system

Abstract

We provide the system. [Solution] An interface for inputting health checkup results obtained from users, An artificial intelligence means for performing data analysis based on the aforementioned health checkup results, A generation means for generating dietary and exercise suggestions based on the aforementioned analysis results, A display means for displaying the generated proposal to the user, A health management support system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, due to the diversification of lifestyles, time constraints, and lack of exercise, many people tend to neglect health management. In addition, conventional health management methods generally have problems such as the complexity of data input by hand and the lack of personalization of proposals, making it difficult for users to conduct effective and continuous health management. Therefore, there is a need to provide more convenient and accurate personalized health management.

Means for Solving the Problems

[0005] This invention includes an interface for inputting health checkup results obtained from users, and an artificial intelligence means for performing data analysis based on the health checkup results. This enables easy personalized health management by automatically generating and displaying dietary and exercise suggestions based on the analysis results. In particular, the interface employs barcode recognition and voice input to reduce the complexity of input, and the generation means provides suggestions, including cooking procedures, based on the inputted food information.

[0006] "User" refers to an individual who uses this system to receive suggestions for health management, diet, and exercise.

[0007] "Health checkup results" refers to data showing the results of health checkups received by users at medical institutions, etc.

[0008] "Interface means" refers to the means by which users input their health checkup results into this system, and includes barcode recognition and voice input.

[0009] "Artificial intelligence means" refers to a program or algorithm that analyzes acquired data and generates suggestions for meals and exercises suitable for the user.

[0010] "Data analysis" refers to the process of evaluating a person's health status based on health checkup results and other input information, and deriving analysis results.

[0011] "Generation method" refers to a method of creating personalized diet and exercise suggestions based on the results of data analysis.

[0012] "Display means" refers to devices or applications that visually present the generated proposals to the user.

[0013] "Food ingredient information" refers to information about food ingredients that the user owns or can purchase.

[0014] "Cooking instructions" refer to step-by-step process information for creating a dish using ingredients. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when combined with an emotion engine.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the 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.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0029] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system that provides an interface for users to manage their own health. The user first inputs their health checkup results into a terminal. Input methods include barcode scanning and voice input, which significantly reduces the effort required for manual input.

[0037] The terminal sends the entered data to the server, which then receives the data. Based on the health check results, the server uses an AI algorithm to analyze the data. This analysis evaluates the user's health status and generates an analysis result.

[0038] Next, the server generates meal and exercise plans tailored to the user based on the analysis results. The meal plans are proposed with cooking instructions, taking into account the ingredients the user has in their refrigerator. They also include suggested menu items to choose when eating out or buying from convenience stores.

[0039] The generated plan is presented to the user via their device. The user can then manage and practice their daily diet and exercise according to the displayed health management plan.

[0040] For example, if a user enters a health checkup result indicating high cholesterol levels, the server will use that information to suggest including oatmeal and nuts in their breakfast. It will also display a recipe for a fish-based dinner menu and provide cooking instructions. In this way, users can easily maintain their health while cooking at home.

[0041] This system contributes to preventive medicine based on daily lifestyle habits and enables health management tailored to the individual needs of each user.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users input their health checkup results through the terminal's interface. By utilizing methods such as barcode scanning and voice recognition, the effort required for manual input is reduced.

[0045] Step 2:

[0046] The terminal sends the entered data to the server. The transmitted data is stored on the server and accumulated in the database. This process prepares the data for analysis.

[0047] Step 3:

[0048] The server analyzes the received data. Using an AI algorithm, it evaluates the user's health status based on health checkup data and identifies abnormal values ​​and areas for improvement.

[0049] Step 4:

[0050] The server generates optimal meal and exercise plans for the user based on the analysis results. The meal plans include recipes using information about the ingredients in the user's refrigerator, as well as menu plans that can be used when eating out.

[0051] Step 5:

[0052] The generated meal and exercise suggestions are sent to the device and presented to the user visually. The device displays the suggestions on its interface in a way that is easy for the user to understand.

[0053] Step 6:

[0054] Users follow the displayed plan to implement daily dietary and exercise routines. By inputting the results of their plan implementation and feedback into their device, users can reflect areas for improvement and further needs in the system.

[0055] Step 7:

[0056] The server analyzes user feedback and adjusts the plan as needed. This ensures that users are always provided with an optimized health management plan.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] Enabling personalized health management based on health checkup results is crucial for maintaining users' health. However, manual data entry and general planning methods are time-consuming and inaccurate. Furthermore, the lack of specific suggestions based on the ingredients in the refrigerator and lifestyle makes it difficult for users to implement the plan.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes information input means, machine learning means, plan generation means, visualization means, and information transmission means. This enables rapid and accurate data analysis based on health checkup results and the generation of personalized health management suggestions. It can also suggest specific cooking procedures based on the food information in the user's refrigerator, realizing highly practical health management.

[0062] "Information input means" refers to methods that allow users to input their health check results into a terminal using code recognition or voice input.

[0063] "Machine learning methods" refer to techniques for analyzing data based on received health checkup results and evaluating the user's health status.

[0064] The "plan generation means" is a means of generating dietary and exercise suggestions tailored to individual users based on the analysis results.

[0065] A "visualization means" is a means of visually displaying suggestions generated on a server to the user via a terminal.

[0066] "Information transmission means" refers to a means of securely and efficiently transmitting diagnostic data from a terminal to a server.

[0067] Embodiments for carrying out this invention are described below.

[0068] Users input their health check results using a terminal. Input can be done via code recognition (e.g., barcode scanning) or voice input, significantly reducing manual data entry. This allows users to easily send clean data to the system from devices such as smartphones and tablets.

[0069] The terminal sends the entered information to the server for analysis. Since secure communication is required for data transmission, the HTTPS protocol is used, and the data is sent in a structured format (e.g., JSON). The terminal also features a dedicated application that provides users with opportunities to review and correct their data through a visually intuitive interface.

[0070] The server performs detailed analysis using the received data, employing machine learning techniques. Specifically, a model trained with TENSORFLOW® analyzes the user's health status based on health checkup results, providing risk assessments and health recommendation strengths. In this process, numerous servers collaborate in a cloud environment to provide the necessary infrastructure for data analysis.

[0071] After the analysis results are obtained, the server uses a plan generation mechanism to generate personalized diet and exercise suggestions. These suggestions are based on information about the food items in the user's refrigerator, and a common relational database (e.g., MySQL®) is used for the database management system. As a result, each user is provided with an optimized plan.

[0072] Finally, the generated plan is visualized for the user via their device, showing detailed information about meals and exercise. This visualization and follow-up process utilizes techniques to display intuitive and visual content on digital devices to make it easier for users to put the suggestions into action.

[0073] For example, if a user is diagnosed with "high cholesterol," the AI ​​will receive a prompt such as "Please generate a meal plan to lower cholesterol," and will provide a breakfast recipe including oatmeal and nuts, as well as a dinner recipe using fish from the refrigerator. In this way, the system helps users make effective use of the information they have on hand to lead a healthy daily life.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user enters their health checkup results into the terminal. The input methods used by the user are code recognition or voice input. Specifically, the user scans the barcode of the checkup results with their smartphone camera or inputs numerical values ​​by voice. The entered data is stored in the system in JSON format.

[0077] Step 2:

[0078] The terminal sends the entered health check results to the server. This process securely sends the input data to the server using the HTTPS protocol. The terminal initiates data transmission when the start button is pressed, and displays a confirmation message to the user after transmission is complete. The input consists of the health check results data, and the output must be securely delivered to the server.

[0079] Step 3:

[0080] The server analyzes the received health checkup data using an AI algorithm. Based on the received data, the server applies a machine learning model using TensorFlow to perform an analysis of the user's health status. Specifically, the AI ​​model evaluates cholesterol levels and blood pressure and calculates a cardiovascular disease risk score. The input is the submitted diagnostic data, and the output is the analysis results regarding the user's health status.

[0081] Step 4:

[0082] The server generates meal and exercise plans based on the analysis results. It utilizes a plan generation mechanism to retrieve the user's food information from a MySQL database. Based on this, it develops recipes and exercise plans tailored to the ingredients in the user's refrigerator. For example, the server creates a weekly exercise menu tailored to the user's preferences. The input is the AI ​​analysis results and food information, and the output is a specific health plan.

[0083] Step 5:

[0084] The device visually displays the generated plan to the user. Through the device's application, meal and exercise suggestions are presented in a user-friendly interface. Specifically, the app includes a calendar view and reminder functions to support the user in efficiently managing their daily health plan. Input is health plan information from the server, and output is visual content displayed on the user's screen.

[0085] (Application Example 1)

[0086] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0087] In modern times, it is difficult for consumers to make appropriate food choices based on their health condition, and this is especially true when shopping at physical stores, where time constraints and lack of information can hinder healthy choices. Therefore, there is a need for a system that enables consumers to make food choices based on their individual health conditions and efficiently supports their daily health management.

[0088] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0089] In this invention, the server includes an interface means for inputting health-related information obtained from the user, an artificial intelligence means for performing data analysis based on the health-related information, a generation means for generating suggestions for meals, exercise, and products based on the analysis results, and a display means for displaying the generated suggestions to the user and assisting in product selection. This enables users to make food choices based on their individual health conditions, realizing efficient and healthy purchasing activities in physical stores.

[0090] A "user" is an individual who uses the system for health management purposes.

[0091] "Health-related information" refers to diagnostic results and physical data that indicate the user's health status.

[0092] "Interface means" refers to a device or function for users to input health-related information.

[0093] "Artificial intelligence means" refers to a program or device that performs intelligent processing to analyze input health-related information and evaluate the user's health status.

[0094] The "generation means" refers to a function that creates suggestions regarding diet, exercise, and products suitable for the user based on the analyzed health information.

[0095] "Display means" refers to a device or function that visually displays the generated proposal so that the user can confirm it.

[0096] "Supporting product selection" means helping users choose products that are suitable for their individual health conditions.

[0097] The system for implementing this invention is designed to support users in managing their health. This system includes a function that allows users to input health-related information using a smartphone or other mobile device. Specifically, the device is equipped with barcode recognition and voice input functions, allowing users to easily input data about their health status using these functions.

[0098] The input data is transmitted to the server via the network. The server has artificial intelligence capabilities using a generative AI model to analyze the received health data. This AI model processes the data using the latest analytical techniques and evaluates the user's health status.

[0099] Based on the evaluation results, the server uses a generation mechanism to create personalized meal plans, exercise plans, and product recommendations for the user. These recommendations may include, for example, recommended foods, types of exercise, or guidance on product selection in physical stores.

[0100] The device also features a display mechanism for showing suggestions received from the server as meal and exercise schedules. This allows users to manage their health in real time.

[0101] As a concrete example, when a user shops at a supermarket, there is a function that allows them to scan suggested products based on their health checkup results using their smartphone. The product information is then analyzed, and it is displayed whether the product matches the user's health plan. Based on this information, the user can select products that are good for their health.

[0102] An example of a prompt using a generative AI model is: "Design an application that analyzes the nutritional value of specific products in a supermarket based on health checkup results and provides information that recommends or warns against purchasing them." This is designed so that the AI ​​links health checkup results with product data and generates advice tailored to individual health needs.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The user inputs health-related information into the terminal using an interface. The terminal then uses barcode recognition and voice input to obtain the user's health checkup results and food intake information. As a result, the terminal receives raw data related to the user's health.

[0106] Step 2:

[0107] The terminal sends the entered health information to the server. In this transmission process, the entered data is securely transferred to the server over the network. The server receives this as received data and prepares it for the next analysis step. Here, the server performs preprocessing to convert the data into a format suitable for the AI ​​model.

[0108] Step 3:

[0109] The server uses a generative AI model to analyze the received health information. Specifically, the server passes numerical data from health checkups to the AI ​​layer to evaluate the user's health status. This data analysis outputs information about the user's health risks and areas for improvement. The AI ​​model uses pattern recognition technology to extract features from the data and generate analysis results.

[0110] Step 4:

[0111] Based on the analysis results, the server generates optimal meal plans, exercise plans, and product selections for the user. In this step, the generation mechanism uses the analysis data to create specific recommendations and compiles them into information to be presented to the user. The output suggestions include recommended menus, exercises, and product options.

[0112] Step 5:

[0113] The server sends the generated suggestions to the terminal, which then presents them to the user using a display device. The user reviews the displayed health management suggestions and uses them as a reference for purchasing activities at physical stores or convenience stores. The terminal then updates the displayed content in response to user actions and provides feedback on the user's choices.

[0114] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0115] This invention is a healthcare system for simultaneously managing a user's health and emotional state. Based on health checkup results entered by the user, the system uses AI to analyze the data and provide suggestions regarding diet and exercise. Furthermore, by incorporating an emotion engine, the system can detect the user's emotional state and dynamically adjust the suggestions accordingly.

[0116] First, the user enters their health checkup results into the terminal. Barcode recognition and voice input improve input efficiency, making it easy to register data. The data entered into the terminal is sent to a server, where AI performs data analysis. As a result, a diet and exercise plan tailored to the individual's health condition is generated.

[0117] The system's key feature, the emotion engine, acquires user voice and facial expression information through the device and analyzes this data to identify the user's emotional state. For example, if the system detects that the user is stressed, it will recommend foods that have a calming effect or light exercise. If the system determines that the user is feeling down, it can recommend foods that boost mood or energetic exercise.

[0118] For example, when a user is feeling stressed at work, this information is detected by the emotion engine. The server takes this emotional state into consideration and suggests relaxing chamomile tea or deep breathing exercises. In this way, the suggestions are adjusted based on the user's emotions, providing them with more personalized and effective health management.

[0119] In this way, this system aims to improve the user's health condition and, by flexibly responding to changes in emotional state, comprehensively supports the physical and mental health of daily life.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] Users input their health checkup results through the terminal's interface. Input is performed using barcode scanners and voice recognition, enabling rapid data registration.

[0123] Step 2:

[0124] The terminal sends the entered health check data to the server. The data is stored in a database on the server and ready for analysis.

[0125] Step 3:

[0126] Based on the health check data received by the server, an AI algorithm is used to analyze the user's health status. As a result of the analysis, risks and areas for improvement related to the user's health are identified.

[0127] Step 4:

[0128] The server generates an optimal meal plan and exercise plan for the user based on the analysis results. This time, the user's emotional state will also be taken into consideration.

[0129] Step 5:

[0130] The device collects user emotion data using voice input or camera functions. The emotion engine analyzes this data to identify the user's current emotional state.

[0131] Step 6:

[0132] The server takes into account the emotional state identified by the emotion engine and appropriately adjusts the aforementioned meal and exercise plans. For example, if the user is feeling stressed, it will suggest relaxing meals and light exercise.

[0133] Step 7:

[0134] The adjusted meal and exercise plans are sent to the device and displayed to the user. The device presents the suggestions in a user-friendly format.

[0135] Step 8:

[0136] The user follows the displayed plan and enters feedback on the device upon completion. This feedback is used to improve future suggestions.

[0137] Step 9:

[0138] The server analyzes user feedback and adjusts the original plan and sentiment recognition methods as needed. This makes it possible to always provide users with suitable suggestions.

[0139] (Example 2)

[0140] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0141] In modern society, comprehensive health management based on individual health conditions is required. However, conventional systems have the problem of providing fixed suggestions based on health checkup results, and it is difficult to provide dynamic advice that reflects the emotional state of the user. In particular, health management that takes stress and mental balance into consideration has been difficult to address on an individual basis.

[0142] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0143] In this invention, the server includes a device for inputting health checkup results obtained from the user, an intelligent system for performing information analysis based on the health checkup results, and a function for dynamically changing suggestions based on the user's emotional state. This enables a comprehensive analysis of the user's health and emotional state, and allows for personalized health and emotional management suggestions.

[0144] "Users" refer to individuals or organizations that use the system and are the entities that provide health checkup results and emotional information.

[0145] "Health checkup results" refer to data that indicates the user's physical health status, and include information such as blood test results and physical measurements.

[0146] "Device" is a general term for hardware and software used for data input and emotional information acquisition.

[0147] An "intelligent system" is a mechanism that utilizes artificial intelligence to analyze input data and generate results.

[0148] "Nutritional intake and exercise suggestions" refers to specific instructions and recommendations regarding diet and physical activity for users, generated based on the analysis results.

[0149] "Mechanism" refers to the methods and means of providing generated suggestions to users through screens or other means.

[0150] "Emotional information" refers to data about the emotional state obtained from the user's voice and facial expressions.

[0151] The "dynamically changing function" refers to the ability to adjust the generated suggestions in a timely manner based on the user's emotional information.

[0152] The embodiments for carrying out the present invention will now be described. First, the user enters their health checkup results into a terminal. This terminal is equipped with an identification code recognition function and a voice input function, which allows the user to efficiently register data. The registered data is transmitted to a server via a secure communication protocol. The server uses an intelligent system to analyze the received health checkup results. This intelligent system utilizes a machine learning framework such as TensorFlow to generate suggestions for nutrition and exercise according to the user's health status.

[0153] Furthermore, the device acquires emotional information through the user's voice and facial expressions. This utilizes voice processing software and facial recognition libraries. This emotional information is sent to a server for analysis of the emotional state. Based on the analysis results, the server has the ability to dynamically change its suggestions. For example, if the user is feeling stressed, it can suggest foods with relaxing effects or light exercise.

[0154] For example, if a user uses the system at the end of a stressful day at work, the system will suggest drinking chamomile tea and doing deep breathing exercises. Another example of a prompt to the generating AI model is, "Please suggest a personalized diet and exercise plan based on my health checkup results. Also, please include advice that takes my emotional state into consideration."

[0155] This invention provides users with more personalized health management support that takes into account both their physical and emotional state.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The user enters their health check results into the terminal. The terminal uses an identification code to recognize the paper health check results and directly acquires the data via voice input. This input data includes the user's basic health status (e.g., blood pressure, blood sugar levels, etc.). The raw data is then sent to the server.

[0159] Step 2:

[0160] The server receives health checkup results sent from the terminal. Based on the received data, the server starts analysis using an intelligent system. The machine learning model used here (e.g., TensorFlow) performs health analysis that takes into account medical history and lifestyle habits based on past data. Through pattern recognition and inference of the input data, nutritional intake and exercise plans corresponding to the individual's health condition are generated. This analysis outputs specific suggestions regarding diet and exercise.

[0161] Step 3:

[0162] The device collects the user's voice and facial expressions through sensors. A microphone and camera are used for this purpose. The data acquired is used to analyze the user's emotional state, such as tone of voice and facial movements. Real-time emotional information is input and sent to a server where the current emotional state is analyzed.

[0163] Step 4:

[0164] The server analyzes emotional information transmitted from the terminal and uses an emotion engine to identify the user's emotional state. It identifies stress levels and energy levels from the emotional data and selects appropriate suggestions accordingly. For example, if high stress levels are estimated, the server incorporates suggestions to help the user relax. The output is health suggestions optimized for the user's emotional state.

[0165] Step 5:

[0166] The server generates dynamically adjusted suggestions based on analysis of health checkup results and emotional information. These generated suggestions are sent to the terminal in text or multimedia format. Both analysis results are used as input, while the output provides comprehensive health management advice that takes into account both health and emotional states.

[0167] Step 6:

[0168] The device displays suggestions sent from the server to the user. These include suggestions for improving lifestyle habits, specific action plans, and relaxation methods. The user receives the presented information and makes a decision on how to implement them. At this step, the user can receive feedback from the system and take subsequent actions as needed.

[0169] (Application Example 2)

[0170] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0171] In modern times, comprehensively managing users' health and emotional states is difficult. In particular, there is a need for real-time, personalized health advice and product / service recommendations that respond immediately to users' emotional states, but there is a lack of appropriate systems to effectively achieve this.

[0172] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0173] In this invention, the server includes information processing means for inputting health checkup results and emotional state data obtained from the user, artificial intelligence means for performing data analysis based on the health checkup results, and generation means for generating diet and exercise suggestions based on the analysis results and emotional state data. This makes it possible to provide users with personalized health management suggestions along with recommendations for products and services that match their emotional state.

[0174] "Information processing means" refers to a device or tool for efficiently inputting data from users regarding health checkup results and emotional state, and for appropriately processing that data.

[0175] "Artificial intelligence means" refers to an algorithm or program that performs data analysis based on input health check results and generates advice and suggestions appropriate to the user's health condition.

[0176] The "generation method" is a mechanism that utilizes analysis results and emotional state data to create appropriate diet and exercise suggestions for users.

[0177] A "display mechanism" is an interface that clearly shows the generated suggestions and recommendations to the user and provides feedback as needed.

[0178] This invention is a system for effectively managing health checkup results and emotional state data obtained from users. This system mainly consists of three elements: a server, an information processing terminal, and the user.

[0179] The server receives health checkup results and emotional state data transmitted from information processing terminals. This uses interfaces that efficiently capture data using barcode reading and voice input technologies. The server analyzes this data using cloud-based AI services, such as Google Cloud AI, to generate personalized diet and exercise recommendations for the user. Preprocessing, including data cleaning and normalization, is performed during the analysis, allowing the AI ​​model to identify patterns in health status.

[0180] Furthermore, the server dynamically adjusts recommended products and services based on emotional state data. The emotion engine analyzes emotions from the user's voice and facial expressions, for example, using Microsoft® Azure® Face API. Based on these results, it incorporates products and services that are expected to have a relaxation effect into the analysis results.

[0181] The device visualizes and presents the proposed content to the user, providing an interface for obtaining feedback. This interface is implemented on smartphones and other mobile devices and is used in a constantly accessible manner.

[0182] For example, if a user feels stressed after work, that information entered from their device is sent to the server, and suggestions to help reduce stress, such as chamomile tea or yoga classes, are displayed.

[0183] An example of a prompt message is, "After entering your health checkup results, if stress is indicated, what relaxing beverages would you suggest?" Using prompts like this allows for suggestions that cater to the diverse needs of users.

[0184] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0185] Step 1:

[0186] The terminal receives health checkup results and emotional state data from the user. This data entry utilizes a barcode reader or voice input function. The entered data is converted to a digital format and sent to the server.

[0187] Step 2:

[0188] The server receives health and emotional data transmitted from the terminal. The received data is cleaned in preparation for analysis, removing unnecessary or inaccurate data. The data is then normalized and ready for AI analysis.

[0189] Step 3:

[0190] The server uses cloud-based AI models, such as Google Cloud AI, to analyze normalized health data. The analysis generates personalized meal and exercise plans for the user. This analysis process involves pattern recognition and prediction based on historical data.

[0191] Step 4:

[0192] The server performs sentiment analysis using a separate process. Specifically, it uses the Microsoft Azure Face API to analyze the user's facial expressions and voice data to identify their emotional state. Based on the detected emotional state, recommendations for products and services that will stabilize the user's mental state are made.

[0193] Step 5:

[0194] The server integrates the generated suggestions with relaxation products and services appropriate to the emotional state, and sends these results to the terminal.

[0195] Step 6:

[0196] The device displays personalized suggestions based on information received from the server. These suggestions include specific meal plans, exercise plans, and relaxation products and services tailored to the user's emotional state.

[0197] Step 7:

[0198] Users can review the presented suggestions and provide feedback as needed. This information is then incorporated back into the system as feedback and used to improve future suggestions.

[0199] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0200] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0201] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0205] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0206] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0207] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0208] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0209] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0210] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0211] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0212] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0213] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0214] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0215] This invention is a system that provides an interface for users to manage their own health. The user first inputs their health checkup results into a terminal. Input methods include barcode scanning and voice input, which significantly reduces the effort required for manual input.

[0216] The terminal sends the entered data to the server, which then receives the data. Based on the health check results, the server uses an AI algorithm to analyze the data. This analysis evaluates the user's health status and generates an analysis result.

[0217] Next, the server generates meal and exercise plans tailored to the user based on the analysis results. The meal plans are proposed with cooking instructions, taking into account the ingredients the user has in their refrigerator. They also include suggested menu items to choose when eating out or buying from convenience stores.

[0218] The generated plan is presented to the user via their device. The user can then manage and practice their daily diet and exercise according to the displayed health management plan.

[0219] For example, if a user enters a health checkup result indicating high cholesterol levels, the server will use that information to suggest including oatmeal and nuts in their breakfast. It will also display a recipe for a fish-based dinner menu and provide cooking instructions. In this way, users can easily maintain their health while cooking at home.

[0220] This system contributes to preventive medicine based on daily lifestyle habits and enables health management tailored to the individual needs of each user.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] Users input their health checkup results through the terminal's interface. By utilizing methods such as barcode scanning and voice recognition, the effort required for manual input is reduced.

[0224] Step 2:

[0225] The terminal sends the entered data to the server. The transmitted data is stored on the server and accumulated in the database. This process prepares the data for analysis.

[0226] Step 3:

[0227] The server analyzes the received data. Using an AI algorithm, it evaluates the user's health status based on health checkup data and identifies abnormal values ​​and areas for improvement.

[0228] Step 4:

[0229] The server generates optimal meal and exercise plans for the user based on the analysis results. The meal plans include recipes using information about the ingredients in the user's refrigerator, as well as menu plans that can be used when eating out.

[0230] Step 5:

[0231] The generated meal and exercise suggestions are sent to the device and presented to the user visually. The device displays the suggestions on its interface in a way that is easy for the user to understand.

[0232] Step 6:

[0233] Users follow the displayed plan to implement daily dietary and exercise routines. By inputting the results of their plan implementation and feedback into their device, users can reflect areas for improvement and further needs in the system.

[0234] Step 7:

[0235] The server analyzes user feedback and adjusts the plan as needed. This ensures that users are always provided with an optimized health management plan.

[0236] (Example 1)

[0237] Next, we will describe Example 1. 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."

[0238] Enabling personalized health management based on health checkup results is crucial for maintaining users' health. However, manual data entry and general planning methods are time-consuming and inaccurate. Furthermore, the lack of specific suggestions based on the ingredients in the refrigerator and lifestyle makes it difficult for users to implement the plan.

[0239] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0240] In this invention, the server includes information input means, machine learning means, plan generation means, visualization means, and information transmission means. This enables rapid and accurate data analysis based on health checkup results and the generation of personalized health management suggestions. It can also suggest specific cooking procedures based on the food information in the user's refrigerator, realizing highly practical health management.

[0241] "Information input means" refers to methods that allow users to input their health check results into a terminal using code recognition or voice input.

[0242] "Machine learning methods" refer to techniques for analyzing data based on received health checkup results and evaluating the user's health status.

[0243] The "plan generation means" is a means of generating dietary and exercise suggestions tailored to individual users based on the analysis results.

[0244] A "visualization means" is a means of visually displaying suggestions generated on a server to the user via a terminal.

[0245] "Information transmission means" refers to a means of securely and efficiently transmitting diagnostic data from a terminal to a server.

[0246] Embodiments for carrying out this invention are described below.

[0247] Users input their health check results using a terminal. Input can be done via code recognition (e.g., barcode scanning) or voice input, significantly reducing manual data entry. This allows users to easily send clean data to the system from devices such as smartphones and tablets.

[0248] The terminal sends the entered information to the server for analysis. Since secure communication is required for data transmission, the HTTPS protocol is used, and the data is sent in a structured format (e.g., JSON). The terminal also features a dedicated application that provides users with opportunities to review and correct their data through a visually intuitive interface.

[0249] The server performs detailed analysis using the received data, employing machine learning techniques. Specifically, a model trained with TensorFlow analyzes the user's health status based on health checkup results, providing risk assessments and health recommendation strengths. In this process, numerous servers collaborate in a cloud environment to provide the necessary infrastructure for data analysis.

[0250] After the analysis results are obtained, the server uses a plan generation mechanism to generate personalized diet and exercise suggestions. These suggestions are based on information about the food items in the user's refrigerator, and a common relational database (e.g., MySQL) is used for the database management system. As a result, each user is provided with an optimized plan.

[0251] Finally, the generated plan is visualized for the user via their device, showing detailed information about meals and exercise. This visualization and follow-up process utilizes techniques to display intuitive and visual content on digital devices to make it easier for users to put the suggestions into action.

[0252] For example, if a user is diagnosed with "high cholesterol," the AI ​​will receive a prompt such as "Please generate a meal plan to lower cholesterol," and will provide a breakfast recipe including oatmeal and nuts, as well as a dinner recipe using fish from the refrigerator. In this way, the system helps users make effective use of the information they have on hand to lead a healthy daily life.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] The user enters their health checkup results into the terminal. The input methods used by the user are code recognition or voice input. Specifically, the user scans the barcode of the checkup results with their smartphone camera or inputs numerical values ​​by voice. The entered data is stored in the system in JSON format.

[0256] Step 2:

[0257] The terminal sends the entered health check results to the server. This process securely sends the input data to the server using the HTTPS protocol. The terminal initiates data transmission when the start button is pressed, and displays a confirmation message to the user after transmission is complete. The input consists of the health check results data, and the output must be securely delivered to the server.

[0258] Step 3:

[0259] The server analyzes the received health checkup data using an AI algorithm. Based on the received data, the server applies a machine learning model using TensorFlow to perform an analysis of the user's health status. Specifically, the AI ​​model evaluates cholesterol levels and blood pressure and calculates a cardiovascular disease risk score. The input is the submitted diagnostic data, and the output is the analysis results regarding the user's health status.

[0260] Step 4:

[0261] The server generates meal and exercise plans based on the analysis results. It utilizes a plan generation mechanism to retrieve the user's food information from a MySQL database. Based on this, it develops recipes and exercise plans tailored to the ingredients in the user's refrigerator. For example, the server creates a weekly exercise menu tailored to the user's preferences. The input is the AI ​​analysis results and food information, and the output is a specific health plan.

[0262] Step 5:

[0263] The device visually displays the generated plan to the user. Through the device's application, meal and exercise suggestions are presented in a user-friendly interface. Specifically, the app includes a calendar view and reminder functions to support the user in efficiently managing their daily health plan. Input is health plan information from the server, and output is visual content displayed on the user's screen.

[0264] (Application Example 1)

[0265] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0266] In modern times, it is difficult for consumers to make appropriate food choices based on their health condition, and this is especially true when shopping at physical stores, where time constraints and lack of information can hinder healthy choices. Therefore, there is a need for a system that enables consumers to make food choices based on their individual health conditions and efficiently supports their daily health management.

[0267] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0268] In this invention, the server includes an interface means for inputting health-related information obtained from the user, an artificial intelligence means for performing data analysis based on the health-related information, a generation means for generating suggestions for meals, exercise, and products based on the analysis results, and a display means for displaying the generated suggestions to the user and assisting in product selection. This enables users to make food choices based on their individual health conditions, realizing efficient and healthy purchasing activities in physical stores.

[0269] A "user" is an individual who uses the system for health management purposes.

[0270] "Health-related information" refers to diagnostic results and physical data that indicate the user's health status.

[0271] "Interface means" refers to a device or function for users to input health-related information.

[0272] "Artificial intelligence means" refers to a program or device that performs intelligent processing to analyze input health-related information and evaluate the user's health status.

[0273] The "generation means" refers to a function that creates suggestions regarding diet, exercise, and products suitable for the user based on the analyzed health information.

[0274] "Display means" refers to a device or function that visually displays the generated proposal so that the user can confirm it.

[0275] "Supporting product selection" means helping users choose products that are suitable for their individual health conditions.

[0276] The system for implementing this invention is designed to support users in managing their health. This system includes a function that allows users to input health-related information using a smartphone or other mobile device. Specifically, the device is equipped with barcode recognition and voice input functions, allowing users to easily input data about their health status using these functions.

[0277] The input data is transmitted to the server via the network. The server has artificial intelligence capabilities using a generative AI model to analyze the received health data. This AI model processes the data using the latest analytical techniques and evaluates the user's health status.

[0278] Based on the evaluation results, the server uses a generation mechanism to create personalized meal plans, exercise plans, and product recommendations for the user. These recommendations may include, for example, recommended foods, types of exercise, or guidance on product selection in physical stores.

[0279] The device also features a display mechanism for showing suggestions received from the server as meal and exercise schedules. This allows users to manage their health in real time.

[0280] As a concrete example, when a user shops at a supermarket, there is a function that allows them to scan suggested products based on their health checkup results using their smartphone. The product information is then analyzed, and it is displayed whether the product matches the user's health plan. Based on this information, the user can select products that are good for their health.

[0281] An example of a prompt using a generative AI model is: "Design an application that analyzes the nutritional value of specific products in a supermarket based on health checkup results and provides information that recommends or warns against purchasing them." This is designed so that the AI ​​links health checkup results with product data and generates advice tailored to individual health needs.

[0282] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0283] Step 1:

[0284] The user inputs health-related information into the terminal using the interface means. At this time, the terminal uses barcode recognition or voice input to obtain the user's health examination results and food ingredient information. As a result, raw data related to the user's health is input into the terminal.

[0285] Step 2:

[0286] The terminal transmits the input health-related information to the server. In this transmission process, the input data is securely transferred to the server via the network. The server takes this as received data and prepares the data for the next analysis step. Here, the server performs preprocessing to convert the data into a format suitable for the AI model.

[0287] Step 3:

[0288] The server analyzes the received health-related information using the generated AI model. Specifically, the server passes the numerical data of the health examination to the AI layer to evaluate the user's health status. Through this data analysis, information regarding the user's health risks and improvement points is output. Here, the AI model utilizes pattern recognition technology to extract the features of the data and generate the analysis results.

[0289] Step 4:

[0290] Based on the analysis results, the server generates an optimal diet plan, exercise plan, and product selection for the user. In this step, the generation means uses the analysis data to create specific recommendations and summarizes them as information to be presented to the user. The output proposals include recommended menu items, exercises, and product candidates.

[0291] Step 5:

[0292] The server sends the generated suggestions to the terminal, which then presents them to the user using a display device. The user reviews the displayed health management suggestions and uses them as a reference for purchasing activities at physical stores or convenience stores. The terminal then updates the displayed content in response to user actions and provides feedback on the user's choices.

[0293] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0294] This invention is a healthcare system for simultaneously managing a user's health and emotional state. Based on health checkup results entered by the user, the system uses AI to analyze the data and provide suggestions regarding diet and exercise. Furthermore, by incorporating an emotion engine, the system can detect the user's emotional state and dynamically adjust the suggestions accordingly.

[0295] First, the user enters their health checkup results into the terminal. Barcode recognition and voice input improve input efficiency, making it easy to register data. The data entered into the terminal is sent to a server, where AI performs data analysis. As a result, a diet and exercise plan tailored to the individual's health condition is generated.

[0296] The system's key feature, the emotion engine, acquires user voice and facial expression information through the device and analyzes this data to identify the user's emotional state. For example, if the system detects that the user is stressed, it will recommend foods that have a calming effect or light exercise. If the system determines that the user is feeling down, it can recommend foods that boost mood or energetic exercise.

[0297] As a specific example, when a user feels stressed at work, this information is detected by the emotion engine. The server takes this emotional state into account and proposes chamomile tea with a relaxing effect or breathing exercises. In this way, by adjusting the proposed content according to the emotion, more personalized and effective health management is provided to the user.

[0298] In this way, this system aims to improve the user's health condition, and by further flexibly responding to changes in the emotional state, it comprehensively supports the physical and mental health in daily life.

[0299] The following describes the processing flow.

[0300] Step 1:

[0301] The user inputs the health diagnosis result through the interface of the terminal. The input is performed using a barcode scanner or voice recognition, which enables rapid data registration.

[0302] Step 2:

[0303] The terminal sends the input health diagnosis data to the server. The data is stored in the database on the server, and preparations for analysis are completed.

[0304] [[ID=·27]] Step 3:

[0305] Based on the health diagnosis data received by the server, the AI algorithm is used to analyze the user's health condition. As the analysis result, the risks and improvement points related to the user's health are identified.

[0306] Step 4:

[0307] Based on the analysis result, the server generates an optimal diet plan and exercise plan for the user. This time, the user's emotional state will also be taken into consideration.

[0308] Step 5:

[0309] The device collects user emotion data using voice input or camera functions. The emotion engine analyzes this data to identify the user's current emotional state.

[0310] Step 6:

[0311] The server takes into account the emotional state identified by the emotion engine and appropriately adjusts the aforementioned meal and exercise plans. For example, if the user is feeling stressed, it will suggest relaxing meals and light exercise.

[0312] Step 7:

[0313] The adjusted meal and exercise plans are sent to the device and displayed to the user. The device presents the suggestions in a user-friendly format.

[0314] Step 8:

[0315] The user follows the displayed plan and enters feedback on the device upon completion. This feedback is used to improve future suggestions.

[0316] Step 9:

[0317] The server analyzes user feedback and adjusts the original plan and sentiment recognition methods as needed. This makes it possible to always provide users with suitable suggestions.

[0318] (Example 2)

[0319] Next, we will describe Example 2. 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".

[0320] In modern society, comprehensive health management based on individual health conditions is required. However, conventional systems have the problem of providing fixed suggestions based on health checkup results, and it is difficult to provide dynamic advice that reflects the emotional state of the user. In particular, health management that takes stress and mental balance into consideration has been difficult to address on an individual basis.

[0321] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0322] In this invention, the server includes a device for inputting health checkup results obtained from the user, an intelligent system for performing information analysis based on the health checkup results, and a function for dynamically changing suggestions based on the user's emotional state. This enables a comprehensive analysis of the user's health and emotional state, and allows for personalized health and emotional management suggestions.

[0323] "Users" refer to individuals or organizations that use the system and are the entities that provide health checkup results and emotional information.

[0324] "Health checkup results" refer to data that indicates the user's physical health status, and include information such as blood test results and physical measurements.

[0325] "Device" is a general term for hardware and software used for data input and emotional information acquisition.

[0326] An "intelligent system" is a mechanism that utilizes artificial intelligence to analyze input data and generate results.

[0327] "Nutritional intake and exercise suggestions" refers to specific instructions and recommendations regarding diet and physical activity for users, generated based on the analysis results.

[0328] "Mechanism" refers to the methods and means of providing generated suggestions to users through screens or other means.

[0329] "Emotional information" refers to data about the emotional state obtained from the user's voice and facial expressions.

[0330] The "dynamically changing function" refers to the ability to adjust the generated suggestions in a timely manner based on the user's emotional information.

[0331] The embodiments for carrying out the present invention will now be described. First, the user enters their health checkup results into a terminal. This terminal is equipped with an identification code recognition function and a voice input function, which allows the user to efficiently register data. The registered data is transmitted to a server via a secure communication protocol. The server uses an intelligent system to analyze the received health checkup results. This intelligent system utilizes a machine learning framework such as TensorFlow to generate suggestions for nutrition and exercise according to the user's health status.

[0332] Furthermore, the device acquires emotional information through the user's voice and facial expressions. This utilizes voice processing software and facial recognition libraries. This emotional information is sent to a server for analysis of the emotional state. Based on the analysis results, the server has the ability to dynamically change its suggestions. For example, if the user is feeling stressed, it can suggest foods with relaxing effects or light exercise.

[0333] For example, if a user uses the system at the end of a stressful day at work, the system will suggest drinking chamomile tea and doing deep breathing exercises. Another example of a prompt to the generating AI model is, "Please suggest a personalized diet and exercise plan based on my health checkup results. Also, please include advice that takes my emotional state into consideration."

[0334] This invention provides users with more personalized health management support that takes into account both their physical and emotional state.

[0335] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0336] Step 1:

[0337] The user enters their health check results into the terminal. The terminal uses an identification code to recognize the paper health check results and directly acquires the data via voice input. This input data includes the user's basic health status (e.g., blood pressure, blood sugar levels, etc.). The raw data is then sent to the server.

[0338] Step 2:

[0339] The server receives health checkup results sent from the terminal. Based on the received data, the server starts analysis using an intelligent system. The machine learning model used here (e.g., TensorFlow) performs health analysis that takes into account medical history and lifestyle habits based on past data. Through pattern recognition and inference of the input data, nutritional intake and exercise plans corresponding to the individual's health condition are generated. This analysis outputs specific suggestions regarding diet and exercise.

[0340] Step 3:

[0341] The device collects the user's voice and facial expressions through sensors. A microphone and camera are used for this purpose. The data acquired is used to analyze the user's emotional state, such as tone of voice and facial movements. Real-time emotional information is input and sent to a server where the current emotional state is analyzed.

[0342] Step 4:

[0343] The server analyzes emotional information transmitted from the terminal and uses an emotion engine to identify the user's emotional state. It identifies stress levels and energy levels from the emotional data and selects appropriate suggestions accordingly. For example, if high stress levels are estimated, the server incorporates suggestions to help the user relax. The output is health suggestions optimized for the user's emotional state.

[0344] Step 5:

[0345] The server generates dynamically adjusted suggestions based on analysis of health checkup results and emotional information. These generated suggestions are sent to the terminal in text or multimedia format. Both analysis results are used as input, while the output provides comprehensive health management advice that takes into account both health and emotional states.

[0346] Step 6:

[0347] The device displays suggestions sent from the server to the user. These include suggestions for improving lifestyle habits, specific action plans, and relaxation methods. The user receives the presented information and makes a decision on how to implement them. At this step, the user can receive feedback from the system and take subsequent actions as needed.

[0348] (Application Example 2)

[0349] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0350] In modern times, comprehensively managing users' health and emotional states is difficult. In particular, there is a need for real-time, personalized health advice and product / service recommendations that respond immediately to users' emotional states, but there is a lack of appropriate systems to effectively achieve this.

[0351] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0352] In this invention, the server includes information processing means for inputting health checkup results and emotional state data obtained from the user, artificial intelligence means for performing data analysis based on the health checkup results, and generation means for generating diet and exercise suggestions based on the analysis results and emotional state data. This makes it possible to provide users with personalized health management suggestions along with recommendations for products and services that match their emotional state.

[0353] "Information processing means" refers to a device or tool for efficiently inputting data from users regarding health checkup results and emotional state, and for appropriately processing that data.

[0354] "Artificial intelligence means" refers to an algorithm or program that performs data analysis based on input health check results and generates advice and suggestions appropriate to the user's health condition.

[0355] The "generation method" is a mechanism that utilizes analysis results and emotional state data to create appropriate diet and exercise suggestions for users.

[0356] A "display mechanism" is an interface that clearly shows the generated suggestions and recommendations to the user and provides feedback as needed.

[0357] This invention is a system for effectively managing health checkup results and emotional state data obtained from users. This system mainly consists of three elements: a server, an information processing terminal, and the user.

[0358] The server receives health checkup results and emotional state data transmitted from information processing terminals. This uses interfaces that efficiently capture data using barcode reading and voice input technologies. The server analyzes this data using cloud-based AI services, such as Google Cloud AI, to generate personalized diet and exercise recommendations for the user. Preprocessing, including data cleaning and normalization, is performed during the analysis, allowing the AI ​​model to identify patterns in health status.

[0359] Furthermore, the server dynamically adjusts recommended products and services based on emotional state data. The emotion engine analyzes emotions from the user's voice and facial expressions, for example, using the Microsoft Azure Face API. Based on these results, it incorporates products and services that are expected to have a relaxation effect into the analysis.

[0360] The device visualizes and presents the proposed content to the user, providing an interface for obtaining feedback. This interface is implemented on smartphones and other mobile devices and is used in a constantly accessible manner.

[0361] For example, if a user feels stressed after work, that information entered from their device is sent to the server, and suggestions to help reduce stress, such as chamomile tea or yoga classes, are displayed.

[0362] An example of a prompt message is, "After entering your health checkup results, if stress is indicated, what relaxing beverages would you suggest?" Using prompts like this allows for suggestions that cater to the diverse needs of users.

[0363] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0364] Step 1:

[0365] The terminal receives health checkup results and emotional state data from the user. This data entry utilizes a barcode reader or voice input function. The entered data is converted to a digital format and sent to the server.

[0366] Step 2:

[0367] The server receives health and emotional data transmitted from the terminal. The received data is cleaned in preparation for analysis, removing unnecessary or inaccurate data. The data is then normalized and ready for AI analysis.

[0368] Step 3:

[0369] The server uses cloud-based AI models, such as Google Cloud AI, to analyze normalized health data. The analysis generates personalized meal and exercise plans for the user. This analysis process involves pattern recognition and prediction based on historical data.

[0370] Step 4:

[0371] The server performs sentiment analysis using a separate process. Specifically, it uses the Microsoft Azure Face API to analyze the user's facial expressions and voice data to identify their emotional state. Based on the detected emotional state, recommendations for products and services that will stabilize the user's mental state are made.

[0372] Step 5:

[0373] The server integrates the generated suggestions with relaxation products and services appropriate to the emotional state, and sends these results to the terminal.

[0374] Step 6:

[0375] The device displays personalized suggestions based on information received from the server. These suggestions include specific meal plans, exercise plans, and relaxation products and services tailored to the user's emotional state.

[0376] Step 7:

[0377] Users can review the presented suggestions and provide feedback as needed. This information is then incorporated back into the system as feedback and used to improve future suggestions.

[0378] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0379] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0380] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0381] [Third Embodiment]

[0382] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0383] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0384] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0385] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0386] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0387] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0388] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0389] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0390] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0391] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0392] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0393] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0394] This invention is a system that provides an interface for users to manage their own health. The user first inputs their health checkup results into a terminal. Input methods include barcode scanning and voice input, which significantly reduces the effort required for manual input.

[0395] The terminal sends the entered data to the server, which then receives the data. Based on the health check results, the server uses an AI algorithm to analyze the data. This analysis evaluates the user's health status and generates an analysis result.

[0396] Next, the server generates meal and exercise plans tailored to the user based on the analysis results. The meal plans are proposed with cooking instructions, taking into account the ingredients the user has in their refrigerator. They also include suggested menu items to choose when eating out or buying from convenience stores.

[0397] The generated plan is presented to the user via their device. The user can then manage and practice their daily diet and exercise according to the displayed health management plan.

[0398] For example, if a user enters a health checkup result indicating high cholesterol levels, the server will use that information to suggest including oatmeal and nuts in their breakfast. It will also display a recipe for a fish-based dinner menu and provide cooking instructions. In this way, users can easily maintain their health while cooking at home.

[0399] This system contributes to preventive medicine based on daily lifestyle habits and enables health management tailored to the individual needs of each user.

[0400] The following describes the processing flow.

[0401] Step 1:

[0402] Users input their health checkup results through the terminal's interface. By utilizing methods such as barcode scanning and voice recognition, the effort required for manual input is reduced.

[0403] Step 2:

[0404] The terminal sends the entered data to the server. The transmitted data is stored on the server and accumulated in the database. This process prepares the data for analysis.

[0405] Step 3:

[0406] The server analyzes the received data. Using an AI algorithm, it evaluates the user's health status based on health checkup data and identifies abnormal values ​​and areas for improvement.

[0407] Step 4:

[0408] The server generates optimal meal and exercise plans for the user based on the analysis results. The meal plans include recipes using information about the ingredients in the user's refrigerator, as well as menu plans that can be used when eating out.

[0409] Step 5:

[0410] The generated meal and exercise suggestions are sent to the device and presented to the user visually. The device displays the suggestions on its interface in a way that is easy for the user to understand.

[0411] Step 6:

[0412] Users follow the displayed plan to implement daily dietary and exercise routines. By inputting the results of their plan implementation and feedback into their device, users can reflect areas for improvement and further needs in the system.

[0413] Step 7:

[0414] The server analyzes user feedback and adjusts the plan as needed. This ensures that users are always provided with an optimized health management plan.

[0415] (Example 1)

[0416] Next, we will describe Example 1. 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."

[0417] Enabling personalized health management based on health checkup results is crucial for maintaining users' health. However, manual data entry and general planning methods are time-consuming and inaccurate. Furthermore, the lack of specific suggestions based on the ingredients in the refrigerator and lifestyle makes it difficult for users to implement the plan.

[0418] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0419] In this invention, the server includes information input means, machine learning means, plan generation means, visualization means, and information transmission means. This enables rapid and accurate data analysis based on health checkup results and the generation of personalized health management suggestions. It can also suggest specific cooking procedures based on the food information in the user's refrigerator, realizing highly practical health management.

[0420] "Information input means" refers to methods that allow users to input their health check results into a terminal using code recognition or voice input.

[0421] "Machine learning methods" refer to techniques for analyzing data based on received health checkup results and evaluating the user's health status.

[0422] The "plan generation means" is a means of generating dietary and exercise suggestions tailored to individual users based on the analysis results.

[0423] A "visualization means" is a means of visually displaying suggestions generated on a server to the user via a terminal.

[0424] "Information transmission means" refers to a means of securely and efficiently transmitting diagnostic data from a terminal to a server.

[0425] Embodiments for carrying out this invention are described below.

[0426] Users input their health check results using a terminal. Input can be done via code recognition (e.g., barcode scanning) or voice input, significantly reducing manual data entry. This allows users to easily send clean data to the system from devices such as smartphones and tablets.

[0427] The terminal sends the entered information to the server for analysis. Since secure communication is required for data transmission, the HTTPS protocol is used, and the data is sent in a structured format (e.g., JSON). The terminal also features a dedicated application that provides users with opportunities to review and correct their data through a visually intuitive interface.

[0428] The server performs detailed analysis using the received data, employing machine learning techniques. Specifically, a model trained with TensorFlow analyzes the user's health status based on health checkup results, providing risk assessments and health recommendation strengths. In this process, numerous servers collaborate in a cloud environment to provide the necessary infrastructure for data analysis.

[0429] After the analysis results are obtained, the server uses a plan generation mechanism to generate personalized diet and exercise suggestions. These suggestions are based on information about the food items in the user's refrigerator, and a common relational database (e.g., MySQL) is used for the database management system. As a result, each user is provided with an optimized plan.

[0430] Finally, the generated plan is visualized for the user via their device, showing detailed information about meals and exercise. This visualization and follow-up process utilizes techniques to display intuitive and visual content on digital devices to make it easier for users to put the suggestions into action.

[0431] For example, if a user is diagnosed with "high cholesterol," the AI ​​will receive a prompt such as "Please generate a meal plan to lower cholesterol," and will provide a breakfast recipe including oatmeal and nuts, as well as a dinner recipe using fish from the refrigerator. In this way, the system helps users make effective use of the information they have on hand to lead a healthy daily life.

[0432] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0433] Step 1:

[0434] The user enters their health checkup results into the terminal. The input methods used by the user are code recognition or voice input. Specifically, the user scans the barcode of the checkup results with their smartphone camera or inputs numerical values ​​by voice. The entered data is stored in the system in JSON format.

[0435] Step 2:

[0436] The terminal sends the entered health check results to the server. This process securely sends the input data to the server using the HTTPS protocol. The terminal initiates data transmission when the start button is pressed, and displays a confirmation message to the user after transmission is complete. The input consists of the health check results data, and the output must be securely delivered to the server.

[0437] Step 3:

[0438] The server analyzes the received health checkup data using an AI algorithm. Based on the received data, the server applies a machine learning model using TensorFlow to perform an analysis of the user's health status. Specifically, the AI ​​model evaluates cholesterol levels and blood pressure and calculates a cardiovascular disease risk score. The input is the submitted diagnostic data, and the output is the analysis results regarding the user's health status.

[0439] Step 4:

[0440] The server generates meal and exercise plans based on the analysis results. It utilizes a plan generation mechanism to retrieve the user's food information from a MySQL database. Based on this, it develops recipes and exercise plans tailored to the ingredients in the user's refrigerator. For example, the server creates a weekly exercise menu tailored to the user's preferences. The input is the AI ​​analysis results and food information, and the output is a specific health plan.

[0441] Step 5:

[0442] The device visually displays the generated plan to the user. Through the device's application, meal and exercise suggestions are presented in a user-friendly interface. Specifically, the app includes a calendar view and reminder functions to support the user in efficiently managing their daily health plan. Input is health plan information from the server, and output is visual content displayed on the user's screen.

[0443] (Application Example 1)

[0444] Next, we will explain Application Example 1. In the following explanation, 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."

[0445] In modern times, it is difficult for consumers to make appropriate food choices based on their health condition, and this is especially true when shopping at physical stores, where time constraints and lack of information can hinder healthy choices. Therefore, there is a need for a system that enables consumers to make food choices based on their individual health conditions and efficiently supports their daily health management.

[0446] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0447] In this invention, the server includes an interface means for inputting health-related information obtained from the user, an artificial intelligence means for performing data analysis based on the health-related information, a generation means for generating suggestions for meals, exercise, and products based on the analysis results, and a display means for displaying the generated suggestions to the user and assisting in product selection. This enables users to make food choices based on their individual health conditions, realizing efficient and healthy purchasing activities in physical stores.

[0448] A "user" is an individual who uses the system for health management purposes.

[0449] "Health-related information" refers to diagnostic results and physical data that indicate the user's health status.

[0450] "Interface means" refers to a device or function for users to input health-related information.

[0451] "Artificial intelligence means" refers to a program or device that performs intelligent processing to analyze input health-related information and evaluate the user's health status.

[0452] The "generation means" refers to a function that creates suggestions regarding diet, exercise, and products suitable for the user based on the analyzed health information.

[0453] "Display means" refers to a device or function that visually displays the generated proposal so that the user can confirm it.

[0454] "Supporting product selection" means helping users choose products that are suitable for their individual health conditions.

[0455] The system for implementing this invention is designed to support users in managing their health. This system includes a function that allows users to input health-related information using a smartphone or other mobile device. Specifically, the device is equipped with barcode recognition and voice input functions, allowing users to easily input data about their health status using these functions.

[0456] The input data is transmitted to the server via the network. The server has artificial intelligence capabilities using a generative AI model to analyze the received health data. This AI model processes the data using the latest analytical techniques and evaluates the user's health status.

[0457] Based on the evaluation results, the server uses a generation mechanism to create personalized meal plans, exercise plans, and product recommendations for the user. These recommendations may include, for example, recommended foods, types of exercise, or guidance on product selection in physical stores.

[0458] The device also features a display mechanism for showing suggestions received from the server as meal and exercise schedules. This allows users to manage their health in real time.

[0459] As a concrete example, when a user shops at a supermarket, there is a function that allows them to scan suggested products based on their health checkup results using their smartphone. The product information is then analyzed, and it is displayed whether the product matches the user's health plan. Based on this information, the user can select products that are good for their health.

[0460] An example of a prompt using a generative AI model is: "Design an application that analyzes the nutritional value of specific products in a supermarket based on health checkup results and provides information that recommends or warns against purchasing them." This is designed so that the AI ​​links health checkup results with product data and generates advice tailored to individual health needs.

[0461] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0462] Step 1:

[0463] The user inputs health-related information into the terminal using an interface. The terminal then uses barcode recognition and voice input to obtain the user's health checkup results and food intake information. As a result, the terminal receives raw data related to the user's health.

[0464] Step 2:

[0465] The terminal sends the entered health information to the server. In this transmission process, the entered data is securely transferred to the server over the network. The server receives this as received data and prepares it for the next analysis step. Here, the server performs preprocessing to convert the data into a format suitable for the AI ​​model.

[0466] Step 3:

[0467] The server uses a generative AI model to analyze the received health information. Specifically, the server passes numerical data from health checkups to the AI ​​layer to evaluate the user's health status. This data analysis outputs information about the user's health risks and areas for improvement. The AI ​​model uses pattern recognition technology to extract features from the data and generate analysis results.

[0468] Step 4:

[0469] Based on the analysis results, the server generates optimal meal plans, exercise plans, and product selections for the user. In this step, the generation mechanism uses the analysis data to create specific recommendations and compiles them into information to be presented to the user. The output suggestions include recommended menus, exercises, and product options.

[0470] Step 5:

[0471] The server sends the generated suggestions to the terminal, which then presents them to the user using a display device. The user reviews the displayed health management suggestions and uses them as a reference for purchasing activities at physical stores or convenience stores. The terminal then updates the displayed content in response to user actions and provides feedback on the user's choices.

[0472] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0473] This invention is a healthcare system for simultaneously managing a user's health and emotional state. Based on health checkup results entered by the user, the system uses AI to analyze the data and provide suggestions regarding diet and exercise. Furthermore, by incorporating an emotion engine, the system can detect the user's emotional state and dynamically adjust the suggestions accordingly.

[0474] First, the user enters their health checkup results into the terminal. Barcode recognition and voice input improve input efficiency, making it easy to register data. The data entered into the terminal is sent to a server, where AI performs data analysis. As a result, a diet and exercise plan tailored to the individual's health condition is generated.

[0475] The system's key feature, the emotion engine, acquires user voice and facial expression information through the device and analyzes this data to identify the user's emotional state. For example, if the system detects that the user is stressed, it will recommend foods that have a calming effect or light exercise. If the system determines that the user is feeling down, it can recommend foods that boost mood or energetic exercise.

[0476] For example, when a user is feeling stressed at work, this information is detected by the emotion engine. The server takes this emotional state into consideration and suggests relaxing chamomile tea or deep breathing exercises. In this way, the suggestions are adjusted based on the user's emotions, providing them with more personalized and effective health management.

[0477] In this way, this system aims to improve the user's health condition and, by flexibly responding to changes in emotional state, comprehensively supports the physical and mental health of daily life.

[0478] The following describes the processing flow.

[0479] Step 1:

[0480] Users input their health checkup results through the terminal's interface. Input is performed using barcode scanners and voice recognition, enabling rapid data registration.

[0481] Step 2:

[0482] The terminal sends the entered health check data to the server. The data is stored in a database on the server and ready for analysis.

[0483] Step 3:

[0484] Based on the health check data received by the server, an AI algorithm is used to analyze the user's health status. As a result of the analysis, risks and areas for improvement related to the user's health are identified.

[0485] Step 4:

[0486] The server generates an optimal meal plan and exercise plan for the user based on the analysis results. This time, the user's emotional state will also be taken into consideration.

[0487] Step 5:

[0488] The device collects user emotion data using voice input or camera functions. The emotion engine analyzes this data to identify the user's current emotional state.

[0489] Step 6:

[0490] The server takes into account the emotional state identified by the emotion engine and appropriately adjusts the aforementioned meal and exercise plans. For example, if the user is feeling stressed, it will suggest relaxing meals and light exercise.

[0491] Step 7:

[0492] The adjusted meal and exercise plans are sent to the device and displayed to the user. The device presents the suggestions in a user-friendly format.

[0493] Step 8:

[0494] The user follows the displayed plan and enters feedback on the device upon completion. This feedback is used to improve future suggestions.

[0495] Step 9:

[0496] The server analyzes user feedback and adjusts the original plan and sentiment recognition methods as needed. This makes it possible to always provide users with suitable suggestions.

[0497] (Example 2)

[0498] Next, we will describe Example 2. 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."

[0499] In modern society, comprehensive health management based on individual health conditions is required. However, conventional systems have the problem of providing fixed suggestions based on health checkup results, and it is difficult to provide dynamic advice that reflects the emotional state of the user. In particular, health management that takes stress and mental balance into consideration has been difficult to address on an individual basis.

[0500] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0501] In this invention, the server includes a device for inputting health checkup results obtained from the user, an intelligent system for performing information analysis based on the health checkup results, and a function for dynamically changing suggestions based on the user's emotional state. This enables a comprehensive analysis of the user's health and emotional state, and allows for personalized health and emotional management suggestions.

[0502] "Users" refer to individuals or organizations that use the system and are the entities that provide health checkup results and emotional information.

[0503] "Health checkup results" refer to data that indicates the user's physical health status, and include information such as blood test results and physical measurements.

[0504] "Device" is a general term for hardware and software used for data input and emotional information acquisition.

[0505] An "intelligent system" is a mechanism that utilizes artificial intelligence to analyze input data and generate results.

[0506] "Nutritional intake and exercise suggestions" refers to specific instructions and recommendations regarding diet and physical activity for users, generated based on the analysis results.

[0507] "Mechanism" refers to the methods and means of providing generated suggestions to users through screens or other means.

[0508] "Emotional information" refers to data about the emotional state obtained from the user's voice and facial expressions.

[0509] The "dynamically changing function" refers to the ability to adjust the generated suggestions in a timely manner based on the user's emotional information.

[0510] The embodiments for carrying out the present invention will now be described. First, the user enters their health checkup results into a terminal. This terminal is equipped with an identification code recognition function and a voice input function, which allows the user to efficiently register data. The registered data is transmitted to a server via a secure communication protocol. The server uses an intelligent system to analyze the received health checkup results. This intelligent system utilizes a machine learning framework such as TensorFlow to generate suggestions for nutrition and exercise according to the user's health status.

[0511] Furthermore, the device acquires emotional information through the user's voice and facial expressions. This utilizes voice processing software and facial recognition libraries. This emotional information is sent to a server for analysis of the emotional state. Based on the analysis results, the server has the ability to dynamically change its suggestions. For example, if the user is feeling stressed, it can suggest foods with relaxing effects or light exercise.

[0512] For example, if a user uses the system at the end of a stressful day at work, the system will suggest drinking chamomile tea and doing deep breathing exercises. Another example of a prompt to the generating AI model is, "Please suggest a personalized diet and exercise plan based on my health checkup results. Also, please include advice that takes my emotional state into consideration."

[0513] This invention provides users with more personalized health management support that takes into account both their physical and emotional state.

[0514] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0515] Step 1:

[0516] The user enters their health check results into the terminal. The terminal uses an identification code to recognize the paper health check results and directly acquires the data via voice input. This input data includes the user's basic health status (e.g., blood pressure, blood sugar levels, etc.). The raw data is then sent to the server.

[0517] Step 2:

[0518] The server receives health checkup results sent from the terminal. Based on the received data, the server starts analysis using an intelligent system. The machine learning model used here (e.g., TensorFlow) performs health analysis that takes into account medical history and lifestyle habits based on past data. Through pattern recognition and inference of the input data, nutritional intake and exercise plans corresponding to the individual's health condition are generated. This analysis outputs specific suggestions regarding diet and exercise.

[0519] Step 3:

[0520] The device collects the user's voice and facial expressions through sensors. A microphone and camera are used for this purpose. The data acquired is used to analyze the user's emotional state, such as tone of voice and facial movements. Real-time emotional information is input and sent to a server where the current emotional state is analyzed.

[0521] Step 4:

[0522] The server analyzes emotional information transmitted from the terminal and uses an emotion engine to identify the user's emotional state. It identifies stress levels and energy levels from the emotional data and selects appropriate suggestions accordingly. For example, if high stress levels are estimated, the server incorporates suggestions to help the user relax. The output is health suggestions optimized for the user's emotional state.

[0523] Step 5:

[0524] The server generates dynamically adjusted suggestions based on analysis of health checkup results and emotional information. These generated suggestions are sent to the terminal in text or multimedia format. Both analysis results are used as input, while the output provides comprehensive health management advice that takes into account both health and emotional states.

[0525] Step 6:

[0526] The device displays suggestions sent from the server to the user. These include suggestions for improving lifestyle habits, specific action plans, and relaxation methods. The user receives the presented information and makes a decision on how to implement them. At this step, the user can receive feedback from the system and take subsequent actions as needed.

[0527] (Application Example 2)

[0528] Next, we will explain application example 2. In the following explanation, 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."

[0529] In modern times, comprehensively managing users' health and emotional states is difficult. In particular, there is a need for real-time, personalized health advice and product / service recommendations that respond immediately to users' emotional states, but there is a lack of appropriate systems to effectively achieve this.

[0530] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0531] In this invention, the server includes information processing means for inputting health checkup results and emotional state data obtained from the user, artificial intelligence means for performing data analysis based on the health checkup results, and generation means for generating diet and exercise suggestions based on the analysis results and emotional state data. This makes it possible to provide users with personalized health management suggestions along with recommendations for products and services that match their emotional state.

[0532] "Information processing means" refers to a device or tool for efficiently inputting data from users regarding health checkup results and emotional state, and for appropriately processing that data.

[0533] "Artificial intelligence means" refers to an algorithm or program that performs data analysis based on input health check results and generates advice and suggestions appropriate to the user's health condition.

[0534] The "generation method" is a mechanism that utilizes analysis results and emotional state data to create appropriate diet and exercise suggestions for users.

[0535] A "display mechanism" is an interface that clearly shows the generated suggestions and recommendations to the user and provides feedback as needed.

[0536] This invention is a system for effectively managing health checkup results and emotional state data obtained from users. This system mainly consists of three elements: a server, an information processing terminal, and the user.

[0537] The server receives health checkup results and emotional state data transmitted from information processing terminals. This uses interfaces that efficiently capture data using barcode reading and voice input technologies. The server analyzes this data using cloud-based AI services, such as Google Cloud AI, to generate personalized diet and exercise recommendations for the user. Preprocessing, including data cleaning and normalization, is performed during the analysis, allowing the AI ​​model to identify patterns in health status.

[0538] Furthermore, the server dynamically adjusts recommended products and services based on emotional state data. The emotion engine analyzes emotions from the user's voice and facial expressions, for example, using the Microsoft Azure Face API. Based on these results, it incorporates products and services that are expected to have a relaxation effect into the analysis.

[0539] The device visualizes and presents the proposed content to the user, providing an interface for obtaining feedback. This interface is implemented on smartphones and other mobile devices and is used in a constantly accessible manner.

[0540] For example, if a user feels stressed after work, that information entered from their device is sent to the server, and suggestions to help reduce stress, such as chamomile tea or yoga classes, are displayed.

[0541] An example of a prompt message is, "After entering your health checkup results, if stress is indicated, what relaxing beverages would you suggest?" Using prompts like this allows for suggestions that cater to the diverse needs of users.

[0542] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0543] Step 1:

[0544] The terminal receives health checkup results and emotional state data from the user. This data entry utilizes a barcode reader or voice input function. The entered data is converted to a digital format and sent to the server.

[0545] Step 2:

[0546] The server receives health and emotional data transmitted from the terminal. The received data is cleaned in preparation for analysis, removing unnecessary or inaccurate data. The data is then normalized and ready for AI analysis.

[0547] Step 3:

[0548] The server uses cloud-based AI models, such as Google Cloud AI, to analyze normalized health data. The analysis generates personalized meal and exercise plans for the user. This analysis process involves pattern recognition and prediction based on historical data.

[0549] Step 4:

[0550] The server performs sentiment analysis using a separate process. Specifically, it uses the Microsoft Azure Face API to analyze the user's facial expressions and voice data to identify their emotional state. Based on the detected emotional state, recommendations for products and services that will stabilize the user's mental state are made.

[0551] Step 5:

[0552] The server integrates the generated suggestions with relaxation products and services appropriate to the emotional state, and sends these results to the terminal.

[0553] Step 6:

[0554] The device displays personalized suggestions based on information received from the server. These suggestions include specific meal plans, exercise plans, and relaxation products and services tailored to the user's emotional state.

[0555] Step 7:

[0556] Users can review the presented suggestions and provide feedback as needed. This information is then incorporated back into the system as feedback and used to improve future suggestions.

[0557] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0558] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0559] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0560] [Fourth Embodiment]

[0561] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0562] As shown in Figure 7, the 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.

[0563] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0564] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0565] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0566] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0567] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0568] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0569] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0570] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0571] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0572] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0573] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0574] This invention is a system that provides an interface for users to manage their own health. The user first inputs their health checkup results into a terminal. Input methods include barcode scanning and voice input, which significantly reduces the effort required for manual input.

[0575] The terminal sends the entered data to the server, which then receives the data. Based on the health check results, the server uses an AI algorithm to analyze the data. This analysis evaluates the user's health status and generates an analysis result.

[0576] Next, the server generates meal and exercise plans tailored to the user based on the analysis results. The meal plans are proposed with cooking instructions, taking into account the ingredients the user has in their refrigerator. They also include suggested menu items to choose when eating out or buying from convenience stores.

[0577] The generated plan is presented to the user via their device. The user can then manage and practice their daily diet and exercise according to the displayed health management plan.

[0578] For example, if a user enters a health checkup result indicating high cholesterol levels, the server will use that information to suggest including oatmeal and nuts in their breakfast. It will also display a recipe for a fish-based dinner menu and provide cooking instructions. In this way, users can easily maintain their health while cooking at home.

[0579] This system contributes to preventive medicine based on daily lifestyle habits and enables health management tailored to the individual needs of each user.

[0580] The following describes the processing flow.

[0581] Step 1:

[0582] Users input their health checkup results through the terminal's interface. By utilizing methods such as barcode scanning and voice recognition, the effort required for manual input is reduced.

[0583] Step 2:

[0584] The terminal sends the entered data to the server. The transmitted data is stored on the server and accumulated in the database. This process prepares the data for analysis.

[0585] Step 3:

[0586] The server analyzes the received data. Using an AI algorithm, it evaluates the user's health status based on health checkup data and identifies abnormal values ​​and areas for improvement.

[0587] Step 4:

[0588] The server generates optimal meal and exercise plans for the user based on the analysis results. The meal plans include recipes using information about the ingredients in the user's refrigerator, as well as menu plans that can be used when eating out.

[0589] Step 5:

[0590] The generated meal and exercise suggestions are sent to the device and presented to the user visually. The device displays the suggestions on its interface in a way that is easy for the user to understand.

[0591] Step 6:

[0592] Users follow the displayed plan to implement daily dietary and exercise routines. By inputting the results of their plan implementation and feedback into their device, users can reflect areas for improvement and further needs in the system.

[0593] Step 7:

[0594] The server analyzes user feedback and adjusts the plan as needed. This ensures that users are always provided with an optimized health management plan.

[0595] (Example 1)

[0596] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0597] Enabling personalized health management based on health checkup results is crucial for maintaining users' health. However, manual data entry and general planning methods are time-consuming and inaccurate. Furthermore, the lack of specific suggestions based on the ingredients in the refrigerator and lifestyle makes it difficult for users to implement the plan.

[0598] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0599] In this invention, the server includes information input means, machine learning means, plan generation means, visualization means, and information transmission means. This enables rapid and accurate data analysis based on health checkup results and the generation of personalized health management suggestions. It can also suggest specific cooking procedures based on the food information in the user's refrigerator, realizing highly practical health management.

[0600] "Information input means" refers to methods that allow users to input their health check results into a terminal using code recognition or voice input.

[0601] "Machine learning methods" refer to techniques for analyzing data based on received health checkup results and evaluating the user's health status.

[0602] The "plan generation means" is a means of generating dietary and exercise suggestions tailored to individual users based on the analysis results.

[0603] A "visualization means" is a means of visually displaying suggestions generated on a server to the user via a terminal.

[0604] "Information transmission means" refers to a means of securely and efficiently transmitting diagnostic data from a terminal to a server.

[0605] Embodiments for carrying out this invention are described below.

[0606] Users input their health check results using a terminal. Input can be done via code recognition (e.g., barcode scanning) or voice input, significantly reducing manual data entry. This allows users to easily send clean data to the system from devices such as smartphones and tablets.

[0607] The terminal sends the entered information to the server for analysis. Since secure communication is required for data transmission, the HTTPS protocol is used, and the data is sent in a structured format (e.g., JSON). The terminal also features a dedicated application that provides users with opportunities to review and correct their data through a visually intuitive interface.

[0608] The server performs detailed analysis using the received data, employing machine learning techniques. Specifically, a model trained with TensorFlow analyzes the user's health status based on health checkup results, providing risk assessments and health recommendation strengths. In this process, numerous servers collaborate in a cloud environment to provide the necessary infrastructure for data analysis.

[0609] After the analysis results are obtained, the server uses a plan generation mechanism to generate personalized diet and exercise suggestions. These suggestions are based on information about the food items in the user's refrigerator, and a common relational database (e.g., MySQL) is used for the database management system. As a result, each user is provided with an optimized plan.

[0610] Finally, the generated plan is visualized for the user via their device, showing detailed information about meals and exercise. This visualization and follow-up process utilizes techniques to display intuitive and visual content on digital devices to make it easier for users to put the suggestions into action.

[0611] For example, if a user is diagnosed with "high cholesterol," the AI ​​will receive a prompt such as "Please generate a meal plan to lower cholesterol," and will provide a breakfast recipe including oatmeal and nuts, as well as a dinner recipe using fish from the refrigerator. In this way, the system helps users make effective use of the information they have on hand to lead a healthy daily life.

[0612] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0613] Step 1:

[0614] The user enters their health checkup results into the terminal. The input methods used by the user are code recognition or voice input. Specifically, the user scans the barcode of the checkup results with their smartphone camera or inputs numerical values ​​by voice. The entered data is stored in the system in JSON format.

[0615] Step 2:

[0616] The terminal sends the entered health check results to the server. This process securely sends the input data to the server using the HTTPS protocol. The terminal initiates data transmission when the start button is pressed, and displays a confirmation message to the user after transmission is complete. The input consists of the health check results data, and the output must be securely delivered to the server.

[0617] Step 3:

[0618] The server analyzes the received health checkup data using an AI algorithm. Based on the received data, the server applies a machine learning model using TensorFlow to perform an analysis of the user's health status. Specifically, the AI ​​model evaluates cholesterol levels and blood pressure and calculates a cardiovascular disease risk score. The input is the submitted diagnostic data, and the output is the analysis results regarding the user's health status.

[0619] Step 4:

[0620] The server generates meal and exercise plans based on the analysis results. It utilizes a plan generation mechanism to retrieve the user's food information from a MySQL database. Based on this, it develops recipes and exercise plans tailored to the ingredients in the user's refrigerator. For example, the server creates a weekly exercise menu tailored to the user's preferences. The input is the AI ​​analysis results and food information, and the output is a specific health plan.

[0621] Step 5:

[0622] The device visually displays the generated plan to the user. Through the device's application, meal and exercise suggestions are presented in a user-friendly interface. Specifically, the app includes a calendar view and reminder functions to support the user in efficiently managing their daily health plan. Input is health plan information from the server, and output is visual content displayed on the user's screen.

[0623] (Application Example 1)

[0624] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0625] In modern times, it is difficult for consumers to make appropriate food choices based on their health condition, and this is especially true when shopping at physical stores, where time constraints and lack of information can hinder healthy choices. Therefore, there is a need for a system that enables consumers to make food choices based on their individual health conditions and efficiently supports their daily health management.

[0626] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0627] In this invention, the server includes an interface means for inputting health-related information obtained from the user, an artificial intelligence means for performing data analysis based on the health-related information, a generation means for generating suggestions for meals, exercise, and products based on the analysis results, and a display means for displaying the generated suggestions to the user and assisting in product selection. This enables users to make food choices based on their individual health conditions, realizing efficient and healthy purchasing activities in physical stores.

[0628] A "user" is an individual who uses the system for health management purposes.

[0629] "Health-related information" refers to diagnostic results and physical data that indicate the user's health status.

[0630] "Interface means" refers to a device or function for users to input health-related information.

[0631] "Artificial intelligence means" refers to a program or device that performs intelligent processing to analyze input health-related information and evaluate the user's health status.

[0632] The "generation means" refers to a function that creates suggestions regarding diet, exercise, and products suitable for the user based on the analyzed health information.

[0633] "Display means" refers to a device or function that visually displays the generated proposal so that the user can confirm it.

[0634] "Supporting product selection" means helping users choose products that are suitable for their individual health conditions.

[0635] The system for implementing this invention is designed to support users in managing their health. This system includes a function that allows users to input health-related information using a smartphone or other mobile device. Specifically, the device is equipped with barcode recognition and voice input functions, allowing users to easily input data about their health status using these functions.

[0636] The input data is transmitted to the server via the network. The server has artificial intelligence capabilities using a generative AI model to analyze the received health data. This AI model processes the data using the latest analytical techniques and evaluates the user's health status.

[0637] Based on the evaluation results, the server uses a generation mechanism to create personalized meal plans, exercise plans, and product recommendations for the user. These recommendations may include, for example, recommended foods, types of exercise, or guidance on product selection in physical stores.

[0638] The device also features a display mechanism for showing suggestions received from the server as meal and exercise schedules. This allows users to manage their health in real time.

[0639] As a concrete example, when a user shops at a supermarket, there is a function that allows them to scan suggested products based on their health checkup results using their smartphone. The product information is then analyzed, and it is displayed whether the product matches the user's health plan. Based on this information, the user can select products that are good for their health.

[0640] An example of a prompt using a generative AI model is: "Design an application that analyzes the nutritional value of specific products in a supermarket based on health checkup results and provides information that recommends or warns against purchasing them." This is designed so that the AI ​​links health checkup results with product data and generates advice tailored to individual health needs.

[0641] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0642] Step 1:

[0643] The user inputs health-related information into the terminal using an interface. The terminal then uses barcode recognition and voice input to obtain the user's health checkup results and food intake information. As a result, the terminal receives raw data related to the user's health.

[0644] Step 2:

[0645] The terminal sends the entered health information to the server. In this transmission process, the entered data is securely transferred to the server over the network. The server receives this as received data and prepares it for the next analysis step. Here, the server performs preprocessing to convert the data into a format suitable for the AI ​​model.

[0646] Step 3:

[0647] The server uses a generative AI model to analyze the received health information. Specifically, the server passes numerical data from health checkups to the AI ​​layer to evaluate the user's health status. This data analysis outputs information about the user's health risks and areas for improvement. The AI ​​model uses pattern recognition technology to extract features from the data and generate analysis results.

[0648] Step 4:

[0649] Based on the analysis results, the server generates optimal meal plans, exercise plans, and product selections for the user. In this step, the generation mechanism uses the analysis data to create specific recommendations and compiles them into information to be presented to the user. The output suggestions include recommended menus, exercises, and product options.

[0650] Step 5:

[0651] The server sends the generated suggestions to the terminal, which then presents them to the user using a display device. The user reviews the displayed health management suggestions and uses them as a reference for purchasing activities at physical stores or convenience stores. The terminal then updates the displayed content in response to user actions and provides feedback on the user's choices.

[0652] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0653] This invention is a healthcare system for simultaneously managing a user's health and emotional state. Based on health checkup results entered by the user, the system uses AI to analyze the data and provide suggestions regarding diet and exercise. Furthermore, by incorporating an emotion engine, the system can detect the user's emotional state and dynamically adjust the suggestions accordingly.

[0654] First, the user enters their health checkup results into the terminal. Barcode recognition and voice input improve input efficiency, making it easy to register data. The data entered into the terminal is sent to a server, where AI performs data analysis. As a result, a diet and exercise plan tailored to the individual's health condition is generated.

[0655] The system's key feature, the emotion engine, acquires user voice and facial expression information through the device and analyzes this data to identify the user's emotional state. For example, if the system detects that the user is stressed, it will recommend foods that have a calming effect or light exercise. If the system determines that the user is feeling down, it can recommend foods that boost mood or energetic exercise.

[0656] For example, when a user is feeling stressed at work, this information is detected by the emotion engine. The server takes this emotional state into consideration and suggests relaxing chamomile tea or deep breathing exercises. In this way, the suggestions are adjusted based on the user's emotions, providing them with more personalized and effective health management.

[0657] In this way, this system aims to improve the user's health condition and, by flexibly responding to changes in emotional state, comprehensively supports the physical and mental health of daily life.

[0658] The following describes the processing flow.

[0659] Step 1:

[0660] Users input their health checkup results through the terminal's interface. Input is performed using barcode scanners and voice recognition, enabling rapid data registration.

[0661] Step 2:

[0662] The terminal sends the entered health check data to the server. The data is stored in a database on the server and ready for analysis.

[0663] Step 3:

[0664] Based on the health check data received by the server, an AI algorithm is used to analyze the user's health status. As a result of the analysis, risks and areas for improvement related to the user's health are identified.

[0665] Step 4:

[0666] The server generates an optimal meal plan and exercise plan for the user based on the analysis results. This time, the user's emotional state will also be taken into consideration.

[0667] Step 5:

[0668] The device collects user emotion data using voice input or camera functions. The emotion engine analyzes this data to identify the user's current emotional state.

[0669] Step 6:

[0670] The server takes into account the emotional state identified by the emotion engine and appropriately adjusts the aforementioned meal and exercise plans. For example, if the user is feeling stressed, it will suggest relaxing meals and light exercise.

[0671] Step 7:

[0672] The adjusted meal and exercise plans are sent to the device and displayed to the user. The device presents the suggestions in a user-friendly format.

[0673] Step 8:

[0674] The user follows the displayed plan and enters feedback on the device upon completion. This feedback is used to improve future suggestions.

[0675] Step 9:

[0676] The server analyzes user feedback and adjusts the original plan and sentiment recognition methods as needed. This makes it possible to always provide users with suitable suggestions.

[0677] (Example 2)

[0678] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0679] In modern society, comprehensive health management based on individual health conditions is required. However, conventional systems have the problem of providing fixed suggestions based on health checkup results, and it is difficult to provide dynamic advice that reflects the emotional state of the user. In particular, health management that takes stress and mental balance into consideration has been difficult to address on an individual basis.

[0680] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0681] In this invention, the server includes a device for inputting health checkup results obtained from the user, an intelligent system for performing information analysis based on the health checkup results, and a function for dynamically changing suggestions based on the user's emotional state. This enables a comprehensive analysis of the user's health and emotional state, and allows for personalized health and emotional management suggestions.

[0682] "Users" refer to individuals or organizations that use the system and are the entities that provide health checkup results and emotional information.

[0683] "Health checkup results" refer to data that indicates the user's physical health status, and include information such as blood test results and physical measurements.

[0684] "Device" is a general term for hardware and software used for data input and emotional information acquisition.

[0685] An "intelligent system" is a mechanism that utilizes artificial intelligence to analyze input data and generate results.

[0686] "Nutritional intake and exercise suggestions" refers to specific instructions and recommendations regarding diet and physical activity for users, generated based on the analysis results.

[0687] "Mechanism" refers to the methods and means of providing generated suggestions to users through screens or other means.

[0688] "Emotional information" refers to data about the emotional state obtained from the user's voice and facial expressions.

[0689] The "dynamically changing function" refers to the ability to adjust the generated suggestions in a timely manner based on the user's emotional information.

[0690] The embodiments for carrying out the present invention will now be described. First, the user enters their health checkup results into a terminal. This terminal is equipped with an identification code recognition function and a voice input function, which allows the user to efficiently register data. The registered data is transmitted to a server via a secure communication protocol. The server uses an intelligent system to analyze the received health checkup results. This intelligent system utilizes a machine learning framework such as TensorFlow to generate suggestions for nutrition and exercise according to the user's health status.

[0691] Furthermore, the device acquires emotional information through the user's voice and facial expressions. This utilizes voice processing software and facial recognition libraries. This emotional information is sent to a server for analysis of the emotional state. Based on the analysis results, the server has the ability to dynamically change its suggestions. For example, if the user is feeling stressed, it can suggest foods with relaxing effects or light exercise.

[0692] For example, if a user uses the system at the end of a stressful day at work, the system will suggest drinking chamomile tea and doing deep breathing exercises. Another example of a prompt to the generating AI model is, "Please suggest a personalized diet and exercise plan based on my health checkup results. Also, please include advice that takes my emotional state into consideration."

[0693] This invention provides users with more personalized health management support that takes into account both their physical and emotional state.

[0694] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0695] Step 1:

[0696] The user enters their health check results into the terminal. The terminal uses an identification code to recognize the paper health check results and directly acquires the data via voice input. This input data includes the user's basic health status (e.g., blood pressure, blood sugar levels, etc.). The raw data is then sent to the server.

[0697] Step 2:

[0698] The server receives health checkup results sent from the terminal. Based on the received data, the server starts analysis using an intelligent system. The machine learning model used here (e.g., TensorFlow) performs health analysis that takes into account medical history and lifestyle habits based on past data. Through pattern recognition and inference of the input data, nutritional intake and exercise plans corresponding to the individual's health condition are generated. This analysis outputs specific suggestions regarding diet and exercise.

[0699] Step 3:

[0700] The device collects the user's voice and facial expressions through sensors. A microphone and camera are used for this purpose. The data acquired is used to analyze the user's emotional state, such as tone of voice and facial movements. Real-time emotional information is input and sent to a server where the current emotional state is analyzed.

[0701] Step 4:

[0702] The server analyzes emotional information transmitted from the terminal and uses an emotion engine to identify the user's emotional state. It identifies stress levels and energy levels from the emotional data and selects appropriate suggestions accordingly. For example, if high stress levels are estimated, the server incorporates suggestions to help the user relax. The output is health suggestions optimized for the user's emotional state.

[0703] Step 5:

[0704] The server generates dynamically adjusted suggestions based on analysis of health checkup results and emotional information. These generated suggestions are sent to the terminal in text or multimedia format. Both analysis results are used as input, while the output provides comprehensive health management advice that takes into account both health and emotional states.

[0705] Step 6:

[0706] The device displays suggestions sent from the server to the user. These include suggestions for improving lifestyle habits, specific action plans, and relaxation methods. The user receives the presented information and makes a decision on how to implement them. At this step, the user can receive feedback from the system and take subsequent actions as needed.

[0707] (Application Example 2)

[0708] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0709] In modern times, comprehensively managing users' health and emotional states is difficult. In particular, there is a need for real-time, personalized health advice and product / service recommendations that respond immediately to users' emotional states, but there is a lack of appropriate systems to effectively achieve this.

[0710] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0711] In this invention, the server includes information processing means for inputting health checkup results and emotional state data obtained from the user, artificial intelligence means for performing data analysis based on the health checkup results, and generation means for generating diet and exercise suggestions based on the analysis results and emotional state data. This makes it possible to provide users with personalized health management suggestions along with recommendations for products and services that match their emotional state.

[0712] "Information processing means" refers to a device or tool for efficiently inputting data from users regarding health checkup results and emotional state, and for appropriately processing that data.

[0713] "Artificial intelligence means" refers to an algorithm or program that performs data analysis based on input health check results and generates advice and suggestions appropriate to the user's health condition.

[0714] The "generation method" is a mechanism that utilizes analysis results and emotional state data to create appropriate diet and exercise suggestions for users.

[0715] A "display mechanism" is an interface that clearly shows the generated suggestions and recommendations to the user and provides feedback as needed.

[0716] This invention is a system for effectively managing health checkup results and emotional state data obtained from users. This system mainly consists of three elements: a server, an information processing terminal, and the user.

[0717] The server receives health checkup results and emotional state data transmitted from information processing terminals. This uses interfaces that efficiently capture data using barcode reading and voice input technologies. The server analyzes this data using cloud-based AI services, such as Google Cloud AI, to generate personalized diet and exercise recommendations for the user. Preprocessing, including data cleaning and normalization, is performed during the analysis, allowing the AI ​​model to identify patterns in health status.

[0718] Furthermore, the server dynamically adjusts recommended products and services based on emotional state data. The emotion engine analyzes emotions from the user's voice and facial expressions, for example, using the Microsoft Azure Face API. Based on these results, it incorporates products and services that are expected to have a relaxation effect into the analysis.

[0719] The device visualizes and presents the proposed content to the user, providing an interface for obtaining feedback. This interface is implemented on smartphones and other mobile devices and is used in a constantly accessible manner.

[0720] For example, if a user feels stressed after work, that information entered from their device is sent to the server, and suggestions to help reduce stress, such as chamomile tea or yoga classes, are displayed.

[0721] An example of a prompt message is, "After entering your health checkup results, if stress is indicated, what relaxing beverages would you suggest?" Using prompts like this allows for suggestions that cater to the diverse needs of users.

[0722] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0723] Step 1:

[0724] The terminal receives health checkup results and emotional state data from the user. This data entry utilizes a barcode reader or voice input function. The entered data is converted to a digital format and sent to the server.

[0725] Step 2:

[0726] The server receives health and emotional data transmitted from the terminal. The received data is cleaned in preparation for analysis, removing unnecessary or inaccurate data. The data is then normalized and ready for AI analysis.

[0727] Step 3:

[0728] The server uses cloud-based AI models, such as Google Cloud AI, to analyze normalized health data. The analysis generates personalized meal and exercise plans for the user. This analysis process involves pattern recognition and prediction based on historical data.

[0729] Step 4:

[0730] The server performs sentiment analysis using a separate process. Specifically, it uses the Microsoft Azure Face API to analyze the user's facial expressions and voice data to identify their emotional state. Based on the detected emotional state, recommendations for products and services that will stabilize the user's mental state are made.

[0731] Step 5:

[0732] The server integrates the generated suggestions with relaxation products and services appropriate to the emotional state, and sends these results to the terminal.

[0733] Step 6:

[0734] The device displays personalized suggestions based on information received from the server. These suggestions include specific meal plans, exercise plans, and relaxation products and services tailored to the user's emotional state.

[0735] Step 7:

[0736] Users can review the presented suggestions and provide feedback as needed. This information is then incorporated back into the system as feedback and used to improve future suggestions.

[0737] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0738] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0739] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0740] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0741] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0742] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0743] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0744] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0745] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0746] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0747] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0748] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0749] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0750] 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.

[0751] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0752] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0753] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0754] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0755] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0756] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0757] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0758] The following is further disclosed regarding the embodiments described above.

[0759] (Claim 1)

[0760] An interface for inputting health checkup results obtained from users,

[0761] An artificial intelligence means for performing data analysis based on the aforementioned health checkup results,

[0762] A generation means for generating dietary and exercise suggestions based on the aforementioned analysis results,

[0763] A display means for displaying the generated proposal to the user,

[0764] A health management support system that includes this.

[0765] (Claim 2)

[0766] The system according to claim 1, characterized in that the interface means includes barcode recognition and voice input.

[0767] (Claim 3)

[0768] The system according to claim 1, characterized in that the generation means makes suggestions including cooking procedures based on input ingredient information.

[0769] "Example 1"

[0770] (Claim 1)

[0771] A means for inputting information to enter health checkup results obtained from users,

[0772] A machine learning method that performs data analysis based on the aforementioned health checkup results,

[0773] A plan generation means that generates diet and exercise suggestions based on the aforementioned analysis results,

[0774] A visualization means for displaying the generated proposal to the user,

[0775] Information transmission means for which a user inputs diagnostic data via the visualization means,

[0776] An information processing system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1, characterized in that the information input means includes code recognition and voice input.

[0779] (Claim 3)

[0780] The system according to claim 1, characterized in that the plan generation means makes suggestions including cooking procedures based on registered food information.

[0781] "Application Example 1"

[0782] (Claim 1)

[0783] An interface for inputting health information obtained from users,

[0784] An artificial intelligence means that performs data analysis based on the aforementioned health information,

[0785] A generation means that generates suggestions for meals, exercise, and products based on the aforementioned analysis results,

[0786] A display means for displaying the generated suggestions to the user and assisting in product selection,

[0787] A system that includes this.

[0788] (Claim 2)

[0789] The system according to claim 1, characterized in that the interface means includes symbol recognition and voice input.

[0790] (Claim 3)

[0791] The system according to claim 1, characterized in that the generating means makes suggestions, including cooking procedures, based on information about food that has been input, and supports commercial transactions.

[0792] "Example 2 of combining an emotion engine"

[0793] (Claim 1)

[0794] A device for inputting health checkup results obtained from users,

[0795] An intelligent system that performs information analysis based on the aforementioned health checkup results,

[0796] Based on the aforementioned analysis results, the function generates suggestions for nutritional intake and exercise,

[0797] A mechanism for displaying the aforementioned proposal to users,

[0798] A device for acquiring emotional information and analyzing the emotional state of users,

[0799] A function to dynamically change the aforementioned suggestions based on the user's emotional state,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, characterized in that the device includes identification code recognition and voice data input.

[0803] (Claim 3)

[0804] The system according to claim 1, characterized in that the aforementioned function makes suggestions, including cooking procedures, based on the input ingredient information.

[0805] "Application example 2 when combining with an emotional engine"

[0806] (Claim 1)

[0807] Information processing means for inputting health check results and emotional state data obtained from users,

[0808] An artificial intelligence means for performing data analysis based on the aforementioned health checkup results,

[0809] A generation means for generating dietary and exercise suggestions based on the aforementioned analysis results and emotional state data,

[0810] A display means that displays the generated suggestions to the user and dynamically recommends relaxation products and services,

[0811] A health management support system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, characterized in that the information processing means includes barcode reading and voice input.

[0814] (Claim 3)

[0815] The system according to claim 1, characterized in that the generating means makes suggestions including cooking procedures and lifestyle improvement methods based on the input item information. [Explanation of symbols]

[0816] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An interface for inputting health checkup results obtained from users, An artificial intelligence means for performing data analysis based on the aforementioned health checkup results, A generation means for generating dietary and exercise suggestions based on the aforementioned analysis results, A display means for displaying the generated proposal to the user, A health management support system that includes this.

2. The system according to claim 1, characterized in that the interface means includes barcode recognition and voice input.

3. The system according to claim 1, characterized in that the generation means makes suggestions including cooking procedures based on input ingredient information.

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