system

Abstract

Provide a system. 【Solution means】 Means for receiving user information, Means for analyzing the health status based on the user information, Means for setting individual health goals from the analysis results, Means for providing advice related to the health goals, Means for monitoring the user activities after the advice is provided and generating feedback, Means for receiving user information via voice input and visual display, Means for obtaining body information in real time based on the analysis, Means for providing the feedback using vision and hearing, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, despite the increase in lifestyle diseases and the growing awareness of individual health management, effective health management and early detection and prevention of risks are considered difficult. In particular, continuous and personalized health guidance considering individual health conditions according to age and living environment is not sufficiently provided, resulting in an increase in medical costs and a delay in improving the overall health level of society.

Means for Solving the Problems

[0005] To address this challenge, the present invention proposes a system that collects user information, analyzes their health status based on that information, sets individual health goals, and provides specific advice. This system continuously monitors the user's activities even after providing advice and generates feedback, thereby promoting specific behavioral changes for health improvement tailored to the user. Furthermore, by performing risk assessments as needed and enabling data acquisition from wearable devices, it achieves even more accurate health management.

[0006] "User information" refers to data about an individual's basic attributes and health status, including, for example, age, gender, height, weight, dietary history, exercise habits, and sleep patterns.

[0007] "Health status" refers to an indicator used to assess an individual's degree of physical and mental health, and includes the risk of lifestyle-related diseases, physical fitness, nutritional status, and mental health.

[0008] "Analysis" refers to the process of analyzing data based on user information to evaluate the user's health status.

[0009] "Health goals" refer to specific behavioral targets or achievement items set to improve the user's health status.

[0010] "Advice" refers to specific instructions and suggestions provided by the system to help users achieve their health goals.

[0011] "Monitoring" refers to the continuous observation and collection of data regarding changes in users' activities and lifestyle patterns.

[0012] "Feedback" refers to the process of providing information that evaluates and suggests improvements to health based on the user's activity results.

[0013] "Risk assessment" means using the user's health data to determine the likelihood of future health problems occurring.

[0014] A "wearable device" refers to a wearable electronic device that can collect a user's biometric data and activity information in real time and transmit it to an external device or system. [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the 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 one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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), and the like.

[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, and the like.

[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 analyzes a user's health status, sets individually customized health goals, and provides advice. This system functions in conjunction with a server, a terminal, and a wearable device. The server plays a central role, holding and analyzing information provided by the user. The terminal acts as an interface with the user, used for data input and receiving advice.

[0037] Users first enter their personal health information through a device. This information includes height, weight, age, gender, daily eating and exercise habits, and sleep patterns. If necessary, wearable devices can be used to record heart rate, steps, activity time, and other data in real time.

[0038] The server collects data submitted by users and stores it in a database. Based on this data, the system uses AI technology to analyze it and assess the user's health status and potential risks. This analysis includes calculating BMI, comparing it with past health data, and detecting imbalances in nutrition.

[0039] Based on the evaluation results, the server sets individual health goals. In this process, specific goals optimized for each user are proposed and notified to the user via their device. For example, realistic goals aimed at maintaining or improving the user's health are presented, such as "exercise 150 minutes of aerobic exercise per week" or "limit sugar intake in meals to less than 50g per day."

[0040] The server then provides specific advice to help users achieve these goals. This advice includes nutritional guidelines, exercise plans, and habits and improvements to incorporate into daily life. Using the advice displayed on the device as a guide, users can make changes to their daily lives and improve their health.

[0041] Subsequently, users continue to record their daily activities and provide feedback on their progress to the server via their device. Based on this feedback, the server evaluates the progress and, if necessary, revises goals and updates advice. In addition, if positive results are achieved, feedback and incentives are provided to maintain motivation.

[0042] As a concrete example, consider a user who uses this system for the purpose of weight loss and prevention of lifestyle-related diseases. Suppose this user aims to improve fatty liver disease. The server analyzes the user's BMI to be on the higher side and notifies the user of the importance of controlling calorie intake and exercising regularly. It also suggests meal plans to reduce the risk of fatty liver disease and monitors the user's diet and exercise history regularly to check the progress of their weight loss and provide feedback. In this way, the user can continuously receive guidance from the system and incorporate efforts toward improving their health into their daily life.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users input health information through their devices. This includes data related to their daily lives, such as height, weight, age, gender, dietary history, and exercise status. Users can also synchronize data obtained in real time via wearable devices.

[0046] Step 2:

[0047] The device sends health information collected from the user to the server. The data is encrypted and transmitted securely.

[0048] Step 3:

[0049] The server stores the received data in a database and passes it to the AI ​​analysis module. The AI ​​analysis module uses this data to evaluate the user's current health status and past trends.

[0050] Step 4:

[0051] The server understands the user's health status based on the evaluation results of the AI ​​analysis module and identifies potential health risks. Based on these results, it sets individually optimized health goals.

[0052] Step 5:

[0053] The server sends health goals and progress instructions to the device. The device notifies the user of these goals and presents an action plan. Specific examples include "aerobic exercise three times a week" and "increasing daily vegetable intake."

[0054] Step 6:

[0055] Users record their daily activities on their devices. They input data such as what they ate, how much exercise they did, and changes in their weight, and manage their progress.

[0056] Step 7:

[0057] The terminal sends user records to the server, which then monitors them. The server evaluates goal achievement and generates feedback and corrective advice as needed.

[0058] Step 8:

[0059] The server generates feedback and sends it to the device. The device then presents this feedback to the user, helping to boost motivation and set new goals. This feedback may include messages of praise based on achievement and suggestions for the next challenge.

[0060] (Example 1)

[0061] 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."

[0062] In modern society, individual health management is a crucial issue, but conventional systems have struggled to provide personalized health maintenance goals and advice. In particular, the lack of real-time health status analysis and feedback has hindered user motivation and goal achievement. Therefore, there is a need to develop a system that enables the setting of user-optimized health maintenance goals and the provision of specific advice aligned with those goals.

[0063] 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.

[0064] In this invention, the server includes means for receiving the user's physical information, means for analyzing the health status using a generative AI model, and means for setting individual health maintenance goals. This makes it possible to set health maintenance goals optimized for each user and provide specific advice for achieving them.

[0065] "User physical information" refers to data that indicates an individual's health status, such as height, weight, age, gender, eating and exercise habits, sleep patterns, heart rate, and steps taken.

[0066] A "generative AI model" is an algorithm or program built on artificial intelligence technology that is used to analyze the physical information collected from users.

[0067] "Health status analysis" is a process that evaluates the user's current health status and health risks based on their physical information.

[0068] "Individual health maintenance goals" are specific and achievable health improvement goals set based on each user's health status and lifestyle.

[0069] "Health maintenance advice" refers to specific action guidelines and suggestions provided to users based on their set health maintenance goals.

[0070] "Recording user activity" refers to the process of tracking users' daily actions and health-related activities and saving them as data.

[0071] "Evaluating progress" is an activity that measures the degree to which users have achieved their goals and confirms the effectiveness of current efforts.

[0072] "Updating health maintenance goals and advice" refers to reviewing and adjusting existing goals and advice in a timely manner based on user activity feedback.

[0073] This invention utilizes servers, terminals, and wearable devices to build a system that supports users' health management. This makes it possible to collect and analyze personal health information and provide an optimal health maintenance plan.

[0074] The server plays a central role in the system, receiving health-related data entered by users. The terminal allows users to input data via an interface and facilitates data transmission and reception between the terminal and the server. Wearable devices are used to collect real-time data such as heart rate and steps, enabling detailed monitoring of the user's health.

[0075] First, users input their individual health information into the system using a terminal. This information includes height, weight, age, gender, daily eating and exercise habits, and sleep patterns. Furthermore, by wearing a wearable device, specific physical activity data is continuously collected and transmitted to the server.

[0076] The server uses a generative AI model to analyze the collected data and assess the user's health status. This analysis process uses prompts to specify the algorithms and calculation procedures the model will use. For example, a prompt might say, "Enter the user's BMI and past exercise data, and generate advice to help them achieve their health goals." Based on this, the model calculates the BMI, compares it to past data, and detects anomalies.

[0077] Based on the analysis results, the server sets health maintenance goals optimized for the user. The set goals are notified to the user via their device, and the user can work on improving their daily life by following the specific advice provided.

[0078] Subsequently, users continue to record their activities and provide feedback to the server via their device. Based on this feedback, the server evaluates the user's progress and determines whether updates to health maintenance goals and advice are necessary. This allows users to consistently manage their health appropriately and receive support towards achieving their goals.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user enters their health information into the device. This information includes height, weight, age, gender, dietary and exercise habits, and sleep patterns. The purpose of this input is to collect data to understand the individual's health status. The device then organizes this data and prepares it for transmission to the server.

[0082] Step 2:

[0083] The terminal transmits health information obtained from the user and real-time data collected through wearable devices to the server. Wearable devices provide data such as heart rate, steps taken, and activity time. The server stores the received data in a database and uses it as input for analysis.

[0084] Step 3:

[0085] The server uses a generated AI model based on stored data to analyze the user's health status. Specific data processing includes calculating BMI using height and weight, and detecting imbalances in nutrition based on daily lifestyle data. An example of a prompt message used is, "Please use the user's BMI and past exercise data to assess health risks and set appropriate health goals." The output includes analysis results and a risk assessment.

[0086] Step 4:

[0087] The server sets individual health maintenance goals for each user based on the analysis results of the generated AI model. These goals may include suggestions for improving the user's eating habits or recommending a certain amount of exercise. This goal setting output is sent to the device as a specific health maintenance plan and notified to the user.

[0088] Step 5:

[0089] The device displays a health maintenance plan sent from the server to the user and provides health maintenance advice. The user can then take actions to maintain their health, such as improving their diet or exercising, based on the specific advice provided. At this stage, the advice is presented in a clear and easy-to-follow format.

[0090] Step 6:

[0091] Users record their health maintenance activities and feed this data back to the server via their device. This feedback allows the server to understand the user's progress and current position relative to their goals. Based on this feedback, the server evaluates progress and updates health maintenance goals and advice as needed. This enables users to continuously review and improve their health management.

[0092] (Application Example 1)

[0093] 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."

[0094] In modern society, it is difficult for individuals to accurately understand their own health status and manage their health sustainably. In particular, there is a need for personalized health goals tailored to each user's different lifestyle and health condition, as well as health improvement advice that is integrated into daily life. Furthermore, because there is no system that acquires this data in real time and provides appropriate feedback as needed, it is difficult for users to maintain their own motivation.

[0095] 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.

[0096] In this invention, the server includes means for receiving user information via voice input and visual display, means for acquiring physical information in real time based on the analysis, and means for providing the feedback using visual and auditory means. This allows users to understand their health status in real time, make it easier to implement individually customized advice in their daily lives, and increase their motivation to maintain their health.

[0097] "User information" refers to data representing the user's health status, specifically including information such as height, weight, age, gender, eating habits, exercise habits, and sleep patterns.

[0098] "Health status" refers to a comprehensive representation of a user's current physical and mental health, and is evaluated using various physiological indicators such as body fat percentage, blood pressure, heart rate, and activity level.

[0099] "Analysis" refers to the process of using collected user information to analyze health status using AI technology, assessing health risks based on the results, and deriving appropriate guidelines.

[0100] "Health goals" are specific behavioral targets set according to individual circumstances with the aim of maintaining or improving a user's health, and include, for example, exercise time and calorie intake.

[0101] "Advice" refers to information that provides specific guidance on actions and habits that users should take in their daily lives in accordance with their health goals, and includes things like meal plans and exercise menus.

[0102] "Monitoring" is the process of continuously tracking the activities that users perform based on their set health goals and evaluating the results.

[0103] "Feedback" refers to providing comments and evaluations based on the user's activity results, in order to help them modify their behavior and maintain their motivation.

[0104] "Reception" refers to the system taking in information provided by the user, and this is done through voice input or a visual interface.

[0105] "Real-time" means collecting and processing data instantly without delay and providing the results to the user.

[0106] "Providing" means showing the generated advice and feedback to the user, especially through audio or visual means.

[0107] The system that implements this application effectively manages users' health information and supports continuous health improvement. This system operates in conjunction with a server, terminals, and wearable devices.

[0108] The server receives health information from users via voice input and visual display. Hardware used includes computer devices such as Raspberry Pi, and the Google® Speech-to-Text API is used for speech recognition software. This information is stored in a database and used as basic data for analysis.

[0109] The server uses AI technologies such as TENSORFLOW® to analyze the collected data. This analysis assesses the user's health status and also acquires real-time physical information. Based on this information, individual health goals are generated, and the server provides the user with appropriate advice.

[0110] The device provides users with analysis results and advice through a web application built with Flask, utilizing both visual and auditory feedback. Through this feedback, users can adjust their daily behaviors to achieve their health goals.

[0111] For example, if a user asks the device in the morning, "Tell me my exercise plan for today," the system will analyze past data and provide a specific action plan such as, "Today, I recommend a 30-minute walk and 10 minutes of stretching."

[0112] An example of a prompt to input into the generating AI model would be, "Based on the user's data from the past week, please suggest a health improvement plan for next week." This allows for health management optimized for each individual user.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user enters health information via a voice or visual interface. For voice input, the device receives input through the microphone and converts the speech to text using the Google Speech-to-Text API. For visual input, the user enters the required information into a form on the screen. The output at this stage is user health information in text format.

[0116] Step 2:

[0117] The server receives user health information in text format sent from the terminal. This information is stored in a database and integrated with historical data to form a baseline of the user's health status. The output is an integrated dataset prepared for analysis.

[0118] Step 3:

[0119] The server uses the integrated dataset to launch an AI model, specifically an analysis algorithm using TensorFlow. This model allows the server to assess health status and identify potential risks. The output of this step is an assessment of the user's current health status.

[0120] Step 4:

[0121] The server generates individual health goals based on the analysis results. Using a generation AI model, it designs a customized action plan that takes into account each user's past data and goal achievement status. The output consists of specific health goals and daily action plans.

[0122] Step 5:

[0123] The server sends analysis results, health goals, and specific advice to the user's device via a Flask-based web application. The user receives this information visually and audibly through their device. The output includes user-friendly feedback.

[0124] Step 6:

[0125] Based on the feedback received, users adjust their daily activities and aim to achieve their goals. They then send feedback from their device back to the server regarding the results of each activity. Based on this feedback, advice and goals for the next activity are adjusted as needed. The output of this step is the user's activity result data.

[0126] 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.

[0127] This invention relates to a system that supports user health management, and in particular, has the function of recognizing the user's emotional state and adjusting health goals and feedback accordingly. The system includes a server, a terminal, and an emotion engine. The server manages user information and works with the emotion engine to analyze data. The terminal functions as an interface with the user and presents feedback from the emotion engine to the user.

[0128] Users input health information such as height, weight, daily diet and exercise, and sleep patterns through the device. In addition, the device uses a camera and microphone to analyze the user's facial expressions and voice tone, and an emotion engine recognizes their emotional state. This recognition includes basic emotions such as smiling, stress, anger, and sadness.

[0129] The server receives health information sent by the user and emotional data analyzed by the emotion engine. Based on this data, AI technology is used to analyze the user's overall health status and identify the presence or absence of lifestyle-related disease risks and areas that need improvement. After the health status is analyzed, the server sets the most appropriate individual health goals for the user. Based on the emotional state, the goals may be adjusted, for example, by recommending rest or light exercise to relax if the user is feeling stressed.

[0130] The server generates specific advice related to health goals and sends it to the device. On the device, the user reviews the advice and uses it in their daily life. For example, if the emotion engine determines that the user is feeling down, it will display advice on eating habits and exercise that will lead to more proactive lifestyle improvements.

[0131] Users continuously record their daily activities on their devices and provide progress feedback to the server. The server analyzes this feedback and generates encouraging messages and motivational feedback tailored to the user's emotional state. For example, if the user is emotionally stable, the server will offer new suggestions for their next health goal, supporting continuous self-improvement.

[0132] By incorporating an emotion engine, the system can provide flexible health management that takes into account the user's emotional state, and is expected to significantly contribute to improving health by making users aware of changes in their own emotions.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] Users input health information via their device. This includes height, weight, dietary history, exercise habits, and sleep patterns. Users record this information in input forms within the application.

[0136] Step 2:

[0137] The device uses its camera and microphone to capture the user's facial expressions and voice, and transmits them to the emotion engine. The emotion engine uses the latest facial recognition and voice analysis technologies to determine the user's emotional state.

[0138] Step 3:

[0139] The server receives health information and emotional data transmitted from the terminal and stores them in a database. Based on this stored data, the server uses an AI analysis module to analyze the user's health status.

[0140] Step 4:

[0141] The server sets individual health goals based on the user's health status, emotional state, and historical data trends. The server adjusts the amount and content of tasks according to the user's emotions, formulating goals optimized for the user.

[0142] Step 5:

[0143] The server sends health goals set by the server and specific advice to the terminal. The terminal notifies the user and displays an interface showing an action plan. For example, if relaxation is needed, meditation or deep breathing programs may be recommended.

[0144] Step 6:

[0145] Users record their daily activities and feed this information back to the server via their devices. This feedback includes details about meals, exercise history, and emotional notes.

[0146] Step 7:

[0147] The server analyzes the feedback it receives and evaluates the user's progress. Based on sentiment data, it generates feedback and suggestions to increase the user's motivation.

[0148] Step 8:

[0149] The server generates feedback and sends it to the device, which then displays it to the user. The user reviews the feedback and decides on actions to take toward new goals. For example, if the user's emotions are stable, positive suggestions for achieving new health goals may be presented.

[0150] (Example 2)

[0151] 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".

[0152] In health management systems, there is a need for flexible health goal setting that takes into account the user's emotional state, and for the provision of individually tailored advice. However, conventional systems lack the functionality to dynamically adjust goals and advice in response to the user's emotions, which makes it difficult to effectively support the user's sustainable health improvement.

[0153] 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.

[0154] In this invention, the server includes means for receiving and recording user information, means for analyzing the user's health status using a generative AI model, and means for recognizing the user's emotional state and setting individually tailored health goals. This enables personalized responses that take into account the user's emotional state, allowing for effective health management and continuous improvement for the user.

[0155] "User information" refers to health-related data provided by the user (e.g., weight, height, diet, exercise level, sleep patterns).

[0156] "Generative AI models" refer to artificial intelligence technologies used to analyze a user's health and emotional state.

[0157] "Health goals" refer to the setting of specific and individual goals aimed at improving the user's health.

[0158] "Emotional state" refers to the psychological state or emotion recognized by analyzing the user's facial expressions and tone of voice.

[0159] "Advice" refers to specific lifestyle improvement suggestions provided based on analyzed health and emotional states.

[0160] "Feedback" refers to information that includes evaluations of user activities and advice for the next steps.

[0161] This invention is a system that supports user health management, and in particular utilizes a generative AI model to set health goals that take into account the user's emotional state. This system includes a server, a terminal, and an emotion engine.

[0162] The server receives and records health information provided by the user. This health information includes data such as height, weight, diet, exercise level, and sleep patterns, which the user enters into the terminal. The terminal is equipped with an input interface and hardware such as a camera and microphone, which record the user's facial expressions and voice tone and transmit them to the emotion engine.

[0163] The emotion engine analyzes recorded facial and voice data and uses machine learning techniques (e.g., TensorFlow, PyTorch) to recognize the user's emotional state. The server comprehensively analyzes the emotional state analysis results from the emotion engine and health information using a generative AI model to assess the user's overall health status.

[0164] Based on the analysis results, the server sets optimal health goals for each user. This setting takes into account emotional states, resulting in individually tailored health goals, such as recommending relaxation activities for users experiencing high stress levels. This helps support users in their continuous health improvement.

[0165] For example, if a user records on their device that "Today's meal was well-balanced" and the camera analyzes their smile, the server can recognize this positive emotional state and suggest a new exercise routine to the user as the next step.

[0166] An example of a prompt to input into the generating AI model is, "What health goals would you recommend for a user who is in an optimistic emotional state?" Such prompts allow the system to generate effective health support strategies for the user.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The user manually enters health information (e.g., height, weight, diet, exercise level, sleep patterns) into the terminal. The terminal sends this data to an internal data management system. The entered data is then converted into a format that can be sent to the server. This process includes format checks to ensure data integrity and accuracy. The output is health information data in a format that the server can receive.

[0170] Step 2:

[0171] The device automatically records the user's facial expressions and voice using its built-in camera and microphone. This raw data is prepared for transmission to an emotion engine and analyzed in real time. Inputs are audio and image data, and output are emotional features (e.g., voice tone analysis, facial feature extraction).

[0172] Step 3:

[0173] The server receives health information transmitted from the terminal and emotional data sent from the emotion engine. The server centrally records this data and analyzes it using a generative AI model. The input is health information and emotional data, and the output is an analysis result representing the user's overall health status. At this stage, the generative AI model processes the data and identifies correlations and trends.

[0174] Step 4:

[0175] The server sets individual health goals for the user based on the analysis results. This process involves goal setting that takes emotional state into account, based on predefined rules. The input is the analysis results, and the output is the customized health goals. The system then develops optimal health strategies tailored to the user's emotional state.

[0176] Step 5:

[0177] The server generates and sends the set health goals and related advice to the terminal. The terminal displays this information to the user. The input is the health goal, and the output is specific advice for the user. The user uses this feedback to work towards their goals in their daily life.

[0178] Step 6:

[0179] Users continuously record their daily activity data on their devices and periodically send feedback to the server. Based on this feedback, the server re-evaluates the impact on the user's emotional state and their progress. The inputs are activity data and emotional impact data, and the output is feedback for continuous improvement. This supports continuous health improvement.

[0180] (Application Example 2)

[0181] 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".

[0182] Modern health management is becoming increasingly diverse, requiring comprehensive management of mental and emotional aspects in addition to traditional physical data. There is a need to recognize the user's emotional state in real time and provide flexible health goal setting and feedback based on that understanding. This invention aims to provide a comprehensive health management system that takes the user's emotional state into consideration.

[0183] 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.

[0184] In this invention, the server includes means for receiving user information, means for analyzing the user's health status based on the user information, means for setting individual health goals from the analysis results, means for providing advice related to the health goals, means for monitoring user activity after the advice is provided and generating feedback, means for recognizing the user's emotional state, adjusting the health goals and feedback based on the emotional state, and generating health advice corresponding to the emotional response, and means for using an image acquisition device and a voice acquisition device to analyze the emotional state. This enables comprehensive and flexible health management based on the user's emotions and physical health information.

[0185] "User information" refers to all data about the user, and specifically includes various data related to health and information about emotional state.

[0186] "Means for analyzing health status" refers to methods and devices for evaluating an individual's health level and physical condition based on information obtained from the user.

[0187] "Means for setting individual health goals" refers to systems and processes for formulating optimal health goals for each user.

[0188] "Means of providing advice" refer to methods and tools that present users with guidelines for improvement or achievement.

[0189] A "means for monitoring user activity and generating feedback" is a system that tracks user behavior and uses the results to provide information for further improvement or to encourage caution.

[0190] "Means for recognizing the user's emotional state and adjusting health goals and feedback based on said emotional state" refers to a process of analyzing the user's emotions and using the results of that analysis to appropriately modify health-related goals and feedback.

[0191] A "means for generating health advice that responds to emotional responses" refers to a device or system that takes into account the user's emotional state and has the ability to make specific health suggestions based on that information.

[0192] "Means for using image acquisition devices and sound acquisition devices" refers to methods for acquiring a user's facial expressions, voice tone, etc., as digital data using devices such as cameras and microphones.

[0193] To implement this invention, a system is needed to support the user's health management. This system consists of a server, a terminal, and an emotion analysis engine.

[0194] Users can input their health information using a smartphone or smart speaker as their terminal. The terminal is equipped with a camera and microphone, which capture the user's facial expressions and voice tone. The camera uses OpenCV software for real-time facial analysis, and a speech recognition API is used for voice analysis.

[0195] The acquired data is analyzed in real time by an emotion analysis engine to recognize the user's emotional state. The server integrates this emotional data with other health information sent by the user (e.g., height, weight, dietary history, exercise records, sleep patterns) and analyzes it using AI technology. AI libraries such as TensorFlow and scikit-learn are used for this analysis. Based on the analysis results, the server sets the most appropriate health goals for the user and adjusts those goals and feedback based on the emotional state.

[0196] The advice provided as feedback by the device is tailored to the user's emotional and physical state. For example, if the user is feeling stressed, the device will suggest ways to relax and play relaxing music.

[0197] Furthermore, the server periodically updates the generated feedback and suggestions to support continuous health improvement, and applies the new advice to the user through the terminal. An example of a prompt message is, "Generate advice on how to relax if the user is feeling stressed."

[0198] This system allows users to manage their health more effectively while being aware of their own emotional changes.

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] Users enter their basic health information into the terminal. This data includes height, weight, daily diet, exercise, and sleep patterns. This data is digitized and sent directly to the server.

[0202] Step 2:

[0203] The device uses its camera and microphone to capture the user's facial expressions and voice in real time. The input data here consists of the user's image and voice, which are sent to the emotion analysis engine. OpenCV is used to analyze facial expressions, and the tone of voice is analyzed via the Emotion Recognition API. The result of this analysis is the user's emotional state, which is classified into major emotion categories (e.g., joy, anger, sadness, etc.).

[0204] Step 3:

[0205] The server receives user health information and emotional state data and analyzes the user's health status based on this data. The input here is health information and emotional data, and the overall health status is evaluated using AI technologies such as TensorFlow or scikit-learn. This analysis yields outputs such as the presence or absence of lifestyle-related disease risks and items that need improvement.

[0206] Step 4:

[0207] Based on the analysis results, the server sets individual health goals tailored to the user. It also considers the user's emotional state and generates adjusted goals for relaxation activities, diet, and exercise as needed. In this step, the analyzed health and emotional states are used as input, and specific health goals reflecting these adjustments are output.

[0208] Step 5:

[0209] The device receives feedback from the server and provides the user with specific advice related to their health goals. The device also offers suggestions for relaxation methods tailored to the user's emotions (e.g., appropriate music playback) and ways to improve their emotional state. The input consists of feedback and advice sent from the server, which the device outputs through voice and screen displays.

[0210] Step 6:

[0211] Users continuously record their daily activities on their devices, and a server monitors their progress. The server retrieves the user's progress data and generates encouraging messages tailored to their emotional state. The input is continuous activity data, and the output is highly motivating feedback messages.

[0212] Step 7:

[0213] The generated feedback and suggestions are tailored to the user's emotions and health state, and typically use prompts such as, "Generate advice on how to relax if the user is feeling stressed," to provide support that meets individual needs.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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".

[0230] This invention is a system that analyzes a user's health status, sets individually customized health goals, and provides advice. This system functions in conjunction with a server, a terminal, and a wearable device. The server plays a central role, holding and analyzing information provided by the user. The terminal acts as an interface with the user, used for data input and receiving advice.

[0231] Users first enter their personal health information through a device. This information includes height, weight, age, gender, daily eating and exercise habits, and sleep patterns. If necessary, wearable devices can be used to record heart rate, steps, activity time, and other data in real time.

[0232] The server collects data submitted by users and stores it in a database. Based on this data, the system uses AI technology to analyze it and assess the user's health status and potential risks. This analysis includes calculating BMI, comparing it with past health data, and detecting imbalances in nutrition.

[0233] Based on the evaluation results, the server sets individual health goals. In this process, specific goals optimized for each user are proposed and notified to the user via their device. For example, realistic goals aimed at maintaining or improving the user's health are presented, such as "exercise 150 minutes of aerobic exercise per week" or "limit sugar intake in meals to less than 50g per day."

[0234] The server then provides specific advice to help users achieve these goals. This advice includes nutritional guidelines, exercise plans, and habits and improvements to incorporate into daily life. Using the advice displayed on the device as a guide, users can make changes to their daily lives and improve their health.

[0235] Subsequently, users continue to record their daily activities and provide feedback on their progress to the server via their device. Based on this feedback, the server evaluates the progress and, if necessary, revises goals and updates advice. In addition, if positive results are achieved, feedback and incentives are provided to maintain motivation.

[0236] As a concrete example, consider a user who uses this system for the purpose of weight loss and prevention of lifestyle-related diseases. Suppose this user aims to improve fatty liver disease. The server analyzes the user's BMI to be on the higher side and notifies the user of the importance of controlling calorie intake and exercising regularly. It also suggests meal plans to reduce the risk of fatty liver disease and monitors the user's diet and exercise history regularly to check the progress of their weight loss and provide feedback. In this way, the user can continuously receive guidance from the system and incorporate efforts toward improving their health into their daily life.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] Users input health information through their devices. This includes data related to their daily lives, such as height, weight, age, gender, dietary history, and exercise status. Users can also synchronize data obtained in real time via wearable devices.

[0240] Step 2:

[0241] The device sends health information collected from the user to the server. The data is encrypted and transmitted securely.

[0242] Step 3:

[0243] The server stores the received data in a database and passes it to the AI ​​analysis module. The AI ​​analysis module uses this data to evaluate the user's current health status and past trends.

[0244] Step 4:

[0245] The server understands the user's health status based on the evaluation results of the AI ​​analysis module and identifies potential health risks. Based on these results, it sets individually optimized health goals.

[0246] Step 5:

[0247] The server sends health goals and progress instructions to the device. The device notifies the user of these goals and presents an action plan. Specific examples include "aerobic exercise three times a week" and "increasing daily vegetable intake."

[0248] Step 6:

[0249] Users record their daily activities on their devices. They input data such as what they ate, how much exercise they did, and changes in their weight, and manage their progress.

[0250] Step 7:

[0251] The terminal sends user records to the server, which then monitors them. The server evaluates goal achievement and generates feedback and corrective advice as needed.

[0252] Step 8:

[0253] The server generates feedback and sends it to the device. The device then presents this feedback to the user, helping to boost motivation and set new goals. This feedback may include messages of praise based on achievement and suggestions for the next challenge.

[0254] (Example 1)

[0255] 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."

[0256] In modern society, individual health management is a crucial issue, but conventional systems have struggled to provide personalized health maintenance goals and advice. In particular, the lack of real-time health status analysis and feedback has hindered user motivation and goal achievement. Therefore, there is a need to develop a system that enables the setting of user-optimized health maintenance goals and the provision of specific advice aligned with those goals.

[0257] 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.

[0258] In this invention, the server includes means for receiving the user's physical information, means for analyzing the health status using a generative AI model, and means for setting individual health maintenance goals. This makes it possible to set health maintenance goals optimized for each user and provide specific advice for achieving them.

[0259] "User physical information" refers to data that indicates an individual's health status, such as height, weight, age, gender, eating and exercise habits, sleep patterns, heart rate, and steps taken.

[0260] A "generative AI model" is an algorithm or program built on artificial intelligence technology that is used to analyze the physical information collected from users.

[0261] "Health status analysis" is a process that evaluates the user's current health status and health risks based on their physical information.

[0262] "Individual health maintenance goals" are specific and achievable health improvement goals set based on each user's health status and lifestyle.

[0263] "Health maintenance advice" refers to specific action guidelines and suggestions provided to users based on their set health maintenance goals.

[0264] "Recording user activity" refers to the process of tracking users' daily actions and health-related activities and saving them as data.

[0265] "Evaluating progress" is an activity that measures the degree to which users have achieved their goals and confirms the effectiveness of current efforts.

[0266] "Updating health maintenance goals and advice" refers to reviewing and adjusting existing goals and advice in a timely manner based on user activity feedback.

[0267] This invention utilizes servers, terminals, and wearable devices to build a system that supports users' health management. This makes it possible to collect and analyze personal health information and provide an optimal health maintenance plan.

[0268] The server plays a central role in the system, receiving health-related data entered by users. The terminal allows users to input data via an interface and facilitates data transmission and reception between the terminal and the server. Wearable devices are used to collect real-time data such as heart rate and steps, enabling detailed monitoring of the user's health.

[0269] First, users input their individual health information into the system using a terminal. This information includes height, weight, age, gender, daily eating and exercise habits, and sleep patterns. Furthermore, by wearing a wearable device, specific physical activity data is continuously collected and transmitted to the server.

[0270] The server uses a generative AI model to analyze the collected data and assess the user's health status. This analysis process uses prompts to specify the algorithms and calculation procedures the model will use. For example, a prompt might say, "Enter the user's BMI and past exercise data, and generate advice to help them achieve their health goals." Based on this, the model calculates the BMI, compares it to past data, and detects anomalies.

[0271] Based on the analysis results, the server sets health maintenance goals optimized for the user. The set goals are notified to the user via their device, and the user can work on improving their daily life by following the specific advice provided.

[0272] Subsequently, users continue to record their activities and provide feedback to the server via their device. Based on this feedback, the server evaluates the user's progress and determines whether updates to health maintenance goals and advice are necessary. This allows users to consistently manage their health appropriately and receive support towards achieving their goals.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The user enters their health information into the device. This information includes height, weight, age, gender, dietary and exercise habits, and sleep patterns. The purpose of this input is to collect data to understand the individual's health status. The device then organizes this data and prepares it for transmission to the server.

[0276] Step 2:

[0277] The terminal sends the health information obtained from the user and the real-time data collected through the wearable device to the server. The wearable device provides data such as heart rate, number of steps, and activity time. The server stores the received data in a database and uses it as input for analysis.

[0278] Step 3:

[0279] The server analyzes the health status using the AI model generated based on the stored data. Specific data processing includes calculating BMI using height and weight, and detecting biases in nutritional balance based on daily living habit data. An example of the prompt sentence used here is "Please evaluate the health risk using the user's BMI and past exercise data and set appropriate health goals." As output, analysis results are obtained and risk assessments are made.

[0280] Step 4:

[0281] Based on the analysis results of the generated AI model, the server sets individual health maintenance goals for each user. For example, it includes improvement suggestions for the user's eating habits and health goals recommending a certain amount of exercise. The output of this goal setting is sent to the terminal as a specific health maintenance plan and notified to the user.

[0282] Step 5:

[0283] The terminal displays the health maintenance plan sent from the server to the user and provides health maintenance advice. The user can take actions towards health maintenance, such as improving diet and performing exercise, according to the specific advice presented. At this stage, the content of the advice is output in an easy-to-understand and easy-to-practice form.

[0284] Step 6:

[0285] The user records the health maintenance activities performed and feeds back the activity data to the server via the terminal. Through the feedback, the user's progress and current position relative to the goal are grasped. The server evaluates the progress based on this feedback information and updates the health maintenance goals and advice as necessary. As a result, the user can continuously review and improve their own health management.

[0286] (Application Example 1)

[0287] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] In modern society, it is difficult for individuals to accurately grasp their own health status and conduct continuous health management. In particular, there is a need for setting individual health goals according to different lifestyles and health conditions for each user, as well as advice on health improvement that is integrated into daily life. Also, since there is no system that can acquire these data in real time and provide appropriate feedback at any time, there is a problem that it is difficult to maintain the user's own motivation.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0290] In this invention, the server includes means for receiving user information via voice input and visual display, means for acquiring physical information in real time based on the analysis, and means for providing the feedback using vision and hearing. As a result, the user can grasp their own health status in real time, easily practice individually customized advice in daily life, and it becomes possible to enhance the motivation for health maintenance.

[0291] "User information" is data representing the user's health status, specifically including information such as height, weight, age, gender, eating habits, exercise habits, sleep patterns, etc.

[0292] "Health status" refers to a comprehensive representation of a user's current physical and mental health, and is evaluated using various physiological indicators such as body fat percentage, blood pressure, heart rate, and activity level.

[0293] "Analysis" refers to the process of using collected user information to analyze health status using AI technology, assessing health risks based on the results, and deriving appropriate guidelines.

[0294] "Health goals" are specific behavioral targets set according to individual circumstances with the aim of maintaining or improving a user's health, and include, for example, exercise time and calorie intake.

[0295] "Advice" refers to information that provides specific guidance on actions and habits that users should take in their daily lives in accordance with their health goals, and includes things like meal plans and exercise menus.

[0296] "Monitoring" is the process of continuously tracking the activities that users perform based on their set health goals and evaluating the results.

[0297] "Feedback" refers to providing comments and evaluations based on the user's activity results, in order to help them modify their behavior and maintain their motivation.

[0298] "Reception" refers to the system taking in information provided by the user, and this is done through voice input or a visual interface.

[0299] "Real-time" means collecting and processing data instantly without delay and providing the results to the user.

[0300] "Providing" means showing the generated advice and feedback to the user, especially through audio or visual means.

[0301] The system that implements this application effectively manages users' health information and supports continuous health improvement. This system operates in conjunction with a server, terminals, and wearable devices.

[0302] The server receives health information from users via voice input and visual display. Hardware used includes computer devices such as Raspberry Pi, and the Google Speech-to-Text API is used for speech recognition software. This information is stored in a database and used as basic data for analysis.

[0303] The server uses AI technologies such as TensorFlow to analyze the collected data. This analysis assesses the user's health status and also obtains real-time physical information. Based on this information, individual health goals are generated, and the server provides the user with appropriate advice.

[0304] The device provides users with analysis results and advice through a web application built with Flask, utilizing both visual and auditory feedback. Through this feedback, users can adjust their daily behaviors to achieve their health goals.

[0305] For example, if a user asks the device in the morning, "Tell me my exercise plan for today," the system will analyze past data and provide a specific action plan such as, "Today, I recommend a 30-minute walk and 10 minutes of stretching."

[0306] An example of a prompt to input into the generating AI model would be, "Based on the user's data from the past week, please suggest a health improvement plan for next week." This allows for health management optimized for each individual user.

[0307] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0308] Step 1:

[0309] The user inputs health information from the voice or visual interface. In the case of voice, the terminal receives the input through the microphone and uses the Google Speech-to-Text API to convert the voice into text data. In the case of the visual interface, the user inputs the necessary information into the form on the screen. The output at this stage is the user's health information in text format.

[0310] Step 2:

[0311] The server receives the user's health information in text format sent from the terminal. This information is saved in the database and integrated with past data to form a baseline of the user's health status. As this output, an integrated dataset prepared for analysis is obtained.

[0312] Step 3:

[0313] The server activates an AI model, specifically an analysis algorithm using TensorFlow, with the integrated dataset. The server uses this model to evaluate the health status and identify potential risks. The output of this step is the evaluation result regarding the user's current health status.

[0314] Step 4:

[0315] The server generates individual health goals based on the analysis results. Using the generation AI model, considering each user's past data and goal achievement status, a customized action plan is designed. The output is specific health goals and a daily action plan.

[0316] Step 5:

[0317] The server sends analysis results, health goals, and specific advice to the user's device via a Flask-based web application. The user receives this information visually and audibly through their device. The output includes user-friendly feedback.

[0318] Step 6:

[0319] Based on the feedback received, users adjust their daily activities and aim to achieve their goals. They then send feedback from their device back to the server regarding the results of each activity. Based on this feedback, advice and goals for the next activity are adjusted as needed. The output of this step is the user's activity result data.

[0320] 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.

[0321] This invention relates to a system that supports user health management, and in particular, has the function of recognizing the user's emotional state and adjusting health goals and feedback accordingly. The system includes a server, a terminal, and an emotion engine. The server manages user information and works with the emotion engine to analyze data. The terminal functions as an interface with the user and presents feedback from the emotion engine to the user.

[0322] Users input health information such as height, weight, daily diet and exercise, and sleep patterns through the device. In addition, the device uses a camera and microphone to analyze the user's facial expressions and voice tone, and an emotion engine recognizes their emotional state. This recognition includes basic emotions such as smiling, stress, anger, and sadness.

[0323] The server receives health information sent by the user and emotional data analyzed by the emotion engine. Based on this data, AI technology is used to analyze the user's overall health status and identify the presence or absence of lifestyle-related disease risks and areas that need improvement. After the health status is analyzed, the server sets the most appropriate individual health goals for the user. Based on the emotional state, the goals may be adjusted, for example, by recommending rest or light exercise to relax if the user is feeling stressed.

[0324] The server generates specific advice related to health goals and sends it to the device. On the device, the user reviews the advice and uses it in their daily life. For example, if the emotion engine determines that the user is feeling down, it will display advice on eating habits and exercise that will lead to more proactive lifestyle improvements.

[0325] Users continuously record their daily activities on their devices and provide progress feedback to the server. The server analyzes this feedback and generates encouraging messages and motivational feedback tailored to the user's emotional state. For example, if the user is emotionally stable, the server will offer new suggestions for their next health goal, supporting continuous self-improvement.

[0326] By incorporating an emotion engine, the system can provide flexible health management that takes into account the user's emotional state, and is expected to significantly contribute to improving health by making users aware of changes in their own emotions.

[0327] The following describes the processing flow.

[0328] Step 1:

[0329] Users input health information via their device. This includes height, weight, dietary history, exercise habits, and sleep patterns. Users record this information in input forms within the application.

[0330] Step 2:

[0331] The device uses its camera and microphone to capture the user's facial expressions and voice, and transmits them to the emotion engine. The emotion engine uses the latest facial recognition and voice analysis technologies to determine the user's emotional state.

[0332] Step 3:

[0333] The server receives health information and emotional data transmitted from the terminal and stores them in a database. Based on this stored data, the server uses an AI analysis module to analyze the user's health status.

[0334] Step 4:

[0335] The server sets individual health goals based on the user's health status, emotional state, and historical data trends. The server adjusts the amount and content of tasks according to the user's emotions, formulating goals optimized for the user.

[0336] Step 5:

[0337] The server sends health goals set by the server and specific advice to the terminal. The terminal notifies the user and displays an interface showing an action plan. For example, if relaxation is needed, meditation or deep breathing programs may be recommended.

[0338] Step 6:

[0339] Users record their daily activities and feed this information back to the server via their devices. This feedback includes details about meals, exercise history, and emotional notes.

[0340] Step 7:

[0341] The server analyzes the feedback it receives and evaluates the user's progress. Based on sentiment data, it generates feedback and suggestions to increase the user's motivation.

[0342] Step 8:

[0343] The server generates feedback and sends it to the device, which then displays it to the user. The user reviews the feedback and decides on actions to take toward new goals. For example, if the user's emotions are stable, positive suggestions for achieving new health goals may be presented.

[0344] (Example 2)

[0345] 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".

[0346] In health management systems, there is a need for flexible health goal setting that takes into account the user's emotional state, and for the provision of individually tailored advice. However, conventional systems lack the functionality to dynamically adjust goals and advice in response to the user's emotions, which makes it difficult to effectively support the user's sustainable health improvement.

[0347] 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.

[0348] In this invention, the server includes means for receiving and recording user information, means for analyzing the user's health status using a generative AI model, and means for recognizing the user's emotional state and setting individually tailored health goals. This enables personalized responses that take into account the user's emotional state, allowing for effective health management and continuous improvement for the user.

[0349] "User information" refers to health-related data provided by the user (e.g., weight, height, diet, exercise level, sleep patterns).

[0350] "Generative AI models" refer to artificial intelligence technologies used to analyze a user's health and emotional state.

[0351] "Health goals" refer to the setting of specific and individual goals aimed at improving the user's health.

[0352] "Emotional state" refers to the psychological state or emotion recognized by analyzing the user's facial expressions and tone of voice.

[0353] "Advice" refers to specific lifestyle improvement suggestions provided based on analyzed health and emotional states.

[0354] "Feedback" refers to information that includes evaluations of user activities and advice for the next steps.

[0355] This invention is a system that supports user health management, and in particular utilizes a generative AI model to set health goals that take into account the user's emotional state. This system includes a server, a terminal, and an emotion engine.

[0356] The server receives and records health information provided by the user. This health information includes data such as height, weight, diet, exercise level, and sleep patterns, which the user enters into the terminal. The terminal is equipped with an input interface and hardware such as a camera and microphone, which record the user's facial expressions and voice tone and transmit them to the emotion engine.

[0357] The emotion engine analyzes recorded facial and voice data and uses machine learning techniques (e.g., TensorFlow, PyTorch) to recognize the user's emotional state. The server comprehensively analyzes the emotional state analysis results from the emotion engine and health information using a generative AI model to assess the user's overall health status.

[0358] Based on the analysis results, the server sets optimal health goals for each user. This setting takes into account emotional states, resulting in individually tailored health goals, such as recommending relaxation activities for users experiencing high stress levels. This helps support users in their continuous health improvement.

[0359] For example, if a user records on their device that "Today's meal was well-balanced" and the camera analyzes their smile, the server can recognize this positive emotional state and suggest a new exercise routine to the user as the next step.

[0360] An example of a prompt to input into the generating AI model is, "What health goals would you recommend for a user who is in an optimistic emotional state?" Such prompts allow the system to generate effective health support strategies for the user.

[0361] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0362] Step 1:

[0363] The user manually enters health information (e.g., height, weight, diet, exercise level, sleep patterns) into the terminal. The terminal sends this data to an internal data management system. The entered data is then converted into a format that can be sent to the server. This process includes format checks to ensure data integrity and accuracy. The output is health information data in a format that the server can receive.

[0364] Step 2:

[0365] The device automatically records the user's facial expressions and voice using its built-in camera and microphone. This raw data is prepared for transmission to an emotion engine and analyzed in real time. Inputs are audio and image data, and output are emotional features (e.g., voice tone analysis, facial feature extraction).

[0366] Step 3:

[0367] The server receives health information transmitted from the terminal and emotional data sent from the emotion engine. The server centrally records this data and analyzes it using a generative AI model. The input is health information and emotional data, and the output is an analysis result representing the user's overall health status. At this stage, the generative AI model processes the data and identifies correlations and trends.

[0368] Step 4:

[0369] The server sets individual health goals for the user based on the analysis results. This process involves goal setting that takes emotional state into account, based on predefined rules. The input is the analysis results, and the output is the customized health goals. The system then develops optimal health strategies tailored to the user's emotional state.

[0370] Step 5:

[0371] The server generates and sends the set health goals and related advice to the terminal. The terminal displays this information to the user. The input is the health goal, and the output is specific advice for the user. The user uses this feedback to work towards their goals in their daily life.

[0372] Step 6:

[0373] Users continuously record their daily activity data on their devices and periodically send feedback to the server. Based on this feedback, the server re-evaluates the impact on the user's emotional state and their progress. The inputs are activity data and emotional impact data, and the output is feedback for continuous improvement. This supports continuous health improvement.

[0374] (Application Example 2)

[0375] 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 as the "terminal".

[0376] Modern health management is becoming increasingly diverse, requiring comprehensive management of mental and emotional aspects in addition to traditional physical data. There is a need to recognize the user's emotional state in real time and provide flexible health goal setting and feedback based on that understanding. This invention aims to provide a comprehensive health management system that takes the user's emotional state into consideration.

[0377] 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.

[0378] In this invention, the server includes means for receiving user information, means for analyzing the user's health status based on the user information, means for setting individual health goals from the analysis results, means for providing advice related to the health goals, means for monitoring user activity after the advice is provided and generating feedback, means for recognizing the user's emotional state, adjusting the health goals and feedback based on the emotional state, and generating health advice corresponding to the emotional response, and means for using an image acquisition device and a voice acquisition device to analyze the emotional state. This enables comprehensive and flexible health management based on the user's emotions and physical health information.

[0379] "User information" refers to all data about the user, and specifically includes various data related to health and information about emotional state.

[0380] "Means for analyzing health status" refers to methods and devices for evaluating an individual's health level and physical condition based on information obtained from the user.

[0381] "Means for setting individual health goals" refers to systems and processes for formulating optimal health goals for each user.

[0382] "Means of providing advice" refer to methods and tools that present users with guidelines for improvement or achievement.

[0383] A "means for monitoring user activity and generating feedback" is a system that tracks user behavior and uses the results to provide information for further improvement or to encourage caution.

[0384] "Means for recognizing the user's emotional state and adjusting health goals and feedback based on said emotional state" refers to a process of analyzing the user's emotions and using the results of that analysis to appropriately modify health-related goals and feedback.

[0385] A "means for generating health advice that responds to emotional responses" refers to a device or system that takes into account the user's emotional state and has the ability to make specific health suggestions based on that information.

[0386] "Means for using image acquisition devices and sound acquisition devices" refers to methods for acquiring a user's facial expressions, voice tone, etc., as digital data using devices such as cameras and microphones.

[0387] To implement this invention, a system is needed to support the user's health management. This system consists of a server, a terminal, and an emotion analysis engine.

[0388] Users can input their health information using a smartphone or smart speaker as their terminal. The terminal is equipped with a camera and microphone, which capture the user's facial expressions and voice tone. The camera uses OpenCV software for real-time facial analysis, and a speech recognition API is used for voice analysis.

[0389] The acquired data is analyzed in real time by an emotion analysis engine to recognize the user's emotional state. The server integrates this emotional data with other health information sent by the user (e.g., height, weight, dietary history, exercise records, sleep patterns) and analyzes it using AI technology. AI libraries such as TensorFlow and scikit-learn are used for this analysis. Based on the analysis results, the server sets the most appropriate health goals for the user and adjusts those goals and feedback based on the emotional state.

[0390] The advice provided as feedback by the device is tailored to the user's emotional and physical state. For example, if the user is feeling stressed, the device will suggest ways to relax and play relaxing music.

[0391] Furthermore, the server periodically updates the generated feedback and suggestions to support continuous health improvement, and applies the new advice to the user through the terminal. An example of a prompt message is, "Generate advice on how to relax if the user is feeling stressed."

[0392] This system allows users to manage their health more effectively while being aware of their own emotional changes.

[0393] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0394] Step 1:

[0395] Users enter their basic health information into the terminal. This data includes height, weight, daily diet, exercise, and sleep patterns. This data is digitized and sent directly to the server.

[0396] Step 2:

[0397] The device uses its camera and microphone to capture the user's facial expressions and voice in real time. The input data here consists of the user's image and voice, which are sent to the emotion analysis engine. OpenCV is used to analyze facial expressions, and the tone of voice is analyzed via the Emotion Recognition API. The result of this analysis is the user's emotional state, which is classified into major emotion categories (e.g., joy, anger, sadness, etc.).

[0398] Step 3:

[0399] The server receives user health information and emotional state data and analyzes the user's health status based on this data. The input here is health information and emotional data, and the overall health status is evaluated using AI technologies such as TensorFlow or scikit-learn. This analysis yields outputs such as the presence or absence of lifestyle-related disease risks and items that need improvement.

[0400] Step 4:

[0401] Based on the analysis results, the server sets individual health goals tailored to the user. It also considers the user's emotional state and generates adjusted goals for relaxation activities, diet, and exercise as needed. In this step, the analyzed health and emotional states are used as input, and specific health goals reflecting these adjustments are output.

[0402] Step 5:

[0403] The device receives feedback from the server and provides the user with specific advice related to their health goals. The device also offers suggestions for relaxation methods tailored to the user's emotions (e.g., appropriate music playback) and ways to improve their emotional state. The input consists of feedback and advice sent from the server, which the device outputs through voice and screen displays.

[0404] Step 6:

[0405] Users continuously record their daily activities on their devices, and a server monitors their progress. The server retrieves the user's progress data and generates encouraging messages tailored to their emotional state. The input is continuous activity data, and the output is highly motivating feedback messages.

[0406] Step 7:

[0407] The generated feedback and suggestions are tailored to the user's emotions and health state, and typically use prompts such as, "Generate advice on how to relax if the user is feeling stressed," to provide support that meets individual needs.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] [Third Embodiment]

[0412] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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".

[0424] This invention is a system that analyzes a user's health status, sets individually customized health goals, and provides advice. This system functions in conjunction with a server, a terminal, and a wearable device. The server plays a central role, holding and analyzing information provided by the user. The terminal acts as an interface with the user, used for data input and receiving advice.

[0425] Users first enter their personal health information through a device. This information includes height, weight, age, gender, daily eating and exercise habits, and sleep patterns. If necessary, wearable devices can be used to record heart rate, steps, activity time, and other data in real time.

[0426] The server collects data submitted by users and stores it in a database. Based on this data, the system uses AI technology to analyze it and assess the user's health status and potential risks. This analysis includes calculating BMI, comparing it with past health data, and detecting imbalances in nutrition.

[0427] Based on the evaluation results, the server sets individual health goals. In this process, specific goals optimized for each user are proposed and notified to the user via their device. For example, realistic goals aimed at maintaining or improving the user's health are presented, such as "exercise 150 minutes of aerobic exercise per week" or "limit sugar intake in meals to less than 50g per day."

[0428] The server then provides specific advice to help users achieve these goals. This advice includes nutritional guidelines, exercise plans, and habits and improvements to incorporate into daily life. Using the advice displayed on the device as a guide, users can make changes to their daily lives and improve their health.

[0429] Subsequently, users continue to record their daily activities and provide feedback on their progress to the server via their device. Based on this feedback, the server evaluates the progress and, if necessary, revises goals and updates advice. In addition, if positive results are achieved, feedback and incentives are provided to maintain motivation.

[0430] As a concrete example, consider a user who uses this system for the purpose of weight loss and prevention of lifestyle-related diseases. Suppose this user aims to improve fatty liver disease. The server analyzes the user's BMI to be on the higher side and notifies the user of the importance of controlling calorie intake and exercising regularly. It also suggests meal plans to reduce the risk of fatty liver disease and monitors the user's diet and exercise history regularly to check the progress of their weight loss and provide feedback. In this way, the user can continuously receive guidance from the system and incorporate efforts toward improving their health into their daily life.

[0431] The following describes the processing flow.

[0432] Step 1:

[0433] Users input health information through their devices. This includes data related to their daily lives, such as height, weight, age, gender, dietary history, and exercise status. Users can also synchronize data obtained in real time via wearable devices.

[0434] Step 2:

[0435] The device sends health information collected from the user to the server. The data is encrypted and transmitted securely.

[0436] Step 3:

[0437] The server stores the received data in a database and passes it to the AI ​​analysis module. The AI ​​analysis module uses this data to evaluate the user's current health status and past trends.

[0438] Step 4:

[0439] The server understands the user's health status based on the evaluation results of the AI ​​analysis module and identifies potential health risks. Based on these results, it sets individually optimized health goals.

[0440] Step 5:

[0441] The server sends health goals and progress instructions to the device. The device notifies the user of these goals and presents an action plan. Specific examples include "aerobic exercise three times a week" and "increasing daily vegetable intake."

[0442] Step 6:

[0443] Users record their daily activities on their devices. They input data such as what they ate, how much exercise they did, and changes in their weight, and manage their progress.

[0444] Step 7:

[0445] The terminal sends user records to the server, which then monitors them. The server evaluates goal achievement and generates feedback and corrective advice as needed.

[0446] Step 8:

[0447] The server generates feedback and sends it to the device. The device then presents this feedback to the user, helping to boost motivation and set new goals. This feedback may include messages of praise based on achievement and suggestions for the next challenge.

[0448] (Example 1)

[0449] 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."

[0450] In modern society, individual health management is a crucial issue, but conventional systems have struggled to provide personalized health maintenance goals and advice. In particular, the lack of real-time health status analysis and feedback has hindered user motivation and goal achievement. Therefore, there is a need to develop a system that enables the setting of user-optimized health maintenance goals and the provision of specific advice aligned with those goals.

[0451] 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.

[0452] In this invention, the server includes means for receiving the user's physical information, means for analyzing the health status using a generative AI model, and means for setting individual health maintenance goals. This makes it possible to set health maintenance goals optimized for each user and provide specific advice for achieving them.

[0453] "User physical information" refers to data that indicates an individual's health status, such as height, weight, age, gender, eating and exercise habits, sleep patterns, heart rate, and steps taken.

[0454] A "generative AI model" is an algorithm or program built on artificial intelligence technology that is used to analyze the physical information collected from users.

[0455] "Health status analysis" is a process that evaluates the user's current health status and health risks based on their physical information.

[0456] "Individual health maintenance goals" are specific and achievable health improvement goals set based on each user's health status and lifestyle.

[0457] "Health maintenance advice" refers to specific action guidelines and suggestions provided to users based on their set health maintenance goals.

[0458] "Recording user activity" refers to the process of tracking users' daily actions and health-related activities and saving them as data.

[0459] "Evaluating progress" is an activity that measures the degree to which users have achieved their goals and confirms the effectiveness of current efforts.

[0460] "Updating health maintenance goals and advice" refers to reviewing and adjusting existing goals and advice in a timely manner based on user activity feedback.

[0461] This invention utilizes servers, terminals, and wearable devices to build a system that supports users' health management. This makes it possible to collect and analyze personal health information and provide an optimal health maintenance plan.

[0462] The server plays a central role in the system, receiving health-related data entered by users. The terminal allows users to input data via an interface and facilitates data transmission and reception between the terminal and the server. Wearable devices are used to collect real-time data such as heart rate and steps, enabling detailed monitoring of the user's health.

[0463] First, users input their individual health information into the system using a terminal. This information includes height, weight, age, gender, daily eating and exercise habits, and sleep patterns. Furthermore, by wearing a wearable device, specific physical activity data is continuously collected and transmitted to the server.

[0464] The server uses a generative AI model to analyze the collected data and assess the user's health status. This analysis process uses prompts to specify the algorithms and calculation procedures the model will use. For example, a prompt might say, "Enter the user's BMI and past exercise data, and generate advice to help them achieve their health goals." Based on this, the model calculates the BMI, compares it to past data, and detects anomalies.

[0465] Based on the analysis results, the server sets health maintenance goals optimized for the user. The set goals are notified to the user via their device, and the user can work on improving their daily life by following the specific advice provided.

[0466] Subsequently, users continue to record their activities and provide feedback to the server via their device. Based on this feedback, the server evaluates the user's progress and determines whether updates to health maintenance goals and advice are necessary. This allows users to consistently manage their health appropriately and receive support towards achieving their goals.

[0467] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0468] Step 1:

[0469] The user enters their health information into the device. This information includes height, weight, age, gender, dietary and exercise habits, and sleep patterns. The purpose of this input is to collect data to understand the individual's health status. The device then organizes this data and prepares it for transmission to the server.

[0470] Step 2:

[0471] The terminal transmits health information obtained from the user and real-time data collected through wearable devices to the server. Wearable devices provide data such as heart rate, steps taken, and activity time. The server stores the received data in a database and uses it as input for analysis.

[0472] Step 3:

[0473] The server uses a generated AI model based on stored data to analyze the user's health status. Specific data processing includes calculating BMI using height and weight, and detecting imbalances in nutrition based on daily lifestyle data. An example of a prompt message used is, "Please use the user's BMI and past exercise data to assess health risks and set appropriate health goals." The output includes analysis results and a risk assessment.

[0474] Step 4:

[0475] The server sets individual health maintenance goals for each user based on the analysis results of the generated AI model. These goals may include suggestions for improving the user's eating habits or recommending a certain amount of exercise. This goal setting output is sent to the device as a specific health maintenance plan and notified to the user.

[0476] Step 5:

[0477] The device displays a health maintenance plan sent from the server to the user and provides health maintenance advice. The user can then take actions to maintain their health, such as improving their diet or exercising, based on the specific advice provided. At this stage, the advice is presented in a clear and easy-to-follow format.

[0478] Step 6:

[0479] Users record their health maintenance activities and feed this data back to the server via their device. This feedback allows the server to understand the user's progress and current position relative to their goals. Based on this feedback, the server evaluates progress and updates health maintenance goals and advice as needed. This enables users to continuously review and improve their health management.

[0480] (Application Example 1)

[0481] 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."

[0482] In modern society, it is difficult for individuals to accurately understand their own health status and manage their health sustainably. In particular, there is a need for personalized health goals tailored to each user's different lifestyle and health condition, as well as health improvement advice that is integrated into daily life. Furthermore, because there is no system that acquires this data in real time and provides appropriate feedback as needed, it is difficult for users to maintain their own motivation.

[0483] 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.

[0484] In this invention, the server includes means for receiving user information via voice input and visual display, means for acquiring physical information in real time based on the analysis, and means for providing the feedback using visual and auditory means. This allows users to understand their health status in real time, make it easier to implement individually customized advice in their daily lives, and increase their motivation to maintain their health.

[0485] "User information" refers to data representing the user's health status, specifically including information such as height, weight, age, gender, eating habits, exercise habits, and sleep patterns.

[0486] "Health status" refers to a comprehensive representation of a user's current physical and mental health, and is evaluated using various physiological indicators such as body fat percentage, blood pressure, heart rate, and activity level.

[0487] "Analysis" refers to the process of using collected user information to analyze health status using AI technology, assessing health risks based on the results, and deriving appropriate guidelines.

[0488] "Health goals" are specific behavioral targets set according to individual circumstances with the aim of maintaining or improving a user's health, and include, for example, exercise time and calorie intake.

[0489] "Advice" refers to information that provides specific guidance on actions and habits that users should take in their daily lives in accordance with their health goals, and includes things like meal plans and exercise menus.

[0490] "Monitoring" is the process of continuously tracking the activities that users perform based on their set health goals and evaluating the results.

[0491] "Feedback" refers to providing comments and evaluations based on the user's activity results, in order to help them modify their behavior and maintain their motivation.

[0492] "Reception" refers to the system taking in information provided by the user, and this is done through voice input or a visual interface.

[0493] "Real-time" means collecting and processing data instantly without delay and providing the results to the user.

[0494] "Providing" means showing the generated advice and feedback to the user, especially through audio or visual means.

[0495] The system that implements this application effectively manages users' health information and supports continuous health improvement. This system operates in conjunction with a server, terminals, and wearable devices.

[0496] The server receives health information from users via voice input and visual display. Hardware used includes computer devices such as Raspberry Pi, and the Google Speech-to-Text API is used for speech recognition software. This information is stored in a database and used as basic data for analysis.

[0497] The server uses AI technologies such as TensorFlow to analyze the collected data. This analysis assesses the user's health status and also obtains real-time physical information. Based on this information, individual health goals are generated, and the server provides the user with appropriate advice.

[0498] The device provides users with analysis results and advice through a web application built with Flask, utilizing both visual and auditory feedback. Through this feedback, users can adjust their daily behaviors to achieve their health goals.

[0499] For example, if a user asks the device in the morning, "Tell me my exercise plan for today," the system will analyze past data and provide a specific action plan such as, "Today, I recommend a 30-minute walk and 10 minutes of stretching."

[0500] An example of a prompt to input into the generating AI model would be, "Based on the user's data from the past week, please suggest a health improvement plan for next week." This allows for health management optimized for each individual user.

[0501] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0502] Step 1:

[0503] The user enters health information via a voice or visual interface. For voice input, the device receives input through the microphone and converts the speech to text using the Google Speech-to-Text API. For visual input, the user enters the required information into a form on the screen. The output at this stage is user health information in text format.

[0504] Step 2:

[0505] The server receives user health information in text format sent from the terminal. This information is stored in a database and integrated with historical data to form a baseline of the user's health status. The output is an integrated dataset prepared for analysis.

[0506] Step 3:

[0507] The server uses the integrated dataset to launch an AI model, specifically an analysis algorithm using TensorFlow. This model allows the server to assess health status and identify potential risks. The output of this step is an assessment of the user's current health status.

[0508] Step 4:

[0509] The server generates individual health goals based on the analysis results. Using a generation AI model, it designs a customized action plan that takes into account each user's past data and goal achievement status. The output consists of specific health goals and daily action plans.

[0510] Step 5:

[0511] The server sends analysis results, health goals, and specific advice to the user's device via a Flask-based web application. The user receives this information visually and audibly through their device. The output includes user-friendly feedback.

[0512] Step 6:

[0513] Based on the feedback received, users adjust their daily activities and aim to achieve their goals. They then send feedback from their device back to the server regarding the results of each activity. Based on this feedback, advice and goals for the next activity are adjusted as needed. The output of this step is the user's activity result data.

[0514] 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.

[0515] This invention relates to a system that supports user health management, and in particular, has the function of recognizing the user's emotional state and adjusting health goals and feedback accordingly. The system includes a server, a terminal, and an emotion engine. The server manages user information and works with the emotion engine to analyze data. The terminal functions as an interface with the user and presents feedback from the emotion engine to the user.

[0516] Users input health information such as height, weight, daily diet and exercise, and sleep patterns through the device. In addition, the device uses a camera and microphone to analyze the user's facial expressions and voice tone, and an emotion engine recognizes their emotional state. This recognition includes basic emotions such as smiling, stress, anger, and sadness.

[0517] The server receives health information sent by the user and emotional data analyzed by the emotion engine. Based on this data, AI technology is used to analyze the user's overall health status and identify the presence or absence of lifestyle-related disease risks and areas that need improvement. After the health status is analyzed, the server sets the most appropriate individual health goals for the user. Based on the emotional state, the goals may be adjusted, for example, by recommending rest or light exercise to relax if the user is feeling stressed.

[0518] The server generates specific advice related to health goals and sends it to the device. On the device, the user reviews the advice and uses it in their daily life. For example, if the emotion engine determines that the user is feeling down, it will display advice on eating habits and exercise that will lead to more proactive lifestyle improvements.

[0519] Users continuously record their daily activities on their devices and provide progress feedback to the server. The server analyzes this feedback and generates encouraging messages and motivational feedback tailored to the user's emotional state. For example, if the user is emotionally stable, the server will offer new suggestions for their next health goal, supporting continuous self-improvement.

[0520] By incorporating an emotion engine, the system can provide flexible health management that takes into account the user's emotional state, and is expected to significantly contribute to improving health by making users aware of changes in their own emotions.

[0521] The following describes the processing flow.

[0522] Step 1:

[0523] Users input health information via their device. This includes height, weight, dietary history, exercise habits, and sleep patterns. Users record this information in input forms within the application.

[0524] Step 2:

[0525] The device uses its camera and microphone to capture the user's facial expressions and voice, and transmits them to the emotion engine. The emotion engine uses the latest facial recognition and voice analysis technologies to determine the user's emotional state.

[0526] Step 3:

[0527] The server receives health information and emotional data transmitted from the terminal and stores them in a database. Based on this stored data, the server uses an AI analysis module to analyze the user's health status.

[0528] Step 4:

[0529] The server sets individual health goals based on the user's health status, emotional state, and historical data trends. The server adjusts the amount and content of tasks according to the user's emotions, formulating goals optimized for the user.

[0530] Step 5:

[0531] The server sends health goals set by the server and specific advice to the terminal. The terminal notifies the user and displays an interface showing an action plan. For example, if relaxation is needed, meditation or deep breathing programs may be recommended.

[0532] Step 6:

[0533] Users record their daily activities and feed this information back to the server via their devices. This feedback includes details about meals, exercise history, and emotional notes.

[0534] Step 7:

[0535] The server analyzes the feedback it receives and evaluates the user's progress. Based on sentiment data, it generates feedback and suggestions to increase the user's motivation.

[0536] Step 8:

[0537] The server generates feedback and sends it to the device, which then displays it to the user. The user reviews the feedback and decides on actions to take toward new goals. For example, if the user's emotions are stable, positive suggestions for achieving new health goals may be presented.

[0538] (Example 2)

[0539] 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."

[0540] In health management systems, there is a need for flexible health goal setting that takes into account the user's emotional state, and for the provision of individually tailored advice. However, conventional systems lack the functionality to dynamically adjust goals and advice in response to the user's emotions, which makes it difficult to effectively support the user's sustainable health improvement.

[0541] 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.

[0542] In this invention, the server includes means for receiving and recording user information, means for analyzing the user's health status using a generative AI model, and means for recognizing the user's emotional state and setting individually tailored health goals. This enables personalized responses that take into account the user's emotional state, allowing for effective health management and continuous improvement for the user.

[0543] "User information" refers to health-related data provided by the user (e.g., weight, height, diet, exercise level, sleep patterns).

[0544] "Generative AI models" refer to artificial intelligence technologies used to analyze a user's health and emotional state.

[0545] "Health goals" refer to the setting of specific and individual goals aimed at improving the user's health.

[0546] "Emotional state" refers to the psychological state or emotion recognized by analyzing the user's facial expressions and tone of voice.

[0547] "Advice" refers to specific lifestyle improvement suggestions provided based on analyzed health and emotional states.

[0548] "Feedback" refers to information that includes evaluations of user activities and advice for the next steps.

[0549] This invention is a system that supports user health management, and in particular utilizes a generative AI model to set health goals that take into account the user's emotional state. This system includes a server, a terminal, and an emotion engine.

[0550] The server receives and records health information provided by the user. This health information includes data such as height, weight, diet, exercise level, and sleep patterns, which the user enters into the terminal. The terminal is equipped with an input interface and hardware such as a camera and microphone, which record the user's facial expressions and voice tone and transmit them to the emotion engine.

[0551] The emotion engine analyzes recorded facial and voice data and uses machine learning techniques (e.g., TensorFlow, PyTorch) to recognize the user's emotional state. The server comprehensively analyzes the emotional state analysis results from the emotion engine and health information using a generative AI model to assess the user's overall health status.

[0552] Based on the analysis results, the server sets optimal health goals for each user. This setting takes into account emotional states, resulting in individually tailored health goals, such as recommending relaxation activities for users experiencing high stress levels. This helps support users in their continuous health improvement.

[0553] For example, if a user records on their device that "Today's meal was well-balanced" and the camera analyzes their smile, the server can recognize this positive emotional state and suggest a new exercise routine to the user as the next step.

[0554] An example of a prompt to input into the generating AI model is, "What health goals would you recommend for a user who is in an optimistic emotional state?" Such prompts allow the system to generate effective health support strategies for the user.

[0555] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0556] Step 1:

[0557] The user manually enters health information (e.g., height, weight, diet, exercise level, sleep patterns) into the terminal. The terminal sends this data to an internal data management system. The entered data is then converted into a format that can be sent to the server. This process includes format checks to ensure data integrity and accuracy. The output is health information data in a format that the server can receive.

[0558] Step 2:

[0559] The device automatically records the user's facial expressions and voice using its built-in camera and microphone. This raw data is prepared for transmission to an emotion engine and analyzed in real time. Inputs are audio and image data, and output are emotional features (e.g., voice tone analysis, facial feature extraction).

[0560] Step 3:

[0561] The server receives health information transmitted from the terminal and emotional data sent from the emotion engine. The server centrally records this data and analyzes it using a generative AI model. The input is health information and emotional data, and the output is an analysis result representing the user's overall health status. At this stage, the generative AI model processes the data and identifies correlations and trends.

[0562] Step 4:

[0563] The server sets individual health goals for the user based on the analysis results. This process involves goal setting that takes emotional state into account, based on predefined rules. The input is the analysis results, and the output is the customized health goals. The system then develops optimal health strategies tailored to the user's emotional state.

[0564] Step 5:

[0565] The server generates and sends the set health goals and related advice to the terminal. The terminal displays this information to the user. The input is the health goal, and the output is specific advice for the user. The user uses this feedback to work towards their goals in their daily life.

[0566] Step 6:

[0567] Users continuously record their daily activity data on their devices and periodically send feedback to the server. Based on this feedback, the server re-evaluates the impact on the user's emotional state and their progress. The inputs are activity data and emotional impact data, and the output is feedback for continuous improvement. This supports continuous health improvement.

[0568] (Application Example 2)

[0569] 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."

[0570] Modern health management is becoming increasingly diverse, requiring comprehensive management of mental and emotional aspects in addition to traditional physical data. There is a need to recognize the user's emotional state in real time and provide flexible health goal setting and feedback based on that understanding. This invention aims to provide a comprehensive health management system that takes the user's emotional state into consideration.

[0571] 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.

[0572] In this invention, the server includes means for receiving user information, means for analyzing the user's health status based on the user information, means for setting individual health goals from the analysis results, means for providing advice related to the health goals, means for monitoring user activity after the advice is provided and generating feedback, means for recognizing the user's emotional state, adjusting the health goals and feedback based on the emotional state, and generating health advice corresponding to the emotional response, and means for using an image acquisition device and a voice acquisition device to analyze the emotional state. This enables comprehensive and flexible health management based on the user's emotions and physical health information.

[0573] "User information" refers to all data about the user, and specifically includes various data related to health and information about emotional state.

[0574] "Means for analyzing health status" refers to methods and devices for evaluating an individual's health level and physical condition based on information obtained from the user.

[0575] "Means for setting individual health goals" refers to systems and processes for formulating optimal health goals for each user.

[0576] "Means of providing advice" refer to methods and tools that present users with guidelines for improvement or achievement.

[0577] A "means for monitoring user activity and generating feedback" is a system that tracks user behavior and uses the results to provide information for further improvement or to encourage caution.

[0578] "Means for recognizing the user's emotional state and adjusting health goals and feedback based on said emotional state" refers to a process of analyzing the user's emotions and using the results of that analysis to appropriately modify health-related goals and feedback.

[0579] A "means for generating health advice that responds to emotional responses" refers to a device or system that takes into account the user's emotional state and has the ability to make specific health suggestions based on that information.

[0580] "Means for using image acquisition devices and sound acquisition devices" refers to methods for acquiring a user's facial expressions, voice tone, etc., as digital data using devices such as cameras and microphones.

[0581] To implement this invention, a system is needed to support the user's health management. This system consists of a server, a terminal, and an emotion analysis engine.

[0582] Users can input their health information using a smartphone or smart speaker as their terminal. The terminal is equipped with a camera and microphone, which capture the user's facial expressions and voice tone. The camera uses OpenCV software for real-time facial analysis, and a speech recognition API is used for voice analysis.

[0583] The acquired data is analyzed in real time by an emotion analysis engine to recognize the user's emotional state. The server integrates this emotional data with other health information sent by the user (e.g., height, weight, dietary history, exercise records, sleep patterns) and analyzes it using AI technology. AI libraries such as TensorFlow and scikit-learn are used for this analysis. Based on the analysis results, the server sets the most appropriate health goals for the user and adjusts those goals and feedback based on the emotional state.

[0584] The advice provided as feedback by the device is tailored to the user's emotional and physical state. For example, if the user is feeling stressed, the device will suggest ways to relax and play relaxing music.

[0585] Furthermore, the server periodically updates the generated feedback and suggestions to support continuous health improvement, and applies the new advice to the user through the terminal. An example of a prompt message is, "Generate advice on how to relax if the user is feeling stressed."

[0586] This system allows users to manage their health more effectively while being aware of their own emotional changes.

[0587] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0588] Step 1:

[0589] Users enter their basic health information into the terminal. This data includes height, weight, daily diet, exercise, and sleep patterns. This data is digitized and sent directly to the server.

[0590] Step 2:

[0591] The device uses its camera and microphone to capture the user's facial expressions and voice in real time. The input data here consists of the user's image and voice, which are sent to the emotion analysis engine. OpenCV is used to analyze facial expressions, and the tone of voice is analyzed via the Emotion Recognition API. The result of this analysis is the user's emotional state, which is classified into major emotion categories (e.g., joy, anger, sadness, etc.).

[0592] Step 3:

[0593] The server receives user health information and emotional state data and analyzes the user's health status based on this data. The input here is health information and emotional data, and the overall health status is evaluated using AI technologies such as TensorFlow or scikit-learn. This analysis yields outputs such as the presence or absence of lifestyle-related disease risks and items that need improvement.

[0594] Step 4:

[0595] Based on the analysis results, the server sets individual health goals tailored to the user. It also considers the user's emotional state and generates adjusted goals for relaxation activities, diet, and exercise as needed. In this step, the analyzed health and emotional states are used as input, and specific health goals reflecting these adjustments are output.

[0596] Step 5:

[0597] The device receives feedback from the server and provides the user with specific advice related to their health goals. The device also offers suggestions for relaxation methods tailored to the user's emotions (e.g., appropriate music playback) and ways to improve their emotional state. The input consists of feedback and advice sent from the server, which the device outputs through voice and screen displays.

[0598] Step 6:

[0599] Users continuously record their daily activities on their devices, and a server monitors their progress. The server retrieves the user's progress data and generates encouraging messages tailored to their emotional state. The input is continuous activity data, and the output is highly motivating feedback messages.

[0600] Step 7:

[0601] The generated feedback and suggestions are tailored to the user's emotions and health state, and typically use prompts such as, "Generate advice on how to relax if the user is feeling stressed," to provide support that meets individual needs.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] [Fourth Embodiment]

[0606] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0607] 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.

[0608] 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).

[0609] 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.

[0610] 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.

[0611] 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).

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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".

[0619] This invention is a system that analyzes a user's health status, sets individually customized health goals, and provides advice. This system functions in conjunction with a server, a terminal, and a wearable device. The server plays a central role, holding and analyzing information provided by the user. The terminal acts as an interface with the user, used for data input and receiving advice.

[0620] Users first enter their personal health information through a device. This information includes height, weight, age, gender, daily eating and exercise habits, and sleep patterns. If necessary, wearable devices can be used to record heart rate, steps, activity time, and other data in real time.

[0621] The server collects data submitted by users and stores it in a database. Based on this data, the system uses AI technology to analyze it and assess the user's health status and potential risks. This analysis includes calculating BMI, comparing it with past health data, and detecting imbalances in nutrition.

[0622] Based on the evaluation results, the server sets individual health goals. In this process, specific goals optimized for each user are proposed and notified to the user via their device. For example, realistic goals aimed at maintaining or improving the user's health are presented, such as "exercise 150 minutes of aerobic exercise per week" or "limit sugar intake in meals to less than 50g per day."

[0623] The server then provides specific advice to help users achieve these goals. This advice includes nutritional guidelines, exercise plans, and habits and improvements to incorporate into daily life. Using the advice displayed on the device as a guide, users can make changes to their daily lives and improve their health.

[0624] Subsequently, users continue to record their daily activities and provide feedback on their progress to the server via their device. Based on this feedback, the server evaluates the progress and, if necessary, revises goals and updates advice. In addition, if positive results are achieved, feedback and incentives are provided to maintain motivation.

[0625] As a concrete example, consider a user who uses this system for the purpose of weight loss and prevention of lifestyle-related diseases. Suppose this user aims to improve fatty liver disease. The server analyzes the user's BMI to be on the higher side and notifies the user of the importance of controlling calorie intake and exercising regularly. It also suggests meal plans to reduce the risk of fatty liver disease and monitors the user's diet and exercise history regularly to check the progress of their weight loss and provide feedback. In this way, the user can continuously receive guidance from the system and incorporate efforts toward improving their health into their daily life.

[0626] The following describes the processing flow.

[0627] Step 1:

[0628] Users input health information through their devices. This includes data related to their daily lives, such as height, weight, age, gender, dietary history, and exercise status. Users can also synchronize data obtained in real time via wearable devices.

[0629] Step 2:

[0630] The device sends health information collected from the user to the server. The data is encrypted and transmitted securely.

[0631] Step 3:

[0632] The server stores the received data in a database and passes it to the AI ​​analysis module. The AI ​​analysis module uses this data to evaluate the user's current health status and past trends.

[0633] Step 4:

[0634] The server understands the user's health status based on the evaluation results of the AI ​​analysis module and identifies potential health risks. Based on these results, it sets individually optimized health goals.

[0635] Step 5:

[0636] The server sends health goals and progress instructions to the device. The device notifies the user of these goals and presents an action plan. Specific examples include "aerobic exercise three times a week" and "increasing daily vegetable intake."

[0637] Step 6:

[0638] Users record their daily activities on their devices. They input data such as what they ate, how much exercise they did, and changes in their weight, and manage their progress.

[0639] Step 7:

[0640] The terminal sends user records to the server, which then monitors them. The server evaluates goal achievement and generates feedback and corrective advice as needed.

[0641] Step 8:

[0642] The server generates feedback and sends it to the device. The device then presents this feedback to the user, helping to boost motivation and set new goals. This feedback may include messages of praise based on achievement and suggestions for the next challenge.

[0643] (Example 1)

[0644] 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".

[0645] In modern society, individual health management is a crucial issue, but conventional systems have struggled to provide personalized health maintenance goals and advice. In particular, the lack of real-time health status analysis and feedback has hindered user motivation and goal achievement. Therefore, there is a need to develop a system that enables the setting of user-optimized health maintenance goals and the provision of specific advice aligned with those goals.

[0646] 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.

[0647] In this invention, the server includes means for receiving the user's physical information, means for analyzing the health status using a generative AI model, and means for setting individual health maintenance goals. This makes it possible to set health maintenance goals optimized for each user and provide specific advice for achieving them.

[0648] "User physical information" refers to data that indicates an individual's health status, such as height, weight, age, gender, eating and exercise habits, sleep patterns, heart rate, and steps taken.

[0649] A "generative AI model" is an algorithm or program built on artificial intelligence technology that is used to analyze the physical information collected from users.

[0650] "Health status analysis" is a process that evaluates the user's current health status and health risks based on their physical information.

[0651] "Individual health maintenance goals" are specific and achievable health improvement goals set based on each user's health status and lifestyle.

[0652] "Health maintenance advice" refers to specific action guidelines and suggestions provided to users based on their set health maintenance goals.

[0653] "Recording user activity" refers to the process of tracking users' daily actions and health-related activities and saving them as data.

[0654] "Evaluating progress" is an activity that measures the degree to which users have achieved their goals and confirms the effectiveness of current efforts.

[0655] "Updating health maintenance goals and advice" refers to reviewing and adjusting existing goals and advice in a timely manner based on user activity feedback.

[0656] This invention utilizes servers, terminals, and wearable devices to build a system that supports users' health management. This makes it possible to collect and analyze personal health information and provide an optimal health maintenance plan.

[0657] The server plays a central role in the system, receiving health-related data entered by users. The terminal allows users to input data via an interface and facilitates data transmission and reception between the terminal and the server. Wearable devices are used to collect real-time data such as heart rate and steps, enabling detailed monitoring of the user's health.

[0658] First, users input their individual health information into the system using a terminal. This information includes height, weight, age, gender, daily eating and exercise habits, and sleep patterns. Furthermore, by wearing a wearable device, specific physical activity data is continuously collected and transmitted to the server.

[0659] The server uses a generative AI model to analyze the collected data and assess the user's health status. This analysis process uses prompts to specify the algorithms and calculation procedures the model will use. For example, a prompt might say, "Enter the user's BMI and past exercise data, and generate advice to help them achieve their health goals." Based on this, the model calculates the BMI, compares it to past data, and detects anomalies.

[0660] Based on the analysis results, the server sets health maintenance goals optimized for the user. The set goals are notified to the user via their device, and the user can work on improving their daily life by following the specific advice provided.

[0661] Subsequently, users continue to record their activities and provide feedback to the server via their device. Based on this feedback, the server evaluates the user's progress and determines whether updates to health maintenance goals and advice are necessary. This allows users to consistently manage their health appropriately and receive support towards achieving their goals.

[0662] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0663] Step 1:

[0664] The user enters their health information into the device. This information includes height, weight, age, gender, dietary and exercise habits, and sleep patterns. The purpose of this input is to collect data to understand the individual's health status. The device then organizes this data and prepares it for transmission to the server.

[0665] Step 2:

[0666] The terminal transmits health information obtained from the user and real-time data collected through wearable devices to the server. Wearable devices provide data such as heart rate, steps taken, and activity time. The server stores the received data in a database and uses it as input for analysis.

[0667] Step 3:

[0668] The server uses a generated AI model based on stored data to analyze the user's health status. Specific data processing includes calculating BMI using height and weight, and detecting imbalances in nutrition based on daily lifestyle data. An example of a prompt message used is, "Please use the user's BMI and past exercise data to assess health risks and set appropriate health goals." The output includes analysis results and a risk assessment.

[0669] Step 4:

[0670] The server sets individual health maintenance goals for each user based on the analysis results of the generated AI model. These goals may include suggestions for improving the user's eating habits or recommending a certain amount of exercise. This goal setting output is sent to the device as a specific health maintenance plan and notified to the user.

[0671] Step 5:

[0672] The device displays a health maintenance plan sent from the server to the user and provides health maintenance advice. The user can then take actions to maintain their health, such as improving their diet or exercising, based on the specific advice provided. At this stage, the advice is presented in a clear and easy-to-follow format.

[0673] Step 6:

[0674] Users record their health maintenance activities and feed this data back to the server via their device. This feedback allows the server to understand the user's progress and current position relative to their goals. Based on this feedback, the server evaluates progress and updates health maintenance goals and advice as needed. This enables users to continuously review and improve their health management.

[0675] (Application Example 1)

[0676] 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".

[0677] In modern society, it is difficult for individuals to accurately understand their own health status and manage their health sustainably. In particular, there is a need for personalized health goals tailored to each user's different lifestyle and health condition, as well as health improvement advice that is integrated into daily life. Furthermore, because there is no system that acquires this data in real time and provides appropriate feedback as needed, it is difficult for users to maintain their own motivation.

[0678] 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.

[0679] In this invention, the server includes means for receiving user information via voice input and visual display, means for acquiring physical information in real time based on the analysis, and means for providing the feedback using visual and auditory means. This allows users to understand their health status in real time, make it easier to implement individually customized advice in their daily lives, and increase their motivation to maintain their health.

[0680] "User information" refers to data representing the user's health status, specifically including information such as height, weight, age, gender, eating habits, exercise habits, and sleep patterns.

[0681] "Health status" refers to a comprehensive representation of a user's current physical and mental health, and is evaluated using various physiological indicators such as body fat percentage, blood pressure, heart rate, and activity level.

[0682] "Analysis" refers to the process of using collected user information to analyze health status using AI technology, assessing health risks based on the results, and deriving appropriate guidelines.

[0683] "Health goals" are specific behavioral targets set according to individual circumstances with the aim of maintaining or improving a user's health, and include, for example, exercise time and calorie intake.

[0684] "Advice" refers to information that provides specific guidance on actions and habits that users should take in their daily lives in accordance with their health goals, and includes things like meal plans and exercise menus.

[0685] "Monitoring" is the process of continuously tracking the activities that users perform based on their set health goals and evaluating the results.

[0686] "Feedback" refers to providing comments and evaluations based on the user's activity results, in order to help them modify their behavior and maintain their motivation.

[0687] "Reception" refers to the system taking in information provided by the user, and this is done through voice input or a visual interface.

[0688] "Real-time" means collecting and processing data instantly without delay and providing the results to the user.

[0689] "Providing" means showing the generated advice and feedback to the user, especially through audio or visual means.

[0690] The system that implements this application effectively manages users' health information and supports continuous health improvement. This system operates in conjunction with a server, terminals, and wearable devices.

[0691] The server receives health information from users via voice input and visual display. Hardware used includes computer devices such as Raspberry Pi, and the Google Speech-to-Text API is used for speech recognition software. This information is stored in a database and used as basic data for analysis.

[0692] The server uses AI technologies such as TensorFlow to analyze the collected data. This analysis assesses the user's health status and also obtains real-time physical information. Based on this information, individual health goals are generated, and the server provides the user with appropriate advice.

[0693] The device provides users with analysis results and advice through a web application built with Flask, utilizing both visual and auditory feedback. Through this feedback, users can adjust their daily behaviors to achieve their health goals.

[0694] For example, if a user asks the device in the morning, "Tell me my exercise plan for today," the system will analyze past data and provide a specific action plan such as, "Today, I recommend a 30-minute walk and 10 minutes of stretching."

[0695] An example of a prompt to input into the generating AI model would be, "Based on the user's data from the past week, please suggest a health improvement plan for next week." This allows for health management optimized for each individual user.

[0696] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0697] Step 1:

[0698] The user enters health information via a voice or visual interface. For voice input, the device receives input through the microphone and converts the speech to text using the Google Speech-to-Text API. For visual input, the user enters the required information into a form on the screen. The output at this stage is user health information in text format.

[0699] Step 2:

[0700] The server receives user health information in text format sent from the terminal. This information is stored in a database and integrated with historical data to form a baseline of the user's health status. The output is an integrated dataset prepared for analysis.

[0701] Step 3:

[0702] The server uses the integrated dataset to launch an AI model, specifically an analysis algorithm using TensorFlow. This model allows the server to assess health status and identify potential risks. The output of this step is an assessment of the user's current health status.

[0703] Step 4:

[0704] The server generates individual health goals based on the analysis results. Using a generation AI model, it designs a customized action plan that takes into account each user's past data and goal achievement status. The output consists of specific health goals and daily action plans.

[0705] Step 5:

[0706] The server sends analysis results, health goals, and specific advice to the user's device via a Flask-based web application. The user receives this information visually and audibly through their device. The output includes user-friendly feedback.

[0707] Step 6:

[0708] Based on the feedback received, users adjust their daily activities and aim to achieve their goals. They then send feedback from their device back to the server regarding the results of each activity. Based on this feedback, advice and goals for the next activity are adjusted as needed. The output of this step is the user's activity result data.

[0709] 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.

[0710] This invention relates to a system that supports user health management, and in particular, has the function of recognizing the user's emotional state and adjusting health goals and feedback accordingly. The system includes a server, a terminal, and an emotion engine. The server manages user information and works with the emotion engine to analyze data. The terminal functions as an interface with the user and presents feedback from the emotion engine to the user.

[0711] Users input health information such as height, weight, daily diet and exercise, and sleep patterns through the device. In addition, the device uses a camera and microphone to analyze the user's facial expressions and voice tone, and an emotion engine recognizes their emotional state. This recognition includes basic emotions such as smiling, stress, anger, and sadness.

[0712] The server receives health information sent by the user and emotional data analyzed by the emotion engine. Based on this data, AI technology is used to analyze the user's overall health status and identify the presence or absence of lifestyle-related disease risks and areas that need improvement. After the health status is analyzed, the server sets the most appropriate individual health goals for the user. Based on the emotional state, the goals may be adjusted, for example, by recommending rest or light exercise to relax if the user is feeling stressed.

[0713] The server generates specific advice related to health goals and sends it to the device. On the device, the user reviews the advice and uses it in their daily life. For example, if the emotion engine determines that the user is feeling down, it will display advice on eating habits and exercise that will lead to more proactive lifestyle improvements.

[0714] Users continuously record their daily activities on their devices and provide progress feedback to the server. The server analyzes this feedback and generates encouraging messages and motivational feedback tailored to the user's emotional state. For example, if the user is emotionally stable, the server will offer new suggestions for their next health goal, supporting continuous self-improvement.

[0715] By incorporating an emotion engine, the system can provide flexible health management that takes into account the user's emotional state, and is expected to significantly contribute to improving health by making users aware of changes in their own emotions.

[0716] The following describes the processing flow.

[0717] Step 1:

[0718] Users input health information via their device. This includes height, weight, dietary history, exercise habits, and sleep patterns. Users record this information in input forms within the application.

[0719] Step 2:

[0720] The device uses its camera and microphone to capture the user's facial expressions and voice, and transmits them to the emotion engine. The emotion engine uses the latest facial recognition and voice analysis technologies to determine the user's emotional state.

[0721] Step 3:

[0722] The server receives health information and emotional data transmitted from the terminal and stores them in a database. Based on this stored data, the server uses an AI analysis module to analyze the user's health status.

[0723] Step 4:

[0724] The server sets individual health goals based on the user's health status, emotional state, and historical data trends. The server adjusts the amount and content of tasks according to the user's emotions, formulating goals optimized for the user.

[0725] Step 5:

[0726] The server sends health goals set by the server and specific advice to the terminal. The terminal notifies the user and displays an interface showing an action plan. For example, if relaxation is needed, meditation or deep breathing programs may be recommended.

[0727] Step 6:

[0728] Users record their daily activities and feed this information back to the server via their devices. This feedback includes details about meals, exercise history, and emotional notes.

[0729] Step 7:

[0730] The server analyzes the feedback it receives and evaluates the user's progress. Based on sentiment data, it generates feedback and suggestions to increase the user's motivation.

[0731] Step 8:

[0732] The server generates feedback and sends it to the device, which then displays it to the user. The user reviews the feedback and decides on actions to take toward new goals. For example, if the user's emotions are stable, positive suggestions for achieving new health goals may be presented.

[0733] (Example 2)

[0734] 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".

[0735] In health management systems, there is a need for flexible health goal setting that takes into account the user's emotional state, and for the provision of individually tailored advice. However, conventional systems lack the functionality to dynamically adjust goals and advice in response to the user's emotions, which makes it difficult to effectively support the user's sustainable health improvement.

[0736] 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.

[0737] In this invention, the server includes means for receiving and recording user information, means for analyzing the user's health status using a generative AI model, and means for recognizing the user's emotional state and setting individually tailored health goals. This enables personalized responses that take into account the user's emotional state, allowing for effective health management and continuous improvement for the user.

[0738] "User information" refers to health-related data provided by the user (e.g., weight, height, diet, exercise level, sleep patterns).

[0739] "Generative AI models" refer to artificial intelligence technologies used to analyze a user's health and emotional state.

[0740] "Health goals" refer to the setting of specific and individual goals aimed at improving the user's health.

[0741] "Emotional state" refers to the psychological state or emotion recognized by analyzing the user's facial expressions and tone of voice.

[0742] "Advice" refers to specific lifestyle improvement suggestions provided based on analyzed health and emotional states.

[0743] "Feedback" refers to information that includes evaluations of user activities and advice for the next steps.

[0744] This invention is a system that supports user health management, and in particular utilizes a generative AI model to set health goals that take into account the user's emotional state. This system includes a server, a terminal, and an emotion engine.

[0745] The server receives and records health information provided by the user. This health information includes data such as height, weight, diet, exercise level, and sleep patterns, which the user enters into the terminal. The terminal is equipped with an input interface and hardware such as a camera and microphone, which record the user's facial expressions and voice tone and transmit them to the emotion engine.

[0746] The emotion engine analyzes recorded facial and voice data and uses machine learning techniques (e.g., TensorFlow, PyTorch) to recognize the user's emotional state. The server comprehensively analyzes the emotional state analysis results from the emotion engine and health information using a generative AI model to assess the user's overall health status.

[0747] Based on the analysis results, the server sets optimal health goals for each user. This setting takes into account emotional states, resulting in individually tailored health goals, such as recommending relaxation activities for users experiencing high stress levels. This helps support users in their continuous health improvement.

[0748] For example, if a user records on their device that "Today's meal was well-balanced" and the camera analyzes their smile, the server can recognize this positive emotional state and suggest a new exercise routine to the user as the next step.

[0749] An example of a prompt to input into the generating AI model is, "What health goals would you recommend for a user who is in an optimistic emotional state?" Such prompts allow the system to generate effective health support strategies for the user.

[0750] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0751] Step 1:

[0752] The user manually enters health information (e.g., height, weight, diet, exercise level, sleep patterns) into the terminal. The terminal sends this data to an internal data management system. The entered data is then converted into a format that can be sent to the server. This process includes format checks to ensure data integrity and accuracy. The output is health information data in a format that the server can receive.

[0753] Step 2:

[0754] The device automatically records the user's facial expressions and voice using its built-in camera and microphone. This raw data is prepared for transmission to an emotion engine and analyzed in real time. Inputs are audio and image data, and output are emotional features (e.g., voice tone analysis, facial feature extraction).

[0755] Step 3:

[0756] The server receives health information transmitted from the terminal and emotional data sent from the emotion engine. The server centrally records this data and analyzes it using a generative AI model. The input is health information and emotional data, and the output is an analysis result representing the user's overall health status. At this stage, the generative AI model processes the data and identifies correlations and trends.

[0757] Step 4:

[0758] The server sets individual health goals for the user based on the analysis results. This process involves goal setting that takes emotional state into account, based on predefined rules. The input is the analysis results, and the output is the customized health goals. The system then develops optimal health strategies tailored to the user's emotional state.

[0759] Step 5:

[0760] The server generates and sends the set health goals and related advice to the terminal. The terminal displays this information to the user. The input is the health goal, and the output is specific advice for the user. The user uses this feedback to work towards their goals in their daily life.

[0761] Step 6:

[0762] Users continuously record their daily activity data on their devices and periodically send feedback to the server. Based on this feedback, the server re-evaluates the impact on the user's emotional state and their progress. The inputs are activity data and emotional impact data, and the output is feedback for continuous improvement. This supports continuous health improvement.

[0763] (Application Example 2)

[0764] 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".

[0765] Modern health management is becoming increasingly diverse, requiring comprehensive management of mental and emotional aspects in addition to traditional physical data. There is a need to recognize the user's emotional state in real time and provide flexible health goal setting and feedback based on that understanding. This invention aims to provide a comprehensive health management system that takes the user's emotional state into consideration.

[0766] 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.

[0767] In this invention, the server includes means for receiving user information, means for analyzing the user's health status based on the user information, means for setting individual health goals from the analysis results, means for providing advice related to the health goals, means for monitoring user activity after the advice is provided and generating feedback, means for recognizing the user's emotional state, adjusting the health goals and feedback based on the emotional state, and generating health advice corresponding to the emotional response, and means for using an image acquisition device and a voice acquisition device to analyze the emotional state. This enables comprehensive and flexible health management based on the user's emotions and physical health information.

[0768] "User information" refers to all data about the user, and specifically includes various data related to health and information about emotional state.

[0769] "Means for analyzing health status" refers to methods and devices for evaluating an individual's health level and physical condition based on information obtained from the user.

[0770] "Means for setting individual health goals" refers to systems and processes for formulating optimal health goals for each user.

[0771] "Means of providing advice" refer to methods and tools that present users with guidelines for improvement or achievement.

[0772] A "means for monitoring user activity and generating feedback" is a system that tracks user behavior and uses the results to provide information for further improvement or to encourage caution.

[0773] "Means for recognizing the user's emotional state and adjusting health goals and feedback based on said emotional state" refers to a process of analyzing the user's emotions and using the results of that analysis to appropriately modify health-related goals and feedback.

[0774] A "means for generating health advice that responds to emotional responses" refers to a device or system that takes into account the user's emotional state and has the ability to make specific health suggestions based on that information.

[0775] "Means for using image acquisition devices and sound acquisition devices" refers to methods for acquiring a user's facial expressions, voice tone, etc., as digital data using devices such as cameras and microphones.

[0776] To implement this invention, a system is needed to support the user's health management. This system consists of a server, a terminal, and an emotion analysis engine.

[0777] Users can input their health information using a smartphone or smart speaker as their terminal. The terminal is equipped with a camera and microphone, which capture the user's facial expressions and voice tone. The camera uses OpenCV software for real-time facial analysis, and a speech recognition API is used for voice analysis.

[0778] The acquired data is analyzed in real time by an emotion analysis engine to recognize the user's emotional state. The server integrates this emotional data with other health information sent by the user (e.g., height, weight, dietary history, exercise records, sleep patterns) and analyzes it using AI technology. AI libraries such as TensorFlow and scikit-learn are used for this analysis. Based on the analysis results, the server sets the most appropriate health goals for the user and adjusts those goals and feedback based on the emotional state.

[0779] The advice provided as feedback by the device is tailored to the user's emotional and physical state. For example, if the user is feeling stressed, the device will suggest ways to relax and play relaxing music.

[0780] Furthermore, the server periodically updates the generated feedback and suggestions to support continuous health improvement, and applies the new advice to the user through the terminal. An example of a prompt message is, "Generate advice on how to relax if the user is feeling stressed."

[0781] This system allows users to manage their health more effectively while being aware of their own emotional changes.

[0782] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0783] Step 1:

[0784] Users enter their basic health information into the terminal. This data includes height, weight, daily diet, exercise, and sleep patterns. This data is digitized and sent directly to the server.

[0785] Step 2:

[0786] The device uses its camera and microphone to capture the user's facial expressions and voice in real time. The input data here consists of the user's image and voice, which are sent to the emotion analysis engine. OpenCV is used to analyze facial expressions, and the tone of voice is analyzed via the Emotion Recognition API. The result of this analysis is the user's emotional state, which is classified into major emotion categories (e.g., joy, anger, sadness, etc.).

[0787] Step 3:

[0788] The server receives user health information and emotional state data and analyzes the user's health status based on this data. The input here is health information and emotional data, and the overall health status is evaluated using AI technologies such as TensorFlow or scikit-learn. This analysis yields outputs such as the presence or absence of lifestyle-related disease risks and items that need improvement.

[0789] Step 4:

[0790] Based on the analysis results, the server sets individual health goals tailored to the user. It also considers the user's emotional state and generates adjusted goals for relaxation activities, diet, and exercise as needed. In this step, the analyzed health and emotional states are used as input, and specific health goals reflecting these adjustments are output.

[0791] Step 5:

[0792] The device receives feedback from the server and provides the user with specific advice related to their health goals. The device also offers suggestions for relaxation methods tailored to the user's emotions (e.g., appropriate music playback) and ways to improve their emotional state. The input consists of feedback and advice sent from the server, which the device outputs through voice and screen displays.

[0793] Step 6:

[0794] Users continuously record their daily activities on their devices, and a server monitors their progress. The server retrieves the user's progress data and generates encouraging messages tailored to their emotional state. The input is continuous activity data, and the output is highly motivating feedback messages.

[0795] Step 7:

[0796] The generated feedback and suggestions are tailored to the user's emotions and health state, and typically use prompts such as, "Generate advice on how to relax if the user is feeling stressed," to provide support that meets individual needs.

[0797] 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.

[0798] 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.

[0799] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0800] 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.

[0801] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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."

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] The following is further disclosed regarding the embodiments described above.

[0819] (Claim 1)

[0820] Means for receiving user information,

[0821] A means for analyzing the health status based on the aforementioned user information,

[0822] A means for setting individual health goals based on the aforementioned analysis results,

[0823] Means for providing advice related to the aforementioned health goals,

[0824] A means for monitoring user activity after the provision of the aforementioned advice and generating feedback,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, further comprising means for performing a risk assessment based on the user's health status.

[0828] (Claim 3)

[0829] The system according to claim 1, further comprising means for acquiring user activity data from an existing wearable device.

[0830] "Example 1"

[0831] (Claim 1)

[0832] A means of receiving the user's physical information,

[0833] A means for analyzing the user's health status using a generated AI model based on the aforementioned physical information of the user,

[0834] A means for setting individual health maintenance goals based on the aforementioned analysis results,

[0835] A means of providing specific health maintenance advice in relation to the aforementioned health maintenance goals,

[0836] A means for recording the user's activity status and evaluating its progress after providing the aforementioned advice,

[0837] A means of updating the aforementioned health maintenance goals and advice according to the progress of goal achievement,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, further comprising means for evaluating the degree of risk from the user's health information.

[0841] (Claim 3)

[0842] The system according to claim 1, further comprising means for acquiring user behavior data from a general-purpose device.

[0843] "Application Example 1"

[0844] (Claim 1)

[0845] Means for receiving user information,

[0846] A means for analyzing the health status based on the aforementioned user information,

[0847] A means for setting individual health goals based on the aforementioned analysis results,

[0848] Means for providing advice related to the aforementioned health goals,

[0849] A means for monitoring user activity after the provision of the aforementioned advice and generating feedback,

[0850] Means for receiving user information via voice input and visual display,

[0851] A means for acquiring physical information in real time based on the aforementioned analysis,

[0852] Means for providing the aforementioned feedback using visual and auditory information,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, further comprising means for performing a risk assessment based on the user's health status.

[0856] (Claim 3)

[0857] The system according to claim 1, further comprising means for acquiring user activity data from an existing detection device.

[0858] "Example 2 of combining an emotion engine"

[0859] (Claim 1)

[0860] Means for receiving and recording user information,

[0861] A means for analyzing the health status using a generated AI model based on the aforementioned user information,

[0862] Based on the aforementioned analysis results, a means for recognizing the user's emotional state and setting individually adjusted health goals,

[0863] A means for providing advice based on the aforementioned health goals and the user's emotional state,

[0864] A means for monitoring user activity based on the aforementioned advice and generating continuous feedback,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, further comprising means for flexibly adjusting goals based on the user's emotional state.

[0868] (Claim 3)

[0869] The system according to claim 1, further comprising means for acquiring data from existing measuring devices in order to analyze the user's physiological data.

[0870] "Application example 2 when combining with an emotional engine"

[0871] (Claim 1)

[0872] Means for receiving user information,

[0873] A means for analyzing the health status based on the aforementioned user information,

[0874] A means for setting individual health goals based on the aforementioned analysis results,

[0875] Means for providing advice related to the aforementioned health goals,

[0876] A means for monitoring user activity after the provision of the aforementioned advice and generating feedback,

[0877] A means for recognizing the user's emotional state, adjusting health goals and feedback based on the said emotional state, and generating health advice corresponding to emotional responses,

[0878] To analyze the aforementioned emotional state, means are provided for using an image acquisition device and an audio acquisition device.

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, further comprising means for performing a risk assessment based on the user's health status.

[0882] (Claim 3)

[0883] The system according to claim 1, further comprising means for acquiring user activity data from existing wearable information devices. [Explanation of Symbols]

[0884] 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. Means for receiving user information, A means for analyzing the health status based on the aforementioned user information, A means for setting individual health goals based on the aforementioned analysis results, Means for providing advice related to the aforementioned health goals, A means for monitoring user activity after the provision of the aforementioned advice and generating feedback, Means for receiving user information via voice input and visual display, A means for acquiring physical information in real time based on the aforementioned analysis, Means for providing the aforementioned feedback using visual and auditory information, A system that includes this.

2. The system according to claim 1, further comprising means for performing a risk assessment based on the user's health status.

3. The system according to claim 1, further comprising means for acquiring user activity data from an existing detection device.

Images