number of steps. This data is temporarily stored on the device and sent to a server at regular intervals. At the same time, an emotion engine is used to obtain emotional data from the user's facial expressions and voice.

[1301] Sending data

[1302] The device sends the collected physical and emotional data to a server using Wi-Fi, Bluetooth, or other communication methods, and the data is sent securely.

[1303] Metabolic calculation and calorie / exercise calculation

[1304] The server calculates the user's basal metabolic rate using the Harris-Benedict equation based on the received physical data, and then calculates the user's daily calorie intake and exercise requirements based on the user's target weight and activity level.

[1305] Providing audio advice

[1306] The server uses a text-to-speech engine (e.g., Google Text-to-Speech API) to generate audio advice based on required calorie intake and exercise volume. The content and tone of the advice are also adjusted based on the emotional data. The generated audio advice is sent to the device and provided to the user.

[1307] Input and analysis of meal contents

[1308] Users input their meal plans through a dedicated app, and the device sends the information to a server, which analyzes the received meal data, calculates calories and nutritional balance, and provides feedback on whether the user's meal plans are in line with their set health goals.

[1309] Creation and management of exercise menus

[1310] The server generates a personalized exercise menu based on the user's physical and emotional data. The exercise menu can also be adjusted based on the emotional data. The generated exercise menu is sent to the device, which provides it to the user and tracks their progress.

[1311] Generating statistical data and utilizing it in medical settings

[1312] The server aggregates data collected from many users and generates statistical data by age and gender, which is used in medical settings to optimize diagnosis and treatment.

[1313] Customization services for corporations

[1314] The server generates customized reports and analytical data for corporate clients, allowing them to efficiently manage the health of their employees. The server provides these reports to corporate clients on a regular basis and also provides consulting services as needed.

[1315] Specific examples

[1316] For example, if a user has data on their weight (70 kg), body fat percentage (25%), and heart rate (72 bpm), the device that collected this data sends it to the server. The server calculates their basal metabolic rate, for example, 1500 kcal / day. The server then takes the user's target weight into account and sets the required calorie intake at 1800 kcal / day, suggesting three times a week of exercise.

[1317] At the same time, if the emotion engine detects "stress" from the user's facial expressions and voice, it will provide voice advice such as, "We recommend a 30-minute walk to relax." Furthermore, if the user inputs "200g of chicken breast, salad, and one bowl of brown rice" for lunch, the server will calculate the calories to 500kcal and can also analyze the balance of total calorie intake. Feedback will be provided in the form of, "Today's calorie intake is 90% of your goal. We recommend something light for dinner."

[1318] Prompt Sentence Examples

[1319] "Write pseudocode in natural language for a system that creates a personalized health management plan based on a user's weight, body fat percentage, heart rate, number of steps, diet, etc., along with an explanation of each step."

[1320] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1321] Step 1:

[1322] The user puts on a wearable tool. This tool uses built-in sensors to collect real-time physical data such as weight, body fat, heart rate, and number of steps. The collected data is temporarily stored on the device. The input is the user's physical data, and the output is the data stored on the device.

[1323] Step 2:

[1324] At regular intervals, the device sends the collected physiological data to a server. The communication method is Wi-Fi or Bluetooth. Specifically, the data stored in the device is sent to the server via the network. The input is the physiological data in the device, and the output is the physiological data sent to the server.

[1325] Step 3:

[1326] The device also collects the user's facial expressions and voice to generate emotion data. The emotion engine analyzes this data and identifies the user's emotional state (e.g., stress, joy, fatigue, etc.). This emotion data is also periodically sent to the server. The input is the user's facial expressions and voice, and the output is the emotion data sent to the server.

[1327] Step 4:

[1328] The server calculates the basal metabolic rate (BMR) using the Harris-Benedict equation based on the received physical data. Specifically, it calculates the BMR using the user's weight, height, age, and gender. The input is the received physical data, and the output is the calculated basal metabolic rate.

[1329] Step 5:

[1330] The server calculates the required calorie intake and exercise amount based on the user's basal metabolic rate, taking into account the user's target weight and activity level. Specifically, it calculates the total daily energy expenditure (TDEE) by applying a correction factor for each activity level, and then sets the calorie intake and exercise goals based on the user's goals. The inputs are the basal metabolic rate, target weight, and activity level, and the output is the required calorie intake and exercise amount.

[1331] Step 6:

[1332] The server uses a text-to-speech engine to generate audio advice based on the required calorie intake and amount of exercise. Furthermore, it adjusts the content and tone of the advice using emotional data. The generated audio advice is sent to the device. The input is the calculated required calorie intake and emotional data, and the output is the generated audio advice.

[1333] Step 7:

[1334] The terminal provides the generated voice advice to the user. The terminal uses a voice playback function to convey the advice to the user. The input is the generated voice advice, and the output is the voice advice provided to the user.

[1335] Step 8:

[1336] The user inputs the details of their meal through a dedicated app. The device sends this data to the server. The input is the user's meal data, and the output is the meal data sent to the server.

[1337] Step 9:

[1338] The server analyzes the received meal data, calculates calories and nutritional balance, and generates feedback based on the analysis results and sends it to the device. The input is the received meal data, and the output is the calculated calories and feedback.

[1339] Step 10:

[1340] The server generates a customized exercise menu based on the user's data. It can also adjust the exercise menu taking into account emotional data. The generated exercise menu is sent to the terminal. The input is the user's physical data and emotional data, and the output is the generated exercise menu.

[1341] Step 11:

[1342] The terminal provides the user with an exercise menu and tracks the progress. The input is the generated exercise menu, and the output is the exercise menu provided to the user and tracking data.

[1343] (Application example 2)

[1344] 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."

[1345] In recent years, interest in health management has grown, creating a demand for systems that can provide advice and recommendations tailored to individual users. However, existing systems tend to be limited to providing information based on physical data and lack personalized recommendations that take into account the user's emotional state. Furthermore, it has been difficult to recommend optimal products and services based on the user's health condition and emotions during the shopping experience in a physical store. Therefore, there is a demand for systems that can provide more comprehensive and personalized health management services.

[1346] 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.

[1347] In this invention, the server includes means for acquiring physical data, means for calculating the required calorie intake and amount of exercise, means for acquiring user emotion data, means for recommending products or services based on the emotion data, and means for displaying and providing audio guidance on the smart device with the recommended content. This enables personalized recommendations based on the user's health condition and emotions, and makes it possible to provide optimal products and services even in a shopping experience at a physical store.

[1348] "Physical data" refers to data related to the user's physical condition, such as the user's weight, body fat percentage, heart rate, and number of steps.

[1349] The "server" is a computer system that receives and analyzes the physical data and emotional data, calculates the required calorie intake and amount of exercise, and recommends products and services.

[1350] "Metabolic rate" is a calculated value used to calculate the user's basal metabolic rate, and indicates the basic amount of energy the user consumes in a day.

[1351] "Required calorie intake" indicates the amount of energy a user should take in per day, and is calculated based on the user's target weight and activity level.

[1352] "Voice advice" means spoken instructions or recommendations provided to the user by the system and generated using text-to-speech technology.

[1353] An "exercise menu" is a plan that indicates the exercise content that a user should do based on the user's health goals.

[1354] "Statistical data" refers to data obtained by aggregating and analyzing data collected from a large number of users, and is used as a reference for diagnosis and treatment in medical settings.

[1355] "Emotion data" is data that indicates the emotional state of the user, obtained from the user's facial expressions and voice.

[1356] "Product or service recommendation" means proposing optimal products or services to a user based on the user's physical data and emotional data.

[1357] "Smart devices" are devices that have the function of displaying information and providing voice guidance, such as smartphones, smart glasses, and head-mounted displays.

[1358] The present invention is a system that comprehensively supports users' health management, and in particular provides personalized recommendations by utilizing emotional data. The system includes physical and emotional data collection, data analysis, recommendation generation, display, and voice guidance.

[1359] System configuration

[1360] The system consists of the following main hardware and software:

[1361] Wearable tool: A device that collects health data (weight, body fat percentage, heart rate, number of steps, etc.) in real time and transmits it to a device.

[1362] Smart Device: Smart glasses are used as a device to display health and emotional data and provide voice guidance.

[1363] Server: The computer system that receives and analyzes the collected data.

[1364] Emotion recognition engine: Software that recognizes emotions from the user's facial expressions and voice.

[1365] Text-to-Speech Engine: Software that generates spoken recommendations and advice.

[1366] Recommendation engine: Software that recommends the most suitable products and services to users based on their physical and emotional data.

[1367] System Operation

[1368] Data collection

[1369] By wearing the wearable tool, the user can collect real-time health data such as weight, body fat percentage, heart rate, and number of steps. This data is transmitted to a device via smart glasses. In addition, an emotion recognition engine uses the smart glasses' camera and microphone to analyze the user's facial expressions and voice to obtain emotional data.

[1370] Data analysis and recommendation generation

[1371] The server calculates the user's metabolic rate based on the received health data, and calculates the required calorie intake and amount of exercise.The recommendation engine also considers the emotional data acquired by the emotion recognition engine and selects the most suitable products and services for the user.

[1372] Display and audio guidance of recommendations

[1373] The generated recommendation is displayed on the smart device and audibly guided using a text-to-speech engine. For example, if the user is feeling stressed, the recommendation may be displayed and spoken, such as "We recommend herbal tea for its relaxing effects."

[1374] Specific examples

[1375] For example, if a user has data showing a weight of 70 kg, a body fat percentage of 25%, and a heart rate of 80 bpm, and the emotion recognition engine detects "stress," the system operates as follows: The server receives the health data and calculates the metabolic rate. Next, the recommendation engine selects "relaxing herbal teas and aromatic oils" based on the emotion data. The smart glasses will display a message saying, "We recommend relaxing herbal teas," and the text-to-speech engine will provide a similar message via voice.

[1376] Also, when a user inputs a health care question into the generative AI model via a smart device, the following prompt can be used: "If the user is feeling stressed, please recommend products that have a relaxation effect, such as herbal tea or aromatic oil."

[1377] In this way, the system of the present invention provides personalized recommendations based on the user's health and emotional state, enhancing the shopping experience in brick-and-mortar stores.

[1378] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1379] Step 1:

[1380] Users wear a wearable tool that collects health data (weight, body fat percentage, heart rate, number of steps, etc.) in real time.

[1381] Input: Health data from wearable tools

[1382] Output: Health data sent to smart glasses

[1383] How it works: The wearable tool is worn by the user, and the built-in sensors detect data such as weight, body fat percentage, heart rate, and number of steps, and send this information to the device in real time.

[1384] Step 2:

[1385] Using the camera and microphone of the smart glasses, the emotion recognition engine analyzes the user's facial expressions and voice to obtain emotional data.

[1386] Input: User's facial expression, voice

[1387] Output: Emotion data

[1388] How it works: The smart glasses' camera captures the user's facial expressions and microphone records their voice, which are then sent to an emotion recognition engine to analyze the user's emotional state (e.g., stress, joy, anxiety, etc.).

[1389] Step 3:

[1390] The smart glasses send the collected health and emotional data to a server.

[1391] Input: Health data, emotion data

[1392] Output: Health and emotion data sent to the server

[1393] How it works: The smart glasses send health and emotion data to the server, which then receives it. The data is encrypted before transmission.

[1394] Step 4:

[1395] The server calculates the metabolic rate based on the health data received, and calculates the required calorie intake and amount of exercise.

[1396] Input: Health data

[1397] Output: metabolic rate, calorie needs, and exercise volume

[1398] How it works: The server analyzes health data and calculates metabolic rate using the Harris-Benedict equation, etc. It then calculates the required calorie intake and amount of exercise based on the user's target weight and activity level.

[1399] Step 5:

[1400] The server takes into account the emotional data, and the recommendation engine generates the most suitable products and services for the user.

[1401] Input: Emotion data, calculation results from health data

[1402] Output: Recommendation (product or service)

[1403] How it works: The server analyzes the emotional data to understand the user's current emotional state. The recommendation engine then selects products and services that will have a relaxing effect for the user based on the health and emotional data.

[1404] Step 6:

[1405] The generated recommendation content is sent to the smart device, where it is displayed and provided as audio guidance.

[1406] Input: Recommendation from the server

[1407] Output: Recommendations displayed on smart devices and audio guidance

[1408] Specific operation: The recommendation content is sent to the smart glasses and displayed on the screen. At the same time, the text-to-speech engine generates a voice prompt and delivers it to the user through the smart glasses' speaker.

[1409] In this way, the system supports more personalized health management by understanding the user's health and emotional state in detail at each step and recommending optimal products and services.

[1410] 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.

[1411] 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.

[1412] 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.

[1413] [Fourth embodiment]

[1414] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1415] 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.

[1416] 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).

[1417] 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.

[1418] 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.

[1419] 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).

[1420] 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.

[1421] 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.

[1422] 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.

[1423] 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.

[1424] 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.

[1425] 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.

[1426] 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."

[1427] DETAILED DESCRIPTION OF THE INVENTION The present invention relates to a wearable tool and a related system that comprehensively supports a user's health management. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail.

[1428] Data collection

[1429] When a user wears a wearable device, the device collects real-time physical data such as weight, body fat, heart rate, and number of steps taken. This data is temporarily stored on the device and sent to a server at regular intervals.

[1430] Metabolic calculation and calculation of required calorie intake and exercise amount

[1431] The server calculates the user's basal metabolic rate (BMR) based on the received physical data. The BMR is calculated using, for example, the Harris-Benedict equation. The server then calculates the user's calorie intake and exercise needs based on the user's target weight and activity level.

[1432] Providing audio advice

[1433] The server generates an appropriate weight loss plan based on the calculation results and creates voice advice using a text-to-speech engine. The voice advice is then sent to the device and provided to the user.

[1434] Enter your meal details and calculate nutritional information

[1435] When a user enters their meal information through the app, the device sends that information to a server, which analyzes the received meal data and calculates calories and nutritional balance. This allows the server to provide feedback on whether the user's meal is in line with their health goals.

[1436] Exercise menu management

[1437] The server generates an individually customized exercise menu based on the user's data, which is then sent to the device, where it is provided to the user and progress is tracked.

[1438] Generating statistical data and utilizing it in medical settings

[1439] The server aggregates data collected from many users and generates statistical data, such as the average body fat percentage by age and gender. This statistical data is useful in medical settings for individual diagnosis and optimization of treatment.

[1440] Customization services for corporations

[1441] The server generates customized reports and analytical data for corporate clients, allowing them to efficiently manage the health of their employees. The server provides these reports to corporate clients on a regular basis and also provides consulting services as needed.

[1442] Specific examples

[1443] For example, suppose a user has data on their weight: 70 kg, body fat percentage: 25%, and heart rate: 72 bpm. The device collects this data and sends it to the server. The server calculates their basal metabolic rate, for example, 1500 kcal / day. The server then considers the user's target weight and suggests a required calorie intake of 1800 kcal / day and exercise three times a week.

[1444] The generated voice advice is provided to the user via the device in the form of "Today's target calorie intake is 1800 kcal. We recommend a 30-minute walk." Furthermore, if the user inputs "200 g of chicken breast, salad, and one bowl of brown rice" for lunch, the server calculates the calories to 500 kcal and also analyzes the balance of the total calorie intake.

[1445] In this way, by using the system of the present invention, users can perform efficient health management, and medical facilities and companies can also manage and utilize data appropriately.

[1446] The processing flow will be explained below.

[1447] Step 1:

[1448] The user puts on a wearable tool, which is equipped with sensors such as a weight scale, a body fat scale, a heart rate monitor, and a pedometer.

[1449] Step 2:

[1450] The device collects data from the wearable device, specifically physical data such as weight, body fat percentage, heart rate, and number of steps taken, in real time.

[1451] Step 3:

[1452] The device temporarily stores the acquired physiological data in memory, ready to be sent to a server later.

[1453] Step 4:

[1454] The device sends the collected data to the server at regular intervals, using Bluetooth or Wi-Fi as the communication method.

[1455] Step 5:

[1456] The server receives the data sent from the device and checks the integrity of the data, for example, checking whether the data is missing or contains errors.

[1457] Step 6:

[1458] The server calculates the user's basal metabolic rate based on the received data, for example, using the Harris-Benedict equation.

[1459] Step 7:

[1460] The server calculates the required calorie intake and recommended exercise amount based on the user's basal metabolic rate and target weight. The required calorie intake is then displayed as a specific number, such as 1800 kcal / day.

[1461] Step 8:

[1462] The server generates textual audio advice based on the calculation results. For example, it might say, "Today's target calorie intake is 1,800 kcal. We recommend walking for 30 minutes."

[1463] Step 9:

[1464] The server uses a text-to-speech engine to convert the generated text into an audio file, which is then sent to the device.

[1465] Step 10:

[1466] The device plays the audio file received from the server, based on the timing or request specified by the user.

[1467] Step 11:

[1468] The user inputs the details of their meal into the app. For example, they input a specific meal such as "200g of chicken breast, salad, and one bowl of brown rice."

[1469] Step 12:

[1470] The device sends the inputted meal data to the server, which uses the data to evaluate calorie and nutritional balance.

[1471] Step 13:

[1472] The server analyzes the received dietary data, calculates calories and nutritional balance, and generates feedback on the user's total calorie intake and nutritional balance based on this data.

[1473] Step 14:

[1474] The server sends feedback data to the device, such as "Today's calorie intake is 90% of your goal. I recommend having a light meal for dinner."

[1475] Step 15:

[1476] The server generates a personalized exercise menu based on the user's data, including specific exercises and their frequency.

[1477] Step 16:

[1478] The server sends the generated exercise menu to the terminal, which displays the menu to the user and tracks the exercise progress.

[1479] Step 17:

[1480] The server aggregates data collected from many users and generates statistical data, such as calculating the average body fat percentage by age and gender.

[1481] Step 18:

[1482] The server provides the generated statistical data to medical facilities, providing doctors with data that can help them make individual diagnoses and optimize treatment.

[1483] Step 19:

[1484] The server generates customized reports and analytical data for corporate clients, such as reports summarizing employee health management status.

[1485] Step 20:

[1486] The server provides regular reports and analytical data to corporate clients, and also provides health management consulting services as needed.

[1487] Example 1

[1488] 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."

[1489] Current systems for efficient health management lack the means to comprehensively understand a user's physical condition and provide appropriate advice. Furthermore, it is difficult to provide customized feedback and exercise menus for individual users. Furthermore, there is a lack of statistical data available for use in businesses and medical settings. To address these issues, a more comprehensive and customized healthcare system is needed.

[1490] 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.

[1491] In this invention, the server includes means for the user to acquire physical data, means for the terminal to transmit the physical data to the server, means for the server to calculate metabolic rate, means for the server to calculate required calorie intake and exercise amount, means for the server to generate audio advice, means for the server to manage exercise menus, and means for the server to generate statistical data and use it in medical settings, thereby enabling efficient health management for users and appropriate data management and use in medical settings and companies.

[1492] "User" refers to an individual who uses the system to manage their own health.

[1493] "Terminal" refers to a wearable tool worn by a user or a device for transmitting data.

[1494] A "server" refers to a computer system that receives data sent by users and performs analysis and calculations.

[1495] "Physical data" refers to measurement data about the user's body, such as weight, body fat, heart rate, and number of steps.

[1496] "Metabolic rate" is a numerical representation of the user's basal metabolic rate, and is an index for calculating energy expenditure.

[1497] "Required calorie intake" refers to the amount of calories a user should consume per day.

[1498] "Movement" refers to the recommended target amount of physical activity a user is recommended to achieve within a particular period of time.

[1499] "Voice advice" refers to instructions or suggestions in the form of voice that are generated based on the results of calculations.

[1500] An "exercise menu" refers to a specific exercise plan proposed to maintain or improve the user's health.

[1501] "Statistical data" refers to statistical information obtained by comprehensively analyzing data collected from multiple users.

[1502] "Healthcare setting" refers to a location where health care services are provided, such as a hospital or clinic.

[1503] "Dietary data" refers to information about the contents of meals consumed by a user.

[1504] "Calories" refers to a unit of measurement that indicates the amount of energy in food.

[1505] "Nutritional balance" refers to the appropriate proportions of nutrients such as protein, fat, and carbohydrates.

[1506] "Corporate Customer" refers to a company, organization, or other entity that uses this system for business purposes.

[1507] "Customized Report" means a report of analytical data tailored to the needs of a particular Corporate Client.

[1508] MODE FOR CARRYING OUT THE INVENTION

[1509] The present invention is a system that comprehensively supports a user's health management, and specific embodiments thereof will be described in detail below.

[1510] overview

[1511] This system collects and manages physical data such as weight, body fat, heart rate, and number of steps taken through a wearable device worn by the user in real time. The data is sent via the device to a server, which performs various calculations and analyses based on the collected data. It also provides users with customized health advice and exercise menus, and supports use in businesses and medical settings.

[1512] Data collection

[1513] When a user wears a wearable device, the device collects physical data in real time. The device used for this is a wearable tool equipped with multiple sensors. An example is a smartwatch with a built-in scale and heart rate monitor. The collected data is temporarily stored on the device and encrypted and sent to a server at regular intervals.

[1514] Data analysis and feedback

[1515] The server analyzes the received data and calculates the user's basal metabolic rate. The Harris-Benedict equation is used to calculate the basal metabolic rate: 88.362 + (13.397 × weight) + (4.799 × height) - (5.677 × age). The necessary calculations are performed based on the user's input data. The server then calculates the required calorie intake and exercise volume based on the user's target weight and activity level.

[1516] The generated results are provided to the user as voice advice. The voice advice generated using a text-to-speech engine is sent to the terminal, which then notifies the user directly.

[1517] Dietary data and nutritional analysis

[1518] When a user enters their meal details through the app, the device sends the data to a server. The server analyzes the received meal data and calculates calories and nutritional balance. This allows the device to provide feedback on how well the user's meal matches their health goals. For example, if a user enters "200g chicken breast, salad, and one bowl of brown rice" for lunch, the device calculates the calories and analyzes the balance based on the total calorie intake.

[1519] Providing individual exercise menus

[1520] The server generates a personalized exercise menu based on the user's health data, taking into account information such as the user's weight, basal metabolic rate, and target weight. The generated exercise menu is sent to the device and provided to the user. The device also has the function of tracking exercise progress.

[1521] Generate statistical data

[1522] The server aggregates data collected from many users and generates statistical data, including average body fat percentage by age and gender, which is used in medical settings and companies to optimize diagnosis and treatment.

[1523] Corporate Services

[1524] It also generates customized reports and analytical data for corporate clients. The server periodically generates these reports and provides them to corporate clients. Consulting services are also provided as needed.

[1525] Specific examples

[1526] For example, suppose a user has data showing a weight of 70 kg, a body fat percentage of 25%, and a heart rate of 72 bpm. The device collects this data and sends it to the server. The server calculates the user's basal metabolic rate, for example, 1500 kcal / day. The server then takes the user's target weight into account and suggests a required calorie intake of 1800 kcal / day and three exercise sessions per week. The generated audio advice is provided to the user via the device in the form of, "Today's target calorie intake is 1800 kcal. I recommend a 30-minute walk." If the user also inputs "200 g of chicken breast, salad, and one bowl of brown rice" for lunch, the server calculates the calories and analyzes the balance of the total calorie intake.

[1527] Example prompts to input to the generative AI model

[1528] "Calculate the user's basal metabolic rate based on their weight, body fat percentage, and heart rate data, and create voice advice on the amount of calories they need to eat and exercise. Use the following data:

[1529] Weight: 70kg

[1530] Body fat percentage: 25%

[1531] Heart rate: 72 bpm

[1532] Also, please calculate the calories of the 200g chicken breast, salad, and bowl of brown rice that the user ate for lunch, and provide feedback on the balance of the total calorie intake.

[1533] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1534] Step 1:

[1535] The user puts on a wearable device, which measures real-time data such as weight, body fat, heart rate, and steps taken.

[1536] Step 2:

[1537] The terminal receives measurement data from the wearable device, which is temporarily stored in the terminal.

[1538] Input: Body data from wearable devices

[1539] Output: Data temporarily stored in the device

[1540] Step 3:

[1541] The device sends the collected data to the server at regular intervals. The data is encrypted before transmission.

[1542] Input: Temporary data in the terminal

[1543] Output: Data sent to the server

[1544] Step 4:

[1545] The server receives the data sent from the terminal and stores it in a database.

[1546] Input: Data sent from the terminal

[1547] Output: Data stored in the database on the server

[1548] Step 5:

[1549] The server calculates the user's basal metabolic rate based on the received data. The calculation is performed using the Harris-Benedict equation.

[1550] Input: Physical data in the database

[1551] Output: Calculated basal metabolic rate

[1552] Step 6:

[1553] The server calculates the required calorie intake and amount of exercise, taking into account the user's target weight and activity level.

[1554] Input: Basal metabolic rate, target weight, activity level

[1555] Output: Required calorie intake and exercise amount

[1556] Step 7:

[1557] The server generates the audio advice, using a text-to-speech engine to create audio advice based on the calculation results.

[1558] Input: Required calorie intake and exercise amount

[1559] Output: Voice advice

[1560] Step 8:

[1561] The server transmits the generated voice advice to the terminal, which notifies the user of the advice.

[1562] Input: Generated voice advice

[1563] Output: Audio advice notification to the user

[1564] Step 9:

[1565] The user inputs the details of their meal through a dedicated app. For example, they input "200g of chicken breast, salad, and one bowl of brown rice for lunch."

[1566] Input: Meal details

[1567] Output: Meal data via a dedicated app

[1568] Step 10:

[1569] The device sends the meal data to the server, and the server receives the meal data.

[1570] Input: Meal data

[1571] Output: Data sent to the server

[1572] Step 11:

[1573] The server analyzes the received dietary data and calculates calories and nutritional balance.

[1574] Input: Meal data

[1575] Output: Calorie and nutritional balance results

[1576] Step 12:

[1577] The server generates feedback based on the meal content, which is sent to the device and can be viewed by the user.

[1578] Input: Calories and nutritional balance results

[1579] Output: Feedback

[1580] Step 13:

[1581] The server generates a customized exercise menu based on the user's data.

[1582] Input: User's physical data, health goals

[1583] Output: Customized exercise menu

[1584] Step 14:

[1585] The server sends the generated exercise menu to the device, which displays the menu to the user and tracks their progress.

[1586] Enter: Customized Exercise Menu

[1587] Output: Data sent to the device, progress data

[1588] Step 15:

[1589] The server aggregates the data collected from multiple users and generates statistical data, such as calculating the average body fat percentage by age and gender.

[1590] Input: A lot of user data

[1591] Output:Statistical data

[1592] Step 16:

[1593] The server provides the generated statistical data to medical facilities, which helps optimize diagnosis and treatment.

[1594] Input:Statistical data

[1595] Output: Data provided to medical facilities

[1596] Step 17:

[1597] The server generates and provides customized reports and analytical data for companies, periodically sending reports to corporate clients and providing consulting services as needed.

[1598] Input: Corporate customer data

[1599] Output: Customized reports and analytical data

[1600] (Application example 1)

[1601] 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."

[1602] In modern society, users are expected to manage their health efficiently and comprehensively. However, while current systems manage individual health data, they lack functionality such as proposing optimal products in a virtual store or offering virtual experiences with exercise equipment. Furthermore, conventional systems face challenges in providing sufficient accuracy in terms of dietary feedback and voice advice.

[1603] 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.

[1604] In this invention, the server includes means for proposing optimal products based on the user's health data within the virtual store, means for virtually experiencing exercise equipment, means for tracking progress, means for providing health advice using a generative AI model, means for providing dietary feedback using a generative AI model, and means for managing statistical data on health-related products within the virtual store, thereby enabling users to comprehensively manage their health and optimally purchase and experience health-related products within the virtual store.

[1605] "Physical data" refers to physiological data such as the user's weight, body fat percentage, heart rate, and number of steps.

[1606] A "server" is a computer that receives, processes, and stores data via a network.

[1607] "Metabolic rate" is an index of energy consumption calculated based on the user's basal metabolic rate.

[1608] "Caloric intake requirement" refers to the daily calorie intake required by a user to maintain health or achieve a goal.

[1609] "Exercise volume" refers to the intensity and amount of exercise a user should do per day.

[1610] "Voice Advice" means audio health advice provided to a user that is created using a generative AI model.

[1611] An "exercise menu" is an exercise plan that is individually customized based on the user's goals.

[1612] "Statistical data" refers to statistical information obtained by aggregating data collected from many users.

[1613] A "virtual store" is a virtual environment where users can purchase products in a digital space, rather than in a real store.

[1614] "Means for suggesting optimal products" refers to algorithms or systems that suggest optimal health-related products to users based on their health data.

[1615] The "means for virtually experiencing exercise equipment" refers to a simulation system that allows users to try out exercise equipment in a digital space.

[1616] A "progress tracking means" is a system for recording and managing a user's exercise and health management progress.

[1617] A "generative AI model" is a model that uses artificial intelligence to analyze data, make predictions, and generate advice.

[1618] A "means for providing health advice" is a system that uses a generative AI model to provide health advice to a user.

[1619] "Means for providing dietary feedback" refers to a system that calculates calories and nutritional balance based on dietary data entered by the user and provides feedback.

[1620] The "means for managing statistical data on health-related products" refers to a system that collects, analyzes, and manages data such as sales and popularity of health-related products sold in the virtual store.

[1621] In order to implement the present invention, the system uses various hardware and software. The following describes the system program that realizes this application example.

[1622] System Configuration

[1623] Hardware:

[1624] 1. Wearable tools: Devices worn by the user that collect physical data such as weight, body fat percentage, heart rate, and number of steps.

[1625] 2. Smartphone or tablet: A device where users input health and dietary data, which receives data from the wearable tool and transmits it to the server.

[1626] 3. Server: Analyzes and stores data, and provides voice advice and feedback using generative AI models.

[1627] software:

[1628] 1. Google Text-to-Speech (gTTS): Software for providing voice advice to users.

[1629] 2. Flask Web Framework: A web framework that handles server-side processing.

[1630] 3. Generative AI model: An AI model that generates advice and feedback based on health and dietary data.

[1631] Data processing and calculation

[1632] Physical data acquisition and transmission

[1633] When a user wears the wearable tool, it collects real-time physical data and transmits it to a smartphone.

[1634] The smartphone sends this data to a server at regular intervals.

[1635] Calculating metabolic rate and calorie intake targets

[1636] The server uses the received physical data to calculate the user's basal metabolic rate using the Harris-Benedict equation or similar.

[1637] Next, the target calorie intake is calculated based on the basal metabolic rate, the user's target weight, and activity level.

[1638] Generating health advice

[1639] The server uses a generative AI model to generate appropriate health advice based on the calculation results, which is then generated as audio advice using Google Text-to-Speech and sent to the smartphone.

[1640] Meal data entry and feedback

[1641] When a user enters their meal details into their smartphone, the information is sent to the server.

[1642] The server analyzes the submitted meal data, calculates calories and nutritional balance, and provides feedback using a generative AI model.

[1643] Exercise menu management

[1644] The server generates an individually customized exercise menu based on the user's data and sends it to the smartphone.

[1645] Users can track their progress through their smartphones.

[1646] Applications in virtual stores

[1647] The server suggests optimal products in the virtual store based on the user's health data.

[1648] We provide a simulation system that allows users to virtually experience exercise equipment and tracks their progress.

[1649] Specific examples

[1650] For example, let's consider the case where a user sends data showing a weight of 70 kg, a body fat percentage of 25%, a heart rate of 72 bpm, and a step count of 5,000. The server that receives this data calculates the basal metabolic rate and calculates the target calorie intake. It also uses an AI model to generate voice advice such as, "Today's target calorie intake is 1,800 kcal. We recommend walking for 30 minutes."

[1651] If you enter your meal data for lunch, such as "200g chicken breast, salad, and one bowl of brown rice," the server will calculate the calories and provide you with feedback on your meal.

[1652] Prompt Sentence Examples

[1653] "Calculate today's target calorie intake based on weight: 70 kg, body fat percentage: 25%, heart rate: 72 bpm, and steps: 5000."

[1654] "Please analyze the dietary data of a lunch consisting of 200g of chicken breast, salad, and one bowl of brown rice, and provide feedback on the calories and nutritional balance."

[1655] This system enables users to manage their health efficiently and allows them to optimally select and experience health-related products in a virtual store.

[1656] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1657] Step 1:

[1658] The user wears a wearable tool to collect physiological data such as weight, body fat percentage, heart rate, and number of steps. The input is physiological data obtained from the wearable tool worn by the user, which transmits this data in real time to a smartphone. The output is physiological data stored on the smartphone.

[1659] Step 2:

[1660] The smartphone collects and transmits the collected physiological data at regular intervals to a server. The input is the physiological data collected by the wearable tool, and the smartphone transmits the data to the server. The output is the user's physiological data, which is stored on the server. The specific operation is to transmit the data to the server using a communication protocol.

[1661] Step 3:

[1662] The server calculates the basal metabolic rate (BMR) based on the received physical data. The input is the user's physiological data sent to the server, and the data calculation is performed based on the Harris-Benedict equation. The output is the user's basal metabolic rate (BMR).

[1663] Step 4:

[1664] The server calculates the required calorie intake and amount of exercise based on the user's basal metabolic rate, taking into account the user's target weight and activity level. The input is the basal metabolic rate, target weight, and activity level, and based on these, it performs data calculations to calculate the required calorie intake and amount of exercise. The output is the required calorie intake and amount of exercise.

[1665] Step 5:

[1666] The server uses the calculation results to generate voice advice using a generative AI model. The input is data on required calorie intake and exercise amount, and the generative AI model is used to generate advice. Specifically, it uses Google Text-to-Speech to generate an audio file and send it back to the smartphone. The output is an audio file.

[1667] Step 6:

[1668] The user inputs the details of their meal into their smartphone and sends the data to the server. The input is the meal data entered by the user into their smartphone and sent to the server. The output is the meal data stored on the server.

[1669] Step 7:

[1670] The server analyzes the received meal data and calculates calories and nutritional balance. The input is the meal data sent to the server, and the server's data processing algorithm (generative AI model) is used to calculate calories and nutritional balance. The output is feedback data.

[1671] Step 8:

[1672] The server generates an individually customized exercise menu based on the user's data and sends it to the smartphone. The input is the user's physiological data and goal information stored on the server, and the exercise menu is generated using an algorithm within the server. The output is the customized exercise menu.

[1673] Step 9:

[1674] The user checks the exercise menu through their smartphone and tracks their progress. The input is the exercise menu sent from the server and displayed on the smartphone. Progress data is obtained by inputting the exercise content the user will perform. The output is the user's progress data.

[1675] Step 10:

[1676] The server proposes optimal products in a virtual store based on the user's health data. The input is the user's health data and the metadata of the products in the virtual store, and the optimal products are determined based on data analysis. The output is a list of suggested products.

[1677] Step 11:

[1678] The server provides a virtual simulation of the exercise equipment experience and tracks the user's progress. The input is data about the exercise equipment in the virtual store and the user's exercise data, and the simulation system provides the exercise experience. The output is progress tracking data.

[1679] This system enables users to manage their health comprehensively and to optimally select and experience health-related products in a virtual store.

[1680] 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.

[1681] The present invention provides a wearable tool and related system that comprehensively supports a user's health management, and aims to provide more personalized services by combining it with an emotion engine that recognizes the user's emotions. Hereinafter, embodiments of the present invention will be described in detail.

[1682] Data collection

[1683] When a user wears a wearable tool, the device collects real-time physical data such as weight, body fat, heart rate, and number of steps. This data is temporarily stored on the device and sent to a server at regular intervals. In addition, an emotion engine is used to obtain emotional data from the user's facial expressions and voice.

[1684] Metabolic calculation and calculation of required calorie intake and exercise amount

[1685] The server calculates the user's basal metabolic rate (BMR) based on the received physical data. The BMR is calculated using, for example, the Harris-Benedict equation. The server then calculates the user's calorie intake and exercise needs based on the user's target weight and activity level.

[1686] Providing audio advice

[1687] The server generates an appropriate weight loss plan based on the calculation results and creates voice advice. This voice advice is generated using a text-to-speech engine. Furthermore, the content and tone of the voice advice are adjusted based on the user's emotional data recognized by the emotion engine. The generated voice advice is sent to the device and provided to the user.

[1688] Enter your meal details and calculate nutritional information

[1689] When a user enters their meal information through the app, the device sends that information to a server, which analyzes the received meal data and calculates calories and nutritional balance. This allows the server to provide feedback on whether the user's meal is in line with their health goals.

[1690] Exercise menu management

[1691] The server generates an individually customized exercise menu based on the user's data. In addition, the exercise menu can be adjusted taking into account the user's emotional data recognized by the emotion engine. The generated exercise menu is sent from the server to the device, which provides it to the user and tracks their progress.

[1692] Generating statistical data and utilizing it in medical settings

[1693] The server aggregates data collected from many users and generates statistical data, such as the average body fat percentage by age and gender. This statistical data is useful in medical settings for individual diagnosis and optimization of treatment.

[1694] Customization services for corporations

[1695] The server generates customized reports and analytical data for corporate clients, allowing them to efficiently manage the health of their employees. The server provides these reports to corporate clients on a regular basis and also provides consulting services as needed.

[1696] Specific examples

[1697] For example, suppose a user has data on their weight: 70 kg, body fat percentage: 25%, and heart rate: 72 bpm. The device collects this data and sends it to the server. The server calculates their basal metabolic rate, for example, 1500 kcal / day. The server then considers the user's target weight and suggests a required calorie intake of 1800 kcal / day and exercise three times a week.

[1698] At the same time, if the emotion engine detects "stress" from the user's facial expressions or voice, the corresponding voice advice will change. Advice will be provided in the form of "Today's target calorie intake is 1800 kcal. We recommend a 30-minute walk to relax."

[1699] Furthermore, if a user inputs "200g chicken breast, salad, and one bowl of brown rice" for lunch, the server calculates the calories to 500kcal and analyzes the balance of the total calorie intake. This provides feedback such as "Today's calorie intake is 90% of your goal. I recommend something light for dinner."

[1700] In this way, by using the system of the present invention, users can efficiently manage their health, and by using the emotion engine, support that takes into account psychological state can be provided. This will enable appropriate data management and utilization in medical settings and companies.

[1701] The processing flow will be explained below.

[1702] Step 1:

[1703] The user puts on a wearable tool, which is equipped with sensors such as a weight scale, a body fat scale, a heart rate monitor, and a pedometer.

[1704] Step 2:

[1705] The device collects data from the wearable device, specifically physical data such as weight, body fat percentage, heart rate, and number of steps taken, in real time.

[1706] Step 3:

[1707] The device temporarily stores the acquired physiological data in memory, ready to be sent to a server later.

[1708] Step 4:

[1709] The device sends the collected data to the server at regular intervals, using Bluetooth or Wi-Fi as the communication method.

[1710] Step 5:

[1711] The server receives the data sent from the device and checks the integrity of the data, for example, checking whether the data is missing or contains errors.

[1712] Step 6:

[1713] The server calculates the user's basal metabolic rate based on the received data, for example, using the Harris-Benedict equation.

[1714] Step 7:

[1715] The server calculates the required calorie intake and recommended exercise amount based on the user's basal metabolic rate and target weight. The required calorie intake is then displayed as a specific number, such as 1800 kcal / day.

[1716] Step 8:

[1717] The device collects the user's facial expressions and voice data and sends it to the emotion engine, which analyzes the user's emotions and generates emotion data such as "stress" or "relaxation."

[1718] Step 9:

[1719] The server generates the most appropriate voice advice text for the user based on the emotion data and metabolic data received from the emotion engine. For example, it might say, "Today's target calorie intake is 1800 kcal. We recommend a 30-minute walk to relax."

[1720] Step 10:

[1721] The server uses a text-to-speech engine to convert the generated text into an audio file, which is then sent to the device.

[1722] Step 11:

[1723] The device plays the audio file received from the server, based on the timing or request specified by the user.

[1724] Step 12:

[1725] The user inputs the details of their meal into the app. For example, they input a specific meal such as "200g of chicken breast, salad, and one bowl of brown rice."

[1726] Step 13:

[1727] The device sends the inputted meal data to the server, which uses the data to evaluate calorie and nutritional balance.

[1728] Step 14:

[1729] The server analyzes the received dietary data, calculates calories and nutritional balance, and generates feedback on the user's total calorie intake and nutritional balance based on this data.

[1730] Step 15:

[1731] The server sends feedback data to the device, such as "Today's calorie intake is 90% of your goal. I recommend having a light meal for dinner."

[1732] Step 16:

[1733] The server uses the user's data to generate a personalized workout routine, including specific exercises and their frequency.

[1734] Step 17:

[1735] The server sends the generated exercise menu to the terminal, which displays the exercise menu to the English user and tracks the exercise progress.

[1736] Step 18:

[1737] The emotion engine analyzes the user's emotional data and adjusts the exercise menu based on the user's psychological state. For example, it recommends relaxing exercises for users with high stress levels.

[1738] Step 19:

[1739] The server aggregates data collected from many users and generates statistical data, such as calculating the average body fat percentage by age and gender.

[1740] Step 20:

[1741] The server provides the generated statistical data to medical facilities, providing doctors with data that can help them make individual diagnoses and optimize treatment.

[1742] Step 21:

[1743] The server generates customized reports and analytical data for corporate clients, such as reports summarizing employee health management status.

[1744] Step 22:

[1745] The server provides regular reports and analytical data to corporate clients, and also provides health management consulting services as needed.

[1746] The above are the specific steps for implementing the present invention. Through this system, comprehensive health management based on the user's physical data and emotional data can be realized.

[1747] Example 2

[1748] 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."

[1749] Conventional health management systems provide management and advice based solely on the user's physical data, but do not take into account the user's emotional state, making efficient and personalized health management difficult. They also lack the functionality to provide detailed feedback on dietary content and nutritional balance, or to offer customized services for corporate clients. This poses a challenge, preventing advice tailored to the user's psychological state and making it difficult for companies to efficiently manage the health of their employees.

[1750] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1751] In this invention, the server includes a means for acquiring physical data, a means for transmitting the physical data to the server, a means for calculating metabolic rate, a means for calculating required calorie intake and exercise volume, a means for generating audio advice, a means for collecting and analyzing emotional data, a means for adjusting the content and tone of the audio advice based on the emotional data, a means for generating and adjusting an exercise menu, and a means for generating statistical data for use in clinical settings. This enables personalized health management that takes into account not only the user's physical data but also their emotional data. Furthermore, since the system also includes the input and analysis of dietary information and the provision of customization services for corporations, it can function as a comprehensive health management system.

[1752] "Physical data" refers to various data that indicate the user's physical condition, such as the user's weight, body fat, heart rate, and number of steps.

[1753] A "server" is a computer system that receives, stores, analyzes various data, and provides the results.

[1754] "Metabolic rate" represents the user's basal metabolic rate, and is a value calculated based on the Harris-Benedict equation or the like.

[1755] "Required calorie intake" is the amount of calories that a user should take in per day, and is a value calculated based on a target weight and activity level.

[1756] The "amount of exercise" refers to the amount of exercise that the user should do in a day, and indicates the specific scale of the exercise, including the time and intensity.

[1757] "Voice advice" refers to voice instructions or advice that is generated based on the required calorie intake and amount of exercise and provided to the user.

[1758] "Emotion data" is data that indicates the user's mental and emotional state, obtained from the user's facial expressions and voice.

[1759] An "exercise menu" refers to an exercise plan or training schedule for each user that is created based on the user's individual data.

[1760] "Statistical data" is data collected from many users and compiled and analyzed according to attributes such as age and gender.

[1761] The present invention is a system that comprehensively supports a user's health management, and aims to provide more personalized services by incorporating emotion data in particular. Hereinafter, embodiments of the present invention will be described in detail.

[1762] Data collection

[1763] When a user wears a wearable tool, the device collects real-time physical data such as weight, body fat, heart rate, and number of steps. This data is temporarily stored on the device and sent to a server at regular intervals. At the same time, an emotion engine is used to obtain emotional data from the user's facial expressions and voice.

[1764] Sending data

[1765] The device sends the collected physical and emotional data to a server using Wi-Fi, Bluetooth, or other communication methods, and the data is sent securely.

[1766] Metabolic calculation and calorie / exercise calculation

[1767] The server calculates the user's basal metabolic rate using the Harris-Benedict equation based on the received physical data, and then calculates the user's daily calorie intake and exercise requirements based on the user's target weight and activity level.

[1768] Providing audio advice

[1769] The server uses a text-to-speech engine (e.g., Google Text-to-Speech API) to generate audio advice based on required calorie intake and exercise volume. The content and tone of the advice are also adjusted based on the emotional data. The generated audio advice is sent to the device and provided to the user.

[1770] Input and analysis of meal contents

[1771] Users input their meal plans through a dedicated app, and the device sends the information to a server, which analyzes the received meal data, calculates calories and nutritional balance, and provides feedback on whether the user's meal plans are in line with their set health goals.

[1772] Creation and management of exercise menus

[1773] The server generates a personalized exercise menu based on the user's physical and emotional data. The exercise menu can also be adjusted based on the emotional data. The generated exercise menu is sent to the device, which provides it to the user and tracks their progress.

[1774] Generating statistical data and utilizing it in medical settings

[1775] The server aggregates data collected from many users and generates statistical data by age and gender, which is used in medical settings to optimize diagnosis and treatment.

[1776] Customization services for corporations

[1777] The server generates customized reports and analytical data for corporate clients, allowing them to efficiently manage the health of their employees. The server provides these reports to corporate clients on a regular basis and also provides consulting services as needed.

[1778] Specific examples

[1779] For example, if a user has data on their weight (70 kg), body fat percentage (25%), and heart rate (72 bpm), the device that collected this data sends it to the server. The server calculates their basal metabolic rate, for example, 1500 kcal / day. The server then takes the user's target weight into account and sets the required calorie intake at 1800 kcal / day, suggesting three times a week of exercise.

[1780] At the same time, if the emotion engine detects "stress" from the user's facial expressions and voice, it will provide voice advice such as, "We recommend a 30-minute walk to relax." Furthermore, if the user inputs "200g of chicken breast, salad, and one bowl of brown rice" for lunch, the server will calculate the calories to 500kcal and can also analyze the balance of total calorie intake. Feedback will be provided in the form of, "Today's calorie intake is 90% of your goal. We recommend something light for dinner."

[1781] Prompt Sentence Examples

[1782] "Write pseudocode in natural language for a system that creates a personalized health management plan based on a user's weight, body fat percentage, heart rate, number of steps, diet, etc., along with an explanation of each step."

[1783] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1784] Step 1:

[1785] The user puts on a wearable tool. This tool uses built-in sensors to collect real-time physical data such as weight, body fat, heart rate, and number of steps. The collected data is temporarily stored on the device. The input is the user's physical data, and the output is the data stored on the device.

[1786] Step 2:

[1787] At regular intervals, the device sends the collected physiological data to a server. The communication method is Wi-Fi or Bluetooth. Specifically, the data stored in the device is sent to the server via the network. The input is the physiological data in the device, and the output is the physiological data sent to the server.

[1788] Step 3:

[1789] The device also collects the user's facial expressions and voice to generate emotion data. The emotion engine analyzes this data and identifies the user's emotional state (e.g., stress, joy, fatigue, etc.). This emotion data is also periodically sent to the server. The input is the user's facial expressions and voice, and the output is the emotion data sent to the server.

[1790] Step 4:

[1791] The server calculates the basal metabolic rate (BMR) using the Harris-Benedict equation based on the received physical data. Specifically, it calculates the BMR using the user's weight, height, age, and gender. The input is the received physical data, and the output is the calculated basal metabolic rate.

[1792] Step 5:

[1793] The server calculates the required calorie intake and exercise amount based on the user's basal metabolic rate, taking into account the user's target weight and activity level. Specifically, it calculates the total daily energy expenditure (TDEE) by applying a correction factor for each activity level, and then sets the calorie intake and exercise goals based on the user's goals. The inputs are the basal metabolic rate, target weight, and activity level, and the output is the required calorie intake and exercise amount.

[1794] Step 6:

[1795] The server uses a text-to-speech engine to generate audio advice based on the required calorie intake and amount of exercise. Furthermore, it adjusts the content and tone of the advice using emotional data. The generated audio advice is sent to the device. The input is the calculated required calorie intake and emotional data, and the output is the generated audio advice.

[1796] Step 7:

[1797] The terminal provides the generated voice advice to the user. The terminal uses a voice playback function to convey the advice to the user. The input is the generated voice advice, and the output is the voice advice provided to the user.

[1798] Step 8:

[1799] The user inputs the details of their meal through a dedicated app. The device sends this data to the server. The input is the user's meal data, and the output is the meal data sent to the server.

[1800] Step 9:

[1801] The server analyzes the received meal data, calculates calories and nutritional balance, and generates feedback based on the analysis results and sends it to the device. The input is the received meal data, and the output is the calculated calories and feedback.

[1802] Step 10:

[1803] The server generates a customized exercise menu based on the user's data. It can also adjust the exercise menu taking into account emotional data. The generated exercise menu is sent to the terminal. The input is the user's physical data and emotional data, and the output is the generated exercise menu.

[1804] Step 11:

[1805] The terminal provides the user with an exercise menu and tracks the progress. The input is the generated exercise menu, and the output is the exercise menu provided to the user and tracking data.

[1806] (Application example 2)

[1807] 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."

[1808] In recent years, interest in health management has grown, creating a demand for systems that can provide advice and recommendations tailored to individual users. However, existing systems tend to be limited to providing information based on physical data and lack personalized recommendations that take into account the user's emotional state. Furthermore, it has been difficult to recommend optimal products and services based on the user's health condition and emotions during the shopping experience in a physical store. Therefore, there is a demand for systems that can provide more comprehensive and personalized health management services.

[1809] 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.

[1810] In this invention, the server includes means for acquiring physical data, means for calculating the required calorie intake and amount of exercise, means for acquiring user emotion data, means for recommending products or services based on the emotion data, and means for displaying and providing audio guidance on the smart device with the recommended content. This enables personalized recommendations based on the user's health condition and emotions, and makes it possible to provide optimal products and services even in a shopping experience at a physical store.

[1811] "Physical data" refers to data related to the user's physical condition, such as the user's weight, body fat percentage, heart rate, and number of steps.

[1812] The "server" is a computer system that receives and analyzes the physical data and emotional data, calculates the required calorie intake and amount of exercise, and recommends products and services.

[1813] "Metabolic rate" is a calculated value used to calculate the user's basal metabolic rate, and indicates the basic amount of energy the user consumes in a day.

[1814] "Required calorie intake" indicates the amount of energy a user should take in per day, and is calculated based on the user's target weight and activity level.

[1815] "Voice advice" means spoken instructions or recommendations provided to the user by the system and generated using text-to-speech technology.

[1816] An "exercise menu" is a plan that indicates the exercise content that a user should do based on the user's health goals.

[1817] "Statistical data" refers to data obtained by aggregating and analyzing data collected from a large number of users, and is used as a reference for diagnosis and treatment in medical settings.

[1818] "Emotion data" is data that indicates the emotional state of the user, obtained from the user's facial expressions and voice.

[1819] "Product or service recommendation" means proposing optimal products or services to a user based on the user's physical data and emotional data.

[1820] "Smart devices" are devices that have the function of displaying information and providing voice guidance, such as smartphones, smart glasses, and head-mounted displays.

[1821] The present invention is a system that comprehensively supports users' health management, and in particular provides personalized recommendations by utilizing emotional data. The system includes physical and emotional data collection, data analysis, recommendation generation, display, and voice guidance.

[1822] System configuration

[1823] The system consists of the following main hardware and software:

[1824] Wearable tool: A device that collects health data (weight, body fat percentage, heart rate, number of steps, etc.) in real time and transmits it to a device.

[1825] Smart Device: Smart glasses are used as a device to display health and emotional data and provide voice guidance.

[1826] Server: The computer system that receives and analyzes the collected data.

[1827] Emotion recognition engine: Software that recognizes emotions from the user's facial expressions and voice.

[1828] Text-to-Speech Engine: Software that generates spoken recommendations and advice.

[1829] Recommendation engine: Software that recommends the most suitable products and services to users based on their physical and emotional data.

[1830] System Operation

[1831] Data collection

[1832] By wearing the wearable tool, the user can collect real-time health data such as weight, body fat percentage, heart rate, and number of steps. This data is transmitted to a device via smart glasses. In addition, an emotion recognition engine uses the smart glasses' camera and microphone to analyze the user's facial expressions and voice to obtain emotional data.

[1833] Data analysis and recommendation generation

[1834] The server calculates the user's metabolic rate based on the received health data, and calculates the required calorie intake and amount of exercise.The recommendation engine also considers the emotional data acquired by the emotion recognition engine and selects the most suitable products and services for the user.

[1835] Display and audio guidance of recommendations

[1836] The generated recommendation is displayed on the smart device and audibly guided using a text-to-speech engine. For example, if the user is feeling stressed, the recommendation may be displayed and spoken, such as "We recommend herbal tea for its relaxing effects."

[1837] Specific examples

[1838] For example, if a user has data showing a weight of 70 kg, a body fat percentage of 25%, and a heart rate of 80 bpm, and the emotion recognition engine detects "stress," the system operates as follows: The server receives the health data and calculates the metabolic rate. Next, the recommendation engine selects "relaxing herbal teas and aromatic oils" based on the emotion data. The smart glasses will display a message saying, "We recommend relaxing herbal teas," and the text-to-speech engine will provide a similar message via voice.

[1839] Also, when a user inputs a health care question into the generative AI model via a smart device, the following prompt can be used: "If the user is feeling stressed, please recommend products that have a relaxation effect, such as herbal tea or aromatic oil."

[1840] In this way, the system of the present invention provides personalized recommendations based on the user's health and emotional state, enhancing the shopping experience in brick-and-mortar stores.

[1841] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1842] Step 1:

[1843] Users wear a wearable tool that collects health data (weight, body fat percentage, heart rate, number of steps, etc.) in real time.

[1844] Input: Health data from wearable tools

[1845] Output: Health data sent to smart glasses

[1846] How it works: The wearable tool is worn by the user, and the built-in sensors detect data such as weight, body fat percentage, heart rate, and number of steps, and send this information to the device in real time.

[1847] Step 2:

[1848] Using the camera and microphone of the smart glasses, the emotion recognition engine analyzes the user's facial expressions and voice to obtain emotional data.

[1849] Input: User's facial expression, voice

[1850] Output: Emotion data

[1851] How it works: The smart glasses' camera captures the user's facial expressions and microphone records their voice, which are then sent to an emotion recognition engine to analyze the user's emotional state (e.g., stress, joy, anxiety, etc.).

[1852] Step 3:

[1853] The smart glasses send the collected health and emotional data to a server.

[1854] Input: Health data, emotion data

[1855] Output: Health and emotion data sent to the server

[1856] How it works: The smart glasses send health and emotion data to the server, which then receives it. The data is encrypted before transmission.

[1857] Step 4:

[1858] The server calculates the metabolic rate based on the health data received, and calculates the required calorie intake and amount of exercise.

[1859] Input: Health data

[1860] Output: metabolic rate, calorie needs, and exercise volume

[1861] How it works: The server analyzes health data and calculates metabolic rate using the Harris-Benedict equation, etc. It then calculates the required calorie intake and amount of exercise based on the user's target weight and activity level.

[1862] Step 5:

[1863] The server takes into account the emotional data, and the recommendation engine generates the most suitable products and services for the user.

[1864] Input: Emotion data, calculation results from health data

[1865] Output: Recommendation (product or service)

[1866] How it works: The server analyzes the emotional data to understand the user's current emotional state. The recommendation engine then selects products and services that will have a relaxing effect for the user based on the health and emotional data.

[1867] Step 6:

[1868] The generated recommendation content is sent to the smart device, where it is displayed and provided as audio guidance.

[1869] Input: Recommendation from the server

[1870] Output: Recommendations displayed on smart devices and audio guidance

[1871] Specific operation: The recommendation content is sent to the smart glasses and displayed on the screen. At the same time, the text-to-speech engine generates a voice prompt and delivers it to the user through the smart glasses' speaker.

[1872] In this way, the system supports more personalized health management by understanding the user's health and emotional state in detail at each step and recommending optimal products and services.

[1873] 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.

[1874] 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.

[1875] 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.

[1876] 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.

[1877] 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.

[1878] 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.

[1879] 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).

[1880] 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.

[1881] 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."

[1882] 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.

[1883] 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).

[1884] 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.

[1885] 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.

[1886] 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.

[1887] 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.

[1888] 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.

[1889] 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.

[1890] 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.

[1891] 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.

[1892] 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.

[1893] 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.

[1894] The following is further disclosed regarding the above embodiment.

[1895] (Claim 1)

[1896] A means for acquiring physical data;

[1897] means for transmitting the physiological data to a server;

[1898] a means for calculating metabolic rate;

[1899] A means for calculating required calorie intake and exercise amount;

[1900] means for generating audio advice;

[1901] A means for managing exercise menus;

[1902] A system that includes a means to generate statistical data and utilize it in medical practice.

[1903] (Claim 2)

[1904] a means for inputting dietary data;

[1905] means for transmitting the meal data to a server;

[1906] 10. The system of claim 1, further comprising means for calculating calorie and nutritional balance.

[1907] (Claim 3)

[1908] A means to generate customized reports and analytical data for corporate clients;

[1909] The system of claim 1 further comprising means for providing the report and analysis data.

[1910] "Example 1"

[1911] (Claim 1)

[1912] a means for a user to acquire physiological data;

[1913] means for transmitting the physiological data to a server by the terminal;

[1914] a means for the server to calculate metabolic rate;

[1915] A server calculates the required calorie intake and amount of exercise;

[1916] means for the server to generate audio advice;

[1917] A means for the server to manage the exercise menu;

[1918] A system including a server that generates statistical data and uses the data in medical settings.

[1919] (Claim 2)

[1920] a means for a user to input meal data;

[1921] A means for the terminal to transmit the meal data to a server;

[1922] 10. The system of claim 1, wherein the server further comprises means for calculating calories and nutritional balance.

[1923] (Claim 3)

[1924] 10. The system of claim 1, wherein the server further comprises means for generating and providing customized reports and analytical data for the business customer.

[1925] "Application Example 1"

[1926] (Claim 1)

[1927] A means for acquiring physical data;

[1928] means for transmitting the physiological data to a server;

[1929] a means for calculating metabolic rate;

[1930] A means for calculating required calorie intake and exercise amount;

[1931] means for generating audio advice;

[1932] A means for managing exercise menus;

[1933] A means of generating statistical data and utilizing it in medical practice,

[1934] A means to suggest optimal products based on the user's health data in a virtual store,

[1935] A means to virtually experience exercise equipment;

[1936] A means of tracking progress;

[1937] A system including a means for providing health advice using a generative AI model.

[1938] (Claim 2)

[1939] a means for inputting dietary data;

[1940] means for transmitting the meal data to a server;

[1941] a means for calculating calories and nutritional balance;

[1942] 10. The system of claim 1, further comprising means for providing dietary feedback using a generative AI model.

[1943] (Claim 3)

[1944] A means to generate customized reports and analytical data for corporate clients;

[1945] means for providing said reports and analytical data;

[1946] 10. The system of claim 1, further comprising means for managing statistical data of health-related products in the virtual store.

[1947] "Example 2: Combining Emotion Engines"

[1948] (Claim 1)

[1949] A means for acquiring physical data;

[1950] means for transmitting the physiological data to a server;

[1951] a means for calculating metabolic rate;

[1952] A means for calculating required calorie intake and exercise amount;

[1953] means for generating audio advice;

[1954] a means for collecting and analyzing emotion data;

[1955] means for adjusting the content and tone of the voice advice based on the emotion data;

[1956] A means for generating and adjusting an exercise menu;

[1957] A system that includes a means to generate statistical data and utilize it in medical practice.

[1958] (Claim 2)

[1959] a means for inputting dietary data;

[1960] means for transmitting the meal data to a server;

[1961] 10. The system of claim 1, further comprising means for calculating calorie and nutritional balance.

[1962] (Claim 3)

[1963] A means to generate customized reports and analytical data for corporate clients; ...

Claims

1. A means for acquiring physical data; means for transmitting the physiological data to a server; a means for calculating metabolic rate; A means for calculating required calorie intake and exercise amount; means for generating audio advice; A means for managing exercise menus; A system including a means for generating statistical data and utilizing it in medical settings.

2. a means for inputting dietary data; means for transmitting the meal data to a server; 10. The system of claim 1, further comprising means for calculating calorie and nutritional balance.

3. A means to generate customized reports and analytical data for corporate clients; The system of claim 1 further comprising means for providing said report and analysis data.

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