system

Abstract

Provide a system. A means for users to scan their body type and input basic information; A means for the device to transmit user data to a server; A means by which the server analyzes the data and compares it with other databases; A means for the server to generate meal menus, exercise plans, and mental care advice; a means for the terminal to provide the generated plan to the user; The user inputs progress data, and the server analyzes the data and adjusts the plan. A system including:

Description

[Technical Field]

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

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

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

[0004] In modern society, many people find it difficult to manage their health and diet in their busy daily lives. It is particularly difficult to find an appropriate diet menu and exercise method that suits each individual's body type and health condition. Furthermore, lack of mental care during the dieting process often leads to discouragement. The objective of this invention is to provide a system that provides optimal health management plans for individual users and supports effective dieting without straining them. [Means for solving the problem]

[0005] The present invention provides a means for users to scan their body shape and send the collected data and basic information to a server. The server analyzes the data and compares it with other databases to generate a meal menu, exercise plan, and mental health advice tailored to each individual user. The device provides the generated plan to the user, and the user inputs progress data, which the server analyzes and adjusts accordingly. This system makes it possible to provide a comprehensive health management plan tailored to each user's individual needs.

[0006] "User" refers to an individual who uses this system for the purpose of managing their own health or dieting.

[0007] "Body scanning" refers to the act of digitizing the shape and dimensions of a user's body, thereby generating a highly accurate 3D model.

[0008] "Basic information" refers to personal data entered by the user, such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, bone structure, etc.

[0009] "Terminal" refers to an electronic device used by a user, such as a smartphone or tablet, which is a means for inputting and outputting data to the system.

[0010] "Server" means a central computer system for processing, storing, and analyzing data submitted by users.

[0011] "Data analysis" is the act of processing collected user data using computational models and algorithms to extract useful information.

[0012] A "database" is a repository where collected and analyzed data is stored and organized in a way that allows it to be collated with other data.

[0013] A "meal menu" is a plan that specifies the contents of a user's daily meals, and is created taking into consideration an individual's basal metabolic rate and nutritional balance.

[0014] An "exercise plan" is a plan that instructs the user on the recommended exercise content, as well as the duration and frequency of the exercise.

[0015] "Mental health advice" refers to suggestions and advice to support the user's mental health, including stress management and relaxation techniques.

[0016] "Providing a plan" refers to the act of delivering the generated meal menu, exercise plan, and mental care advice to the user and making them available for viewing.

[0017] "Progress data" refers to data that records the user's daily exercise, diet, weight fluctuations, physical condition, and other performance data.

[0018] "Adjusting the plan" refers to the act of improving or changing the original plan as appropriate based on progress data from the user. [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] This invention is a system that allows users to scan their own body shape and effectively manage their health and diet. The system analyzes the user's data and compares it with other databases to provide individualized meal menus, exercise plans, and mental health advice.

[0041] Overall system overview

[0042] 1. Obtaining user data

[0043] The user installs and launches a dedicated application on their smartphone or tablet, scans their body shape, and acquires image data.

[0044] The user enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise amount, sleep time, constitution, and bone structure into a form.

[0045] 2. Data transmission and storage

[0046] The device encrypts the scanned body data and basic information and sends it to a server using a secure communication protocol.

[0047] The server anonymizes the data it receives and stores it in a secure database.

[0048] 3. Data analysis and collation

[0049] The server analyzes the user data and extracts important features using a feature extraction algorithm.

[0050] The server searches for similar data in the database and identifies the right plan for the user based on success and failure.

[0051] 4. Generate a plan

[0052] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise.

[0053] The server generates a nutritionally balanced meal menu and suggests it to the user.

[0054] The server generates a safe and effective exercise plan (exercise type, exercise time, exercise frequency) and recommends it to the user.

[0055] The server will create a comprehensive plan that includes advice on mental care such as stress management.

[0056] 5. Offering of Plans

[0057] The server sends the generated plan to the terminal, and the user can receive and confirm it.

[0058] 6. Track and adjust progress

[0059] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[0060] The device encrypts the new data and sends it to the server.

[0061] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed.

[0062] Specific examples

[0063] 1. Obtaining user data

[0064] For example, a 30-year-old woman who is 160 cm tall and weighs 65 kg launches a dedicated app and scans her body shape.

[0065] The user enters information into the form, including basal body temperature of 36.5°C, basal metabolic rate of 1400 kcal, low daily exercise, 7 hours of sleep, lipid-rich body type, and bone mass.

[0066] 2. Data transmission and storage

[0067] The scan results and basic information are encrypted and sent to the server.

[0068] The server anonymizes the received data and stores it securely in a database.

[0069] 3. Data analysis and collation

[0070] The server analyzes user data and extracts features such as basal metabolic rate and physical constitution.

[0071] The server searches the database for similar data and identifies successful cases (plans that have been successful for people with similar body types and constitutions).

[0072] 4. Generate a plan

[0073] The server takes into account the basal metabolic rate of 1400 kcal and sets the daily calorie intake target at 1700 kcal.

[0074] Suggest oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[0075] It is recommended to walk three times a week and do light strength training twice a week.

[0076] Also includes mental health advice on yoga and meditation.

[0077] 5. Offering of Plans

[0078] The generated plan is sent to the terminal, where the user can confirm and execute it.

[0079] 6. Track and adjust progress

[0080] Users enter their exercise routine and dietary habits into the app every day.

[0081] The server analyzes the new data and tweaks the plan as needed.

[0082] For example, if weight loss is too rapid, adjustments can be made, such as increasing calorie intake.

[0083] The system provides health management and diet plans optimized for each individual user.

[0084] The processing flow will be explained below.

[0085] Step 1:

[0086] The user launches the app and scans their body.

[0087] The device uses a camera to 3D scan the user's body and generate a highly accurate 3D model.

[0088] Step 2:

[0089] The user enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise amount, sleep time, constitution, and bone structure into a form.

[0090] Step 3:

[0091] The device encrypts the collected body shape data and basic information and transmits it to a server using a secure communication protocol.

[0092] Step 4:

[0093] The server anonymizes the data it receives and stores it in a secure database.

[0094] Step 5:

[0095] The server analyzes the user data and extracts important features using a feature extraction algorithm.

[0096] Step 6:

[0097] The server searches for similar data in the database and identifies the right plan for the user based on success and failure.

[0098] Step 7:

[0099] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise.

[0100] Step 8:

[0101] The server generates a nutritionally balanced meal menu and suggests it to the user.

[0102] Step 9:

[0103] The server generates a safe and effective exercise plan (exercise type, exercise time, exercise frequency) and recommends it to the user.

[0104] Step 10:

[0105] The server will create a comprehensive plan that includes advice on mental care such as stress management.

[0106] Step 11:

[0107] The server sends the generated plan to the terminal, and the user can receive and confirm it.

[0108] Step 12:

[0109] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[0110] Step 13:

[0111] The device encrypts the new data and sends it to the server.

[0112] Step 14:

[0113] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed.

[0114] Example 1

[0115] 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."

[0116] The present invention relates to a system that allows users to scan their own body shape and effectively manage their health and diet. Conventional health management systems often lack security and accuracy in the acquisition, transmission, storage, and analysis of user data. They also lack effective methods for providing individual users with optimized meal menus, exercise plans, and mental health advice. This makes it difficult for users to implement and maintain their health management and diet plans.

[0117] 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.

[0118] In this invention, the server includes: [means for the user to acquire body type data and input basic information;] [means for the terminal to encrypt the user's data and send it to the server; and [means for the server to anonymize the received data and store it in a secure database.] This improves the safety and accuracy of the data, making it possible to provide health management and diet plans specific to the user.

[0119] "User" refers to an individual who obtains body data and enters basic information.

[0120] "Body shape data" refers to image data and dimensional data obtained by scanning the user's body shape.

[0121] "Basic information" refers to data entered by the user, such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure.

[0122] "Terminal" refers to a computing device used by a User, such as a smartphone or tablet.

[0123] "Encryption" refers to the process of transforming data using encryption technology, such as the Advanced Encryption Standard (AES), to protect the data.

[0124] "Server" refers to a computer system that receives, analyzes, and stores data submitted by users.

[0125] "Anonymization" refers to the process of removing personally identifiable information from received data.

[0126] "Database" means a secure information storage system for storing received data.

[0127] "Feature extraction" refers to the process by which the server identifies and extracts important elements from user data.

[0128] "Meal Menu" refers to a nutritionally balanced meal plan suggested to the user.

[0129] An "exercise plan" refers to an exercise plan that includes safe and effective exercise types, exercise duration, and exercise frequency.

[0130] "Mental health advice" refers to advice to support stress management and psychological well-being.

[0131] "Progress data" refers to data entered by the user daily, such as exercise status, dietary habits, weight fluctuations, and physical condition.

[0132] This invention is a system that allows users to scan their own body shape and effectively manage their health and diet. The system analyzes the user's data and compares it with other databases to provide individualized meal menus, exercise plans, and mental health advice.

[0133] User actions

[0134] 1. Installing the dedicated application

[0135] The user installs and launches a dedicated application on their smartphone or tablet.

[0136] 2. Scan your body and enter basic information

[0137] The user follows the instructions in the application to scan their body shape using the camera and capture image data.

[0138] Next, enter basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure into the form within the app.

[0139] Device operation

[0140] 3. Data Encryption and Transmission

[0141] The body shape data and basic information acquired by the device are encrypted using an encryption protocol such as AES (Advanced Encryption Standard).

[0142] The encrypted data is sent to the server via the HTTPS protocol.

[0143] Server Operations

[0144] 4. Data anonymization and storage

[0145] The server removes any personally identifiable information from the received data and performs anonymization processing.

[0146] The anonymized data is stored in a secure database (e.g., AWS® RDS).

[0147] 5. Data analysis and feature extraction

[0148] The server extracts features from the user data using Python's scikit-learn library, for example, by using principal component analysis (PCA) to identify important features.

[0149] The server searches for similar data in the database and identifies a plan that is suitable for the user based on success and failure.

[0150] 6. Generate a plan

[0151] The server calculates the target calorie intake (e.g., 1700 kcal per day) based on the user's basal metabolic rate (e.g., 1400 kcal) and activity level.

[0152] The server generates a nutritionally balanced meal menu, suggesting oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[0153] The server generates a safe and effective exercise plan, recommending, for example, three days of walking and two days of light strength training per week.

[0154] The server will create a comprehensive plan that includes advice on stress management and mental health, including yoga and meditation as examples.

[0155] Plan Offerings

[0156] 7. Submit the generated plan

[0157] The server sends the generated plan to the terminal so that the user can receive and check it.

[0158] Track and adjust progress

[0159] 8. Enter and submit new data

[0160] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[0161] The device encrypts the new data and sends it to the server.

[0162] 9. Analyze progress data and adjust plans

[0163] The server analyzes the progress data and adjusts the diet and exercise plan as needed, for example, increasing calorie intake if weight loss is too rapid.

[0164] Prompt Sentence Examples

[0165] "A 30-year-old woman, 160cm tall, 65kg weight, basal metabolic rate of 1400kcal, low daily exercise, lipid-rich body. Please suggest the best meal menu and exercise plan for this user."

[0166] "A user is walking three times a week and doing light strength training twice a week, but is losing weight rapidly. Could you please advise how to adjust their calorie intake?"

[0167] This system makes it possible to provide users with optimized health management and diet plans, enabling effective health management.

[0168] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0169] Step 1:

[0170] The user obtains body shape data and enters basic information

[0171] The user installs a dedicated application on a smartphone or tablet and launches it. The input is the launch of the app.

[0172] The user scans their body using a camera and obtains image data. The user then enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure into a form within the app. The obtained body data and basic information are then output.

[0173] Step 2:

[0174] The device encrypts and transmits data

[0175] The device encrypts the body shape data and basic information acquired from the user using encryption technology such as the AES protocol. The input is the body shape data and basic information. The encrypted data is then output.

[0176] The device sends the encrypted data to the server using the HTTPS protocol. The sent encrypted data is output.

[0177] Step 3:

[0178] The server anonymizes and stores the data

[0179] The server decrypts the received encrypted data and performs an anonymization process to remove any personally identifiable information. The input is the encrypted data. The output is anonymized data.

[0180] The server stores the anonymized data in a secure database. The stored data is then output.

[0181] Step 4:

[0182] The server analyzes the data and extracts features

[0183] The server extracts features from user data using Python's scikit-learn library or similar. The input is anonymized data. Specifically, it uses principal component analysis (PCA) to identify important features. The resulting extracted features are output.

[0184] The server searches for similar data in a database and runs SQL queries to identify successes and failures, which results in the output of similar data.

[0185] Step 5:

[0186] The server generates a plan

[0187] The server calculates the target calorie intake based on the user's basal metabolic rate (e.g., 1400 kcal) and activity level, and generates a meal menu. The extracted features are used as input. As a specific example, it suggests oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner. The generated meal menu is then output.

[0188] The server generates a safe and effective exercise plan. For example, it recommends walking three times a week and light strength training twice a week. The generated exercise plan is then output.

[0189] The server creates a comprehensive plan that includes advice on stress management and mental health. For example, it adds advice on yoga and meditation. This outputs a comprehensive plan.

[0190] Step 6:

[0191] The server sends the generated plan to the device.

[0192] The server sends the generated plan to the terminal. The generated plan is taken as input. The transmitted plan is output.

[0193] Step 7:

[0194] The user enters progress data and sends it to the server

[0195] The user inputs data such as daily exercise status, dietary habits, weight fluctuations, and physical condition into the application. Progress data is input, and the input progress data is output.

[0196] The device encrypts this data using protocols such as AES and sends it to the server, which then outputs the encrypted progress data.

[0197] Step 8:

[0198] The server analyzes the progress data and adjusts the plan

[0199] The server analyzes the progress data and adjusts the diet and exercise plan as needed. For example, if weight loss is too rapid, it may increase calorie intake. The input is the progress data sent. The output is an adjusted plan.

[0200] (Application example 1)

[0201] 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."

[0202] In today's security environment, ensuring the safety of personal information is a critical issue, especially in authentication processes that require high security. Traditional passwords and simple two-step authentication are often insufficient. Furthermore, there is a growing need to effectively utilize health management and diet data while safely managing and using that data effectively. Against this background, there is a demand for a system that can perform health management and authentication processes in one place, while providing a higher level of security by using personal health information for security authentication.

[0203] 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.

[0204] In this invention, the server includes a means for the user to scan their body type and input basic information, a means for the terminal to send the user's data to the server, and a means for the server to analyze the data and compare it with other databases. This enables high-security authentication and the provision of comprehensive health management plans.

[0205] "Means for users to scan their body and enter basic information" refers to the interface and device configuration that allows users to use a digital device to scan their body and enter basic health information such as height, weight, and blood pressure.

[0206] "Means for the device to transmit user data to a server" refers to the functionality and protocols that allow the digital device to transmit acquired body shape data and health information to a remote server using a secure communications protocol.

[0207] "Means for the server to analyze data and compare it with other databases" refers to software and algorithms that the server uses to analyze the user data it receives and compare it with existing databases.

[0208] The "means by which the server generates meal menus, exercise plans, and mental care advice" refers to an algorithm that automatically generates meal menus, exercise plans, and mental care advice appropriate for the user based on the data analyzed by the server.

[0209] "Means for the terminal to provide the generated plan to the user" refers to a digital device and interface for providing the server-generated plan so that the user can access it.

[0210] "Means for users to input progress data and for the server to analyze that data and adjust the plan" refers to a function / system that allows users to input their daily progress data (exercise history, diet history, weight fluctuations, etc.) and for the server to analyze that data and optimize the plan.

[0211] "Means for utilizing body scan and health data as security authentication" refers to systems and methods for using a user's body scan data and health information as part of security authentication.

[0212] "Means for encrypting user health data and transmitting it to the server" refers to protocols and algorithms for securely protecting user health data using encryption technology and transmitting that data to the server.

[0213] "Means for the server to analyze received data, perform authentication, and transmit the result to the device" refers to a system and method for the server to analyze received user data, generate an authentication result, and transmit the result to the user's digital device.

[0214] The present invention is a system that uses a user's body shape data and health information to provide high-security authentication and comprehensive health management plans.

[0215] The system is configured as follows:

[0216] 1. Obtaining user data

[0217] The user uses a digital device (e.g., a smartphone) to scan their body shape and input their health information. The smartphone's camera is used to scan the user's body shape and input basic health information such as height, weight, blood pressure, and basal body temperature. This allows the user to obtain basic data.

[0218] 2. Data transmission and storage

[0219] The data is securely transmitted to the server using encryption protocols (such as TLS / SSL) on the device side. The server anonymizes the received data and stores it in a secure database. This anonymization process protects the user's privacy.

[0220] 3. Data analysis and collation

[0221] The server analyzes the data sent by the user using a specific algorithm to extract features, then compares it with existing data in the database to identify data with high similarity, enabling effective plan generation and highly accurate security authentication.

[0222] 4. Authentication and Plan Generation

[0223] After feature extraction and matching are complete, the server automatically generates a meal menu, exercise plan, and mental health advice tailored to the user. It also generates a token required for security authentication, allowing users to authenticate and manage their health through a single application.

[0224] 5. Offering and Certifying the Plan

[0225] The generated plan and authentication token are sent to the user's device, where they can be reviewed and executed, and security access is granted or denied based on the authentication result.

[0226] 6. Tracking and adjusting progress

[0227] Users enter their daily progress data (e.g., exercise history, diet history, weight fluctuations, etc.) into the device. The device then re-encrypts this data and sends it to the server. The server analyzes the new data and fine-tunes the plan as needed. This ensures that users receive ongoing, appropriate support.

[0228] (Example)

[0229] For example, a 30-year-old female user enters basic information into a smartphone app, such as height 160 cm, weight 65 kg, basal metabolic rate 1400 kcal, low daily exercise, sleep time 7 hours, lipid-rich body, and bone mass. This data is encrypted and sent to the server for analysis. The server then generates and recommends breakfast, lunch, and dinner menus to the user, taking into account the basal metabolic rate of 1400 kcal and setting a daily calorie intake goal of 1700 kcal. The generated token is also sent to the user for security authentication.

[0230] (Example of a prompt)

[0231] Please explain the two-factor authentication smartphone application that uses a body scanning app. Please include the process of acquiring body data and basic health data, encrypting it, sending it to a server, and analyzing the data for authentication.

[0232] To implement this invention, hardware and software such as a smartphone, a secure communication protocol (TLS / SSL), an image processing library (OpenCV), and a data encryption library (cryptography) are required.

[0233] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0234] Step 1:

[0235] Users scan their body type and enter basic information.

[0236] Input: Body image data obtained using a smartphone camera, and basic information such as height, weight, blood pressure, and basal body temperature entered by the user into a form.

[0237] Output: The user dataset before encryption.

[0238] How it works: Users install and launch the app on their smartphone. They use the smartphone's camera to scan their body and obtain image data. They then enter basic information such as height and weight into a form within the app.

[0239] Step 2:

[0240] The device sends the user's data to the server.

[0241] Input: User dataset (image data + basic information) before encryption.

[0242] Output: The encrypted user dataset.

[0243] What it does: Encrypts data on the device using the SSL / TLS protocol. Sends the encrypted data to the server, typically via an HTTP POST request.

[0244] Step 3:

[0245] The server anonymizes the data it receives and stores it in a database.

[0246] Input: The encrypted user dataset.

[0247] Output: Anonymized user data stored in a database.

[0248] What it does: The server receives the data, decrypts it, uses an anonymization algorithm to remove any personally identifiable information, and stores it in a secure database.

[0249] Step 4:

[0250] The server analyzes the user data and compares it with other databases.

[0251] Input: Anonymized user data.

[0252] Output: The collated dataset.

[0253] How it works: The server uses a data analysis algorithm (e.g., a machine learning model) to extract features from user data, then compares it with existing databases to identify data with high similarity.

[0254] Step 5:

[0255] The server generates meal menus, exercise plans, and mental care advice.

[0256] Input: The collated dataset.

[0257] Output: User-optimized meal menu, exercise plan, and mental health advice.

[0258] How it works: The server automatically generates meal plans and exercise plans based on the user's basal metabolic rate and physical constitution. The plans are designed to include the appropriate nutrients and exercise levels for the user. They also include advice on yoga and meditation for mental health.

[0259] Step 6:

[0260] The device provides the generated plan and security authentication token to the user.

[0261] Input: The plan and authentication token generated by the server.

[0262] Output: The plan and authentication token displayed on the user's device.

[0263] Specific operation: The server sends the generated plan and security authentication token to the device. The user can receive and check this information through the application.

[0264] Step 7:

[0265] Users input progress data, and the server analyzes the data and adjusts the plan.

[0266] Input: Data such as the user's daily exercise history, diet history, and weight fluctuations.

[0267] Output: Tailored meal plans, exercise plans, and mental health advice.

[0268] How it works: Users enter their daily progress data into the app. The device encrypts this data and sends it to the server. The server analyzes the new data, adjusts the plan if necessary, and presents it to the user again.

[0269] 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.

[0270] This invention is a system that scans a user's body shape and provides effective health management and dieting. The system analyzes the user's data and compares it with other databases to provide meal menus, exercise plans, and mental care advice tailored to each individual user. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it achieves more detailed mental care and plan adjustments.

[0271] Overall system overview

[0272] 1. Obtaining user data

[0273] The user installs and launches a dedicated application on their smartphone or tablet, scans their body shape, and acquires image data.

[0274] The user enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise amount, sleep time, constitution, and bone structure into a form.

[0275] 2. Data transmission and storage

[0276] The device encrypts the scanned body data and basic information and sends it to a server using a secure communication protocol.

[0277] The server anonymizes the data it receives and stores it in a secure database.

[0278] 3. Data analysis and collation

[0279] The server analyzes the user data and extracts important features using a feature extraction algorithm.

[0280] The server searches for similar data in the database and identifies the right plan for the user based on success and failure.

[0281] 4. Generate a plan

[0282] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise.

[0283] The server generates a nutritionally balanced meal menu and suggests it to the user.

[0284] The server generates a safe and effective exercise plan (exercise type, exercise time, exercise frequency) and recommends it to the user.

[0285] The server will create a comprehensive plan that includes advice on mental care such as stress management.

[0286] 5. Offering of Plans

[0287] The server sends the generated plan to the terminal, and the user can receive and confirm it.

[0288] 6. Track and adjust progress

[0289] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[0290] The device encrypts the new data and sends it to the server.

[0291] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed.

[0292] 7. Emotion Recognition with Emotion Engine

[0293] The user uses the emotion engine to obtain emotion data for that day from voice, facial expressions, text data, etc.

[0294] The device collects emotional data, encrypts it, and sends it to a server.

[0295] The server analyzes the emotional data and adjusts mental care advice.

[0296] 8. Adjust your plan based on sentiment data

[0297] The server further adjusts the meal menu and exercise plan based on the user's emotional state.

[0298] For example, if a user is feeling stressed, it will suggest foods that will reduce stress or an exercise plan to help them relax.

[0299] Specific examples

[0300] 1. Obtaining user data

[0301] For example, a 30-year-old woman who is 160 cm tall and weighs 65 kg launches a dedicated app and scans her body shape.

[0302] The user enters information into the form, including basal body temperature of 36.5°C, basal metabolic rate of 1400 kcal, low daily exercise, 7 hours of sleep, lipid-rich body type, and bone mass.

[0303] 2. Data transmission and storage

[0304] The scan results and basic information are encrypted and sent to the server.

[0305] The server anonymizes the received data and stores it securely in a database.

[0306] 3. Data analysis and collation

[0307] The server analyzes user data and extracts features such as basal metabolic rate and physical constitution.

[0308] The server searches the database for similar data and identifies successful cases (plans that have been successful for people with similar body types and constitutions).

[0309] 4. Generate a plan

[0310] The server takes into account the basal metabolic rate of 1400 kcal and sets the daily calorie intake target at 1700 kcal.

[0311] Suggest oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[0312] It is recommended to walk three times a week and do light strength training twice a week.

[0313] Also includes mental health advice on yoga and meditation.

[0314] 5. Offering of Plans

[0315] The generated plan is sent to the terminal, where the user can confirm and execute it.

[0316] 6. Track and adjust progress

[0317] Users enter their exercise routine and dietary habits into the app every day.

[0318] The server analyzes the new data and tweaks the plan as needed.

[0319] For example, if weight loss is too rapid, adjustments can be made, such as increasing calorie intake.

[0320] 7. Emotion Recognition with Emotion Engine

[0321] For example, a user can write a diary entry into the app by voice, and the emotion engine will determine the user's emotional state from the tone and content of the voice.

[0322] If an emotion such as anger or sadness is detected, the data is sent to a server.

[0323] 8. Adjust your plan based on sentiment data

[0324] The server analyzes the emotional data and provides advice to users who are feeling stressed, such as relaxing meals, light exercise, or meditation.

[0325] Specifically, we will suggest herbal teas that are effective in reducing stress and stretching methods for relaxation.

[0326] Thus, the present invention is a system that comprehensively manages the user's health condition and provides an optimal plan that also takes into account the user's emotional state.

[0327] The processing flow will be explained below.

[0328] Step 1:

[0329] The user launches the app and scans their body.

[0330] The device uses a camera to 3D scan the user's body and generate a highly accurate 3D model.

[0331] Step 2:

[0332] The user enters basic information (height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, bone structure, etc.) into a form.

[0333] Step 3:

[0334] The device encrypts the collected body data and basic information and transmits it to a server using a secure communication protocol.

[0335] Step 4:

[0336] The server anonymizes the data it receives and stores it in a secure database.

[0337] Step 5:

[0338] The server analyzes the user data and extracts important features using a feature extraction algorithm.

[0339] Step 6:

[0340] The server searches for similar data in the database and identifies the right plan for the user based on success and failure.

[0341] Step 7:

[0342] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise.

[0343] Step 8:

[0344] The server generates a nutritionally balanced meal menu and suggests it to the user.

[0345] Step 9:

[0346] The server generates a safe and effective exercise plan (exercise type, exercise time, exercise frequency) and recommends it to the user.

[0347] Step 10:

[0348] The server will create a comprehensive plan that includes advice on mental care, such as stress management.

[0349] Step 11:

[0350] The server sends the generated plan to the terminal, and the user can receive and confirm it.

[0351] Step 12:

[0352] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[0353] Step 13:

[0354] The device encrypts the new data and sends it to the server.

[0355] Step 14:

[0356] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed.

[0357] Step 15:

[0358] The user utilizes the emotion engine to obtain emotion data for that day from voice, facial expression, text data, etc.

[0359] Step 16:

[0360] The device collects emotional data, encrypts it, and sends it to a server.

[0361] Step 17:

[0362] The server analyzes the emotional data and adjusts mental care advice.

[0363] Step 18:

[0364] The server further adjusts the meal menu and exercise plan based on the emotional data.

[0365] For users who feel stressed, the server will suggest foods that will reduce stress and exercise plans that will help them relax.

[0366] Specific examples

[0367] Step 1:

[0368] For example, a 30-year-old woman who is 160 cm tall and weighs 65 kg launches a dedicated app and scans her body shape.

[0369] The device uses a camera to digitize the user's body shape and generate a 3D model.

[0370] Step 2:

[0371] The user enters basic information such as basal body temperature of 36.5°C, basal metabolic rate of 1400 kcal, low daily exercise, 7 hours of sleep, lipid-rich constitution, and bone mass.

[0372] Step 3:

[0373] The device encrypts the scan results and basic information and sends them to the server.

[0374] Step 4:

[0375] The server anonymizes the data it receives and stores it in a secure database.

[0376] Step 5:

[0377] The server analyzes user data and extracts features such as basal metabolic rate and physical constitution.

[0378] Step 6:

[0379] The server searches for similar data in a database and identifies an appropriate plan based on successes and failures.

[0380] Step 7:

[0381] The server takes into account the basal metabolic rate of 1400 kcal and sets the daily calorie intake target at 1700 kcal.

[0382] Step 8:

[0383] The server generates a meal menu that suggests oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[0384] Step 9:

[0385] The server generates an exercise plan that recommends walking three times a week and light strength training twice a week.

[0386] Step 10:

[0387] The server will create a comprehensive plan that includes mental care advice such as yoga and meditation.

[0388] Step 11:

[0389] The generated plan is sent to the terminal and confirmed by the user.

[0390] Step 12:

[0391] Users enter their exercise routine and dietary habits into the app every day.

[0392] Step 13:

[0393] The device encrypts the new data and sends it to the server.

[0394] Step 14:

[0395] The server analyzes progress data and adjusts the plan as needed, for example if weight loss is too rapid.

[0396] Step 15:

[0397] For example, the same user can enter their diary entry into the app by voice, and the emotion engine will recognize emotions such as anger or sadness from the tone and content of their voice.

[0398] Step 16:

[0399] The device collects emotional data, encrypts it, and sends it to a server.

[0400] Step 17:

[0401] The server analyzes the emotional data and suggests stress-reducing advice to users who are feeling stressed.

[0402] Step 18:

[0403] Based on the emotional data, the server can make additional suggestions, such as herbal tea that reduces stress or yoga stretches to help you relax.

[0404] Thus, the present invention is a system that comprehensively manages the user's health condition and provides an optimal plan that also takes into account the user's emotional state.

[0405] Example 2

[0406] 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."

[0407] Health management and dieting have become important themes in modern society. However, many users find it difficult to accurately grasp their own body shape and health condition, and are unable to receive appropriate meal menus, exercise plans, or mental care advice. Furthermore, there are few systems that provide comprehensive health support that takes emotional state into account, meaning that many users are unable to receive appropriate healthcare. This makes effective health management difficult, and presents challenges for maintaining sustainable health.

[0408] 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.

[0409] In this invention, the server includes: [means for anonymizing received data and storing it in a secure database]; [means for extracting features of user data and searching for data with high similarity]; and [means for acquiring and analyzing user emotional data.] This makes it possible to comprehensively assess the user's health and emotional state and provide optimal meal menus, exercise plans, and mental care advice.

[0410] "User" refers to a person who provides their body data and basic information using a dedicated application.

[0411] "Terminal" refers to equipment that collects user data and transmits it to a server using encryption technology. Specifically, this includes devices such as smartphones and tablets.

[0412] "Server" refers to a computer system that receives data sent by users and performs anonymization processing, storage, analysis, collation, and plan generation.

[0413] "Body scanning means" refers to the imaging device and image analysis technology used by a user to obtain their body data.

[0414] "Basic information" refers to basic biometric information about the user, such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and skeleton.

[0415] "Encryption Technology" means technology used to keep data confidential. Examples include AES encryption.

[0416] "Anonymization" refers to techniques that process data so that individuals cannot be identified. Examples include k-Anonymity.

[0417] "Database" refers to a system for storing and managing structured data, including Amazon RDS.

[0418] "Features" refer to important information that machine learning models extract from data, allowing for a more accurate understanding of the attributes and state of the data.

[0419] "Meal Menu" refers to a meal plan suggested based on the user's nutritional balance and health goals.

[0420] "Exercise Plan" refers to an exercise plan suggested based on the user's physical abilities and health goals.

[0421] "Mental health advice" refers to advice or activities offered to support a user's mental health, such as yoga or meditation.

[0422] "Emotion engine" refers to technology that analyzes a user's emotional state from their voice, facial expressions, and text data.

[0423] A "protocol" refers to a set of defined rules or procedures for transmitting data. An example is HTTPS.

[0424] A "machine learning algorithm" refers to a computational method for analyzing data and extracting features.

[0425] The present invention provides a system that effectively supports health management and dieting based on a user's body type data and personal information. This system provides the user with an optimal health plan through a series of processes including body type scanning by the user, data transmission, data analysis by the server, and plan generation and provision.

[0426] 1. Obtaining user data

[0427] First, the user installs and launches a dedicated application on their smartphone or tablet. We will use "MyHealthManager" as an example of this application. Next, the user scans their body shape using the app's camera function, and acquires image data. The OpenCV image processing library is used for this body shape scan.

[0428] In addition, users can use the form within the app to enter basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure.

[0429] 2. Data transmission and storage

[0430] The device encrypts the body scan data and input information obtained from the user using AES encryption technology. The encrypted data is sent to the server using the HTTPS protocol. Industry-standard encryption and communication protocols are used to ensure secure communication.

[0431] The server anonymizes the received data using an anonymization algorithm (specifically, k-Anonymity) and stores it in a secure database (e.g., Amazon RDS).

[0432] 3. Data analysis and collation

[0433] The server analyzes the stored user data using machine learning libraries (e.g., Scikit-learn) to extract features related to the user's health and body shape. These features are then matched with similar data in a database to identify successful and unsuccessful cases.

[0434] 4. Plan Generation and Delivery

[0435] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise. For example, if the basal metabolic rate is 1400 kcal, the target calorie intake is set to 1700 kcal. The server then generates a meal menu that takes nutritional balance into consideration. For example, it might suggest oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[0436] The server also generates and provides users with exercise plans, such as walking three times a week and light strength training twice a week, and mental health advice, including yoga and meditation.

[0437] The generated plan is sent to the terminal, where the user can review and execute the plan.

[0438] Specific examples

[0439] For example, consider a 30-year-old woman who is 160 cm tall and weighs 65 kg. She has a basal body temperature of 36.5°C, a basal metabolic rate of 1,400 kcal, little daily exercise, seven hours of sleep, a lipid-rich constitution, and a medium-bone structure. She launches the app, scans her body type, and enters this basic information into a form.

[0440] For this user, the server generates the following plan:

[0441] Meal plan: Oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[0442] Exercise plan: Walking 3 times a week and light strength training 2 times a week

[0443] Mental care advice: Yoga and meditation

[0444] Prompt Sentence Examples

[0445] Below is an example of a prompt sentence to input to the generative AI model.

[0446] "We have a 30-year-old female user who is 160cm tall and weighs 65kg. Her basal body temperature is 36.5°C, her basal metabolic rate is 1400kcal, she exercises little, she sleeps 7 hours a day, she has a lipid-rich constitution, and she has a medium-sized bone. Please provide her with a daily meal plan, exercise plan, and mental health advice that is appropriate for her."

[0447] This allows the system to comprehensively manage the user's health status and provide an optimal plan that also takes into account their emotional state.

[0448] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0449] Step 1:

[0450] The user installs and launches the dedicated application "MyHealthManager" on their smartphone or tablet. The application uses the camera function to scan the user's body shape. The input data is 3D body scan data, which is then processed using the image processing library OpenCV and output as body shape data.

[0451] Step 2:

[0452] The user enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure into a form within the app. The input data is basic information in text format, which is saved in the application and output as the user's basic information.

[0453] Step 3:

[0454] The device encrypts the acquired body shape data and basic user information using AES encryption technology. The input data is body shape data and basic information, and the encrypted data is output.

[0455] Step 4:

[0456] The device sends encrypted data to the server via the HTTPS protocol. The input is encrypted body shape data and basic information, and the output is the data securely sent to the server.

[0457] Step 5:

[0458] The server anonymizes the data it receives using the k-Anonymity algorithm. The input data is encrypted user data, and the output is anonymized data.

[0459] Step 6:

[0460] The server stores the anonymized data in a secure database (e.g., Amazon RDS). The input is the anonymized data, and the output is the stored database entry.

[0461] Step 7:

[0462] The server analyzes the stored user data using the machine learning library Scikit-learn and extracts features. The input is the user data in the database, and the output is the extracted features.

[0463] Step 8:

[0464] The server searches for similar data in the database and identifies successful or unsuccessful cases. The input is the extracted features, and the output is the data of the identified successful or unsuccessful cases.

[0465] Step 9:

[0466] The server calculates the target daily calorie intake based on the basal metabolic rate and daily exercise amount. The input data is the basal metabolic rate and exercise amount, and the output is the target daily calorie intake.

[0467] Step 10:

[0468] The server generates meal menus taking nutritional balance into consideration. The input is the target calorie intake and criteria for nutritional balance, and the output is meal menus for breakfast, lunch, and dinner.

[0469] Step 11:

[0470] The server generates the optimal exercise plan for the user. The input is body type data and basal metabolic rate, and the output is a weekly exercise plan.

[0471] Step 12:

[0472] The server generates advice for mental health, including yoga and meditation. The input is the user's stress level and emotional data, and the output is mental health advice.

[0473] Step 13:

[0474] The server sends the generated plan (meal menu, exercise plan, mental care advice) to the terminal. The input is the generated plan, and the output is the plan display on the terminal.

[0475] Step 14:

[0476] The user inputs data into the app, such as daily exercise, diet, weight fluctuations, and physical condition. The input is the user's daily data, and the output is updated health status data.

[0477] Step 15:

[0478] The device encrypts daily data and sends it to the server. The input is the updated health status data, and the output is the encrypted data sent to the server.

[0479] Step 16:

[0480] The server analyzes the daily data and adjusts the plan as needed. The input is the daily health data and the output is the adjusted plan.

[0481] Step 17:

[0482] The user uses the emotion engine to obtain emotion data from voice, facial expressions, and text data. The input is the user's emotion data, and the output is the analyzed emotion results.

[0483] Step 18:

[0484] The device encrypts the emotion data and sends it to the server. The input is emotion data, and the output is the encrypted emotion data sent to the server.

[0485] Step 19:

[0486] The server analyzes the emotional data and adjusts mental care advice based on the emotional state. The input is the analyzed emotional result, and the output is the adjusted mental care advice.

[0487] (Application example 2)

[0488] 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."

[0489] Conventional health management systems could obtain a user's body type and basic information and provide meal menus, exercise plans, and mental care advice based on that information, but they were unable to provide specific product suggestions or location information for products in stores. They also lacked the ability to fine-tune plans by taking into account the user's emotional state. This made it difficult for users to smoothly find the right products in stores and was unable to receive appropriate advice based on their emotional state.

[0490] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for the user to scan their body type and input basic information, a means for the terminal to send the user's data to the server, a means for the server to analyze the data and compare it with other databases, a means for the server to generate a meal menu, an exercise plan, and mental care advice, a means for the terminal to provide the generated plan to the user, a means for the user to input progress data and the server to analyze the data and adjust the plan, a means for measuring body type using a scanning device in the store, a means for providing the user with optimal food and product placement information, and a means for analyzing the user's emotional data using an emotion engine and fine-tuning the plan based on the data. This makes it easier for users to find optimal products in the store through the health management system and receive more detailed advice tailored to their emotional state.

[0491] "User" means an individual who utilizes the system to provide information for body scanning and health management.

[0492] "Body scanning" refers to using a device to measure the shape and dimensions of a user's body and capture that data in digital form.

[0493] "Basic information" refers to biological data and lifestyle information that users input into the system, including height, weight, basal metabolic rate, daily exercise, sleep duration, and physical constitution.

[0494] A "terminal" is a device that is directly operated by a user, and refers to electronic devices such as smartphones and tablets.

[0495] A "server" is a computing resource that analyzes and stores data and provides information to users, and refers to a high-performance computer located in a cloud environment or on a network.

[0496] "Analyzing the data" means processing the data submitted by the user and extracting meaningful information.

[0497] A "database" is a collection of large amounts of accumulated data that is structured and stored in a format that allows for searching and matching.

[0498] "Meal menu" refers to a combination of ingredients and dishes suggested taking into consideration the user's health condition and nutritional balance.

[0499] "Exercise plan" refers to a plan that specifically indicates the type, time, and frequency of exercise based on the user's health condition and goals.

[0500] "Mental health advice" refers to suggestions and guidance for maintaining and improving a user's mental health.

[0501] "Progress data" refers to data related to diet, exercise, weight fluctuations, physical condition, etc. that users record in their daily lives.

[0502] "In-store scanning device" refers to a device installed in a physical store that allows customers to measure their body measurements.

[0503] "Product placement information" refers to data that indicates the specific placement locations of products within a store that a user is considering purchasing.

[0504] An "emotion engine" refers to software or hardware for analyzing a user's emotional state from their voice, facial expressions, text data, etc.

[0505] "Emotion data" is digital data that indicates the user's emotional state and is analyzed by the emotion engine.

[0506] A "recommendation list" refers to a list of products or actions that are recommended for a user to purchase or perform based on their health information or emotional state.

[0507] A system for implementing this invention includes a means for a user to scan their body type and input basic information, a means for transmitting the data to a server, a means for the server to analyze the data and compare it with other databases, a means for generating a meal menu, an exercise plan, and mental care advice, a means for the user to input progress data and analyze the data, a means for measuring body type using a scanning device in a store, a means for providing optimal food and product placement information, and a means for analyzing the user's emotional data using an emotion engine and fine-tuning the plan based on the data.

[0508] The server anonymizes the data it receives and stores it in a database. This database structures the large amount of accumulated data and stores it in a format that allows for search and matching. The server also extracts features from the user data and generates meal menus, exercise plans, and mental care advice based on data with high similarity.

[0509] In this system, users first install a dedicated application on their smartphone, tablet, or other device. Next, they measure their body shape using a scanning device in the store and enter that data and basic information (e.g., height, weight, basal metabolic rate, daily exercise, sleep duration, physical constitution, etc.) into the application. This information is encrypted and sent to the server using a secure communication protocol (e.g., HTTPS).

[0510] The server receives the transmitted data and analyzes it. During the analysis, it extracts features from the user data and compares them with similar databases to generate optimal meal plans, exercise plans, and mental care advice for the user. It also uses an emotion engine to analyze the user's emotional data and fine-tune the plan as needed. The emotion engine determines the user's emotional state from their voice, facial expressions, or text data.

[0511] The generated plan is then encrypted again and sent to the user's device. The user can then use this plan to select products in the store, and specific product placement information within the store is also provided, allowing the user to efficiently find the products they need.

[0512] For example, a user visits a health food store, measures their body shape with a 3D body scanner, and then enters the results and basic information into a smartphone app. This data is sent to a cloud server for analysis. The generated plan includes recommended foods (e.g., protein bars, low-calorie snacks, etc.) and provides detailed location information for them. The app helps users find products easily, and also suggests stress-reducing foods based on their emotional state.

[0513] Examples of prompts include:

[0514] "I want to create an app that scans a user's body shape and health data and suggests food and exercise products based on their individual health status. The user scans their body shape in the store, enters the necessary data, and based on the information analyzed by the server, provides the most suitable products and their placement information."

[0515] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0516] Step 1:

[0517] The user installs a dedicated application on their smartphone or tablet and creates a profile. At this time, the user enters basic information such as height, weight, basal metabolic rate, daily exercise, sleep time, and physical constitution. The application temporarily saves the entered basic information in local storage. The entered data is used in the next step.

[0518] Step 2:

[0519] The user scans their body shape using a scanning device (e.g., a 3D body scanner) installed in the store. The scanning device captures the user's body shape data (e.g., the outer shape of the body and the dimensions of each part of the body) and sends the data to a smartphone application. The body shape data is encrypted and used in the next step.

[0520] Step 3:

[0521] The device (smartphone or tablet) encrypts the entered basic information and scanned data and sends it to a cloud server using a secure communication protocol (e.g., HTTPS). The sent data is then received by the server.

[0522] Step 4:

[0523] The server analyzes the received data. During the analysis, for example, a Python library is used to preprocess the data (fill in missing values, detect outliers) and extract features from the user data. These extracted features are then compared with other databases to search for data with high similarity. This process provides the information needed to generate meal menus and exercise plans tailored to the user.

[0524] Step 5:

[0525] The server generates optimal meal plans, exercise plans, and mental health advice for each user using machine learning models and rule-based approaches. For example, a generative AI model is used to suggest optimal meal plans that take into account the user's basal metabolic rate and daily activity level. The generated plans are encrypted.

[0526] Step 6:

[0527] The server sends the generated plan to the terminal, which then decodes the received data and displays it to the user, along with specific product location information to help the user easily find the food or product they need in the store.

[0528] Step 7:

[0529] The user selects and purchases the suggested products in the store. During this process, the application records the user's purchase history and selection trends and uses them for future suggestions. This data is temporarily stored in local storage.

[0530] Step 8:

[0531] The user enters progress data (e.g., dietary details, exercise details, weight fluctuations, physical condition, etc.) into the application. The entered data is encrypted and sent to the server, which then receives it.

[0532] Step 9:

[0533] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed. For example, it uses machine learning algorithms to fine-tune calorie intake and exercise intensity based on the user's progress data. The analysis results are then provided back to the user.

[0534] Step 10:

[0535] The user uses the emotion engine to obtain emotional data. Emotional data is collected from voice, facial expressions, text input, etc. and analyzed using voice analysis software and natural language processing models. This analyzed data is encrypted and sent to the server.

[0536] Step 11:

[0537] The server analyzes the emotional data and further adjusts mental health advice, meal plans, and exercise plans as needed. For example, if the user is feeling stressed, it will suggest foods that have a relaxing effect and light exercise. These adjustments are provided to the user on a case-by-case basis.

[0538] 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.

[0539] 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.

[0540] 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.

[0541] [Second embodiment]

[0542] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0543] 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.

[0544] 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).

[0545] 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.

[0546] 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.

[0547] 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).

[0548] 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.

[0549] 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.

[0550] 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.

[0551] 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.

[0552] 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.

[0553] 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."

[0554] This invention is a system that allows users to scan their own body shape and effectively manage their health and diet. The system analyzes the user's data and compares it with other databases to provide individualized meal menus, exercise plans, and mental health advice.

[0555] Overall system overview

[0556] 1. Obtaining user data

[0557] The user installs and launches a dedicated application on their smartphone or tablet, scans their body shape, and acquires image data.

[0558] The user enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise amount, sleep time, constitution, and bone structure into a form.

[0559] 2. Data transmission and storage

[0560] The device encrypts the scanned body data and basic information and sends it to a server using a secure communication protocol.

[0561] The server anonymizes the data it receives and stores it in a secure database.

[0562] 3. Data analysis and collation

[0563] The server analyzes the user data and extracts important features using a feature extraction algorithm.

[0564] The server searches for similar data in the database and identifies the right plan for the user based on success and failure.

[0565] 4. Generate a plan

[0566] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise.

[0567] The server generates a nutritionally balanced meal menu and suggests it to the user.

[0568] The server generates a safe and effective exercise plan (exercise type, exercise time, exercise frequency) and recommends it to the user.

[0569] The server will create a comprehensive plan that includes advice on mental care such as stress management.

[0570] 5. Offering of Plans

[0571] The server sends the generated plan to the terminal, and the user can receive and confirm it.

[0572] 6. Track and adjust progress

[0573] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[0574] The device encrypts the new data and sends it to the server.

[0575] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed.

[0576] Specific examples

[0577] 1. Obtaining user data

[0578] For example, a 30-year-old woman who is 160 cm tall and weighs 65 kg launches a dedicated app and scans her body shape.

[0579] The user enters information into the form, including basal body temperature of 36.5°C, basal metabolic rate of 1400 kcal, low daily exercise, 7 hours of sleep, lipid-rich body type, and bone mass.

[0580] 2. Data transmission and storage

[0581] The scan results and basic information are encrypted and sent to the server.

[0582] The server anonymizes the received data and stores it securely in a database.

[0583] 3. Data analysis and collation

[0584] The server analyzes user data and extracts features such as basal metabolic rate and physical constitution.

[0585] The server searches the database for similar data and identifies successful cases (plans that have been successful for people with similar body types and constitutions).

[0586] 4. Generate a plan

[0587] The server takes into account the basal metabolic rate of 1400 kcal and sets the daily calorie intake target at 1700 kcal.

[0588] Suggest oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[0589] It is recommended to walk three times a week and do light strength training twice a week.

[0590] Also includes mental health advice on yoga and meditation.

[0591] 5. Offering of Plans

[0592] The generated plan is sent to the terminal, where the user can confirm and execute it.

[0593] 6. Track and adjust progress

[0594] Users enter their exercise routine and dietary habits into the app every day.

[0595] The server analyzes the new data and tweaks the plan as needed.

[0596] For example, if weight loss is too rapid, adjustments can be made, such as increasing calorie intake.

[0597] The system provides health management and diet plans optimized for each individual user.

[0598] The processing flow will be explained below.

[0599] Step 1:

[0600] The user launches the app and scans their body.

[0601] The device uses a camera to 3D scan the user's body and generate a highly accurate 3D model.

[0602] Step 2:

[0603] The user enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise amount, sleep time, constitution, and bone structure into a form.

[0604] Step 3:

[0605] The device encrypts the collected body shape data and basic information and transmits it to a server using a secure communication protocol.

[0606] Step 4:

[0607] The server anonymizes the data it receives and stores it in a secure database.

[0608] Step 5:

[0609] The server analyzes the user data and extracts important features using a feature extraction algorithm.

[0610] Step 6:

[0611] The server searches for similar data in the database and identifies the right plan for the user based on success and failure.

[0612] Step 7:

[0613] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise.

[0614] Step 8:

[0615] The server generates a nutritionally balanced meal menu and suggests it to the user.

[0616] Step 9:

[0617] The server generates a safe and effective exercise plan (exercise type, exercise time, exercise frequency) and recommends it to the user.

[0618] Step 10:

[0619] The server will create a comprehensive plan that includes advice on mental care such as stress management.

[0620] Step 11:

[0621] The server sends the generated plan to the terminal, and the user can receive and confirm it.

[0622] Step 12:

[0623] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[0624] Step 13:

[0625] The device encrypts the new data and sends it to the server.

[0626] Step 14:

[0627] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed.

[0628] Example 1

[0629] 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."

[0630] The present invention relates to a system that allows users to scan their own body shape and effectively manage their health and diet. Conventional health management systems often lack security and accuracy in the acquisition, transmission, storage, and analysis of user data. They also lack effective methods for providing individual users with optimized meal menus, exercise plans, and mental health advice. This makes it difficult for users to implement and maintain their health management and diet plans.

[0631] 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.

[0632] In this invention, the server includes: [means for the user to acquire body type data and input basic information;] [means for the terminal to encrypt the user's data and send it to the server; and [means for the server to anonymize the received data and store it in a secure database.] This improves the safety and accuracy of the data, making it possible to provide health management and diet plans specific to the user.

[0633] "User" refers to an individual who obtains body data and enters basic information.

[0634] "Body shape data" refers to image data and dimensional data obtained by scanning the user's body shape.

[0635] "Basic information" refers to data entered by the user, such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure.

[0636] "Terminal" refers to a computing device used by a User, such as a smartphone or tablet.

[0637] "Encryption" refers to the process of transforming data using encryption technology, such as the Advanced Encryption Standard (AES), to protect the data.

[0638] "Server" refers to a computer system that receives, analyzes, and stores data submitted by users.

[0639] "Anonymization" refers to the process of removing personally identifiable information from received data.

[0640] "Database" means a secure information storage system for storing received data.

[0641] "Feature extraction" refers to the process by which the server identifies and extracts important elements from user data.

[0642] "Meal Menu" refers to a nutritionally balanced meal plan suggested to the user.

[0643] An "exercise plan" refers to an exercise plan that includes safe and effective exercise types, exercise duration, and exercise frequency.

[0644] "Mental health advice" refers to advice to support stress management and psychological well-being.

[0645] "Progress data" refers to data entered by the user daily, such as exercise status, dietary habits, weight fluctuations, and physical condition.

[0646] This invention is a system that allows users to scan their own body shape and effectively manage their health and diet. The system analyzes the user's data and compares it with other databases to provide individualized meal menus, exercise plans, and mental health advice.

[0647] User actions

[0648] 1. Installing the dedicated application

[0649] The user installs and launches a dedicated application on their smartphone or tablet.

[0650] 2. Scan your body and enter basic information

[0651] The user follows the instructions in the application to scan their body shape using the camera and capture image data.

[0652] Next, enter basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure into the form within the app.

[0653] Device operation

[0654] 3. Data Encryption and Transmission

[0655] The body shape data and basic information acquired by the device are encrypted using an encryption protocol such as AES (Advanced Encryption Standard).

[0656] The encrypted data is sent to the server via the HTTPS protocol.

[0657] Server Operations

[0658] 4. Data anonymization and storage

[0659] The server removes any personally identifiable information from the received data and performs anonymization processing.

[0660] Store the anonymized data in a secure database (e.g. AWS RDS).

[0661] 5. Data analysis and feature extraction

[0662] The server extracts features from the user data using Python's scikit-learn library, for example, by using principal component analysis (PCA) to identify important features.

[0663] The server searches for similar data in the database and identifies a plan that is suitable for the user based on success and failure.

[0664] 6. Generate a plan

[0665] The server calculates the target calorie intake (e.g., 1700 kcal per day) based on the user's basal metabolic rate (e.g., 1400 kcal) and activity level.

[0666] The server generates a nutritionally balanced meal menu, suggesting oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[0667] The server generates a safe and effective exercise plan, recommending, for example, three days of walking and two days of light strength training per week.

[0668] The server will create a comprehensive plan that includes advice on stress management and mental health, including yoga and meditation as examples.

[0669] Plan Offerings

[0670] 7. Submit the generated plan

[0671] The server sends the generated plan to the terminal so that the user can receive and check it.

[0672] Track and adjust progress

[0673] 8. Enter and submit new data

[0674] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[0675] The device encrypts the new data and sends it to the server.

[0676] 9. Analyze progress data and adjust plans

[0677] The server analyzes the progress data and adjusts the diet and exercise plan as needed, for example, increasing calorie intake if weight loss is too rapid.

[0678] Prompt Sentence Examples

[0679] "A 30-year-old woman, 160cm tall, 65kg weight, basal metabolic rate of 1400kcal, low daily exercise, lipid-rich body. Please suggest the best meal menu and exercise plan for this user."

[0680] "A user is walking three times a week and doing light strength training twice a week, but is losing weight rapidly. Could you please advise how to adjust their calorie intake?"

[0681] This system makes it possible to provide users with optimized health management and diet plans, enabling effective health management.

[0682] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0683] Step 1:

[0684] The user obtains body shape data and enters basic information

[0685] The user installs a dedicated application on a smartphone or tablet and launches it. The input is the launch of the app.

[0686] The user scans their body using a camera and obtains image data. The user then enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure into a form within the app. The obtained body data and basic information are then output.

[0687] Step 2:

[0688] The device encrypts and transmits data

[0689] The device encrypts the body shape data and basic information acquired from the user using encryption technology such as the AES protocol. The input is the body shape data and basic information. The encrypted data is then output.

[0690] The device sends the encrypted data to the server using the HTTPS protocol. The sent encrypted data is output.

[0691] Step 3:

[0692] The server anonymizes and stores the data

[0693] The server decrypts the received encrypted data and performs an anonymization process to remove any personally identifiable information. The input is the encrypted data. The output is anonymized data.

[0694] The server stores the anonymized data in a secure database. The stored data is then output.

[0695] Step 4:

[0696] The server analyzes the data and extracts features

[0697] The server extracts features from user data using Python's scikit-learn library or similar. The input is anonymized data. Specifically, it uses principal component analysis (PCA) to identify important features. The resulting extracted features are output.

[0698] The server searches for similar data in a database and runs SQL queries to identify successes and failures, which results in the output of similar data.

[0699] Step 5:

[0700] The server generates a plan

[0701] The server calculates the target calorie intake based on the user's basal metabolic rate (e.g., 1400 kcal) and activity level, and generates a meal menu. The extracted features are used as input. As a specific example, it suggests oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner. The generated meal menu is then output.

[0702] The server generates a safe and effective exercise plan. For example, it recommends walking three times a week and light strength training twice a week. The generated exercise plan is then output.

[0703] The server creates a comprehensive plan that includes advice on stress management and mental health. For example, it adds advice on yoga and meditation. This outputs a comprehensive plan.

[0704] Step 6:

[0705] The server sends the generated plan to the device.

[0706] The server sends the generated plan to the terminal. The generated plan is taken as input. The transmitted plan is output.

[0707] Step 7:

[0708] The user enters progress data and sends it to the server

[0709] The user inputs data such as daily exercise status, dietary habits, weight fluctuations, and physical condition into the application. Progress data is input, and the input progress data is output.

[0710] The device encrypts this data using protocols such as AES and sends it to the server, which then outputs the encrypted progress data.

[0711] Step 8:

[0712] The server analyzes the progress data and adjusts the plan

[0713] The server analyzes the progress data and adjusts the diet and exercise plan as needed. For example, if weight loss is too rapid, it may increase calorie intake. The input is the progress data sent. The output is an adjusted plan.

[0714] (Application example 1)

[0715] 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."

[0716] In today's security environment, ensuring the safety of personal information is a critical issue, especially in authentication processes that require high security. Traditional passwords and simple two-step authentication are often insufficient. Furthermore, there is a growing need to effectively utilize health management and diet data while safely managing and using that data effectively. Against this background, there is a demand for a system that can perform health management and authentication processes in one place, while providing a higher level of security by using personal health information for security authentication.

[0717] 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.

[0718] In this invention, the server includes a means for the user to scan their body type and input basic information, a means for the terminal to send the user's data to the server, and a means for the server to analyze the data and compare it with other databases. This enables high-security authentication and the provision of comprehensive health management plans.

[0719] "Means for users to scan their body and enter basic information" refers to the interface and device configuration that allows users to use a digital device to scan their body and enter basic health information such as height, weight, and blood pressure.

[0720] "Means for the device to transmit user data to a server" refers to the functionality and protocols that allow the digital device to transmit acquired body shape data and health information to a remote server using a secure communications protocol.

[0721] "Means for the server to analyze data and compare it with other databases" refers to software and algorithms that the server uses to analyze the user data it receives and compare it with existing databases.

[0722] The "means by which the server generates meal menus, exercise plans, and mental care advice" refers to an algorithm that automatically generates meal menus, exercise plans, and mental care advice appropriate for the user based on the data analyzed by the server.

[0723] "Means for the terminal to provide the generated plan to the user" refers to a digital device and interface for providing the server-generated plan so that the user can access it.

[0724] "Means for users to input progress data and for the server to analyze that data and adjust the plan" refers to a function / system that allows users to input their daily progress data (exercise history, diet history, weight fluctuations, etc.) and for the server to analyze that data and optimize the plan.

[0725] "Means for utilizing body scan and health data as security authentication" refers to systems and methods for using a user's body scan data and health information as part of security authentication.

[0726] "Means for encrypting user health data and transmitting it to the server" refers to protocols and algorithms for securely protecting user health data using encryption technology and transmitting that data to the server.

[0727] "Means for the server to analyze received data, perform authentication, and transmit the result to the device" refers to a system and method for the server to analyze received user data, generate an authentication result, and transmit the result to the user's digital device.

[0728] The present invention is a system that uses a user's body shape data and health information to provide high-security authentication and comprehensive health management plans.

[0729] The system is configured as follows:

[0730] 1. Obtaining user data

[0731] The user uses a digital device (e.g., a smartphone) to scan their body shape and input their health information. The smartphone's camera is used to scan the user's body shape and input basic health information such as height, weight, blood pressure, and basal body temperature. This allows the user to obtain basic data.

[0732] 2. Data transmission and storage

[0733] The data is securely transmitted to the server using encryption protocols (such as TLS / SSL) on the device side. The server anonymizes the received data and stores it in a secure database. This anonymization process protects the user's privacy.

[0734] 3. Data analysis and collation

[0735] The server analyzes the data sent by the user using a specific algorithm to extract features, then compares it with existing data in the database to identify data with high similarity, enabling effective plan generation and highly accurate security authentication.

[0736] 4. Authentication and Plan Generation

[0737] After feature extraction and matching are complete, the server automatically generates a meal menu, exercise plan, and mental health advice tailored to the user. It also generates a token required for security authentication, allowing users to authenticate and manage their health through a single application.

[0738] 5. Offering and Certifying the Plan

[0739] The generated plan and authentication token are sent to the user's device, where they can be reviewed and executed, and security access is granted or denied based on the authentication result.

[0740] 6. Tracking and adjusting progress

[0741] Users enter their daily progress data (e.g., exercise history, diet history, weight fluctuations, etc.) into the device. The device then re-encrypts this data and sends it to the server. The server analyzes the new data and fine-tunes the plan as needed. This ensures that users receive ongoing, appropriate support.

[0742] (Example)

[0743] For example, a 30-year-old female user enters basic information into a smartphone app, such as height 160 cm, weight 65 kg, basal metabolic rate 1400 kcal, low daily exercise, sleep time 7 hours, lipid-rich body, and bone mass. This data is encrypted and sent to the server for analysis. The server then generates and recommends breakfast, lunch, and dinner menus to the user, taking into account the basal metabolic rate of 1400 kcal and setting a daily calorie intake goal of 1700 kcal. The generated token is also sent to the user for security authentication.

[0744] (Example of a prompt)

[0745] Please explain the two-factor authentication smartphone application that uses a body scanning app. Please include the process of acquiring body data and basic health data, encrypting it, sending it to a server, and analyzing the data for authentication.

[0746] To implement this invention, hardware and software such as a smartphone, a secure communication protocol (TLS / SSL), an image processing library (OpenCV), and a data encryption library (cryptography) are required.

[0747] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0748] Step 1:

[0749] Users scan their body type and enter basic information.

[0750] Input: Body image data obtained using a smartphone camera, and basic information such as height, weight, blood pressure, and basal body temperature entered by the user into a form.

[0751] Output: The user dataset before encryption.

[0752] How it works: Users install and launch the app on their smartphone. They use the smartphone's camera to scan their body and obtain image data. They then enter basic information such as height and weight into a form within the app.

[0753] Step 2:

[0754] The device sends the user's data to the server.

[0755] Input: User dataset (image data + basic information) before encryption.

[0756] Output: The encrypted user dataset.

[0757] What it does: Encrypts data on the device using the SSL / TLS protocol. Sends the encrypted data to the server, typically via an HTTP POST request.

[0758] Step 3:

[0759] The server anonymizes the data it receives and stores it in a database.

[0760] Input: The encrypted user dataset.

[0761] Output: Anonymized user data stored in a database.

[0762] What it does: The server receives the data, decrypts it, uses an anonymization algorithm to remove any personally identifiable information, and stores it in a secure database.

[0763] Step 4:

[0764] The server analyzes the user data and compares it with other databases.

[0765] Input: Anonymized user data.

[0766] Output: The collated dataset.

[0767] How it works: The server uses a data analysis algorithm (e.g., a machine learning model) to extract features from user data, then compares it with existing databases to identify data with high similarity.

[0768] Step 5:

[0769] The server generates meal menus, exercise plans, and mental care advice.

[0770] Input: The collated dataset.

[0771] Output: User-optimized meal menu, exercise plan, and mental health advice.

[0772] How it works: The server automatically generates meal plans and exercise plans based on the user's basal metabolic rate and physical constitution. The plans are designed to include the appropriate nutrients and exercise levels for the user. They also include advice on yoga and meditation for mental health.

[0773] Step 6:

[0774] The device provides the generated plan and security authentication token to the user.

[0775] Input: The plan and authentication token generated by the server.

[0776] Output: The plan and authentication token displayed on the user's device.

[0777] Specific operation: The server sends the generated plan and security authentication token to the device. The user can receive and check this information through the application.

[0778] Step 7:

[0779] Users input progress data, and the server analyzes the data and adjusts the plan.

[0780] Input: Data such as the user's daily exercise history, diet history, and weight fluctuations.

[0781] Output: Tailored meal plans, exercise plans, and mental health advice.

[0782] How it works: Users enter their daily progress data into the app. The device encrypts this data and sends it to the server. The server analyzes the new data, adjusts the plan if necessary, and presents it to the user again.

[0783] 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.

[0784] This invention is a system that scans a user's body shape and provides effective health management and dieting. The system analyzes the user's data and compares it with other databases to provide meal menus, exercise plans, and mental care advice tailored to each individual user. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it achieves more detailed mental care and plan adjustments.

[0785] Overall system overview

[0786] 1. Obtaining user data

[0787] The user installs and launches a dedicated application on their smartphone or tablet, scans their body shape, and acquires image data.

[0788] The user enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise amount, sleep time, constitution, and bone structure into a form.

[0789] 2. Data transmission and storage

[0790] The device encrypts the scanned body data and basic information and sends it to a server using a secure communication protocol.

[0791] The server anonymizes the data it receives and stores it in a secure database.

[0792] 3. Data analysis and collation

[0793] The server analyzes the user data and extracts important features using a feature extraction algorithm.

[0794] The server searches for similar data in the database and identifies the right plan for the user based on success and failure.

[0795] 4. Generate a plan

[0796] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise.

[0797] The server generates a nutritionally balanced meal menu and suggests it to the user.

[0798] The server generates a safe and effective exercise plan (exercise type, exercise time, exercise frequency) and recommends it to the user.

[0799] The server will create a comprehensive plan that includes advice on mental care such as stress management.

[0800] 5. Offering of Plans

[0801] The server sends the generated plan to the terminal, and the user can receive and confirm it.

[0802] 6. Track and adjust progress

[0803] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[0804] The device encrypts the new data and sends it to the server.

[0805] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed.

[0806] 7. Emotion Recognition with Emotion Engine

[0807] The user uses the emotion engine to obtain emotion data for that day from voice, facial expressions, text data, etc.

[0808] The device collects emotional data, encrypts it, and sends it to a server.

[0809] The server analyzes the emotional data and adjusts mental care advice.

[0810] 8. Adjust your plan based on sentiment data

[0811] The server further adjusts the meal menu and exercise plan based on the user's emotional state.

[0812] For example, if a user is feeling stressed, it will suggest foods that will reduce stress or an exercise plan to help them relax.

[0813] Specific examples

[0814] 1. Obtaining user data

[0815] For example, a 30-year-old woman who is 160 cm tall and weighs 65 kg launches a dedicated app and scans her body shape.

[0816] The user enters information into the form, including basal body temperature of 36.5°C, basal metabolic rate of 1400 kcal, low daily exercise, 7 hours of sleep, lipid-rich body type, and bone mass.

[0817] 2. Data transmission and storage

[0818] The scan results and basic information are encrypted and sent to the server.

[0819] The server anonymizes the received data and stores it securely in a database.

[0820] 3. Data analysis and collation

[0821] The server analyzes user data and extracts features such as basal metabolic rate and physical constitution.

[0822] The server searches the database for similar data and identifies successful cases (plans that have been successful for people with similar body types and constitutions).

[0823] 4. Generate a plan

[0824] The server takes into account the basal metabolic rate of 1400 kcal and sets the daily calorie intake target at 1700 kcal.

[0825] Suggest oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[0826] It is recommended to walk three times a week and do light strength training twice a week.

[0827] Also includes mental health advice on yoga and meditation.

[0828] 5. Offering of Plans

[0829] The generated plan is sent to the terminal, where the user can confirm and execute it.

[0830] 6. Track and adjust progress

[0831] Users enter their exercise routine and dietary habits into the app every day.

[0832] The server analyzes the new data and tweaks the plan as needed.

[0833] For example, if weight loss is too rapid, adjustments can be made, such as increasing calorie intake.

[0834] 7. Emotion Recognition with Emotion Engine

[0835] For example, a user can write a diary entry into the app by voice, and the emotion engine will determine the user's emotional state from the tone and content of the voice.

[0836] If an emotion such as anger or sadness is detected, the data is sent to a server.

[0837] 8. Adjust your plan based on sentiment data

[0838] The server analyzes the emotional data and provides advice to users who are feeling stressed, such as relaxing meals, light exercise, or meditation.

[0839] Specifically, we will suggest herbal teas that are effective in reducing stress and stretching methods for relaxation.

[0840] Thus, the present invention is a system that comprehensively manages the user's health condition and provides an optimal plan that also takes into account the user's emotional state.

[0841] The processing flow will be explained below.

[0842] Step 1:

[0843] The user launches the app and scans their body.

[0844] The device uses a camera to 3D scan the user's body and generate a highly accurate 3D model.

[0845] Step 2:

[0846] The user enters basic information (height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, bone structure, etc.) into a form.

[0847] Step 3:

[0848] The device encrypts the collected body data and basic information and transmits it to a server using a secure communication protocol.

[0849] Step 4:

[0850] The server anonymizes the data it receives and stores it in a secure database.

[0851] Step 5:

[0852] The server analyzes the user data and extracts important features using a feature extraction algorithm.

[0853] Step 6:

[0854] The server searches for similar data in the database and identifies the right plan for the user based on success and failure.

[0855] Step 7:

[0856] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise.

[0857] Step 8:

[0858] The server generates a nutritionally balanced meal menu and suggests it to the user.

[0859] Step 9:

[0860] The server generates a safe and effective exercise plan (exercise type, exercise time, exercise frequency) and recommends it to the user.

[0861] Step 10:

[0862] The server will create a comprehensive plan that includes advice on mental care, such as stress management.

[0863] Step 11:

[0864] The server sends the generated plan to the terminal, and the user can receive and confirm it.

[0865] Step 12:

[0866] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[0867] Step 13:

[0868] The device encrypts the new data and sends it to the server.

[0869] Step 14:

[0870] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed.

[0871] Step 15:

[0872] The user utilizes the emotion engine to obtain emotion data for that day from voice, facial expression, text data, etc.

[0873] Step 16:

[0874] The device collects emotional data, encrypts it, and sends it to a server.

[0875] Step 17:

[0876] The server analyzes the emotional data and adjusts mental care advice.

[0877] Step 18:

[0878] The server further adjusts the meal menu and exercise plan based on the emotional data.

[0879] For users who feel stressed, the server will suggest foods that will reduce stress and exercise plans that will help them relax.

[0880] Specific examples

[0881] Step 1:

[0882] For example, a 30-year-old woman who is 160 cm tall and weighs 65 kg launches a dedicated app and scans her body shape.

[0883] The device uses a camera to digitize the user's body shape and generate a 3D model.

[0884] Step 2:

[0885] The user enters basic information such as basal body temperature of 36.5°C, basal metabolic rate of 1400 kcal, low daily exercise, 7 hours of sleep, lipid-rich constitution, and bone mass.

[0886] Step 3:

[0887] The device encrypts the scan results and basic information and sends them to the server.

[0888] Step 4:

[0889] The server anonymizes the data it receives and stores it in a secure database.

[0890] Step 5:

[0891] The server analyzes user data and extracts features such as basal metabolic rate and physical constitution.

[0892] Step 6:

[0893] The server searches for similar data in a database and identifies an appropriate plan based on successes and failures.

[0894] Step 7:

[0895] The server takes into account the basal metabolic rate of 1400 kcal and sets the daily calorie intake target at 1700 kcal.

[0896] Step 8:

[0897] The server generates a meal menu that suggests oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[0898] Step 9:

[0899] The server generates an exercise plan that recommends walking three times a week and light strength training twice a week.

[0900] Step 10:

[0901] The server will create a comprehensive plan that includes mental care advice such as yoga and meditation.

[0902] Step 11:

[0903] The generated plan is sent to the terminal and confirmed by the user.

[0904] Step 12:

[0905] Users enter their exercise routine and dietary habits into the app every day.

[0906] Step 13:

[0907] The device encrypts the new data and sends it to the server.

[0908] Step 14:

[0909] The server analyzes progress data and adjusts the plan as needed, for example if weight loss is too rapid.

[0910] Step 15:

[0911] For example, the same user can enter their diary entry into the app by voice, and the emotion engine will recognize emotions such as anger or sadness from the tone and content of their voice.

[0912] Step 16:

[0913] The device collects emotional data, encrypts it, and sends it to a server.

[0914] Step 17:

[0915] The server analyzes the emotional data and suggests stress-reducing advice to users who are feeling stressed.

[0916] Step 18:

[0917] Based on the emotional data, the server can make additional suggestions, such as herbal tea that reduces stress or yoga stretches to help you relax.

[0918] Thus, the present invention is a system that comprehensively manages the user's health condition and provides an optimal plan that also takes into account the user's emotional state.

[0919] Example 2

[0920] 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."

[0921] Health management and dieting have become important themes in modern society. However, many users find it difficult to accurately grasp their own body shape and health condition, and are unable to receive appropriate meal menus, exercise plans, or mental care advice. Furthermore, there are few systems that provide comprehensive health support that takes emotional state into account, meaning that many users are unable to receive appropriate healthcare. This makes effective health management difficult, and presents challenges for maintaining sustainable health.

[0922] 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.

[0923] In this invention, the server includes: [means for anonymizing received data and storing it in a secure database]; [means for extracting features of user data and searching for data with high similarity]; and [means for acquiring and analyzing user emotional data.] This makes it possible to comprehensively assess the user's health and emotional state and provide optimal meal menus, exercise plans, and mental care advice.

[0924] "User" refers to a person who provides their body data and basic information using a dedicated application.

[0925] "Terminal" refers to equipment that collects user data and transmits it to a server using encryption technology. Specifically, this includes devices such as smartphones and tablets.

[0926] "Server" refers to a computer system that receives data sent by users and performs anonymization processing, storage, analysis, collation, and plan generation.

[0927] "Body scanning means" refers to the imaging device and image analysis technology used by a user to obtain their body data.

[0928] "Basic information" refers to basic biometric information about the user, such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and skeleton.

[0929] "Encryption Technology" means technology used to keep data confidential. Examples include AES encryption.

[0930] "Anonymization" refers to techniques that process data so that individuals cannot be identified. Examples include k-Anonymity.

[0931] "Database" refers to a system for storing and managing structured data, including Amazon RDS.

[0932] "Features" refer to important information that machine learning models extract from data, allowing for a more accurate understanding of the attributes and state of the data.

[0933] "Meal Menu" refers to a meal plan suggested based on the user's nutritional balance and health goals.

[0934] "Exercise Plan" refers to an exercise plan suggested based on the user's physical abilities and health goals.

[0935] "Mental health advice" refers to advice or activities offered to support a user's mental health, such as yoga or meditation.

[0936] "Emotion engine" refers to technology that analyzes a user's emotional state from their voice, facial expressions, and text data.

[0937] A "protocol" refers to a set of defined rules or procedures for transmitting data. An example is HTTPS.

[0938] A "machine learning algorithm" refers to a computational method for analyzing data and extracting features.

[0939] The present invention provides a system that effectively supports health management and dieting based on a user's body type data and personal information. This system provides the user with an optimal health plan through a series of processes including body type scanning by the user, data transmission, data analysis by the server, and plan generation and provision.

[0940] 1. Obtaining user data

[0941] First, the user installs and launches a dedicated application on their smartphone or tablet. We will use "MyHealthManager" as an example of this application. Next, the user scans their body shape using the app's camera function, and acquires image data. The OpenCV image processing library is used for this body shape scan.

[0942] In addition, users can use the form within the app to enter basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure.

[0943] 2. Data transmission and storage

[0944] The device encrypts the body scan data and input information obtained from the user using AES encryption technology. The encrypted data is sent to the server using the HTTPS protocol. Industry-standard encryption and communication protocols are used to ensure secure communication.

[0945] The server anonymizes the received data using an anonymization algorithm (specifically, k-Anonymity) and stores it in a secure database (e.g., Amazon RDS).

[0946] 3. Data analysis and collation

[0947] The server analyzes the stored user data using machine learning libraries (e.g., Scikit-learn) to extract features related to the user's health and body shape. These features are then matched with similar data in a database to identify successful and unsuccessful cases.

[0948] 4. Plan Generation and Delivery

[0949] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise. For example, if the basal metabolic rate is 1400 kcal, the target calorie intake is set to 1700 kcal. The server then generates a meal menu that takes nutritional balance into consideration. For example, it might suggest oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[0950] The server also generates and provides users with exercise plans, such as walking three times a week and light strength training twice a week, and mental health advice, including yoga and meditation.

[0951] The generated plan is sent to the terminal, where the user can review and execute the plan.

[0952] Specific examples

[0953] For example, consider a 30-year-old woman who is 160 cm tall and weighs 65 kg. She has a basal body temperature of 36.5°C, a basal metabolic rate of 1,400 kcal, little daily exercise, seven hours of sleep, a lipid-rich constitution, and a medium-bone structure. She launches the app, scans her body type, and enters this basic information into a form.

[0954] For this user, the server generates the following plan:

[0955] Meal plan: Oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[0956] Exercise plan: Walking 3 times a week and light strength training 2 times a week

[0957] Mental care advice: Yoga and meditation

[0958] Prompt Sentence Examples

[0959] Below is an example of a prompt sentence to input to the generative AI model.

[0960] "We have a 30-year-old female user who is 160cm tall and weighs 65kg. Her basal body temperature is 36.5°C, her basal metabolic rate is 1400kcal, she exercises little, she sleeps 7 hours a day, she has a lipid-rich constitution, and she has a medium-sized bone. Please provide her with a daily meal plan, exercise plan, and mental health advice that is appropriate for her."

[0961] This allows the system to comprehensively manage the user's health status and provide an optimal plan that also takes into account their emotional state.

[0962] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0963] Step 1:

[0964] The user installs and launches the dedicated application "MyHealthManager" on their smartphone or tablet. The application uses the camera function to scan the user's body shape. The input data is 3D body scan data, which is then processed using the image processing library OpenCV and output as body shape data.

[0965] Step 2:

[0966] The user enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure into a form within the app. The input data is basic information in text format, which is saved in the application and output as the user's basic information.

[0967] Step 3:

[0968] The device encrypts the acquired body shape data and basic user information using AES encryption technology. The input data is body shape data and basic information, and the encrypted data is output.

[0969] Step 4:

[0970] The device sends encrypted data to the server via the HTTPS protocol. The input is encrypted body shape data and basic information, and the output is the data securely sent to the server.

[0971] Step 5:

[0972] The server anonymizes the data it receives using the k-Anonymity algorithm. The input data is encrypted user data, and the output is anonymized data.

[0973] Step 6:

[0974] The server stores the anonymized data in a secure database (e.g., Amazon RDS). The input is the anonymized data, and the output is the stored database entry.

[0975] Step 7:

[0976] The server analyzes the stored user data using the machine learning library Scikit-learn and extracts features. The input is the user data in the database, and the output is the extracted features.

[0977] Step 8:

[0978] The server searches for similar data in the database and identifies successful or unsuccessful cases. The input is the extracted features, and the output is the data of the identified successful or unsuccessful cases.

[0979] Step 9:

[0980] The server calculates the target daily calorie intake based on the basal metabolic rate and daily exercise amount. The input data is the basal metabolic rate and exercise amount, and the output is the target daily calorie intake.

[0981] Step 10:

[0982] The server generates meal menus taking nutritional balance into consideration. The input is the target calorie intake and criteria for nutritional balance, and the output is meal menus for breakfast, lunch, and dinner.

[0983] Step 11:

[0984] The server generates the optimal exercise plan for the user. The input is body type data and basal metabolic rate, and the output is a weekly exercise plan.

[0985] Step 12:

[0986] The server generates advice for mental health, including yoga and meditation. The input is the user's stress level and emotional data, and the output is mental health advice.

[0987] Step 13:

[0988] The server sends the generated plan (meal menu, exercise plan, mental care advice) to the terminal. The input is the generated plan, and the output is the plan display on the terminal.

[0989] Step 14:

[0990] The user inputs data into the app, such as daily exercise, diet, weight fluctuations, and physical condition. The input is the user's daily data, and the output is updated health status data.

[0991] Step 15:

[0992] The device encrypts daily data and sends it to the server. The input is the updated health status data, and the output is the encrypted data sent to the server.

[0993] Step 16:

[0994] The server analyzes the daily data and adjusts the plan as needed. The input is the daily health data and the output is the adjusted plan.

[0995] Step 17:

[0996] The user uses the emotion engine to obtain emotion data from voice, facial expressions, and text data. The input is the user's emotion data, and the output is the analyzed emotion results.

[0997] Step 18:

[0998] The device encrypts the emotion data and sends it to the server. The input is emotion data, and the output is the encrypted emotion data sent to the server.

[0999] Step 19:

[1000] The server analyzes the emotional data and adjusts mental care advice based on the emotional state. The input is the analyzed emotional result, and the output is the adjusted mental care advice.

[1001] (Application example 2)

[1002] 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."

[1003] Conventional health management systems could obtain a user's body type and basic information and provide meal menus, exercise plans, and mental care advice based on that information, but they were unable to provide specific product suggestions or location information for products in stores. They also lacked the ability to fine-tune plans by taking into account the user's emotional state. This made it difficult for users to smoothly find the right products in stores and was unable to receive appropriate advice based on their emotional state.

[1004] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for the user to scan their body type and input basic information, a means for the terminal to send the user's data to the server, a means for the server to analyze the data and compare it with other databases, a means for the server to generate a meal menu, an exercise plan, and mental care advice, a means for the terminal to provide the generated plan to the user, a means for the user to input progress data and the server to analyze the data and adjust the plan, a means for measuring body type using a scanning device in the store, a means for providing the user with optimal food and product placement information, and a means for analyzing the user's emotional data using an emotion engine and fine-tuning the plan based on the data. This makes it easier for users to find optimal products in the store through the health management system and receive more detailed advice tailored to their emotional state.

[1005] "User" means an individual who utilizes the system to provide information for body scanning and health management.

[1006] "Body scanning" refers to using a device to measure the shape and dimensions of a user's body and capture that data in digital form.

[1007] "Basic information" refers to biological data and lifestyle information that users input into the system, including height, weight, basal metabolic rate, daily exercise, sleep duration, and physical constitution.

[1008] A "terminal" is a device that is directly operated by a user, and refers to electronic devices such as smartphones and tablets.

[1009] A "server" is a computing resource that analyzes and stores data and provides information to users, and refers to a high-performance computer located in a cloud environment or on a network.

[1010] "Analyzing the data" means processing the data submitted by the user and extracting meaningful information.

[1011] A "database" is a collection of large amounts of accumulated data that is structured and stored in a format that allows for searching and matching.

[1012] "Meal menu" refers to a combination of ingredients and dishes suggested taking into consideration the user's health condition and nutritional balance.

[1013] "Exercise plan" refers to a plan that specifically indicates the type, time, and frequency of exercise based on the user's health condition and goals.

[1014] "Mental health advice" refers to suggestions and guidance for maintaining and improving a user's mental health.

[1015] "Progress data" refers to data related to diet, exercise, weight fluctuations, physical condition, etc. that users record in their daily lives.

[1016] "In-store scanning device" refers to a device installed in a physical store that allows customers to measure their body measurements.

[1017] "Product placement information" refers to data that indicates the specific placement locations of products within a store that a user is considering purchasing.

[1018] An "emotion engine" refers to software or hardware for analyzing a user's emotional state from their voice, facial expressions, text data, etc.

[1019] "Emotion data" is digital data that indicates the user's emotional state and is analyzed by the emotion engine.

[1020] A "recommendation list" refers to a list of products or actions that are recommended for a user to purchase or perform based on their health information or emotional state.

[1021] A system for implementing this invention includes a means for a user to scan their body type and input basic information, a means for transmitting the data to a server, a means for the server to analyze the data and compare it with other databases, a means for generating a meal menu, an exercise plan, and mental care advice, a means for the user to input progress data and analyze the data, a means for measuring body type using a scanning device in a store, a means for providing optimal food and product placement information, and a means for analyzing the user's emotional data using an emotion engine and fine-tuning the plan based on the data.

[1022] The server anonymizes the data it receives and stores it in a database. This database structures the large amount of accumulated data and stores it in a format that allows for search and matching. The server also extracts features from the user data and generates meal menus, exercise plans, and mental care advice based on data with high similarity.

[1023] In this system, users first install a dedicated application on their smartphone, tablet, or other device. Next, they measure their body shape using a scanning device in the store and enter that data and basic information (e.g., height, weight, basal metabolic rate, daily exercise, sleep duration, physical constitution, etc.) into the application. This information is encrypted and sent to the server using a secure communication protocol (e.g., HTTPS).

[1024] The server receives the transmitted data and analyzes it. During the analysis, it extracts features from the user data and compares them with similar databases to generate optimal meal plans, exercise plans, and mental care advice for the user. It also uses an emotion engine to analyze the user's emotional data and fine-tune the plan as needed. The emotion engine determines the user's emotional state from their voice, facial expressions, or text data.

[1025] The generated plan is then encrypted again and sent to the user's device. The user can then use this plan to select products in the store, and specific product placement information within the store is also provided, allowing the user to efficiently find the products they need.

[1026] For example, a user visits a health food store, measures their body shape with a 3D body scanner, and then enters the results and basic information into a smartphone app. This data is sent to a cloud server for analysis. The generated plan includes recommended foods (e.g., protein bars, low-calorie snacks, etc.) and provides detailed location information for them. The app helps users find products easily, and also suggests stress-reducing foods based on their emotional state.

[1027] Examples of prompts include:

[1028] "I want to create an app that scans a user's body shape and health data and suggests food and exercise products based on their individual health status. The user scans their body shape in the store, enters the necessary data, and based on the information analyzed by the server, provides the most suitable products and their placement information."

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

[1030] Step 1:

[1031] The user installs a dedicated application on their smartphone or tablet and creates a profile. At this time, the user enters basic information such as height, weight, basal metabolic rate, daily exercise, sleep time, and physical constitution. The application temporarily saves the entered basic information in local storage. The entered data is used in the next step.

[1032] Step 2:

[1033] The user scans their body shape using a scanning device (e.g., a 3D body scanner) installed in the store. The scanning device captures the user's body shape data (e.g., the outer shape of the body and the dimensions of each part of the body) and sends the data to a smartphone application. The body shape data is encrypted and used in the next step.

[1034] Step 3:

[1035] The device (smartphone or tablet) encrypts the entered basic information and scanned data and sends it to a cloud server using a secure communication protocol (e.g., HTTPS). The sent data is then received by the server.

[1036] Step 4:

[1037] The server analyzes the received data. During the analysis, for example, a Python library is used to preprocess the data (fill in missing values, detect outliers) and extract features from the user data. These extracted features are then compared with other databases to search for data with high similarity. This process provides the information needed to generate meal menus and exercise plans tailored to the user.

[1038] Step 5:

[1039] The server generates optimal meal plans, exercise plans, and mental health advice for each user using machine learning models and rule-based approaches. For example, a generative AI model is used to suggest optimal meal plans that take into account the user's basal metabolic rate and daily activity level. The generated plans are encrypted.

[1040] Step 6:

[1041] The server sends the generated plan to the terminal, which then decodes the received data and displays it to the user, along with specific product location information to help the user easily find the food or product they need in the store.

[1042] Step 7:

[1043] The user selects and purchases the suggested products in the store. During this process, the application records the user's purchase history and selection trends and uses them for future suggestions. This data is temporarily stored in local storage.

[1044] Step 8:

[1045] The user enters progress data (e.g., dietary details, exercise details, weight fluctuations, physical condition, etc.) into the application. The entered data is encrypted and sent to the server, which then receives it.

[1046] Step 9:

[1047] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed. For example, it uses machine learning algorithms to fine-tune calorie intake and exercise intensity based on the user's progress data. The analysis results are then provided back to the user.

[1048] Step 10:

[1049] The user uses the emotion engine to obtain emotional data. Emotional data is collected from voice, facial expressions, text input, etc. and analyzed using voice analysis software and natural language processing models. This analyzed data is encrypted and sent to the server.

[1050] Step 11:

[1051] The server analyzes the emotional data and further adjusts mental health advice, meal plans, and exercise plans as needed. For example, if the user is feeling stressed, it will suggest foods that have a relaxing effect and light exercise. These adjustments are provided to the user on a case-by-case basis.

[1052] 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.

[1053] 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.

[1054] 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.

[1055] [Third embodiment]

[1056] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1057] 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.

[1058] 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).

[1059] 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.

[1060] 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.

[1061] 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).

[1062] 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.

[1063] 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.

[1064] 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.

[1065] 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.

[1066] 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.

[1067] 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."

[1068] This invention is a system that allows users to scan their own body shape and effectively manage their health and diet. The system analyzes the user's data and compares it with other databases to provide individualized meal menus, exercise plans, and mental health advice.

[1069] Overall system overview

[1070] 1. Obtaining user data

[1071] The user installs and launches a dedicated application on their smartphone or tablet, scans their body shape, and acquires image data.

[1072] The user enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise amount, sleep time, constitution, and bone structure into a form.

[1073] 2. Data transmission and storage

[1074] The device encrypts the scanned body data and basic information and sends it to a server using a secure communication protocol.

[1075] The server anonymizes the data it receives and stores it in a secure database.

[1076] 3. Data analysis and collation

[1077] The server analyzes the user data and extracts important features using a feature extraction algorithm.

[1078] The server searches for similar data in the database and identifies the right plan for the user based on success and failure.

[1079] 4. Generate a plan

[1080] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise.

[1081] The server generates a nutritionally balanced meal menu and suggests it to the user.

[1082] The server generates a safe and effective exercise plan (exercise type, exercise time, exercise frequency) and recommends it to the user.

[1083] The server will create a comprehensive plan that includes advice on mental care such as stress management.

[1084] 5. Offering of Plans

[1085] The server sends the generated plan to the terminal, and the user can receive and confirm it.

[1086] 6. Track and adjust progress

[1087] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[1088] The device encrypts the new data and sends it to the server.

[1089] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed.

[1090] Specific examples

[1091] 1. Obtaining user data

[1092] For example, a 30-year-old woman who is 160 cm tall and weighs 65 kg launches a dedicated app and scans her body shape.

[1093] The user enters information into the form, including basal body temperature of 36.5°C, basal metabolic rate of 1400 kcal, low daily exercise, 7 hours of sleep, lipid-rich body type, and bone mass.

[1094] 2. Data transmission and storage

[1095] The scan results and basic information are encrypted and sent to the server.

[1096] The server anonymizes the received data and stores it securely in a database.

[1097] 3. Data analysis and collation

[1098] The server analyzes user data and extracts features such as basal metabolic rate and physical constitution.

[1099] The server searches the database for similar data and identifies successful cases (plans that have been successful for people with similar body types and constitutions).

[1100] 4. Generate a plan

[1101] The server takes into account the basal metabolic rate of 1400 kcal and sets the daily calorie intake target at 1700 kcal.

[1102] Suggest oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[1103] It is recommended to walk three times a week and do light strength training twice a week.

[1104] Also includes mental health advice on yoga and meditation.

[1105] 5. Offering of Plans

[1106] The generated plan is sent to the terminal, where the user can confirm and execute it.

[1107] 6. Track and adjust progress

[1108] Users enter their exercise routine and dietary habits into the app every day.

[1109] The server analyzes the new data and tweaks the plan as needed.

[1110] For example, if weight loss is too rapid, adjustments can be made, such as increasing calorie intake.

[1111] The system provides health management and diet plans optimized for each individual user.

[1112] The processing flow will be explained below.

[1113] Step 1:

[1114] The user launches the app and scans their body.

[1115] The device uses a camera to 3D scan the user's body and generate a highly accurate 3D model.

[1116] Step 2:

[1117] The user enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise amount, sleep time, constitution, and bone structure into a form.

[1118] Step 3:

[1119] The device encrypts the collected body shape data and basic information and transmits it to a server using a secure communication protocol.

[1120] Step 4:

[1121] The server anonymizes the data it receives and stores it in a secure database.

[1122] Step 5:

[1123] The server analyzes the user data and extracts important features using a feature extraction algorithm.

[1124] Step 6:

[1125] The server searches for similar data in the database and identifies the right plan for the user based on success and failure.

[1126] Step 7:

[1127] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise.

[1128] Step 8:

[1129] The server generates a nutritionally balanced meal menu and suggests it to the user.

[1130] Step 9:

[1131] The server generates a safe and effective exercise plan (exercise type, exercise time, exercise frequency) and recommends it to the user.

[1132] Step 10:

[1133] The server will create a comprehensive plan that includes advice on mental care such as stress management.

[1134] Step 11:

[1135] The server sends the generated plan to the terminal, and the user can receive and confirm it.

[1136] Step 12:

[1137] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[1138] Step 13:

[1139] The device encrypts the new data and sends it to the server.

[1140] Step 14:

[1141] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed.

[1142] Example 1

[1143] 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."

[1144] The present invention relates to a system that allows users to scan their own body shape and effectively manage their health and diet. Conventional health management systems often lack security and accuracy in the acquisition, transmission, storage, and analysis of user data. They also lack effective methods for providing individual users with optimized meal menus, exercise plans, and mental health advice. This makes it difficult for users to implement and maintain their health management and diet plans.

[1145] 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.

[1146] In this invention, the server includes: [means for the user to acquire body type data and input basic information;] [means for the terminal to encrypt the user's data and send it to the server; and [means for the server to anonymize the received data and store it in a secure database.] This improves the safety and accuracy of the data, making it possible to provide health management and diet plans specific to the user.

[1147] "User" refers to an individual who obtains body data and enters basic information.

[1148] "Body shape data" refers to image data and dimensional data obtained by scanning the user's body shape.

[1149] "Basic information" refers to data entered by the user, such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure.

[1150] "Terminal" refers to a computing device used by a User, such as a smartphone or tablet.

[1151] "Encryption" refers to the process of transforming data using encryption technology, such as the Advanced Encryption Standard (AES), to protect the data.

[1152] "Server" refers to a computer system that receives, analyzes, and stores data submitted by users.

[1153] "Anonymization" refers to the process of removing personally identifiable information from received data.

[1154] "Database" means a secure information storage system for storing received data.

[1155] "Feature extraction" refers to the process by which the server identifies and extracts important elements from user data.

[1156] "Meal Menu" refers to a nutritionally balanced meal plan suggested to the user.

[1157] An "exercise plan" refers to an exercise plan that includes safe and effective exercise types, exercise duration, and exercise frequency.

[1158] "Mental health advice" refers to advice to support stress management and psychological well-being.

[1159] "Progress data" refers to data entered by the user daily, such as exercise status, dietary habits, weight fluctuations, and physical condition.

[1160] This invention is a system that allows users to scan their own body shape and effectively manage their health and diet. The system analyzes the user's data and compares it with other databases to provide individualized meal menus, exercise plans, and mental health advice.

[1161] User actions

[1162] 1. Installing the dedicated application

[1163] The user installs and launches a dedicated application on their smartphone or tablet.

[1164] 2. Scan your body and enter basic information

[1165] The user follows the instructions in the application to scan their body shape using the camera and capture image data.

[1166] Next, enter basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure into the form within the app.

[1167] Device operation

[1168] 3. Data Encryption and Transmission

[1169] The body shape data and basic information acquired by the device are encrypted using an encryption protocol such as AES (Advanced Encryption Standard).

[1170] The encrypted data is sent to the server via the HTTPS protocol.

[1171] Server Operations

[1172] 4. Data anonymization and storage

[1173] The server removes any personally identifiable information from the received data and performs anonymization processing.

[1174] Store the anonymized data in a secure database (e.g. AWS RDS).

[1175] 5. Data analysis and feature extraction

[1176] The server extracts features from the user data using Python's scikit-learn library, for example, by using principal component analysis (PCA) to identify important features.

[1177] The server searches for similar data in the database and identifies a plan that is suitable for the user based on success and failure.

[1178] 6. Generate a plan

[1179] The server calculates the target calorie intake (e.g., 1700 kcal per day) based on the user's basal metabolic rate (e.g., 1400 kcal) and activity level.

[1180] The server generates a nutritionally balanced meal menu, suggesting oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[1181] The server generates a safe and effective exercise plan, recommending, for example, three days of walking and two days of light strength training per week.

[1182] The server will create a comprehensive plan that includes advice on stress management and mental health, including yoga and meditation as examples.

[1183] Plan Offerings

[1184] 7. Submit the generated plan

[1185] The server sends the generated plan to the terminal so that the user can receive and check it.

[1186] Track and adjust progress

[1187] 8. Enter and submit new data

[1188] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[1189] The device encrypts the new data and sends it to the server.

[1190] 9. Analyze progress data and adjust plans

[1191] The server analyzes the progress data and adjusts the diet and exercise plan as needed, for example, increasing calorie intake if weight loss is too rapid.

[1192] Prompt Sentence Examples

[1193] "A 30-year-old woman, 160cm tall, 65kg weight, basal metabolic rate of 1400kcal, low daily exercise, lipid-rich body. Please suggest the best meal menu and exercise plan for this user."

[1194] "A user is walking three times a week and doing light strength training twice a week, but is losing weight rapidly. Could you please advise how to adjust their calorie intake?"

[1195] This system makes it possible to provide users with optimized health management and diet plans, enabling effective health management.

[1196] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1197] Step 1:

[1198] The user obtains body shape data and enters basic information

[1199] The user installs a dedicated application on a smartphone or tablet and launches it. The input is the launch of the app.

[1200] The user scans their body using a camera and obtains image data. The user then enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure into a form within the app. The obtained body data and basic information are then output.

[1201] Step 2:

[1202] The device encrypts and transmits data

[1203] The device encrypts the body shape data and basic information acquired from the user using encryption technology such as the AES protocol. The input is the body shape data and basic information. The encrypted data is then output.

[1204] The device sends the encrypted data to the server using the HTTPS protocol. The sent encrypted data is output.

[1205] Step 3:

[1206] The server anonymizes and stores the data

[1207] The server decrypts the received encrypted data and performs an anonymization process to remove any personally identifiable information. The input is the encrypted data. The output is anonymized data.

[1208] The server stores the anonymized data in a secure database. The stored data is then output.

[1209] Step 4:

[1210] The server analyzes the data and extracts features

[1211] The server extracts features from user data using Python's scikit-learn library or similar. The input is anonymized data. Specifically, it uses principal component analysis (PCA) to identify important features. The resulting extracted features are output.

[1212] The server searches for similar data in a database and runs SQL queries to identify successes and failures, which results in the output of similar data.

[1213] Step 5:

[1214] The server generates a plan

[1215] The server calculates the target calorie intake based on the user's basal metabolic rate (e.g., 1400 kcal) and activity level, and generates a meal menu. The extracted features are used as input. As a specific example, it suggests oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner. The generated meal menu is then output.

[1216] The server generates a safe and effective exercise plan. For example, it recommends walking three times a week and light strength training twice a week. The generated exercise plan is then output.

[1217] The server creates a comprehensive plan that includes advice on stress management and mental health. For example, it adds advice on yoga and meditation. This outputs a comprehensive plan.

[1218] Step 6:

[1219] The server sends the generated plan to the device.

[1220] The server sends the generated plan to the terminal. The generated plan is taken as input. The transmitted plan is output.

[1221] Step 7:

[1222] The user enters progress data and sends it to the server

[1223] The user inputs data such as daily exercise status, dietary habits, weight fluctuations, and physical condition into the application. Progress data is input, and the input progress data is output.

[1224] The device encrypts this data using protocols such as AES and sends it to the server, which then outputs the encrypted progress data.

[1225] Step 8:

[1226] The server analyzes the progress data and adjusts the plan

[1227] The server analyzes the progress data and adjusts the diet and exercise plan as needed. For example, if weight loss is too rapid, it may increase calorie intake. The input is the progress data sent. The output is an adjusted plan.

[1228] (Application example 1)

[1229] 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."

[1230] In today's security environment, ensuring the safety of personal information is a critical issue, especially in authentication processes that require high security. Traditional passwords and simple two-step authentication are often insufficient. Furthermore, there is a growing need to effectively utilize health management and diet data while safely managing and using that data effectively. Against this background, there is a demand for a system that can perform health management and authentication processes in one place, while providing a higher level of security by using personal health information for security authentication.

[1231] 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.

[1232] In this invention, the server includes a means for the user to scan their body type and input basic information, a means for the terminal to send the user's data to the server, and a means for the server to analyze the data and compare it with other databases. This enables high-security authentication and the provision of comprehensive health management plans.

[1233] "Means for users to scan their body and enter basic information" refers to the interface and device configuration that allows users to use a digital device to scan their body and enter basic health information such as height, weight, and blood pressure.

[1234] "Means for the device to transmit user data to a server" refers to the functionality and protocols that allow the digital device to transmit acquired body shape data and health information to a remote server using a secure communications protocol.

[1235] "Means for the server to analyze data and compare it with other databases" refers to software and algorithms that the server uses to analyze the user data it receives and compare it with existing databases.

[1236] The "means by which the server generates meal menus, exercise plans, and mental care advice" refers to an algorithm that automatically generates meal menus, exercise plans, and mental care advice appropriate for the user based on the data analyzed by the server.

[1237] "Means for the terminal to provide the generated plan to the user" refers to a digital device and interface for providing the server-generated plan so that the user can access it.

[1238] "Means for users to input progress data and for the server to analyze that data and adjust the plan" refers to a function / system that allows users to input their daily progress data (exercise history, diet history, weight fluctuations, etc.) and for the server to analyze that data and optimize the plan.

[1239] "Means for utilizing body scan and health data as security authentication" refers to systems and methods for using a user's body scan data and health information as part of security authentication.

[1240] "Means for encrypting user health data and transmitting it to the server" refers to protocols and algorithms for securely protecting user health data using encryption technology and transmitting that data to the server.

[1241] "Means for the server to analyze received data, perform authentication, and transmit the result to the device" refers to a system and method for the server to analyze received user data, generate an authentication result, and transmit the result to the user's digital device.

[1242] The present invention is a system that uses a user's body shape data and health information to provide high-security authentication and comprehensive health management plans.

[1243] The system is configured as follows:

[1244] 1. Obtaining user data

[1245] The user uses a digital device (e.g., a smartphone) to scan their body shape and input their health information. The smartphone's camera is used to scan the user's body shape and input basic health information such as height, weight, blood pressure, and basal body temperature. This allows the user to obtain basic data.

[1246] 2. Data transmission and storage

[1247] The data is securely transmitted to the server using encryption protocols (such as TLS / SSL) on the device side. The server anonymizes the received data and stores it in a secure database. This anonymization process protects the user's privacy.

[1248] 3. Data analysis and collation

[1249] The server analyzes the data sent by the user using a specific algorithm to extract features, then compares it with existing data in the database to identify data with high similarity, enabling effective plan generation and highly accurate security authentication.

[1250] 4. Authentication and Plan Generation

[1251] After feature extraction and matching are complete, the server automatically generates a meal menu, exercise plan, and mental health advice tailored to the user. It also generates a token required for security authentication, allowing users to authenticate and manage their health through a single application.

[1252] 5. Offering and Certifying the Plan

[1253] The generated plan and authentication token are sent to the user's device, where they can be reviewed and executed, and security access is granted or denied based on the authentication result.

[1254] 6. Tracking and adjusting progress

[1255] Users enter their daily progress data (e.g., exercise history, diet history, weight fluctuations, etc.) into the device. The device then re-encrypts this data and sends it to the server. The server analyzes the new data and fine-tunes the plan as needed. This ensures that users receive ongoing, appropriate support.

[1256] (Example)

[1257] For example, a 30-year-old female user enters basic information into a smartphone app, such as height 160 cm, weight 65 kg, basal metabolic rate 1400 kcal, low daily exercise, sleep time 7 hours, lipid-rich body, and bone mass. This data is encrypted and sent to the server for analysis. The server then generates and recommends breakfast, lunch, and dinner menus to the user, taking into account the basal metabolic rate of 1400 kcal and setting a daily calorie intake goal of 1700 kcal. The generated token is also sent to the user for security authentication.

[1258] (Example of a prompt)

[1259] Please explain the two-factor authentication smartphone application that uses a body scanning app. Please include the process of acquiring body data and basic health data, encrypting it, sending it to a server, and analyzing the data for authentication.

[1260] To implement this invention, hardware and software such as a smartphone, a secure communication protocol (TLS / SSL), an image processing library (OpenCV), and a data encryption library (cryptography) are required.

[1261] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1262] Step 1:

[1263] Users scan their body type and enter basic information.

[1264] Input: Body image data obtained using a smartphone camera, and basic information such as height, weight, blood pressure, and basal body temperature entered by the user into a form.

[1265] Output: The user dataset before encryption.

[1266] How it works: Users install and launch the app on their smartphone. They use the smartphone's camera to scan their body and obtain image data. They then enter basic information such as height and weight into a form within the app.

[1267] Step 2:

[1268] The device sends the user's data to the server.

[1269] Input: User dataset (image data + basic information) before encryption.

[1270] Output: The encrypted user dataset.

[1271] What it does: Encrypts data on the device using the SSL / TLS protocol. Sends the encrypted data to the server, typically via an HTTP POST request.

[1272] Step 3:

[1273] The server anonymizes the data it receives and stores it in a database.

[1274] Input: The encrypted user dataset.

[1275] Output: Anonymized user data stored in a database.

[1276] What it does: The server receives the data, decrypts it, uses an anonymization algorithm to remove any personally identifiable information, and stores it in a secure database.

[1277] Step 4:

[1278] The server analyzes the user data and compares it with other databases.

[1279] Input: Anonymized user data.

[1280] Output: The collated dataset.

[1281] How it works: The server uses a data analysis algorithm (e.g., a machine learning model) to extract features from user data, then compares it with existing databases to identify data with high similarity.

[1282] Step 5:

[1283] The server generates meal menus, exercise plans, and mental care advice.

[1284] Input: The collated dataset.

[1285] Output: User-optimized meal menu, exercise plan, and mental health advice.

[1286] How it works: The server automatically generates meal plans and exercise plans based on the user's basal metabolic rate and physical constitution. The plans are designed to include the appropriate nutrients and exercise levels for the user. They also include advice on yoga and meditation for mental health.

[1287] Step 6:

[1288] The device provides the generated plan and security authentication token to the user.

[1289] Input: The plan and authentication token generated by the server.

[1290] Output: The plan and authentication token displayed on the user's device.

[1291] Specific operation: The server sends the generated plan and security authentication token to the device. The user can receive and check this information through the application.

[1292] Step 7:

[1293] Users input progress data, and the server analyzes the data and adjusts the plan.

[1294] Input: Data such as the user's daily exercise history, diet history, and weight fluctuations.

[1295] Output: Tailored meal plans, exercise plans, and mental health advice.

[1296] How it works: Users enter their daily progress data into the app. The device encrypts this data and sends it to the server. The server analyzes the new data, adjusts the plan if necessary, and presents it to the user again.

[1297] 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.

[1298] This invention is a system that scans a user's body shape and provides effective health management and dieting. The system analyzes the user's data and compares it with other databases to provide meal menus, exercise plans, and mental care advice tailored to each individual user. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it achieves more detailed mental care and plan adjustments.

[1299] Overall system overview

[1300] 1. Obtaining user data

[1301] The user installs and launches a dedicated application on their smartphone or tablet, scans their body shape, and acquires image data.

[1302] The user enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise amount, sleep time, constitution, and bone structure into a form.

[1303] 2. Data transmission and storage

[1304] The device encrypts the scanned body data and basic information and sends it to a server using a secure communication protocol.

[1305] The server anonymizes the data it receives and stores it in a secure database.

[1306] 3. Data analysis and collation

[1307] The server analyzes the user data and extracts important features using a feature extraction algorithm.

[1308] The server searches for similar data in the database and identifies the right plan for the user based on success and failure.

[1309] 4. Generate a plan

[1310] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise.

[1311] The server generates a nutritionally balanced meal menu and suggests it to the user.

[1312] The server generates a safe and effective exercise plan (exercise type, exercise time, exercise frequency) and recommends it to the user.

[1313] The server will create a comprehensive plan that includes advice on mental care such as stress management.

[1314] 5. Offering of Plans

[1315] The server sends the generated plan to the terminal, and the user can receive and confirm it.

[1316] 6. Track and adjust progress

[1317] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[1318] The device encrypts the new data and sends it to the server.

[1319] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed.

[1320] 7. Emotion Recognition with Emotion Engine

[1321] The user uses the emotion engine to obtain emotion data for that day from voice, facial expressions, text data, etc.

[1322] The device collects emotional data, encrypts it, and sends it to a server.

[1323] The server analyzes the emotional data and adjusts mental care advice.

[1324] 8. Adjust your plan based on sentiment data

[1325] The server further adjusts the meal menu and exercise plan based on the user's emotional state.

[1326] For example, if a user is feeling stressed, it will suggest foods that will reduce stress or an exercise plan to help them relax.

[1327] Specific examples

[1328] 1. Obtaining user data

[1329] For example, a 30-year-old woman who is 160 cm tall and weighs 65 kg launches a dedicated app and scans her body shape.

[1330] The user enters information into the form, including basal body temperature of 36.5°C, basal metabolic rate of 1400 kcal, low daily exercise, 7 hours of sleep, lipid-rich body type, and bone mass.

[1331] 2. Data transmission and storage

[1332] The scan results and basic information are encrypted and sent to the server.

[1333] The server anonymizes the received data and stores it securely in a database.

[1334] 3. Data analysis and collation

[1335] The server analyzes user data and extracts features such as basal metabolic rate and physical constitution.

[1336] The server searches the database for similar data and identifies successful cases (plans that have been successful for people with similar body types and constitutions).

[1337] 4. Generate a plan

[1338] The server takes into account the basal metabolic rate of 1400 kcal and sets the daily calorie intake target at 1700 kcal.

[1339] Suggest oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[1340] It is recommended to walk three times a week and do light strength training twice a week.

[1341] Also includes mental health advice on yoga and meditation.

[1342] 5. Offering of Plans

[1343] The generated plan is sent to the terminal, where the user can confirm and execute it.

[1344] 6. Track and adjust progress

[1345] Users enter their exercise routine and dietary habits into the app every day.

[1346] The server analyzes the new data and tweaks the plan as needed.

[1347] For example, if weight loss is too rapid, adjustments can be made, such as increasing calorie intake.

[1348] 7. Emotion Recognition with Emotion Engine

[1349] For example, a user can write a diary entry into the app by voice, and the emotion engine will determine the user's emotional state from the tone and content of the voice.

[1350] If an emotion such as anger or sadness is detected, the data is sent to a server.

[1351] 8. Adjust your plan based on sentiment data

[1352] The server analyzes the emotional data and provides advice to users who are feeling stressed, such as relaxing meals, light exercise, or meditation.

[1353] Specifically, we will suggest herbal teas that are effective in reducing stress and stretching methods for relaxation.

[1354] Thus, the present invention is a system that comprehensively manages the user's health condition and provides an optimal plan that also takes into account the user's emotional state.

[1355] The processing flow will be explained below.

[1356] Step 1:

[1357] The user launches the app and scans their body.

[1358] The device uses a camera to 3D scan the user's body and generate a highly accurate 3D model.

[1359] Step 2:

[1360] The user enters basic information (height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, bone structure, etc.) into a form.

[1361] Step 3:

[1362] The device encrypts the collected body data and basic information and transmits it to a server using a secure communication protocol.

[1363] Step 4:

[1364] The server anonymizes the data it receives and stores it in a secure database.

[1365] Step 5:

[1366] The server analyzes the user data and extracts important features using a feature extraction algorithm.

[1367] Step 6:

[1368] The server searches for similar data in the database and identifies the right plan for the user based on success and failure.

[1369] Step 7:

[1370] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise.

[1371] Step 8:

[1372] The server generates a nutritionally balanced meal menu and suggests it to the user.

[1373] Step 9:

[1374] The server generates a safe and effective exercise plan (exercise type, exercise time, exercise frequency) and recommends it to the user.

[1375] Step 10:

[1376] The server will create a comprehensive plan that includes advice on mental care, such as stress management.

[1377] Step 11:

[1378] The server sends the generated plan to the terminal, and the user can receive and confirm it.

[1379] Step 12:

[1380] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[1381] Step 13:

[1382] The device encrypts the new data and sends it to the server.

[1383] Step 14:

[1384] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed.

[1385] Step 15:

[1386] The user utilizes the emotion engine to obtain emotion data for that day from voice, facial expression, text data, etc.

[1387] Step 16:

[1388] The device collects emotional data, encrypts it, and sends it to a server.

[1389] Step 17:

[1390] The server analyzes the emotional data and adjusts mental care advice.

[1391] Step 18:

[1392] The server further adjusts the meal menu and exercise plan based on the emotional data.

[1393] For users who feel stressed, the server will suggest foods that will reduce stress and exercise plans that will help them relax.

[1394] Specific examples

[1395] Step 1:

[1396] For example, a 30-year-old woman who is 160 cm tall and weighs 65 kg launches a dedicated app and scans her body shape.

[1397] The device uses a camera to digitize the user's body shape and generate a 3D model.

[1398] Step 2:

[1399] The user enters basic information such as basal body temperature of 36.5°C, basal metabolic rate of 1400 kcal, low daily exercise, 7 hours of sleep, lipid-rich constitution, and bone mass.

[1400] Step 3:

[1401] The device encrypts the scan results and basic information and sends them to the server.

[1402] Step 4:

[1403] The server anonymizes the data it receives and stores it in a secure database.

[1404] Step 5:

[1405] The server analyzes user data and extracts features such as basal metabolic rate and physical constitution.

[1406] Step 6:

[1407] The server searches for similar data in a database and identifies an appropriate plan based on successes and failures.

[1408] Step 7:

[1409] The server takes into account the basal metabolic rate of 1400 kcal and sets the daily calorie intake target at 1700 kcal.

[1410] Step 8:

[1411] The server generates a meal menu that suggests oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[1412] Step 9:

[1413] The server generates an exercise plan that recommends walking three times a week and light strength training twice a week.

[1414] Step 10:

[1415] The server will create a comprehensive plan that includes mental care advice such as yoga and meditation.

[1416] Step 11:

[1417] The generated plan is sent to the terminal and confirmed by the user.

[1418] Step 12:

[1419] Users enter their exercise routine and dietary habits into the app every day.

[1420] Step 13:

[1421] The device encrypts the new data and sends it to the server.

[1422] Step 14:

[1423] The server analyzes progress data and adjusts the plan as needed, for example if weight loss is too rapid.

[1424] Step 15:

[1425] For example, the same user can enter their diary entry into the app by voice, and the emotion engine will recognize emotions such as anger or sadness from the tone and content of their voice.

[1426] Step 16:

[1427] The device collects emotional data, encrypts it, and sends it to a server.

[1428] Step 17:

[1429] The server analyzes the emotional data and suggests stress-reducing advice to users who are feeling stressed.

[1430] Step 18:

[1431] Based on the emotional data, the server can make additional suggestions, such as herbal tea that reduces stress or yoga stretches to help you relax.

[1432] Thus, the present invention is a system that comprehensively manages the user's health condition and provides an optimal plan that also takes into account the user's emotional state.

[1433] Example 2

[1434] 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."

[1435] Health management and dieting have become important themes in modern society. However, many users find it difficult to accurately grasp their own body shape and health condition, and are unable to receive appropriate meal menus, exercise plans, or mental care advice. Furthermore, there are few systems that provide comprehensive health support that takes emotional state into account, meaning that many users are unable to receive appropriate healthcare. This makes effective health management difficult, and presents challenges for maintaining sustainable health.

[1436] 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.

[1437] In this invention, the server includes: [means for anonymizing received data and storing it in a secure database]; [means for extracting features of user data and searching for data with high similarity]; and [means for acquiring and analyzing user emotional data.] This makes it possible to comprehensively assess the user's health and emotional state and provide optimal meal menus, exercise plans, and mental care advice.

[1438] "User" refers to a person who provides their body data and basic information using a dedicated application.

[1439] "Terminal" refers to equipment that collects user data and transmits it to a server using encryption technology. Specifically, this includes devices such as smartphones and tablets.

[1440] "Server" refers to a computer system that receives data sent by users and performs anonymization processing, storage, analysis, collation, and plan generation.

[1441] "Body scanning means" refers to the imaging device and image analysis technology used by a user to obtain their body data.

[1442] "Basic information" refers to basic biometric information about the user, such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and skeleton.

[1443] "Encryption Technology" means technology used to keep data confidential. Examples include AES encryption.

[1444] "Anonymization" refers to techniques that process data so that individuals cannot be identified. Examples include k-Anonymity.

[1445] "Database" refers to a system for storing and managing structured data, including Amazon RDS.

[1446] "Features" refer to important information that machine learning models extract from data, allowing for a more accurate understanding of the attributes and state of the data.

[1447] "Meal Menu" refers to a meal plan suggested based on the user's nutritional balance and health goals.

[1448] "Exercise Plan" refers to an exercise plan suggested based on the user's physical abilities and health goals.

[1449] "Mental health advice" refers to advice or activities offered to support a user's mental health, such as yoga or meditation.

[1450] "Emotion engine" refers to technology that analyzes a user's emotional state from their voice, facial expressions, and text data.

[1451] A "protocol" refers to a set of defined rules or procedures for transmitting data. An example is HTTPS.

[1452] A "machine learning algorithm" refers to a computational method for analyzing data and extracting features.

[1453] The present invention provides a system that effectively supports health management and dieting based on a user's body type data and personal information. This system provides the user with an optimal health plan through a series of processes including body type scanning by the user, data transmission, data analysis by the server, and plan generation and provision.

[1454] 1. Obtaining user data

[1455] First, the user installs and launches a dedicated application on their smartphone or tablet. We will use "MyHealthManager" as an example of this application. Next, the user scans their body shape using the app's camera function, and acquires image data. The OpenCV image processing library is used for this body shape scan.

[1456] In addition, users can use the form within the app to enter basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure.

[1457] 2. Data transmission and storage

[1458] The device encrypts the body scan data and input information obtained from the user using AES encryption technology. The encrypted data is sent to the server using the HTTPS protocol. Industry-standard encryption and communication protocols are used to ensure secure communication.

[1459] The server anonymizes the received data using an anonymization algorithm (specifically, k-Anonymity) and stores it in a secure database (e.g., Amazon RDS).

[1460] 3. Data analysis and collation

[1461] The server analyzes the stored user data using machine learning libraries (e.g., Scikit-learn) to extract features related to the user's health and body shape. These features are then matched with similar data in a database to identify successful and unsuccessful cases.

[1462] 4. Plan Generation and Delivery

[1463] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise. For example, if the basal metabolic rate is 1400 kcal, the target calorie intake is set to 1700 kcal. The server then generates a meal menu that takes nutritional balance into consideration. For example, it might suggest oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[1464] The server also generates and provides users with exercise plans, such as walking three times a week and light strength training twice a week, and mental health advice, including yoga and meditation.

[1465] The generated plan is sent to the terminal, where the user can review and execute the plan.

[1466] Specific examples

[1467] For example, consider a 30-year-old woman who is 160 cm tall and weighs 65 kg. She has a basal body temperature of 36.5°C, a basal metabolic rate of 1,400 kcal, little daily exercise, seven hours of sleep, a lipid-rich constitution, and a medium-bone structure. She launches the app, scans her body type, and enters this basic information into a form.

[1468] For this user, the server generates the following plan:

[1469] Meal plan: Oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[1470] Exercise plan: Walking 3 times a week and light strength training 2 times a week

[1471] Mental care advice: Yoga and meditation

[1472] Prompt Sentence Examples

[1473] Below is an example of a prompt sentence to input to the generative AI model.

[1474] "We have a 30-year-old female user who is 160cm tall and weighs 65kg. Her basal body temperature is 36.5°C, her basal metabolic rate is 1400kcal, she exercises little, she sleeps 7 hours a day, she has a lipid-rich constitution, and she has a medium-sized bone. Please provide her with a daily meal plan, exercise plan, and mental health advice that is appropriate for her."

[1475] This allows the system to comprehensively manage the user's health status and provide an optimal plan that also takes into account their emotional state.

[1476] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1477] Step 1:

[1478] The user installs and launches the dedicated application "MyHealthManager" on their smartphone or tablet. The application uses the camera function to scan the user's body shape. The input data is 3D body scan data, which is then processed using the image processing library OpenCV and output as body shape data.

[1479] Step 2:

[1480] The user enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure into a form within the app. The input data is basic information in text format, which is saved in the application and output as the user's basic information.

[1481] Step 3:

[1482] The device encrypts the acquired body shape data and basic user information using AES encryption technology. The input data is body shape data and basic information, and the encrypted data is output.

[1483] Step 4:

[1484] The device sends encrypted data to the server via the HTTPS protocol. The input is encrypted body shape data and basic information, and the output is the data securely sent to the server.

[1485] Step 5:

[1486] The server anonymizes the data it receives using the k-Anonymity algorithm. The input data is encrypted user data, and the output is anonymized data.

[1487] Step 6:

[1488] The server stores the anonymized data in a secure database (e.g., Amazon RDS). The input is the anonymized data, and the output is the stored database entry.

[1489] Step 7:

[1490] The server analyzes the stored user data using the machine learning library Scikit-learn and extracts features. The input is the user data in the database, and the output is the extracted features.

[1491] Step 8:

[1492] The server searches for similar data in the database and identifies successful or unsuccessful cases. The input is the extracted features, and the output is the data of the identified successful or unsuccessful cases.

[1493] Step 9:

[1494] The server calculates the target daily calorie intake based on the basal metabolic rate and daily exercise amount. The input data is the basal metabolic rate and exercise amount, and the output is the target daily calorie intake.

[1495] Step 10:

[1496] The server generates meal menus taking nutritional balance into consideration. The input is the target calorie intake and criteria for nutritional balance, and the output is meal menus for breakfast, lunch, and dinner.

[1497] Step 11:

[1498] The server generates the optimal exercise plan for the user. The input is body type data and basal metabolic rate, and the output is a weekly exercise plan.

[1499] Step 12:

[1500] The server generates advice for mental health, including yoga and meditation. The input is the user's stress level and emotional data, and the output is mental health advice.

[1501] Step 13:

[1502] The server sends the generated plan (meal menu, exercise plan, mental care advice) to the terminal. The input is the generated plan, and the output is the plan display on the terminal.

[1503] Step 14:

[1504] The user inputs data into the app, such as daily exercise, diet, weight fluctuations, and physical condition. The input is the user's daily data, and the output is updated health status data.

[1505] Step 15:

[1506] The device encrypts daily data and sends it to the server. The input is the updated health status data, and the output is the encrypted data sent to the server.

[1507] Step 16:

[1508] The server analyzes the daily data and adjusts the plan as needed. The input is the daily health data and the output is the adjusted plan.

[1509] Step 17:

[1510] The user uses the emotion engine to obtain emotion data from voice, facial expressions, and text data. The input is the user's emotion data, and the output is the analyzed emotion results.

[1511] Step 18:

[1512] The device encrypts the emotion data and sends it to the server. The input is emotion data, and the output is the encrypted emotion data sent to the server.

[1513] Step 19:

[1514] The server analyzes the emotional data and adjusts mental care advice based on the emotional state. The input is the analyzed emotional result, and the output is the adjusted mental care advice.

[1515] (Application example 2)

[1516] 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."

[1517] Conventional health management systems could obtain a user's body type and basic information and provide meal menus, exercise plans, and mental care advice based on that information, but they were unable to provide specific product suggestions or location information for products in stores. They also lacked the ability to fine-tune plans by taking into account the user's emotional state. This made it difficult for users to smoothly find the right products in stores and was unable to receive appropriate advice based on their emotional state.

[1518] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for the user to scan their body type and input basic information, a means for the terminal to send the user's data to the server, a means for the server to analyze the data and compare it with other databases, a means for the server to generate a meal menu, an exercise plan, and mental care advice, a means for the terminal to provide the generated plan to the user, a means for the user to input progress data and the server to analyze the data and adjust the plan, a means for measuring body type using a scanning device in the store, a means for providing the user with optimal food and product placement information, and a means for analyzing the user's emotional data using an emotion engine and fine-tuning the plan based on the data. This makes it easier for users to find optimal products in the store through the health management system and receive more detailed advice tailored to their emotional state.

[1519] "User" means an individual who utilizes the system to provide information for body scanning and health management.

[1520] "Body scanning" refers to using a device to measure the shape and dimensions of a user's body and capture that data in digital form.

[1521] "Basic information" refers to biological data and lifestyle information that users input into the system, including height, weight, basal metabolic rate, daily exercise, sleep duration, and physical constitution.

[1522] A "terminal" is a device that is directly operated by a user, and refers to electronic devices such as smartphones and tablets.

[1523] A "server" is a computing resource that analyzes and stores data and provides information to users, and refers to a high-performance computer located in a cloud environment or on a network.

[1524] "Analyzing the data" means processing the data submitted by the user and extracting meaningful information.

[1525] A "database" is a collection of large amounts of accumulated data that is structured and stored in a format that allows for searching and matching.

[1526] "Meal menu" refers to a combination of ingredients and dishes suggested taking into consideration the user's health condition and nutritional balance.

[1527] "Exercise plan" refers to a plan that specifically indicates the type, time, and frequency of exercise based on the user's health condition and goals.

[1528] "Mental health advice" refers to suggestions and guidance for maintaining and improving a user's mental health.

[1529] "Progress data" refers to data related to diet, exercise, weight fluctuations, physical condition, etc. that users record in their daily lives.

[1530] "In-store scanning device" refers to a device installed in a physical store that allows customers to measure their body measurements.

[1531] "Product placement information" refers to data that indicates the specific placement locations of products within a store that a user is considering purchasing.

[1532] An "emotion engine" refers to software or hardware for analyzing a user's emotional state from their voice, facial expressions, text data, etc.

[1533] "Emotion data" is digital data that indicates the user's emotional state and is analyzed by the emotion engine.

[1534] A "recommendation list" refers to a list of products or actions that are recommended for a user to purchase or perform based on their health information or emotional state.

[1535] A system for implementing this invention includes a means for a user to scan their body type and input basic information, a means for transmitting the data to a server, a means for the server to analyze the data and compare it with other databases, a means for generating a meal menu, an exercise plan, and mental care advice, a means for the user to input progress data and analyze the data, a means for measuring body type using a scanning device in a store, a means for providing optimal food and product placement information, and a means for analyzing the user's emotional data using an emotion engine and fine-tuning the plan based on the data.

[1536] The server anonymizes the data it receives and stores it in a database. This database structures the large amount of accumulated data and stores it in a format that allows for search and matching. The server also extracts features from the user data and generates meal menus, exercise plans, and mental care advice based on data with high similarity.

[1537] In this system, users first install a dedicated application on their smartphone, tablet, or other device. Next, they measure their body shape using a scanning device in the store and enter that data and basic information (e.g., height, weight, basal metabolic rate, daily exercise, sleep duration, physical constitution, etc.) into the application. This information is encrypted and sent to the server using a secure communication protocol (e.g., HTTPS).

[1538] The server receives the transmitted data and analyzes it. During the analysis, it extracts features from the user data and compares them with similar databases to generate optimal meal plans, exercise plans, and mental care advice for the user. It also uses an emotion engine to analyze the user's emotional data and fine-tune the plan as needed. The emotion engine determines the user's emotional state from their voice, facial expressions, or text data.

[1539] The generated plan is then encrypted again and sent to the user's device. The user can then use this plan to select products in the store, and specific product placement information within the store is also provided, allowing the user to efficiently find the products they need.

[1540] For example, a user visits a health food store, measures their body shape with a 3D body scanner, and then enters the results and basic information into a smartphone app. This data is sent to a cloud server for analysis. The generated plan includes recommended foods (e.g., protein bars, low-calorie snacks, etc.) and provides detailed location information for them. The app helps users find products easily, and also suggests stress-reducing foods based on their emotional state.

[1541] Examples of prompts include:

[1542] "I want to create an app that scans a user's body shape and health data and suggests food and exercise products based on their individual health status. The user scans their body shape in the store, enters the necessary data, and based on the information analyzed by the server, provides the most suitable products and their placement information."

[1543] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1544] Step 1:

[1545] The user installs a dedicated application on their smartphone or tablet and creates a profile. At this time, the user enters basic information such as height, weight, basal metabolic rate, daily exercise, sleep time, and physical constitution. The application temporarily saves the entered basic information in local storage. The entered data is used in the next step.

[1546] Step 2:

[1547] The user scans their body shape using a scanning device (e.g., a 3D body scanner) installed in the store. The scanning device captures the user's body shape data (e.g., the outer shape of the body and the dimensions of each part of the body) and sends the data to a smartphone application. The body shape data is encrypted and used in the next step.

[1548] Step 3:

[1549] The device (smartphone or tablet) encrypts the entered basic information and scanned data and sends it to a cloud server using a secure communication protocol (e.g., HTTPS). The sent data is then received by the server.

[1550] Step 4:

[1551] The server analyzes the received data. During the analysis, for example, a Python library is used to preprocess the data (fill in missing values, detect outliers) and extract features from the user data. These extracted features are then compared with other databases to search for data with high similarity. This process provides the information needed to generate meal menus and exercise plans tailored to the user.

[1552] Step 5:

[1553] The server generates optimal meal plans, exercise plans, and mental health advice for each user using machine learning models and rule-based approaches. For example, a generative AI model is used to suggest optimal meal plans that take into account the user's basal metabolic rate and daily activity level. The generated plans are encrypted.

[1554] Step 6:

[1555] The server sends the generated plan to the terminal, which then decodes the received data and displays it to the user, along with specific product location information to help the user easily find the food or product they need in the store.

[1556] Step 7:

[1557] The user selects and purchases the suggested products in the store. During this process, the application records the user's purchase history and selection trends and uses them for future suggestions. This data is temporarily stored in local storage.

[1558] Step 8:

[1559] The user enters progress data (e.g., dietary details, exercise details, weight fluctuations, physical condition, etc.) into the application. The entered data is encrypted and sent to the server, which then receives it.

[1560] Step 9:

[1561] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed. For example, it uses machine learning algorithms to fine-tune calorie intake and exercise intensity based on the user's progress data. The analysis results are then provided back to the user.

[1562] Step 10:

[1563] The user uses the emotion engine to obtain emotional data. Emotional data is collected from voice, facial expressions, text input, etc. and analyzed using voice analysis software and natural language processing models. This analyzed data is encrypted and sent to the server.

[1564] Step 11:

[1565] The server analyzes the emotional data and further adjusts mental health advice, meal plans, and exercise plans as needed. For example, if the user is feeling stressed, it will suggest foods that have a relaxing effect and light exercise. These adjustments are provided to the user on a case-by-case basis.

[1566] 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.

[1567] 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.

[1568] 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.

[1569] [Fourth embodiment]

[1570] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1571] 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.

[1572] 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).

[1573] 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.

[1574] 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.

[1575] 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).

[1576] 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.

[1577] 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.

[1578] 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.

[1579] 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.

[1580] 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.

[1581] 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.

[1582] 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."

[1583] This invention is a system that allows users to scan their own body shape and effectively manage their health and diet. The system analyzes the user's data and compares it with other databases to provide individualized meal menus, exercise plans, and mental health advice.

[1584] Overall system overview

[1585] 1. Obtaining user data

[1586] The user installs and launches a dedicated application on their smartphone or tablet, scans their body shape, and acquires image data.

[1587] The user enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise amount, sleep time, constitution, and bone structure into a form.

[1588] 2. Data transmission and storage

[1589] The device encrypts the scanned body data and basic information and sends it to a server using a secure communication protocol.

[1590] The server anonymizes the data it receives and stores it in a secure database.

[1591] 3. Data analysis and collation

[1592] The server analyzes the user data and extracts important features using a feature extraction algorithm.

[1593] The server searches for similar data in the database and identifies the right plan for the user based on success and failure.

[1594] 4. Generate a plan

[1595] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise.

[1596] The server generates a nutritionally balanced meal menu and suggests it to the user.

[1597] The server generates a safe and effective exercise plan (exercise type, exercise time, exercise frequency) and recommends it to the user.

[1598] The server will create a comprehensive plan that includes advice on mental care such as stress management.

[1599] 5. Offering of Plans

[1600] The server sends the generated plan to the terminal, and the user can receive and confirm it.

[1601] 6. Track and adjust progress

[1602] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[1603] The device encrypts the new data and sends it to the server.

[1604] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed.

[1605] Specific examples

[1606] 1. Obtaining user data

[1607] For example, a 30-year-old woman who is 160 cm tall and weighs 65 kg launches a dedicated app and scans her body shape.

[1608] The user enters information into the form, including basal body temperature of 36.5°C, basal metabolic rate of 1400 kcal, low daily exercise, 7 hours of sleep, lipid-rich body type, and bone mass.

[1609] 2. Data transmission and storage

[1610] The scan results and basic information are encrypted and sent to the server.

[1611] The server anonymizes the received data and stores it securely in a database.

[1612] 3. Data analysis and collation

[1613] The server analyzes user data and extracts features such as basal metabolic rate and physical constitution.

[1614] The server searches the database for similar data and identifies successful cases (plans that have been successful for people with similar body types and constitutions).

[1615] 4. Generate a plan

[1616] The server takes into account the basal metabolic rate of 1400 kcal and sets the daily calorie intake target at 1700 kcal.

[1617] Suggest oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[1618] It is recommended to walk three times a week and do light strength training twice a week.

[1619] Also includes mental health advice on yoga and meditation.

[1620] 5. Offering of Plans

[1621] The generated plan is sent to the terminal, where the user can confirm and execute it.

[1622] 6. Track and adjust progress

[1623] Users enter their exercise routine and dietary habits into the app every day.

[1624] The server analyzes the new data and tweaks the plan as needed.

[1625] For example, if weight loss is too rapid, adjustments can be made, such as increasing calorie intake.

[1626] The system provides health management and diet plans optimized for each individual user.

[1627] The processing flow will be explained below.

[1628] Step 1:

[1629] The user launches the app and scans their body.

[1630] The device uses a camera to 3D scan the user's body and generate a highly accurate 3D model.

[1631] Step 2:

[1632] The user enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise amount, sleep time, constitution, and bone structure into a form.

[1633] Step 3:

[1634] The device encrypts the collected body shape data and basic information and transmits it to a server using a secure communication protocol.

[1635] Step 4:

[1636] The server anonymizes the data it receives and stores it in a secure database.

[1637] Step 5:

[1638] The server analyzes the user data and extracts important features using a feature extraction algorithm.

[1639] Step 6:

[1640] The server searches for similar data in the database and identifies the right plan for the user based on success and failure.

[1641] Step 7:

[1642] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise.

[1643] Step 8:

[1644] The server generates a nutritionally balanced meal menu and suggests it to the user.

[1645] Step 9:

[1646] The server generates a safe and effective exercise plan (exercise type, exercise time, exercise frequency) and recommends it to the user.

[1647] Step 10:

[1648] The server will create a comprehensive plan that includes advice on mental care such as stress management.

[1649] Step 11:

[1650] The server sends the generated plan to the terminal, and the user can receive and confirm it.

[1651] Step 12:

[1652] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[1653] Step 13:

[1654] The device encrypts the new data and sends it to the server.

[1655] Step 14:

[1656] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed.

[1657] Example 1

[1658] 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."

[1659] The present invention relates to a system that allows users to scan their own body shape and effectively manage their health and diet. Conventional health management systems often lack security and accuracy in the acquisition, transmission, storage, and analysis of user data. They also lack effective methods for providing individual users with optimized meal menus, exercise plans, and mental health advice. This makes it difficult for users to implement and maintain their health management and diet plans.

[1660] 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.

[1661] In this invention, the server includes: [means for the user to acquire body type data and input basic information;] [means for the terminal to encrypt the user's data and send it to the server; and [means for the server to anonymize the received data and store it in a secure database.] This improves the safety and accuracy of the data, making it possible to provide health management and diet plans specific to the user.

[1662] "User" refers to an individual who obtains body data and enters basic information.

[1663] "Body shape data" refers to image data and dimensional data obtained by scanning the user's body shape.

[1664] "Basic information" refers to data entered by the user, such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure.

[1665] "Terminal" refers to a computing device used by a User, such as a smartphone or tablet.

[1666] "Encryption" refers to the process of transforming data using encryption technology, such as the Advanced Encryption Standard (AES), to protect the data.

[1667] "Server" refers to a computer system that receives, analyzes, and stores data submitted by users.

[1668] "Anonymization" refers to the process of removing personally identifiable information from received data.

[1669] "Database" means a secure information storage system for storing received data.

[1670] "Feature extraction" refers to the process by which the server identifies and extracts important elements from user data.

[1671] "Meal Menu" refers to a nutritionally balanced meal plan suggested to the user.

[1672] An "exercise plan" refers to an exercise plan that includes safe and effective exercise types, exercise duration, and exercise frequency.

[1673] "Mental health advice" refers to advice to support stress management and psychological well-being.

[1674] "Progress data" refers to data entered by the user daily, such as exercise status, dietary habits, weight fluctuations, and physical condition.

[1675] This invention is a system that allows users to scan their own body shape and effectively manage their health and diet. The system analyzes the user's data and compares it with other databases to provide individualized meal menus, exercise plans, and mental health advice.

[1676] User actions

[1677] 1. Installing the dedicated application

[1678] The user installs and launches a dedicated application on their smartphone or tablet.

[1679] 2. Scan your body and enter basic information

[1680] The user follows the instructions in the application to scan their body shape using the camera and capture image data.

[1681] Next, enter basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure into the form within the app.

[1682] Device operation

[1683] 3. Data Encryption and Transmission

[1684] The body shape data and basic information acquired by the device are encrypted using an encryption protocol such as AES (Advanced Encryption Standard).

[1685] The encrypted data is sent to the server via the HTTPS protocol.

[1686] Server Operations

[1687] 4. Data anonymization and storage

[1688] The server removes any personally identifiable information from the received data and performs anonymization processing.

[1689] Store the anonymized data in a secure database (e.g. AWS RDS).

[1690] 5. Data analysis and feature extraction

[1691] The server extracts features from the user data using Python's scikit-learn library, for example, by using principal component analysis (PCA) to identify important features.

[1692] The server searches for similar data in the database and identifies a plan that is suitable for the user based on success and failure.

[1693] 6. Generate a plan

[1694] The server calculates the target calorie intake (e.g., 1700 kcal per day) based on the user's basal metabolic rate (e.g., 1400 kcal) and activity level.

[1695] The server generates a nutritionally balanced meal menu, suggesting oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[1696] The server generates a safe and effective exercise plan, recommending, for example, three days of walking and two days of light strength training per week.

[1697] The server will create a comprehensive plan that includes advice on stress management and mental health, including yoga and meditation as examples.

[1698] Plan Offerings

[1699] 7. Submit the generated plan

[1700] The server sends the generated plan to the terminal so that the user can receive and check it.

[1701] Track and adjust progress

[1702] 8. Enter and submit new data

[1703] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[1704] The device encrypts the new data and sends it to the server.

[1705] 9. Analyze progress data and adjust plans

[1706] The server analyzes the progress data and adjusts the diet and exercise plan as needed, for example, increasing calorie intake if weight loss is too rapid.

[1707] Prompt Sentence Examples

[1708] "A 30-year-old woman, 160cm tall, 65kg weight, basal metabolic rate of 1400kcal, low daily exercise, lipid-rich body. Please suggest the best meal menu and exercise plan for this user."

[1709] "A user is walking three times a week and doing light strength training twice a week, but is losing weight rapidly. Could you please advise how to adjust their calorie intake?"

[1710] This system makes it possible to provide users with optimized health management and diet plans, enabling effective health management.

[1711] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1712] Step 1:

[1713] The user obtains body shape data and enters basic information

[1714] The user installs a dedicated application on a smartphone or tablet and launches it. The input is the launch of the app.

[1715] The user scans their body using a camera and obtains image data. The user then enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure into a form within the app. The obtained body data and basic information are then output.

[1716] Step 2:

[1717] The device encrypts and transmits data

[1718] The device encrypts the body shape data and basic information acquired from the user using encryption technology such as the AES protocol. The input is the body shape data and basic information. The encrypted data is then output.

[1719] The device sends the encrypted data to the server using the HTTPS protocol. The sent encrypted data is output.

[1720] Step 3:

[1721] The server anonymizes and stores the data

[1722] The server decrypts the received encrypted data and performs an anonymization process to remove any personally identifiable information. The input is the encrypted data. The output is anonymized data.

[1723] The server stores the anonymized data in a secure database. The stored data is then output.

[1724] Step 4:

[1725] The server analyzes the data and extracts features

[1726] The server extracts features from user data using Python's scikit-learn library or similar. The input is anonymized data. Specifically, it uses principal component analysis (PCA) to identify important features. The resulting extracted features are output.

[1727] The server searches for similar data in a database and runs SQL queries to identify successes and failures, which results in the output of similar data.

[1728] Step 5:

[1729] The server generates a plan

[1730] The server calculates the target calorie intake based on the user's basal metabolic rate (e.g., 1400 kcal) and activity level, and generates a meal menu. The extracted features are used as input. As a specific example, it suggests oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner. The generated meal menu is then output.

[1731] The server generates a safe and effective exercise plan. For example, it recommends walking three times a week and light strength training twice a week. The generated exercise plan is then output.

[1732] The server creates a comprehensive plan that includes advice on stress management and mental health. For example, it adds advice on yoga and meditation. This outputs a comprehensive plan.

[1733] Step 6:

[1734] The server sends the generated plan to the device.

[1735] The server sends the generated plan to the terminal. The generated plan is taken as input. The transmitted plan is output.

[1736] Step 7:

[1737] The user enters progress data and sends it to the server

[1738] The user inputs data such as daily exercise status, dietary habits, weight fluctuations, and physical condition into the application. Progress data is input, and the input progress data is output.

[1739] The device encrypts this data using protocols such as AES and sends it to the server, which then outputs the encrypted progress data.

[1740] Step 8:

[1741] The server analyzes the progress data and adjusts the plan

[1742] The server analyzes the progress data and adjusts the diet and exercise plan as needed. For example, if weight loss is too rapid, it may increase calorie intake. The input is the progress data sent. The output is an adjusted plan.

[1743] (Application example 1)

[1744] 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."

[1745] In today's security environment, ensuring the safety of personal information is a critical issue, especially in authentication processes that require high security. Traditional passwords and simple two-step authentication are often insufficient. Furthermore, there is a growing need to effectively utilize health management and diet data while safely managing and using that data effectively. Against this background, there is a demand for a system that can perform health management and authentication processes in one place, while providing a higher level of security by using personal health information for security authentication.

[1746] 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.

[1747] In this invention, the server includes a means for the user to scan their body type and input basic information, a means for the terminal to send the user's data to the server, and a means for the server to analyze the data and compare it with other databases. This enables high-security authentication and the provision of comprehensive health management plans.

[1748] "Means for users to scan their body and enter basic information" refers to the interface and device configuration that allows users to use a digital device to scan their body and enter basic health information such as height, weight, and blood pressure.

[1749] "Means for the device to transmit user data to a server" refers to the functionality and protocols that allow the digital device to transmit acquired body shape data and health information to a remote server using a secure communications protocol.

[1750] "Means for the server to analyze data and compare it with other databases" refers to software and algorithms that the server uses to analyze the user data it receives and compare it with existing databases.

[1751] The "means by which the server generates meal menus, exercise plans, and mental care advice" refers to an algorithm that automatically generates meal menus, exercise plans, and mental care advice appropriate for the user based on the data analyzed by the server.

[1752] "Means for the terminal to provide the generated plan to the user" refers to a digital device and interface for providing the server-generated plan so that the user can access it.

[1753] "Means for users to input progress data and for the server to analyze that data and adjust the plan" refers to a function / system that allows users to input their daily progress data (exercise history, diet history, weight fluctuations, etc.) and for the server to analyze that data and optimize the plan.

[1754] "Means for utilizing body scan and health data as security authentication" refers to systems and methods for using a user's body scan data and health information as part of security authentication.

[1755] "Means for encrypting user health data and transmitting it to the server" refers to protocols and algorithms for securely protecting user health data using encryption technology and transmitting that data to the server.

[1756] "Means for the server to analyze received data, perform authentication, and transmit the result to the device" refers to a system and method for the server to analyze received user data, generate an authentication result, and transmit the result to the user's digital device.

[1757] The present invention is a system that uses a user's body shape data and health information to provide high-security authentication and comprehensive health management plans.

[1758] The system is configured as follows:

[1759] 1. Obtaining user data

[1760] The user uses a digital device (e.g., a smartphone) to scan their body shape and input their health information. The smartphone's camera is used to scan the user's body shape and input basic health information such as height, weight, blood pressure, and basal body temperature. This allows the user to obtain basic data.

[1761] 2. Data transmission and storage

[1762] The data is securely transmitted to the server using encryption protocols (such as TLS / SSL) on the device side. The server anonymizes the received data and stores it in a secure database. This anonymization process protects the user's privacy.

[1763] 3. Data analysis and collation

[1764] The server analyzes the data sent by the user using a specific algorithm to extract features, then compares it with existing data in the database to identify data with high similarity, enabling effective plan generation and highly accurate security authentication.

[1765] 4. Authentication and Plan Generation

[1766] After feature extraction and matching are complete, the server automatically generates a meal menu, exercise plan, and mental health advice tailored to the user. It also generates a token required for security authentication, allowing users to authenticate and manage their health through a single application.

[1767] 5. Offering and Certifying the Plan

[1768] The generated plan and authentication token are sent to the user's device, where they can be reviewed and executed, and security access is granted or denied based on the authentication result.

[1769] 6. Tracking and adjusting progress

[1770] Users enter their daily progress data (e.g., exercise history, diet history, weight fluctuations, etc.) into the device. The device then re-encrypts this data and sends it to the server. The server analyzes the new data and fine-tunes the plan as needed. This ensures that users receive ongoing, appropriate support.

[1771] (Example)

[1772] For example, a 30-year-old female user enters basic information into a smartphone app, such as height 160 cm, weight 65 kg, basal metabolic rate 1400 kcal, low daily exercise, sleep time 7 hours, lipid-rich body, and bone mass. This data is encrypted and sent to the server for analysis. The server then generates and recommends breakfast, lunch, and dinner menus to the user, taking into account the basal metabolic rate of 1400 kcal and setting a daily calorie intake goal of 1700 kcal. The generated token is also sent to the user for security authentication.

[1773] (Example of a prompt)

[1774] Please explain the two-factor authentication smartphone application that uses a body scanning app. Please include the process of acquiring body data and basic health data, encrypting it, sending it to a server, and analyzing the data for authentication.

[1775] To implement this invention, hardware and software such as a smartphone, a secure communication protocol (TLS / SSL), an image processing library (OpenCV), and a data encryption library (cryptography) are required.

[1776] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1777] Step 1:

[1778] Users scan their body type and enter basic information.

[1779] Input: Body image data obtained using a smartphone camera, and basic information such as height, weight, blood pressure, and basal body temperature entered by the user into a form.

[1780] Output: The user dataset before encryption.

[1781] How it works: Users install and launch the app on their smartphone. They use the smartphone's camera to scan their body and obtain image data. They then enter basic information such as height and weight into a form within the app.

[1782] Step 2:

[1783] The device sends the user's data to the server.

[1784] Input: User dataset (image data + basic information) before encryption.

[1785] Output: The encrypted user dataset.

[1786] What it does: Encrypts data on the device using the SSL / TLS protocol. Sends the encrypted data to the server, typically via an HTTP POST request.

[1787] Step 3:

[1788] The server anonymizes the data it receives and stores it in a database.

[1789] Input: The encrypted user dataset.

[1790] Output: Anonymized user data stored in a database.

[1791] What it does: The server receives the data, decrypts it, uses an anonymization algorithm to remove any personally identifiable information, and stores it in a secure database.

[1792] Step 4:

[1793] The server analyzes the user data and compares it with other databases.

[1794] Input: Anonymized user data.

[1795] Output: The collated dataset.

[1796] How it works: The server uses a data analysis algorithm (e.g., a machine learning model) to extract features from user data, then compares it with existing databases to identify data with high similarity.

[1797] Step 5:

[1798] The server generates meal menus, exercise plans, and mental care advice.

[1799] Input: The collated dataset.

[1800] Output: User-optimized meal menu, exercise plan, and mental health advice.

[1801] How it works: The server automatically generates meal plans and exercise plans based on the user's basal metabolic rate and physical constitution. The plans are designed to include the appropriate nutrients and exercise levels for the user. They also include advice on yoga and meditation for mental health.

[1802] Step 6:

[1803] The device provides the generated plan and security authentication token to the user.

[1804] Input: The plan and authentication token generated by the server.

[1805] Output: The plan and authentication token displayed on the user's device.

[1806] Specific operation: The server sends the generated plan and security authentication token to the device. The user can receive and check this information through the application.

[1807] Step 7:

[1808] Users input progress data, and the server analyzes the data and adjusts the plan.

[1809] Input: Data such as the user's daily exercise history, diet history, and weight fluctuations.

[1810] Output: Tailored meal plans, exercise plans, and mental health advice.

[1811] How it works: Users enter their daily progress data into the app. The device encrypts this data and sends it to the server. The server analyzes the new data, adjusts the plan if necessary, and presents it to the user again.

[1812] 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.

[1813] This invention is a system that scans a user's body shape and provides effective health management and dieting. The system analyzes the user's data and compares it with other databases to provide meal menus, exercise plans, and mental care advice tailored to each individual user. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it achieves more detailed mental care and plan adjustments.

[1814] Overall system overview

[1815] 1. Obtaining user data

[1816] The user installs and launches a dedicated application on their smartphone or tablet, scans their body shape, and acquires image data.

[1817] The user enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise amount, sleep time, constitution, and bone structure into a form.

[1818] 2. Data transmission and storage

[1819] The device encrypts the scanned body data and basic information and sends it to a server using a secure communication protocol.

[1820] The server anonymizes the data it receives and stores it in a secure database.

[1821] 3. Data analysis and collation

[1822] The server analyzes the user data and extracts important features using a feature extraction algorithm.

[1823] The server searches for similar data in the database and identifies the right plan for the user based on success and failure.

[1824] 4. Generate a plan

[1825] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise.

[1826] The server generates a nutritionally balanced meal menu and suggests it to the user.

[1827] The server generates a safe and effective exercise plan (exercise type, exercise time, exercise frequency) and recommends it to the user.

[1828] The server will create a comprehensive plan that includes advice on mental care such as stress management.

[1829] 5. Offering of Plans

[1830] The server sends the generated plan to the terminal, and the user can receive and confirm it.

[1831] 6. Track and adjust progress

[1832] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[1833] The device encrypts the new data and sends it to the server.

[1834] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed.

[1835] 7. Emotion Recognition with Emotion Engine

[1836] The user uses the emotion engine to obtain emotion data for that day from voice, facial expressions, text data, etc.

[1837] The device collects emotional data, encrypts it, and sends it to a server.

[1838] The server analyzes the emotional data and adjusts mental care advice.

[1839] 8. Adjust your plan based on sentiment data

[1840] The server further adjusts the meal menu and exercise plan based on the user's emotional state.

[1841] For example, if a user is feeling stressed, it will suggest foods that will reduce stress or an exercise plan to help them relax.

[1842] Specific examples

[1843] 1. Obtaining user data

[1844] For example, a 30-year-old woman who is 160 cm tall and weighs 65 kg launches a dedicated app and scans her body shape.

[1845] The user enters information into the form, including basal body temperature of 36.5°C, basal metabolic rate of 1400 kcal, low daily exercise, 7 hours of sleep, lipid-rich body type, and bone mass.

[1846] 2. Data transmission and storage

[1847] The scan results and basic information are encrypted and sent to the server.

[1848] The server anonymizes the received data and stores it securely in a database.

[1849] 3. Data analysis and collation

[1850] The server analyzes user data and extracts features such as basal metabolic rate and physical constitution.

[1851] The server searches the database for similar data and identifies successful cases (plans that have been successful for people with similar body types and constitutions).

[1852] 4. Generate a plan

[1853] The server takes into account the basal metabolic rate of 1400 kcal and sets the daily calorie intake target at 1700 kcal.

[1854] Suggest oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[1855] It is recommended to walk three times a week and do light strength training twice a week.

[1856] Also includes mental health advice on yoga and meditation.

[1857] 5. Offering of Plans

[1858] The generated plan is sent to the terminal, where the user can confirm and execute it.

[1859] 6. Track and adjust progress

[1860] Users enter their exercise routine and dietary habits into the app every day.

[1861] The server analyzes the new data and tweaks the plan as needed.

[1862] For example, if weight loss is too rapid, adjustments can be made, such as increasing calorie intake.

[1863] 7. Emotion Recognition with Emotion Engine

[1864] For example, a user can write a diary entry into the app by voice, and the emotion engine will determine the user's emotional state from the tone and content of the voice.

[1865] If an emotion such as anger or sadness is detected, the data is sent to a server.

[1866] 8. Adjust your plan based on sentiment data

[1867] The server analyzes the emotional data and provides advice to users who are feeling stressed, such as relaxing meals, light exercise, or meditation.

[1868] Specifically, we will suggest herbal teas that are effective in reducing stress and stretching methods for relaxation.

[1869] Thus, the present invention is a system that comprehensively manages the user's health condition and provides an optimal plan that also takes into account the user's emotional state.

[1870] The processing flow will be explained below.

[1871] Step 1:

[1872] The user launches the app and scans their body.

[1873] The device uses a camera to 3D scan the user's body and generate a highly accurate 3D model.

[1874] Step 2:

[1875] The user enters basic information (height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, bone structure, etc.) into a form.

[1876] Step 3:

[1877] The device encrypts the collected body data and basic information and transmits it to a server using a secure communication protocol.

[1878] Step 4:

[1879] The server anonymizes the data it receives and stores it in a secure database.

[1880] Step 5:

[1881] The server analyzes the user data and extracts important features using a feature extraction algorithm.

[1882] Step 6:

[1883] The server searches for similar data in the database and identifies the right plan for the user based on success and failure.

[1884] Step 7:

[1885] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise.

[1886] Step 8:

[1887] The server generates a nutritionally balanced meal menu and suggests it to the user.

[1888] Step 9:

[1889] The server generates a safe and effective exercise plan (exercise type, exercise time, exercise frequency) and recommends it to the user.

[1890] Step 10:

[1891] The server will create a comprehensive plan that includes advice on mental care, such as stress management.

[1892] Step 11:

[1893] The server sends the generated plan to the terminal, and the user can receive and confirm it.

[1894] Step 12:

[1895] Users input data such as their daily exercise routine, diet, weight fluctuations, and physical condition into the application.

[1896] Step 13:

[1897] The device encrypts the new data and sends it to the server.

[1898] Step 14:

[1899] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed.

[1900] Step 15:

[1901] The user utilizes the emotion engine to obtain emotion data for that day from voice, facial expression, text data, etc.

[1902] Step 16:

[1903] The device collects emotional data, encrypts it, and sends it to a server.

[1904] Step 17:

[1905] The server analyzes the emotional data and adjusts mental care advice.

[1906] Step 18:

[1907] The server further adjusts the meal menu and exercise plan based on the emotional data.

[1908] For users who feel stressed, the server will suggest foods that will reduce stress and exercise plans that will help them relax.

[1909] Specific examples

[1910] Step 1:

[1911] For example, a 30-year-old woman who is 160 cm tall and weighs 65 kg launches a dedicated app and scans her body shape.

[1912] The device uses a camera to digitize the user's body shape and generate a 3D model.

[1913] Step 2:

[1914] The user enters basic information such as basal body temperature of 36.5°C, basal metabolic rate of 1400 kcal, low daily exercise, 7 hours of sleep, lipid-rich constitution, and bone mass.

[1915] Step 3:

[1916] The device encrypts the scan results and basic information and sends them to the server.

[1917] Step 4:

[1918] The server anonymizes the data it receives and stores it in a secure database.

[1919] Step 5:

[1920] The server analyzes user data and extracts features such as basal metabolic rate and physical constitution.

[1921] Step 6:

[1922] The server searches for similar data in a database and identifies an appropriate plan based on successes and failures.

[1923] Step 7:

[1924] The server takes into account the basal metabolic rate of 1400 kcal and sets the daily calorie intake target at 1700 kcal.

[1925] Step 8:

[1926] The server generates a meal menu that suggests oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[1927] Step 9:

[1928] The server generates an exercise plan that recommends walking three times a week and light strength training twice a week.

[1929] Step 10:

[1930] The server will create a comprehensive plan that includes mental care advice such as yoga and meditation.

[1931] Step 11:

[1932] The generated plan is sent to the terminal and confirmed by the user.

[1933] Step 12:

[1934] Users enter their exercise routine and dietary habits into the app every day.

[1935] Step 13:

[1936] The device encrypts the new data and sends it to the server.

[1937] Step 14:

[1938] The server analyzes progress data and adjusts the plan as needed, for example if weight loss is too rapid.

[1939] Step 15:

[1940] For example, the same user can enter their diary entry into the app by voice, and the emotion engine will recognize emotions such as anger or sadness from the tone and content of their voice.

[1941] Step 16:

[1942] The device collects emotional data, encrypts it, and sends it to a server.

[1943] Step 17:

[1944] The server analyzes the emotional data and suggests stress-reducing advice to users who are feeling stressed.

[1945] Step 18:

[1946] Based on the emotional data, the server can make additional suggestions, such as herbal tea that reduces stress or yoga stretches to help you relax.

[1947] Thus, the present invention is a system that comprehensively manages the user's health condition and provides an optimal plan that also takes into account the user's emotional state.

[1948] Example 2

[1949] 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."

[1950] Health management and dieting have become important themes in modern society. However, many users find it difficult to accurately grasp their own body shape and health condition, and are unable to receive appropriate meal menus, exercise plans, or mental care advice. Furthermore, there are few systems that provide comprehensive health support that takes emotional state into account, meaning that many users are unable to receive appropriate healthcare. This makes effective health management difficult, and presents challenges for maintaining sustainable health.

[1951] 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.

[1952] In this invention, the server includes: [means for anonymizing received data and storing it in a secure database]; [means for extracting features of user data and searching for data with high similarity]; and [means for acquiring and analyzing user emotional data.] This makes it possible to comprehensively assess the user's health and emotional state and provide optimal meal menus, exercise plans, and mental care advice.

[1953] "User" refers to a person who provides their body data and basic information using a dedicated application.

[1954] "Terminal" refers to equipment that collects user data and transmits it to a server using encryption technology. Specifically, this includes devices such as smartphones and tablets.

[1955] "Server" refers to a computer system that receives data sent by users and performs anonymization processing, storage, analysis, collation, and plan generation.

[1956] "Body scanning means" refers to the imaging device and image analysis technology used by a user to obtain their body data.

[1957] "Basic information" refers to basic biometric information about the user, such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and skeleton.

[1958] "Encryption Technology" means technology used to keep data confidential. Examples include AES encryption.

[1959] "Anonymization" refers to techniques that process data so that individuals cannot be identified. Examples include k-Anonymity.

[1960] "Database" refers to a system for storing and managing structured data, including Amazon RDS.

[1961] "Features" refer to important information that machine learning models extract from data, allowing for a more accurate understanding of the attributes and state of the data.

[1962] "Meal Menu" refers to a meal plan suggested based on the user's nutritional balance and health goals.

[1963] "Exercise Plan" refers to an exercise plan suggested based on the user's physical abilities and health goals.

[1964] "Mental health advice" refers to advice or activities offered to support a user's mental health, such as yoga or meditation.

[1965] "Emotion engine" refers to technology that analyzes a user's emotional state from their voice, facial expressions, and text data.

[1966] A "protocol" refers to a set of defined rules or procedures for transmitting data. An example is HTTPS.

[1967] A "machine learning algorithm" refers to a computational method for analyzing data and extracting features.

[1968] The present invention provides a system that effectively supports health management and dieting based on a user's body type data and personal information. This system provides the user with an optimal health plan through a series of processes including body type scanning by the user, data transmission, data analysis by the server, and plan generation and provision.

[1969] 1. Obtaining user data

[1970] First, the user installs and launches a dedicated application on their smartphone or tablet. We will use "MyHealthManager" as an example of this application. Next, the user scans their body shape using the app's camera function, and acquires image data. The OpenCV image processing library is used for this body shape scan.

[1971] In addition, users can use the form within the app to enter basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure.

[1972] 2. Data transmission and storage

[1973] The device encrypts the body scan data and input information obtained from the user using AES encryption technology. The encrypted data is sent to the server using the HTTPS protocol. Industry-standard encryption and communication protocols are used to ensure secure communication.

[1974] The server anonymizes the received data using an anonymization algorithm (specifically, k-Anonymity) and stores it in a secure database (e.g., Amazon RDS).

[1975] 3. Data analysis and collation

[1976] The server analyzes the stored user data using machine learning libraries (e.g., Scikit-learn) to extract features related to the user's health and body shape. These features are then matched with similar data in a database to identify successful and unsuccessful cases.

[1977] 4. Plan Generation and Delivery

[1978] The server calculates the target calorie intake based on the user's basal metabolic rate and daily exercise. For example, if the basal metabolic rate is 1400 kcal, the target calorie intake is set to 1700 kcal. The server then generates a meal menu that takes nutritional balance into consideration. For example, it might suggest oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[1979] The server also generates and provides users with exercise plans, such as walking three times a week and light strength training twice a week, and mental health advice, including yoga and meditation.

[1980] The generated plan is sent to the terminal, where the user can review and execute the plan.

[1981] Specific examples

[1982] For example, consider a 30-year-old woman who is 160 cm tall and weighs 65 kg. She has a basal body temperature of 36.5°C, a basal metabolic rate of 1,400 kcal, little daily exercise, seven hours of sleep, a lipid-rich constitution, and a medium-bone structure. She launches the app, scans her body type, and enters this basic information into a form.

[1983] For this user, the server generates the following plan:

[1984] Meal plan: Oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and stir-fried vegetables for dinner.

[1985] Exercise plan: Walking 3 times a week and light strength training 2 times a week

[1986] Mental care advice: Yoga and meditation

[1987] Prompt Sentence Examples

[1988] Below is an example of a prompt sentence to input to the generative AI model.

[1989] "We have a 30-year-old female user who is 160cm tall and weighs 65kg. Her basal body temperature is 36.5°C, her basal metabolic rate is 1400kcal, she exercises little, she sleeps 7 hours a day, she has a lipid-rich constitution, and she has a medium-sized bone. Please provide her with a daily meal plan, exercise plan, and mental health advice that is appropriate for her."

[1990] This allows the system to comprehensively manage the user's health status and provide an optimal plan that also takes into account their emotional state.

[1991] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1992] Step 1:

[1993] The user installs and launches the dedicated application "MyHealthManager" on their smartphone or tablet. The application uses the camera function to scan the user's body shape. The input data is 3D body scan data, which is then processed using the image processing library OpenCV and output as body shape data.

[1994] Step 2:

[1995] The user enters basic information such as height, weight, basal body temperature, basal metabolic rate, daily exercise, sleep time, constitution, and bone structure into a form within the app. The input data is basic information in text format, which is saved in the application and output as the user's basic information.

[1996] Step 3:

[1997] The device encrypts the acquired body shape data and basic user information using AES encryption technology. The input data is body shape data and basic information, and the encrypted data is output.

[1998] Step 4:

[1999] The device sends encrypted data to the server via the HTTPS protocol. The input is encrypted body shape data and basic information, and the output is the data securely sent to the server.

[2000] Step 5:

[2001] The server anonymizes the data it receives using the k-Anonymity algorithm. The input data is encrypted user data, and the output is anonymized data.

[2002] Step 6:

[2003] The server stores the anonymized data in a secure database (e.g., Amazon RDS). The input is the anonymized data, and the output is the stored database entry.

[2004] Step 7:

[2005] The server analyzes the stored user data using the machine learning library Scikit-learn and extracts features. The input is the user data in the database, and the output is the extracted features.

[2006] Step 8:

[2007] The server searches for similar data in the database and identifies successful or unsuccessful cases. The input is the extracted features, and the output is the data of the identified successful or unsuccessful cases.

[2008] Step 9:

[2009] The server calculates the target daily calorie intake based on the basal metabolic rate and daily exercise amount. The input data is the basal metabolic rate and exercise amount, and the output is the target daily calorie intake.

[2010] Step 10:

[2011] The server generates meal menus taking nutritional balance into consideration. The input is the target calorie intake and criteria for nutritional balance, and the output is meal menus for breakfast, lunch, and dinner.

[2012] Step 11:

[2013] The server generates the optimal exercise plan for the user. The input is body type data and basal metabolic rate, and the output is a weekly exercise plan.

[2014] Step 12:

[2015] The server generates advice for mental health, including yoga and meditation. The input is the user's stress level and emotional data, and the output is mental health advice.

[2016] Step 13:

[2017] The server sends the generated plan (meal menu, exercise plan, mental care advice) to the terminal. The input is the generated plan, and the output is the plan display on the terminal.

[2018] Step 14:

[2019] The user inputs data into the app, such as daily exercise, diet, weight fluctuations, and physical condition. The input is the user's daily data, and the output is updated health status data.

[2020] Step 15:

[2021] The device encrypts daily data and sends it to the server. The input is the updated health status data, and the output is the encrypted data sent to the server.

[2022] Step 16:

[2023] The server analyzes the daily data and adjusts the plan as needed. The input is the daily health data and the output is the adjusted plan.

[2024] Step 17:

[2025] The user uses the emotion engine to obtain emotion data from voice, facial expressions, and text data. The input is the user's emotion data, and the output is the analyzed emotion results.

[2026] Step 18:

[2027] The device encrypts the emotion data and sends it to the server. The input is emotion data, and the output is the encrypted emotion data sent to the server.

[2028] Step 19:

[2029] The server analyzes the emotional data and adjusts mental care advice based on the emotional state. The input is the analyzed emotional result, and the output is the adjusted mental care advice.

[2030] (Application example 2)

[2031] 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."

[2032] Conventional health management systems could obtain a user's body type and basic information and provide meal menus, exercise plans, and mental care advice based on that information, but they were unable to provide specific product suggestions or location information for products in stores. They also lacked the ability to fine-tune plans by taking into account the user's emotional state. This made it difficult for users to smoothly find the right products in stores and was unable to receive appropriate advice based on their emotional state.

[2033] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for the user to scan their body type and input basic information, a means for the terminal to send the user's data to the server, a means for the server to analyze the data and compare it with other databases, a means for the server to generate a meal menu, an exercise plan, and mental care advice, a means for the terminal to provide the generated plan to the user, a means for the user to input progress data and the server to analyze the data and adjust the plan, a means for measuring body type using a scanning device in the store, a means for providing the user with optimal food and product placement information, and a means for analyzing the user's emotional data using an emotion engine and fine-tuning the plan based on the data. This makes it easier for users to find optimal products in the store through the health management system and receive more detailed advice tailored to their emotional state.

[2034] "User" means an individual who utilizes the system to provide information for body scanning and health management.

[2035] "Body scanning" refers to using a device to measure the shape and dimensions of a user's body and capture that data in digital form.

[2036] "Basic information" refers to biological data and lifestyle information that users input into the system, including height, weight, basal metabolic rate, daily exercise, sleep duration, and physical constitution.

[2037] A "terminal" is a device that is directly operated by a user, and refers to electronic devices such as smartphones and tablets.

[2038] A "server" is a computing resource that analyzes and stores data and provides information to users, and refers to a high-performance computer located in a cloud environment or on a network.

[2039] "Analyzing the data" means processing the data submitted by the user and extracting meaningful information.

[2040] A "database" is a collection of large amounts of accumulated data that is structured and stored in a format that allows for searching and matching.

[2041] "Meal menu" refers to a combination of ingredients and dishes suggested taking into consideration the user's health condition and nutritional balance.

[2042] "Exercise plan" refers to a plan that specifically indicates the type, time, and frequency of exercise based on the user's health condition and goals.

[2043] "Mental health advice" refers to suggestions and guidance for maintaining and improving a user's mental health.

[2044] "Progress data" refers to data related to diet, exercise, weight fluctuations, physical condition, etc. that users record in their daily lives.

[2045] "In-store scanning device" refers to a device installed in a physical store that allows customers to measure their body measurements.

[2046] "Product placement information" refers to data that indicates the specific placement locations of products within a store that a user is considering purchasing.

[2047] An "emotion engine" refers to software or hardware for analyzing a user's emotional state from their voice, facial expressions, text data, etc.

[2048] "Emotion data" is digital data that indicates the user's emotional state and is analyzed by the emotion engine.

[2049] A "recommendation list" refers to a list of products or actions that are recommended for a user to purchase or perform based on their health information or emotional state.

[2050] A system for implementing this invention includes a means for a user to scan their body type and input basic information, a means for transmitting the data to a server, a means for the server to analyze the data and compare it with other databases, a means for generating a meal menu, an exercise plan, and mental care advice, a means for the user to input progress data and analyze the data, a means for measuring body type using a scanning device in a store, a means for providing optimal food and product placement information, and a means for analyzing the user's emotional data using an emotion engine and fine-tuning the plan based on the data.

[2051] The server anonymizes the data it receives and stores it in a database. This database structures the large amount of accumulated data and stores it in a format that allows for search and matching. The server also extracts features from the user data and generates meal menus, exercise plans, and mental care advice based on data with high similarity.

[2052] In this system, users first install a dedicated application on their smartphone, tablet, or other device. Next, they measure their body shape using a scanning device in the store and enter that data and basic information (e.g., height, weight, basal metabolic rate, daily exercise, sleep duration, physical constitution, etc.) into the application. This information is encrypted and sent to the server using a secure communication protocol (e.g., HTTPS).

[2053] The server receives the transmitted data and analyzes it. During the analysis, it extracts features from the user data and compares them with similar databases to generate optimal meal plans, exercise plans, and mental care advice for the user. It also uses an emotion engine to analyze the user's emotional data and fine-tune the plan as needed. The emotion engine determines the user's emotional state from their voice, facial expressions, or text data.

[2054] The generated plan is then encrypted again and sent to the user's device. The user can then use this plan to select products in the store, and specific product placement information within the store is also provided, allowing the user to efficiently find the products they need.

[2055] For example, a user visits a health food store, measures their body shape with a 3D body scanner, and then enters the results and basic information into a smartphone app. This data is sent to a cloud server for analysis. The generated plan includes recommended foods (e.g., protein bars, low-calorie snacks, etc.) and provides detailed location information for them. The app helps users find products easily, and also suggests stress-reducing foods based on their emotional state.

[2056] Examples of prompts include:

[2057] "I want to create an app that scans a user's body shape and health data and suggests food and exercise products based on their individual health status. The user scans their body shape in the store, enters the necessary data, and based on the information analyzed by the server, provides the most suitable products and their placement information."

[2058] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2059] Step 1:

[2060] The user installs a dedicated application on their smartphone or tablet and creates a profile. At this time, the user enters basic information such as height, weight, basal metabolic rate, daily exercise, sleep time, and physical constitution. The application temporarily saves the entered basic information in local storage. The entered data is used in the next step.

[2061] Step 2:

[2062] The user scans their body shape using a scanning device (e.g., a 3D body scanner) installed in the store. The scanning device captures the user's body shape data (e.g., the outer shape of the body and the dimensions of each part of the body) and sends the data to a smartphone application. The body shape data is encrypted and used in the next step.

[2063] Step 3:

[2064] The device (smartphone or tablet) encrypts the entered basic information and scanned data and sends it to a cloud server using a secure communication protocol (e.g., HTTPS). The sent data is then received by the server.

[2065] Step 4:

[2066] The server analyzes the received data. During the analysis, for example, a Python library is used to preprocess the data (fill in missing values, detect outliers) and extract features from the user data. These extracted features are then compared with other databases to search for data with high similarity. This process provides the information needed to generate meal menus and exercise plans tailored to the user.

[2067] Step 5:

[2068] The server generates optimal meal plans, exercise plans, and mental health advice for each user using machine learning models and rule-based approaches. For example, a generative AI model is used to suggest optimal meal plans that take into account the user's basal metabolic rate and daily activity level. The generated plans are encrypted.

[2069] Step 6:

[2070] The server sends the generated plan to the terminal, which then decodes the received data and displays it to the user, along with specific product location information to help the user easily find the food or product they need in the store.

[2071] Step 7:

[2072] The user selects and purchases the suggested products in the store. During this process, the application records the user's purchase history and selection trends and uses them for future suggestions. This data is temporarily stored in local storage.

[2073] Step 8:

[2074] The user enters progress data (e.g., dietary details, exercise details, weight fluctuations, physical condition, etc.) into the application. The entered data is encrypted and sent to the server, which then receives it.

[2075] Step 9:

[2076] The server analyzes the progress data and adjusts the meal menu and exercise plan as needed. For example, it uses machine learning algorithms to fine-tune calorie intake and exercise intensity based on the user's progress data. The analysis results are then provided back to the user.

[2077] Step 10:

[2078] The user uses the emotion engine to obtain emotional data. Emotional data is collected from voice, facial expressions, text input, etc. and analyzed using voice analysis software and natural language processing models. This analyzed data is encrypted and sent to the server.

[2079] Step 11:

[2080] The server analyzes the emotional data and further adjusts mental health advice, meal plans, and exercise plans as needed. For example, if the user is feeling stressed, it will suggest foods that have a relaxing effect and light exercise. These adjustments are provided to the user on a case-by-case basis.

[2081] 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.

[2082] 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.

[2083] 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.

[2084] 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.

[2085] 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.

[2086] 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.

[2087] 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).

[2088] 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.

[2089] 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."

[2090] 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.

[2091] 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).

[2092] 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.

[2093] 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.

[2094] 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.

[2095] 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.

[2096] 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.

[2097] 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.

[2098] 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.

[2099] 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.

[2100] 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.

[2101] 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.

[2102] The following is further disclosed regarding the above embodiment.

[2103] (Claim 1)

[2104] [A way for users to scan their body type and enter basic information]

[2105] [Means by which the device transmits user data to the server],

[2106] [Means by which the server analyzes ...

Claims

1. A way for users to scan their body and enter basic information; A means for the device to transmit user data to a server; A means by which the server analyzes the data and compares it with other databases; A means for the server to generate meal menus, exercise plans, and mental care advice; a means for the terminal to provide the generated plan to the user; The user inputs progress data, and the server analyzes the data and adjusts the plan. A system including:

2. Includes a means for the server to anonymize the data received and store it in a database The system of claim 1 .

3. The server extracts features of the user data and extracts data with high similarity. The system of claim 1 .

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