system

Abstract

We provide the system. [Solution] Data collection methods for collecting personal health-related information, An evaluation means for processing the aforementioned health-related information and evaluating the health status, A generation means for generating individual health improvement measures based on the evaluation results obtained by the evaluation means, Output means for outputting the generated improvement measures, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a 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 times, individuals face various minor health problems, which often cause a decline in productivity at work and in daily life. This state is called presenteeism. In many cases, these symptoms do not require a visit to the doctor, so appropriate management and improvement measures have not been taken. Therefore, there is a need for a system that allows individuals to understand their own health status and take appropriate countermeasures.

Means for Solving the Problems

[0005] The present invention provides a health management system that includes data collection means for collecting personal health-related information, evaluation means for processing the health-related information and evaluating the health status, generation means for generating individual health improvement measures based on the evaluation results obtained by the evaluation means, and output means for outputting the generated improvement measures. This allows individuals to receive specific improvement measures while comparing their health status with that of their peers. Specifically, it proposes improvement measures, including visits to medical institutions and daily relaxation methods, based on personal health data, enabling health management tailored to individual needs.

[0006] "Data collection means" refers to a device or process for collecting personal health-related information.

[0007] "Evaluation means" refers to a process or device that analyzes collected health-related information and objectively evaluates an individual's health status.

[0008] "Generating means" refers to a process or apparatus for creating individual health improvement measures based on the results of health status assessments.

[0009] "Output means" refers to a device or process for communicating the generated health improvement measures to an individual.

[0010] "Health-related information" refers to data that indicates an individual's health status, such as heart rate, steps taken, sleep duration, food records, and exercise logs.

[0011] "Health improvement measures" refer to specific actions and advice recommended to improve an individual's health, including advice such as visiting a medical institution or regular exercise methods. [Brief explanation of the drawing]

[0012] [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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It 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 Example 2 when an 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 an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] 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.

[0017] 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.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system for managing and improving an individual's health status, and is configured as follows: The user collects daily health data using a smartphone or wearable device. This data includes heart rate, steps taken, sleep patterns, meal logs, exercise history, etc. The collected data is transmitted to a server in real time or at a fixed frequency (e.g., daily).

[0034] The server centrally manages the received data and first performs data formatting and cleaning. Next, it initiates an analysis process using the data, applying an AI model to assess the user's health status. This assessment is performed by comparing the user's data with the average values ​​for their age group. This process identifies specific risks and signs of presenteeism by clarifying health indicators tailored to each individual user.

[0035] Based on the evaluation results, the server generates personalized health improvement plans for the user. These plans include determining whether a visit to a medical institution is necessary, and providing guidance on stretches, meditation, appropriate exercise, and diet that can be done in daily life. The generated advice is sent to the user's device as a text message, and the device presents it to the user in an easy-to-understand visual format.

[0036] For example, if a user is experiencing chronic fatigue, the server will identify the cause based on sleep data and exercise levels. If it determines that the user is sleep-deprived, the server will notify the user's device of the importance of going to bed early and provide advice on creating a suitable sleep environment.

[0037] Users apply the provided advice to their daily lives to improve their health. The results and feedback from implementing the improvements are sent back to the server, ensuring that a flexible and personalized health management system continues to function.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] Users collect health data such as heart rate, steps taken, and sleep patterns using wearable devices and smartphones. This data is transmitted to a server via the device.

[0041] Step 2:

[0042] The server receives the transmitted health data and performs data formatting and cleaning. It removes incomplete data, standardizes values, and converts the data into the format required for evaluation.

[0043] Step 3:

[0044] The server applies an AI model to the formatted data and calculates an individual health score. This score indicates the user's health status and allows for comparison with others in the same age group.

[0045] Step 4:

[0046] The server uses the output of the AI ​​model to detect anomalies in the user's health status and signs of presenteeism. It generates an alert if specific health risks are detected.

[0047] Step 5:

[0048] Based on the detected results, the server generates health improvement measures tailored to the user. This includes determining whether lifestyle changes are necessary or if a visit to a medical institution is required.

[0049] Step 6:

[0050] The server sends the generated health improvement suggestions to the terminal, and the terminal notifies the user of this information. The suggestions are presented to the user using a visually easy-to-understand dashboard and notification messages.

[0051] Step 7:

[0052] Users review the suggestions they receive and incorporate health improvement measures into their daily lives. They can also send feedback on the results of their implementation to the server via their device.

[0053] Step 8:

[0054] The server analyzes user feedback and new health data to update the accuracy of the AI ​​model. This will enable it to provide more personalized advice in the future.

[0055] (Example 1)

[0056] 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."

[0057] Conventional health management systems struggle to accurately analyze individual health data and provide personalized health improvement strategies. Furthermore, the lack of comparisons using average data for the same age group can lead to insufficient identification of abnormalities and risks. Additionally, there are limited methods for presenting advice based on acquired health data in a way that users can implement in their daily lives. These problems need to be addressed.

[0058] 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.

[0059] In this invention, the server includes information acquisition means for collecting individual biometric data, information processing means for converting the biometric data into a unified format and correcting abnormal values, and state evaluation means for analyzing the biometric data and evaluating the health status using artificial intelligence. This makes it possible to perform precise data analysis to evaluate the health status of individuals and propose appropriate health improvement measures based on that evaluation.

[0060] "Information acquisition means" refers to a device or function for automatically collecting an individual's biometric data.

[0061] "Information processing means" refers to a function that converts acquired biometric data into a unified format and performs processing to correct abnormal values ​​within the data.

[0062] "Condition evaluation means" refers to a function that analyzes biometric data and uses artificial intelligence to evaluate an individual's health status.

[0063] The "proposal generation means" refers to a function that generates individually tailored health improvement measures based on the evaluation results from the condition evaluation means.

[0064] "Presentation means" refers to a function for displaying the generated health improvement measures on the user's communication terminal.

[0065] "Comparative processing" refers to a method of evaluating an individual by comparing their data with standard data from a group of people of the same age.

[0066] This invention is a system that provides personalized health improvement measures by collecting and analyzing biological data. This system mainly consists of a user terminal and a server, each with clearly defined roles. The following describes a specific embodiment of this system.

[0067] Users collect daily biometric data using smartphones and wearable devices. These devices have built-in heart rate sensors, accelerometers, GPS functionality, and other features to automatically record heart rate, steps, distance traveled, sleep patterns, and food intake. The data is transferred to the smartphone using Bluetooth or Wi-Fi.

[0068] The terminal provides an interface for sending collected data to the server. During this process, the data is encrypted and securely transmitted to the server. For example, it's possible to configure the system to send data in batches overnight, reducing the user's communication burden.

[0069] The server centrally manages and processes the received data. The software used includes a database management system, data cleaning tools, and generative AI models. Programming languages ​​such as Python and R are used for data shaping and cleaning, including the removal of outliers and data imputation. Generative AI models are applied to assess each user's health status and calculate risk by comparing it to average data for their age group.

[0070] Based on the evaluation results, the server generates individual health improvement measures using a suggestion generation mechanism. The submitted improvement measures are visually presented to the user on the terminal. The use of a graphical user interface makes the advice easy to understand intuitively.

[0071] For example, if a user inputs a prompt such as, "I've been feeling really tired lately, how can I improve this?" into the AI, the AI ​​will identify the cause of the fatigue from past sleep and heart rate data and provide specific advice for improvement.

[0072] This will enable users to make improvements that are more relevant to their daily lives, resulting in enhanced, personalized health management.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] Users collect biometric data using smartphones and wearable devices. This data includes heart rate, steps taken, sleep patterns, meal logs, and exercise history. The data is transferred to the smartphone via Bluetooth or Wi-Fi. Input data is raw values ​​from sensors, while output is raw data stored on the smartphone. For example, if a user uses a pedometer throughout the day, the number of steps and data broken down by time of day will be collected.

[0076] Step 2:

[0077] The device sends the collected data to the server. Specifically, the smartphone securely transfers the data to the endpoint using an encryption protocol. The input is the raw data stored on the device, and the output is the unformatted data sent to the server. During this process, a process is carried out to prevent the accidental transmission of data. For example, depending on the communication environment, data transmission may be temporarily suspended and sent only when stable communication can be ensured.

[0078] Step 3:

[0079] The server formats and cleans the received data. Here, Python or R programs are used to detect and correct outliers and to impart missing data. The input is the raw data before formatting, and the output is the formatted and cleaned data. For example, if there are outliers in heart rate data, they are corrected as outliers exceeding a predefined range.

[0080] Step 4:

[0081] The server applies a generative AI model using the formatted data to evaluate the user's health status. Here, the data to be analyzed is input into the AI ​​model, and health status scoring and risk assessment are performed. The input is formatted data, and the output is a health assessment report. As a specific example, fluctuations in stress levels are reported based on the user's heart rate data over the past week.

[0082] Step 5:

[0083] The server generates health improvement measures based on the obtained health assessment. This involves a combination of data analysis results and user cases obtained through balance mining to generate suggestions. The input is the health assessment report, and the output is the generated improvement measures. For example, if a lack of exercise is detected, an individualized exercise plan is formulated.

[0084] Step 6:

[0085] The terminal visually presents the improvement suggestions received from the server to the user. Using a designed interface, it clearly displays the suggestions with animations and graphs. The input is the generated improvement suggestions, and the output is a visualization of those suggestions displayed to the user. For example, recommended stretches are presented with illustrations.

[0086] Step 7:

[0087] Users improve their lifestyle habits according to the provided advice and input the results as feedback into the device. The device then sends this data back to the server, where it is used for the next data analysis. The input is the user's feedback data, and the output is the new dataset sent to the server. For example, the system can record the results of a user's exercise plan, which will then be used to inform the next set of advice.

[0088] (Application Example 1)

[0089] 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."

[0090] In modern society, the importance of individual health management is increasing. However, traditional health management methods have difficulty providing personalized, real-time advice, and there is a particular challenge in obtaining health indicators and improvement measures that can be immediately used in exercise facilities and daily life.

[0091] 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.

[0092] In this invention, the server includes data acquisition means for collecting personal health-related information, evaluation means for processing the health-related information and evaluating the health status, generation means for providing individual health improvement guidelines based on the evaluation results obtained by the evaluation means, and presentation means for outputting the generated guidelines to a visual display device in real time. This makes it possible to provide real-time and personalized health advice based on the individual's health status.

[0093] "Health-related information" refers to data consisting of an individual's exercise history, heart rate, steps taken, sleep patterns, and dietary logs, and is used to evaluate an individual's health status.

[0094] "Data acquisition means" refers to a device or software that has the function of collecting personal health-related information.

[0095] "Evaluation means" refers to a device or software used for the purpose of processing collected health-related information and analyzing and evaluating an individual's health status.

[0096] "Generation means" refers to a device or software that provides individual health improvement guidelines based on evaluation results obtained by the evaluation means.

[0097] A "visual display device" is a device used to visually display generated health improvement guidelines to the user, and mainly includes smart glasses and screens.

[0098] "Real-time" refers to the ability to process information instantly and provide the results to users quickly, meaning that data is available in a timely manner without delay.

[0099] The server uses cloud-based data acquisition methods to obtain health-related information from smart glasses worn by individuals in fitness facilities and at home. This information includes heart rate, steps taken, sleep patterns, etc., and is collected in real time from various sensors. The server formats and cleans this data and analyzes the user's health status using a powerful AI model as an evaluation tool. Machine learning software such as TENSORFLOW® is used for the analysis, learning data patterns and generating individualized health improvement guidelines. The generation tool formulates health improvement measures based on the evaluation results, and these measures are output in real time to the smart glasses, which are the visual display devices.

[0100] Based on the provided improvement guidelines, users can train efficiently and safely. For example, if their heart rate suddenly increases during training, the smart glasses will immediately display a warning to the user prompting them to take a break. Furthermore, it can suggest an optimal training plan for the user, taking into account their past exercise history and sleep data. This enables personalized health management.

[0101] An example of a prompt for a generating AI model might be, "Consider the user's recent exercise history and sleep quality, analyze their health status, and generate appropriate health improvement advice." Such a system would allow users to receive more personalized health guidance, which is expected to contribute to maintaining and improving their health.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The server acquires personal health-related information such as heart rate, steps taken, and sleep patterns from smart glasses. The input is real-time data from the sensors, which is stored in a database with high accuracy and preprocessed to link it with historical data. The output is formatted health data.

[0105] Step 2:

[0106] The server uses the acquired health-related information to evaluate the user's health status using an AI model. At this stage, it analyzes heart rate and past exercise data to assess the user's exercise load. The input is the data formatted in step 1, and the output is the evaluation result regarding the user's health status. Specifically, it checks whether the heart rate exceeds a certain threshold, for example.

[0107] Step 3:

[0108] Based on the evaluation results, the server uses a generation mechanism to formulate individual health improvement guidelines. At this stage, the generation AI model takes the prompt "Please suggest the optimal health improvement measures according to the user's health condition" as input and outputs detailed advice as if the user had received individual counseling. The results include information such as the need for rest and the recommended intensity of exercise.

[0109] Step 4:

[0110] The device displays the generated health improvement guidelines in real time on the smart glasses' display. The input is the health improvement guidelines provided by the server, and the output is health advice information that the user can visually confirm. Specifically, the information is overlaid in an easy-to-understand manner within the user's field of view.

[0111] Step 5:

[0112] Users receive advice presented via smart glasses and use it to manage their own health. The input is the advice information provided through the device, and the output is the user's actions to maintain and improve their health. Users take specific actions as needed, such as taking breaks or adjusting their exercise.

[0113] 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.

[0114] This invention incorporates an emotion engine into a system aimed at managing and improving health conditions, thereby comprehensively evaluating the user's health and emotional state and providing personalized improvement measures tailored to individual needs.

[0115] This system collects health-related information using the user's smartphone or wearable device. This includes heart rate, steps taken, sleep patterns, and emotional data such as the user's voice tone and facial expressions. This data is transmitted to a server in real time or at regular intervals.

[0116] The server centrally manages the received data and first cleans and formats it. This removes noise and missing data, making it suitable for analysis. Next, an AI model is applied to assess the user's health status and calculate a health score. In this process, the user's data is compared to the average for their age group to identify specific health risks and signs of presenteeism.

[0117] Furthermore, this system also uses an emotion engine to evaluate emotional states. The emotion engine utilizes voice analysis and facial recognition technology to grasp the user's emotional state in real time, and combines this with health assessments to provide a highly accurate overall evaluation of the user's condition.

[0118] The server generates personalized improvement plans for the user based on a comprehensive assessment of their health and emotional state. These plans include physical advice for maintaining health (e.g., exercise and dietary guidance) as well as mental health support for improving emotions (e.g., positive thinking and stress management techniques). The generated improvement plans are sent to the user's device, which then notifies the user in an intuitively understandable format. This may be presented as visually clear graphs or charts, or as voice notifications for guidance.

[0119] As a concrete example, consider a case where a user experiences anxiety on a daily basis. The emotion engine analyzes emotional data, and if it detects a high level of anxiety, the server suggests appropriate relaxation techniques and breathing exercises, which are then provided to the user as a guided view on their device. In this way, health and emotions are comprehensively managed, aiming to improve the user's quality of life.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] Users collect daily health data and emotional data through voice and facial expressions using smartphones and wearable devices. This data is transmitted from the device to a server.

[0123] Step 2:

[0124] The server receives the incoming data and performs data cleaning. This involves filling in missing data and removing outliers, preparing the data for accurate evaluation in the next step.

[0125] Step 3:

[0126] The server uses an AI model to analyze health data and calculate a user's health score. Based on this score, the user's health status is compared to the average of other people in the same age group.

[0127] Step 4:

[0128] The server uses an emotion engine to analyze emotional data. It evaluates the user's emotional state using voice tone and facial recognition to identify stress and anxiety levels.

[0129] Step 5:

[0130] The server comprehensively assesses both health and emotional states and generates health improvement strategies. These strategies include exercise guidance, dietary advice, and relaxation techniques.

[0131] Step 6:

[0132] The server sends the generated improvement suggestions to the user's device. The device displays the suggestions as a user interface or notification, making them easily accessible to the user.

[0133] Step 7:

[0134] Users can review the improvement suggestions received from the device and incorporate them into their daily lives. For example, they can practice relaxation techniques by following the device's guide.

[0135] Step 8:

[0136] The user inputs the results of the improvements they have made into their device as feedback and sends it back to the server. The server uses this to update the AI ​​model and improve the accuracy of personalized advice.

[0137] (Example 2)

[0138] 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".

[0139] In modern society, there is a need to comprehensively manage an individual's health and emotional state and provide personalized improvement measures. Conventional systems often focus on managing health status, lacking mechanisms for effectively evaluating and improving emotional state. As a result, there is a problem in that it is difficult to improve the overall quality of life of users.

[0140] 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.

[0141] In this invention, the server includes data collection means for collecting personal health-related information and emotional-related information; evaluation means for processing the health-related information and emotional-related information and comprehensively evaluating the health and emotional state; and generation means for generating individual health and emotional improvement measures based on the evaluation results of the health and emotional state obtained by the evaluation means. This makes it possible to comprehensively evaluate the user's health and emotional state and provide comprehensive improvement measures in a timely manner.

[0142] "Data collection means" refers to devices and systems used to acquire personal health-related and emotional-related information, including smartphones and wearable devices.

[0143] "Evaluation tools" refer to functions that process collected health-related and emotion-related information and comprehensively analyze an individual's health and emotional state.

[0144] "Generative means" refers to the function of creating individually tailored strategies to improve health and emotions based on evaluation results.

[0145] "Output means" refers to a mechanism for notifying the user of the generated improvement measures, and this includes visual display devices and voice notification devices.

[0146] An "emotion engine" refers to software or a system that uses voice analysis and facial recognition technology to evaluate an individual's emotional state in real time.

[0147] This invention is a system that comprehensively evaluates an individual's health and emotional state and provides solutions for improvement. Users collect health-related information such as heart rate, steps taken, and sleep patterns in their daily lives using smartphones and wearable devices. These devices also collect emotional data related to voice tone and facial expressions. This data is transmitted to a server by the device in real time or at regular intervals.

[0148] The server centrally manages the received data, performing data cleaning and noise reduction. An AI model is used to assess health status and calculate a health score. This AI model identifies potential health risks by comparing the user's data to the population average.

[0149] Simultaneously, the server uses an emotion engine to assess the user's emotional state. The emotion engine utilizes voice analysis and facial recognition technologies to analyze the user's emotions in real time. This assessment includes tone analysis from voice data and facial recognition using a camera.

[0150] Based on the assessment results of these health and emotional states, the server uses a generation AI module to generate personalized improvement plans. These plans include advice on exercise and diet, as well as stress management methods that focus on mental health. The generated improvement plans are sent to the terminal and notified to the user visually or audibly.

[0151] As a concrete example, consider a case where a user is experiencing chronic anxiety. When the emotion engine detects this anxiety, the server generates a guide on relaxation techniques and breathing exercises and presents it to the user on their device.

[0152] An example of a prompt message might be, "You appear to be feeling anxious. Please tell me some breathing techniques you can try to relax." In this way, the system comprehensively manages the user's health and emotions, improving their quality of life.

[0153] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0154] Step 1:

[0155] Users wear smartphones and wearable devices and collect health-related information while going about their daily lives. This includes heart rate, steps taken, and sleep patterns. The input is biometric data sensed by each device. The output is that these values ​​are temporarily stored in the device and ready to be transmitted later.

[0156] Step 2:

[0157] The device uses voice capture functionality and a camera to collect emotion-related information such as voice tone and facial expressions. Inputs include the user's voice data and video data of their facial expressions. Output is stored on the device as processed voice tone data and facial expression data.

[0158] Step 3:

[0159] The device transmits collected health-related and emotional data to the server. The input consists of all biometric and emotional data temporarily stored on the device. The data is encrypted using security technology, and the output is securely transmitted to the server.

[0160] Step 4:

[0161] The server cleans the received data, removing noise and filling in any missing data. The input is the raw data sent to the server. Data processing includes normalization and filtering, and the output is formatted data.

[0162] Step 5:

[0163] The server applies an AI model to assess health status using pre-processed data. The input is cleaned health-related data. This data is passed through a generating AI model, resulting in a health score as output. This score is compared to that of peers to analyze potential health risks.

[0164] Step 6:

[0165] The server uses an emotion engine to analyze voice and facial expression data and evaluate the user's emotional state. The input is cleaned emotion-related data. The output is the emotion evaluation result obtained from emotional tone and facial expression recognition.

[0166] Step 7:

[0167] The server generates improvement plans using generative AI technology based on assessments of health and emotional states. The inputs are the health score and emotional assessment results. The output is a set of specific improvement plans recommended to the user, which may manifest as suggestions for exercise, diet, or mental health guidance.

[0168] Step 8:

[0169] The terminal receives the generated improvement suggestions and notifies the user. The input is the improvement suggestion information sent from the server. The output is what is displayed or played to the user as a visual graph or audio notification.

[0170] (Application Example 2)

[0171] 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".

[0172] In modern society, comprehensively managing an individual's health and emotional state and providing appropriate improvement measures is becoming increasingly important. However, current systems tend to manage health data and emotional data separately, which can result in insufficient overall assessment and provision of improvement measures. Furthermore, there are few systems designed for use in retail environments, limiting opportunities for users to easily improve their health in their daily lives.

[0173] 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.

[0174] In this invention, the server includes information gathering means for collecting personal health and emotional information, evaluation means for processing the health and emotional information and evaluating the health and emotional state, generation means for generating individual health and emotional improvement measures based on the evaluation results obtained by the evaluation means, and display means for presenting improvement measures tailored to the individual through a display device in the store or a mobile terminal. This allows individual users to instantly grasp their own health and emotional state and easily take concrete actions to improve their health and emotional state in their daily lives.

[0175] "Information gathering means" refers to devices and methods for acquiring and storing information related to an individual's health and emotions.

[0176] "Evaluation means" refers to devices or methods that process collected health and emotional information and use that information to determine an individual's health and emotional state.

[0177] "Generative means" refers to methods and processes for creating individual health and emotional improvement measures from the results obtained through evaluation means.

[0178] "Output means" refers to devices or methods that inform the user of the generated improvement measures in the form of visuals, sounds, or other visual means.

[0179] "Display means" refers to devices or methods that visually show the generated improvement measures so that users can confirm them within the store.

[0180] "Methods of comparison" refer to methods or processes for evaluating an individual's health and emotional state by comparing their evaluation results with the average values ​​of other groups or specific criteria.

[0181] The system for implementing this invention is for the comprehensive management of an individual's health and emotional state. The server collects the user's health information (e.g., heart rate and steps) and emotional information (e.g., tone of voice and facial expressions) through information collection means. This information is obtained from smartphones and wearable devices and transmitted to the server in real time or at regular intervals.

[0182] Upon receiving this data, the server first cleans and formats it. It removes noise and missing data and converts it into a format suitable for analysis. Next, it uses evaluation tools to leverage AI models to assess the user's health and emotional state. This reveals where the user's condition stands compared to their peers and makes it possible to detect specific health risks and emotional anxieties.

[0183] The generation mechanism generates individual health and emotional improvement measures based on the evaluation results. These measures include physical actions (e.g., exercise guidance) and mental support (e.g., stress management techniques). The generated improvement measures are transmitted to the user's terminal via the output mechanism.

[0184] Depending on the display method, improvement measures are presented visually and audibly on in-store display devices or mobile terminals. A specific example is its use in a fitness gym. By having users in the gym wear heart rate sensors, their health status is evaluated in real time during exercise, and after certain exercises, suggestions for relaxation methods are displayed on the counter.

[0185] An example of a prompt for a generative AI model is: "Generate suggestions for the optimal relaxation method when the user's emotional state is unstable. Specific data will be provided in the form of voice tone and facial expression data." In this way, individual users can improve their health and emotional well-being in their daily lives by receiving appropriate health improvement measures.

[0186] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0187] Step 1:

[0188] The server acquires the user's health and emotional information through data collection methods. This information is obtained from smartphones and wearable devices and transmitted to the server in real time or periodically. Inputs include heart rate, step count, voice tone, and facial expression data, which are necessary for health and emotional assessment.

[0189] Step 2:

[0190] The server receives the raw data and performs data cleaning and formatting. It removes noise and missing data and converts it into a format suitable for algorithmic analysis. The cleaned data is then output, serving as the foundational data for subsequent health and emotional assessment processes.

[0191] Step 3:

[0192] The server analyzes cleaned data using evaluation tools and applies an AI model to assess health and emotional state. The input data is fed into the model, which outputs the user's health and emotional scores. By comparing these scores to the average for their age group, specific health risks and emotional anxieties are identified.

[0193] Step 4:

[0194] The server generates individual health and emotional improvement plans based on the evaluation results using a generation mechanism. These plans include specific advice that considers exercise guidance and stress management techniques. The generated improvement plans are output and become input data for subsequent notification processes.

[0195] Step 5:

[0196] The terminal displays improvement suggestions received from the server to the user using a display device. Advice is displayed visually or audibly on in-store display devices and mobile terminals to support users in easily understanding and implementing the suggestions.

[0197] Step 6:

[0198] Users choose actions based on the suggested improvement measures. For example, when receiving exercise instruction at a fitness gym, they can receive real-time feedback by wearing sensors. Based on this information, more effective health management can be achieved.

[0199] This system will enable users to monitor their health and emotional state on a daily basis and take appropriate action.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] [Second Embodiment]

[0204] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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".

[0216] This invention is a system for managing and improving an individual's health status, and is configured as follows: The user collects daily health data using a smartphone or wearable device. This data includes heart rate, steps taken, sleep patterns, meal logs, exercise history, etc. The collected data is transmitted to a server in real time or at a fixed frequency (e.g., daily).

[0217] The server centrally manages the received data and first performs data formatting and cleaning. Next, it initiates an analysis process using the data, applying an AI model to assess the user's health status. This assessment is performed by comparing the user's data with the average values ​​for their age group. This process identifies specific risks and signs of presenteeism by clarifying health indicators tailored to each individual user.

[0218] Based on the evaluation results, the server generates personalized health improvement plans for the user. These plans include determining whether a visit to a medical institution is necessary, and providing guidance on stretches, meditation, appropriate exercise, and diet that can be done in daily life. The generated advice is sent to the user's device as a text message, and the device presents it to the user in an easy-to-understand visual format.

[0219] For example, if a user is experiencing chronic fatigue, the server will identify the cause based on sleep data and exercise levels. If it determines that the user is sleep-deprived, the server will notify the user's device of the importance of going to bed early and provide advice on creating a suitable sleep environment.

[0220] Users apply the provided advice to their daily lives to improve their health. The results and feedback from implementing the improvements are sent back to the server, ensuring that a flexible and personalized health management system continues to function.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] Users collect health data such as heart rate, steps taken, and sleep patterns using wearable devices and smartphones. This data is transmitted to a server via the device.

[0224] Step 2:

[0225] The server receives the transmitted health data and performs data formatting and cleaning. It removes incomplete data, standardizes values, and converts the data into the format required for evaluation.

[0226] Step 3:

[0227] The server applies an AI model to the formatted data and calculates an individual health score. This score indicates the user's health status and allows for comparison with others in the same age group.

[0228] Step 4:

[0229] The server uses the output of the AI ​​model to detect anomalies in the user's health status and signs of presenteeism. It generates an alert if specific health risks are detected.

[0230] Step 5:

[0231] Based on the detected results, the server generates health improvement measures tailored to the user. This includes determining whether lifestyle changes are necessary or if a visit to a medical institution is required.

[0232] Step 6:

[0233] The server sends the generated health improvement suggestions to the terminal, and the terminal notifies the user of this information. The suggestions are presented to the user using a visually easy-to-understand dashboard and notification messages.

[0234] Step 7:

[0235] Users review the suggestions they receive and incorporate health improvement measures into their daily lives. They can also send feedback on the results of their implementation to the server via their device.

[0236] Step 8:

[0237] The server analyzes user feedback and new health data to update the accuracy of the AI ​​model. This will enable it to provide more personalized advice in the future.

[0238] (Example 1)

[0239] 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."

[0240] Conventional health management systems struggle to accurately analyze individual health data and provide personalized health improvement strategies. Furthermore, the lack of comparisons using average data for the same age group can lead to insufficient identification of abnormalities and risks. Additionally, there are limited methods for presenting advice based on acquired health data in a way that users can implement in their daily lives. These problems need to be addressed.

[0241] 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.

[0242] In this invention, the server includes information acquisition means for collecting individual biometric data, information processing means for converting the biometric data into a unified format and correcting abnormal values, and state evaluation means for analyzing the biometric data and evaluating the health status using artificial intelligence. This makes it possible to perform precise data analysis to evaluate the health status of individuals and propose appropriate health improvement measures based on that evaluation.

[0243] "Information acquisition means" refers to a device or function for automatically collecting an individual's biometric data.

[0244] "Information processing means" refers to a function that converts acquired biometric data into a unified format and performs processing to correct abnormal values ​​within the data.

[0245] "Condition evaluation means" refers to a function that analyzes biometric data and uses artificial intelligence to evaluate an individual's health status.

[0246] The "proposal generation means" refers to a function that generates individually tailored health improvement measures based on the evaluation results from the condition evaluation means.

[0247] "Presentation means" refers to a function for displaying the generated health improvement measures on the user's communication terminal.

[0248] "Comparative processing" refers to a method of evaluating an individual by comparing their data with standard data from a group of people of the same age.

[0249] This invention is a system that provides personalized health improvement measures by collecting and analyzing biological data. This system mainly consists of a user terminal and a server, each with clearly defined roles. The following describes a specific embodiment of this system.

[0250] Users collect daily biometric data using smartphones and wearable devices. These devices have built-in heart rate sensors, accelerometers, GPS functionality, and other features to automatically record heart rate, steps, distance traveled, sleep patterns, and food intake. The data is transferred to the smartphone using Bluetooth or Wi-Fi.

[0251] The terminal provides an interface for sending collected data to the server. During this process, the data is encrypted and securely transmitted to the server. For example, it's possible to configure the system to send data in batches overnight, reducing the user's communication burden.

[0252] The server centrally manages and processes the received data. The software used includes a database management system, data cleaning tools, and generative AI models. Programming languages ​​such as Python and R are used for data shaping and cleaning, including the removal of outliers and data imputation. Generative AI models are applied to assess each user's health status and calculate risk by comparing it to average data for their age group.

[0253] Based on the evaluation results, the server generates individual health improvement measures using a suggestion generation mechanism. The submitted improvement measures are visually presented to the user on the terminal. The use of a graphical user interface makes the advice easy to understand intuitively.

[0254] For example, if a user inputs a prompt such as, "I've been feeling really tired lately, how can I improve this?" into the AI, the AI ​​will identify the cause of the fatigue from past sleep and heart rate data and provide specific advice for improvement.

[0255] This will enable users to make improvements that are more relevant to their daily lives, resulting in enhanced, personalized health management.

[0256] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0257] Step 1:

[0258] Users collect biometric data using smartphones and wearable devices. This data includes heart rate, steps taken, sleep patterns, meal logs, and exercise history. The data is transferred to the smartphone via Bluetooth or Wi-Fi. Input data is raw values ​​from sensors, while output is raw data stored on the smartphone. For example, if a user uses a pedometer throughout the day, the number of steps and data broken down by time of day will be collected.

[0259] Step 2:

[0260] The device sends the collected data to the server. Specifically, the smartphone securely transfers the data to the endpoint using an encryption protocol. The input is the raw data stored on the device, and the output is the unformatted data sent to the server. During this process, a process is carried out to prevent the accidental transmission of data. For example, depending on the communication environment, data transmission may be temporarily suspended and sent only when stable communication can be ensured.

[0261] Step 3:

[0262] The server formats and cleans the received data. Here, Python or R programs are used to detect and correct outliers and to impart missing data. The input is the raw data before formatting, and the output is the formatted and cleaned data. For example, if there are outliers in heart rate data, they are corrected as outliers exceeding a predefined range.

[0263] Step 4:

[0264] The server applies a generative AI model using the formatted data to evaluate the user's health status. Here, the data to be analyzed is input into the AI ​​model, and health status scoring and risk assessment are performed. The input is formatted data, and the output is a health assessment report. As a specific example, fluctuations in stress levels are reported based on the user's heart rate data over the past week.

[0265] Step 5:

[0266] The server generates health improvement measures based on the obtained health assessment. This involves a combination of data analysis results and user cases obtained through balance mining to generate suggestions. The input is the health assessment report, and the output is the generated improvement measures. For example, if a lack of exercise is detected, an individualized exercise plan is formulated.

[0267] Step 6:

[0268] The terminal visually presents the improvement suggestions received from the server to the user. Using a designed interface, it clearly displays the suggestions with animations and graphs. The input is the generated improvement suggestions, and the output is a visualization of those suggestions displayed to the user. For example, recommended stretches are presented with illustrations.

[0269] Step 7:

[0270] Users improve their lifestyle habits according to the provided advice and input the results as feedback into the device. The device then sends this data back to the server, where it is used for the next data analysis. The input is the user's feedback data, and the output is the new dataset sent to the server. For example, the system can record the results of a user's exercise plan, which will then be used to inform the next set of advice.

[0271] (Application Example 1)

[0272] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0273] In modern society, the importance of individual health management is increasing. However, traditional health management methods have difficulty providing personalized, real-time advice, and there is a particular challenge in obtaining health indicators and improvement measures that can be immediately used in exercise facilities and daily life.

[0274] 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.

[0275] In this invention, the server includes data acquisition means for collecting personal health-related information, evaluation means for processing the health-related information and evaluating the health status, generation means for providing individual health improvement guidelines based on the evaluation results obtained by the evaluation means, and presentation means for outputting the generated guidelines to a visual display device in real time. This makes it possible to provide real-time and personalized health advice based on the individual's health status.

[0276] "Health-related information" refers to data consisting of an individual's exercise history, heart rate, steps taken, sleep patterns, and dietary logs, and is used to evaluate an individual's health status.

[0277] "Data acquisition means" refers to a device or software that has the function of collecting personal health-related information.

[0278] "Evaluation means" refers to a device or software used for the purpose of processing collected health-related information and analyzing and evaluating an individual's health status.

[0279] "Generation means" refers to a device or software that provides individual health improvement guidelines based on evaluation results obtained by the evaluation means.

[0280] A "visual display device" is a device used to visually display generated health improvement guidelines to the user, and mainly includes smart glasses and screens.

[0281] "Real-time" refers to the ability to process information instantly and provide the results to users quickly, meaning that data is available in a timely manner without delay.

[0282] The server uses cloud-based data acquisition means to obtain health-related information from smart glasses worn by individuals in fitness facilities or at home. This information includes heart rate, number of steps, sleep status, etc., and is collected in real time from various sensors. The server formats and cleans this data and analyzes the user's health status using a powerful AI model as an evaluation means. Machine learning software such as TensorFlow is used for the analysis to learn the patterns of the data and generate individual health improvement guidelines. The generation means formulates health improvement measures based on the evaluation results, and these improvement measures are output in real time to the smart glasses, which are visual display devices.

[0283] Based on the presented improvement guidelines, users can train efficiently and safely. For example, if the heart rate suddenly increases during training, the smart glasses immediately display a warning prompting the user to take a break. It is also possible to propose an optimal training plan for the user considering past exercise history and sleep data. This enables individually optimized health management.

[0284] Examples of prompt sentences for the generation AI model include "Please analyze the user's health status considering the recent exercise history and sleep quality and generate appropriate health improvement advice." With such a system, users can receive more personalized health guidance and are expected to contribute to maintaining and improving their health.

[0285] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0286] Step 1:

[0287] The server obtains personal health-related information such as heart rate, number of steps, and sleep status from the smart glasses. The input is real-time data from the sensors, which is pre-processed by accurately storing it in the database and associating it with past data. The output is formatted health data.

[0288] Step 2:

[0289] The server uses the acquired health-related information to evaluate the user's health status using an AI model. At this stage, it analyzes heart rate and past exercise data to assess the user's exercise load. The input is the data formatted in step 1, and the output is the evaluation result regarding the user's health status. Specifically, it checks whether the heart rate exceeds a certain threshold, for example.

[0290] Step 3:

[0291] Based on the evaluation results, the server uses a generation mechanism to formulate individual health improvement guidelines. At this stage, the generation AI model takes the prompt "Please suggest the optimal health improvement measures according to the user's health condition" as input and outputs detailed advice as if the user had received individual counseling. The results include information such as the need for rest and the recommended intensity of exercise.

[0292] Step 4:

[0293] The device displays the generated health improvement guidelines in real time on the smart glasses' display. The input is the health improvement guidelines provided by the server, and the output is health advice information that the user can visually confirm. Specifically, the information is overlaid in an easy-to-understand manner within the user's field of view.

[0294] Step 5:

[0295] Users receive advice presented via smart glasses and use it to manage their own health. The input is the advice information provided through the device, and the output is the user's actions to maintain and improve their health. Users take specific actions as needed, such as taking breaks or adjusting their exercise.

[0296] 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.

[0297] This invention incorporates an emotion engine into a system aimed at managing and improving health conditions, thereby comprehensively evaluating the user's health and emotional state and providing personalized improvement measures tailored to individual needs.

[0298] This system collects health-related information using the user's smartphone or wearable device. This includes heart rate, steps taken, sleep patterns, and emotional data such as the user's voice tone and facial expressions. This data is transmitted to a server in real time or at regular intervals.

[0299] The server centrally manages the received data and first cleans and formats it. This removes noise and missing data, making it suitable for analysis. Next, an AI model is applied to assess the user's health status and calculate a health score. In this process, the user's data is compared to the average for their age group to identify specific health risks and signs of presenteeism.

[0300] Furthermore, this system also uses an emotion engine to evaluate emotional states. The emotion engine utilizes voice analysis and facial recognition technology to grasp the user's emotional state in real time, and combines this with health assessments to provide a highly accurate overall evaluation of the user's condition.

[0301] Based on the comprehensive evaluation results of the health status and emotional state, the server generates improvement measures suitable for the user. This includes not only physical advice for maintaining health (e.g., exercise and diet guidance), but also mental health support for improving emotions (e.g., practicing positive thinking and stress management methods). The generated improvement measures are sent to the user's terminal, and the terminal notifies the user in a form that is intuitive and easy to understand. For example, it is presented in the form of visually understandable graphs and charts, or voice notifications as guidance.

[0302] As a specific example, consider the case where a user feels anxious on a daily basis. When the emotion engine analyzes the emotion data and detects a high level of anxiety, the server proposes appropriate relaxation and breathing methods and provides them to the user as a guided view on the terminal. In this way, it aims to comprehensively manage health and emotions and improve the user's quality of life.

[0303] The following describes the processing flow.

[0304] Step 1:

[0305] The user uses a smartphone or wearable device to collect daily health data and emotion data through voice and expressions. These data are sent from the terminal to the server.

[0306] Step 2:

[0307] The server receives the received data and performs data cleaning. This is to complement missing data and remove outliers, preparing for accurate evaluation in the next step.

[0308] Step 3:

[0309] The server analyzes the health data using an AI model and calculates the user's health score. Based on this score, the user's health status is compared with the average of other people of the same generation.

[0310] Step 4:

[0311] The server uses an emotion engine to analyze emotional data. It evaluates the user's emotional state using voice tone and facial recognition to identify stress and anxiety levels.

[0312] Step 5:

[0313] The server comprehensively assesses both health and emotional states and generates health improvement strategies. These strategies include exercise guidance, dietary advice, and relaxation techniques.

[0314] Step 6:

[0315] The server sends the generated improvement suggestions to the user's device. The device displays the suggestions as a user interface or notification, making them easily accessible to the user.

[0316] Step 7:

[0317] Users can review the improvement suggestions received from the device and incorporate them into their daily lives. For example, they can practice relaxation techniques by following the device's guide.

[0318] Step 8:

[0319] The user inputs the results of the improvements they have made into their device as feedback and sends it back to the server. The server uses this to update the AI ​​model and improve the accuracy of personalized advice.

[0320] (Example 2)

[0321] 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".

[0322] In modern society, there is a need to comprehensively manage an individual's health and emotional state and provide personalized improvement measures. Conventional systems often focus on managing health status, lacking mechanisms for effectively evaluating and improving emotional state. As a result, there is a problem in that it is difficult to improve the overall quality of life of users.

[0323] 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.

[0324] In this invention, the server includes data collection means for collecting personal health-related information and emotional-related information; evaluation means for processing the health-related information and emotional-related information and comprehensively evaluating the health and emotional state; and generation means for generating individual health and emotional improvement measures based on the evaluation results of the health and emotional state obtained by the evaluation means. This makes it possible to comprehensively evaluate the user's health and emotional state and provide comprehensive improvement measures in a timely manner.

[0325] "Data collection means" refers to devices and systems used to acquire personal health-related and emotional-related information, including smartphones and wearable devices.

[0326] "Evaluation tools" refer to functions that process collected health-related and emotion-related information and comprehensively analyze an individual's health and emotional state.

[0327] "Generative means" refers to the function of creating individually tailored strategies to improve health and emotions based on evaluation results.

[0328] "Output means" refers to a mechanism for notifying the user of the generated improvement measures, and this includes visual display devices and voice notification devices.

[0329] An "emotion engine" refers to software or a system that uses voice analysis and facial recognition technology to evaluate an individual's emotional state in real time.

[0330] This invention is a system that comprehensively evaluates an individual's health and emotional state and provides solutions for improvement. Users collect health-related information such as heart rate, steps taken, and sleep patterns in their daily lives using smartphones and wearable devices. These devices also collect emotional data related to voice tone and facial expressions. This data is transmitted to a server by the device in real time or at regular intervals.

[0331] The server centrally manages the received data, performing data cleaning and noise reduction. An AI model is used to assess health status and calculate a health score. This AI model identifies potential health risks by comparing the user's data to the population average.

[0332] Simultaneously, the server uses an emotion engine to assess the user's emotional state. The emotion engine utilizes voice analysis and facial recognition technologies to analyze the user's emotions in real time. This assessment includes tone analysis from voice data and facial recognition using a camera.

[0333] Based on the assessment results of these health and emotional states, the server uses a generation AI module to generate personalized improvement plans. These plans include advice on exercise and diet, as well as stress management methods that focus on mental health. The generated improvement plans are sent to the terminal and notified to the user visually or audibly.

[0334] As a concrete example, consider a case where a user is experiencing chronic anxiety. When the emotion engine detects this anxiety, the server generates a guide on relaxation techniques and breathing exercises and presents it to the user on their device.

[0335] An example of a prompt message might be, "You appear to be feeling anxious. Please tell me some breathing techniques you can try to relax." In this way, the system comprehensively manages the user's health and emotions, improving their quality of life.

[0336] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0337] Step 1:

[0338] Users wear smartphones and wearable devices and collect health-related information while going about their daily lives. This includes heart rate, steps taken, and sleep patterns. The input is biometric data sensed by each device. The output is that these values ​​are temporarily stored in the device and ready to be transmitted later.

[0339] Step 2:

[0340] The device uses voice capture functionality and a camera to collect emotion-related information such as voice tone and facial expressions. Inputs include the user's voice data and video data of their facial expressions. Output is stored on the device as processed voice tone data and facial expression data.

[0341] Step 3:

[0342] The device transmits collected health-related and emotional data to the server. The input consists of all biometric and emotional data temporarily stored on the device. The data is encrypted using security technology, and the output is securely transmitted to the server.

[0343] Step 4:

[0344] The server cleans the received data, removing noise and filling in any missing data. The input is the raw data sent to the server. Data processing includes normalization and filtering, and the output is formatted data.

[0345] Step 5:

[0346] The server applies an AI model to assess health status using pre-processed data. The input is cleaned health-related data. This data is passed through a generating AI model, resulting in a health score as output. This score is compared to that of peers to analyze potential health risks.

[0347] Step 6:

[0348] The server uses an emotion engine to analyze voice and facial expression data and evaluate the user's emotional state. The input is cleaned emotion-related data. The output is the emotion evaluation result obtained from emotional tone and facial expression recognition.

[0349] Step 7:

[0350] The server generates improvement plans using generative AI technology based on assessments of health and emotional states. The inputs are the health score and emotional assessment results. The output is a set of specific improvement plans recommended to the user, which may manifest as suggestions for exercise, diet, or mental health guidance.

[0351] Step 8:

[0352] The terminal receives the generated improvement suggestions and notifies the user. The input is the improvement suggestion information sent from the server. The output is what is displayed or played to the user as a visual graph or audio notification.

[0353] (Application Example 2)

[0354] 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".

[0355] In modern society, comprehensively managing an individual's health and emotional state and providing appropriate improvement measures is becoming increasingly important. However, current systems tend to manage health data and emotional data separately, which can result in insufficient overall assessment and provision of improvement measures. Furthermore, there are few systems designed for use in retail environments, limiting opportunities for users to easily improve their health in their daily lives.

[0356] 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.

[0357] In this invention, the server includes information gathering means for collecting personal health and emotional information, evaluation means for processing the health and emotional information and evaluating the health and emotional state, generation means for generating individual health and emotional improvement measures based on the evaluation results obtained by the evaluation means, and display means for presenting improvement measures tailored to the individual through a display device in the store or a mobile terminal. This allows individual users to instantly grasp their own health and emotional state and easily take concrete actions to improve their health and emotional state in their daily lives.

[0358] "Information gathering means" refers to devices and methods for acquiring and storing information related to an individual's health and emotions.

[0359] "Evaluation means" refers to devices or methods that process collected health and emotional information and use that information to determine an individual's health and emotional state.

[0360] "Generative means" refers to methods and processes for creating individual health and emotional improvement measures from the results obtained through evaluation means.

[0361] "Output means" refers to devices or methods that inform the user of the generated improvement measures in the form of visuals, sounds, or other visual means.

[0362] "Display means" refers to devices or methods that visually show the generated improvement measures so that users can confirm them within the store.

[0363] "Methods of comparison" refer to methods or processes for evaluating an individual's health and emotional state by comparing their evaluation results with the average values ​​of other groups or specific criteria.

[0364] The system for implementing this invention is for the comprehensive management of an individual's health and emotional state. The server collects the user's health information (e.g., heart rate and steps) and emotional information (e.g., tone of voice and facial expressions) through information collection means. This information is obtained from smartphones and wearable devices and transmitted to the server in real time or at regular intervals.

[0365] Upon receiving this data, the server first cleans and formats it. It removes noise and missing data and converts it into a format suitable for analysis. Next, it uses evaluation tools to leverage AI models to assess the user's health and emotional state. This reveals where the user's condition stands compared to their peers and makes it possible to detect specific health risks and emotional anxieties.

[0366] The generation mechanism generates individual health and emotional improvement measures based on the evaluation results. These measures include physical actions (e.g., exercise guidance) and mental support (e.g., stress management techniques). The generated improvement measures are transmitted to the user's terminal via the output mechanism.

[0367] Depending on the display method, improvement measures are presented visually and audibly on in-store display devices or mobile terminals. A specific example is its use in a fitness gym. By having users in the gym wear heart rate sensors, their health status is evaluated in real time during exercise, and after certain exercises, suggestions for relaxation methods are displayed on the counter.

[0368] An example of a prompt for a generative AI model is: "Generate suggestions for the optimal relaxation method when the user's emotional state is unstable. Specific data will be provided in the form of voice tone and facial expression data." In this way, individual users can improve their health and emotional well-being in their daily lives by receiving appropriate health improvement measures.

[0369] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0370] Step 1:

[0371] The server acquires the user's health and emotional information through data collection methods. This information is obtained from smartphones and wearable devices and transmitted to the server in real time or periodically. Inputs include heart rate, step count, voice tone, and facial expression data, which are necessary for health and emotional assessment.

[0372] Step 2:

[0373] The server receives the raw data and performs data cleaning and formatting. It removes noise and missing data and converts it into a format suitable for algorithmic analysis. The cleaned data is then output, serving as the foundational data for subsequent health and emotional assessment processes.

[0374] Step 3:

[0375] The server analyzes cleaned data using evaluation tools and applies an AI model to assess health and emotional state. The input data is fed into the model, which outputs the user's health and emotional scores. By comparing these scores to the average for their age group, specific health risks and emotional anxieties are identified.

[0376] Step 4:

[0377] The server generates individual health and emotional improvement plans based on the evaluation results using a generation mechanism. These plans include specific advice that considers exercise guidance and stress management techniques. The generated improvement plans are output and become input data for subsequent notification processes.

[0378] Step 5:

[0379] The terminal displays improvement suggestions received from the server to the user using a display device. Advice is displayed visually or audibly on in-store display devices and mobile terminals to support users in easily understanding and implementing the suggestions.

[0380] Step 6:

[0381] Users choose actions based on the suggested improvement measures. For example, when receiving exercise instruction at a fitness gym, they can receive real-time feedback by wearing sensors. Based on this information, more effective health management can be achieved.

[0382] This system will enable users to monitor their health and emotional state on a daily basis and take appropriate action.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] [Third Embodiment]

[0387] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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".

[0399] This invention is a system for managing and improving an individual's health status, and is configured as follows: The user collects daily health data using a smartphone or wearable device. This data includes heart rate, steps taken, sleep patterns, meal logs, exercise history, etc. The collected data is transmitted to a server in real time or at a fixed frequency (e.g., daily).

[0400] The server centrally manages the received data and first performs data formatting and cleaning. Next, it initiates an analysis process using the data, applying an AI model to assess the user's health status. This assessment is performed by comparing the user's data with the average values ​​for their age group. This process identifies specific risks and signs of presenteeism by clarifying health indicators tailored to each individual user.

[0401] Based on the evaluation results, the server generates personalized health improvement plans for the user. These plans include determining whether a visit to a medical institution is necessary, and providing guidance on stretches, meditation, appropriate exercise, and diet that can be done in daily life. The generated advice is sent to the user's device as a text message, and the device presents it to the user in an easy-to-understand visual format.

[0402] For example, if a user is experiencing chronic fatigue, the server will identify the cause based on sleep data and exercise levels. If it determines that the user is sleep-deprived, the server will notify the user's device of the importance of going to bed early and provide advice on creating a suitable sleep environment.

[0403] Users apply the provided advice to their daily lives to improve their health. The results and feedback from implementing the improvements are sent back to the server, ensuring that a flexible and personalized health management system continues to function.

[0404] The following describes the processing flow.

[0405] Step 1:

[0406] Users collect health data such as heart rate, steps taken, and sleep patterns using wearable devices and smartphones. This data is transmitted to a server via the device.

[0407] Step 2:

[0408] The server receives the transmitted health data and performs data formatting and cleaning. It removes incomplete data, standardizes values, and converts the data into the format required for evaluation.

[0409] Step 3:

[0410] The server applies an AI model to the formatted data and calculates an individual health score. This score indicates the user's health status and allows for comparison with others in the same age group.

[0411] Step 4:

[0412] The server uses the output of the AI ​​model to detect anomalies in the user's health status and signs of presenteeism. It generates an alert if specific health risks are detected.

[0413] Step 5:

[0414] Based on the detected results, the server generates health improvement measures tailored to the user. This includes determining whether lifestyle changes are necessary or if a visit to a medical institution is required.

[0415] Step 6:

[0416] The server sends the generated health improvement suggestions to the terminal, and the terminal notifies the user of this information. The suggestions are presented to the user using a visually easy-to-understand dashboard and notification messages.

[0417] Step 7:

[0418] Users review the suggestions they receive and incorporate health improvement measures into their daily lives. They can also send feedback on the results of their implementation to the server via their device.

[0419] Step 8:

[0420] The server analyzes user feedback and new health data to update the accuracy of the AI ​​model. This will enable it to provide more personalized advice in the future.

[0421] (Example 1)

[0422] 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."

[0423] Conventional health management systems struggle to accurately analyze individual health data and provide personalized health improvement strategies. Furthermore, the lack of comparisons using average data for the same age group can lead to insufficient identification of abnormalities and risks. Additionally, there are limited methods for presenting advice based on acquired health data in a way that users can implement in their daily lives. These problems need to be addressed.

[0424] 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.

[0425] In this invention, the server includes information acquisition means for collecting individual biometric data, information processing means for converting the biometric data into a unified format and correcting abnormal values, and state evaluation means for analyzing the biometric data and evaluating the health status using artificial intelligence. This makes it possible to perform precise data analysis to evaluate the health status of individuals and propose appropriate health improvement measures based on that evaluation.

[0426] "Information acquisition means" refers to a device or function for automatically collecting an individual's biometric data.

[0427] "Information processing means" refers to a function that converts acquired biometric data into a unified format and performs processing to correct abnormal values ​​within the data.

[0428] "Condition evaluation means" refers to a function that analyzes biometric data and uses artificial intelligence to evaluate an individual's health status.

[0429] The "proposal generation means" refers to a function that generates individually tailored health improvement measures based on the evaluation results from the condition evaluation means.

[0430] "Presentation means" refers to a function for displaying the generated health improvement measures on the user's communication terminal.

[0431] "Comparative processing" refers to a method of evaluating an individual by comparing their data with standard data from a group of people of the same age.

[0432] This invention is a system that provides personalized health improvement measures by collecting and analyzing biological data. This system mainly consists of a user terminal and a server, each with clearly defined roles. The following describes a specific embodiment of this system.

[0433] Users collect daily biometric data using smartphones and wearable devices. These devices have built-in heart rate sensors, accelerometers, GPS functionality, and other features to automatically record heart rate, steps, distance traveled, sleep patterns, and food intake. The data is transferred to the smartphone using Bluetooth or Wi-Fi.

[0434] The terminal provides an interface for sending collected data to the server. During this process, the data is encrypted and securely transmitted to the server. For example, it's possible to configure the system to send data in batches overnight, reducing the user's communication burden.

[0435] The server centrally manages and processes the received data. The software used includes a database management system, data cleaning tools, and generative AI models. Programming languages ​​such as Python and R are used for data shaping and cleaning, including the removal of outliers and data imputation. Generative AI models are applied to assess each user's health status and calculate risk by comparing it to average data for their age group.

[0436] Based on the evaluation results, the server generates individual health improvement measures using a suggestion generation mechanism. The submitted improvement measures are visually presented to the user on the terminal. The use of a graphical user interface makes the advice easy to understand intuitively.

[0437] For example, if a user inputs a prompt such as, "I've been feeling really tired lately, how can I improve this?" into the AI, the AI ​​will identify the cause of the fatigue from past sleep and heart rate data and provide specific advice for improvement.

[0438] This will enable users to make improvements that are more relevant to their daily lives, resulting in enhanced, personalized health management.

[0439] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0440] Step 1:

[0441] Users collect biometric data using smartphones and wearable devices. This data includes heart rate, steps taken, sleep patterns, meal logs, and exercise history. The data is transferred to the smartphone via Bluetooth or Wi-Fi. Input data is raw values ​​from sensors, while output is raw data stored on the smartphone. For example, if a user uses a pedometer throughout the day, the number of steps and data broken down by time of day will be collected.

[0442] Step 2:

[0443] The device sends the collected data to the server. Specifically, the smartphone securely transfers the data to the endpoint using an encryption protocol. The input is the raw data stored on the device, and the output is the unformatted data sent to the server. During this process, a process is carried out to prevent the accidental transmission of data. For example, depending on the communication environment, data transmission may be temporarily suspended and sent only when stable communication can be ensured.

[0444] Step 3:

[0445] The server formats and cleans the received data. Here, Python or R programs are used to detect and correct outliers and to impart missing data. The input is the raw data before formatting, and the output is the formatted and cleaned data. For example, if there are outliers in heart rate data, they are corrected as outliers exceeding a predefined range.

[0446] Step 4:

[0447] The server applies a generative AI model using the formatted data to evaluate the user's health status. Here, the data to be analyzed is input into the AI ​​model, and health status scoring and risk assessment are performed. The input is formatted data, and the output is a health assessment report. As a specific example, fluctuations in stress levels are reported based on the user's heart rate data over the past week.

[0448] Step 5:

[0449] The server generates health improvement measures based on the obtained health assessment. This involves a combination of data analysis results and user cases obtained through balance mining to generate suggestions. The input is the health assessment report, and the output is the generated improvement measures. For example, if a lack of exercise is detected, an individualized exercise plan is formulated.

[0450] Step 6:

[0451] The terminal visually presents the improvement suggestions received from the server to the user. Using a designed interface, it clearly displays the suggestions with animations and graphs. The input is the generated improvement suggestions, and the output is a visualization of those suggestions displayed to the user. For example, recommended stretches are presented with illustrations.

[0452] Step 7:

[0453] Users improve their lifestyle habits according to the provided advice and input the results as feedback into the device. The device then sends this data back to the server, where it is used for the next data analysis. The input is the user's feedback data, and the output is the new dataset sent to the server. For example, the system can record the results of a user's exercise plan, which will then be used to inform the next set of advice.

[0454] (Application Example 1)

[0455] 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."

[0456] In modern society, the importance of individual health management is increasing. However, traditional health management methods have difficulty providing personalized, real-time advice, and there is a particular challenge in obtaining health indicators and improvement measures that can be immediately used in exercise facilities and daily life.

[0457] 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.

[0458] In this invention, the server includes data acquisition means for collecting personal health-related information, evaluation means for processing the health-related information and evaluating the health status, generation means for providing individual health improvement guidelines based on the evaluation results obtained by the evaluation means, and presentation means for outputting the generated guidelines to a visual display device in real time. This makes it possible to provide real-time and personalized health advice based on the individual's health status.

[0459] "Health-related information" refers to data consisting of an individual's exercise history, heart rate, steps taken, sleep patterns, and dietary logs, and is used to evaluate an individual's health status.

[0460] "Data acquisition means" refers to a device or software that has the function of collecting personal health-related information.

[0461] "Evaluation means" refers to a device or software used for the purpose of processing collected health-related information and analyzing and evaluating an individual's health status.

[0462] "Generation means" refers to a device or software that provides individual health improvement guidelines based on evaluation results obtained by the evaluation means.

[0463] A "visual display device" is a device used to visually display generated health improvement guidelines to the user, and mainly includes smart glasses and screens.

[0464] "Real-time" refers to the ability to process information instantly and provide the results to users quickly, meaning that data is available in a timely manner without delay.

[0465] The server uses cloud-based data acquisition methods to obtain health-related information from smart glasses worn by individuals in fitness facilities and at home. This information includes heart rate, steps taken, sleep patterns, etc., and is collected in real time from various sensors. The server formats and cleans this data and analyzes the user's health status using a powerful AI model as an evaluation tool. Machine learning software such as TensorFlow is used for the analysis, learning data patterns and generating individualized health improvement guidelines. The generation tool formulates health improvement measures based on the evaluation results, and these measures are output in real time to the smart glasses, which are the visual display devices.

[0466] Based on the provided improvement guidelines, users can train efficiently and safely. For example, if their heart rate suddenly increases during training, the smart glasses will immediately display a warning to the user prompting them to take a break. Furthermore, it can suggest an optimal training plan for the user, taking into account their past exercise history and sleep data. This enables personalized health management.

[0467] An example of a prompt for a generating AI model might be, "Consider the user's recent exercise history and sleep quality, analyze their health status, and generate appropriate health improvement advice." Such a system would allow users to receive more personalized health guidance, which is expected to contribute to maintaining and improving their health.

[0468] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0469] Step 1:

[0470] The server acquires personal health-related information such as heart rate, steps taken, and sleep patterns from smart glasses. The input is real-time data from the sensors, which is stored in a database with high accuracy and preprocessed to link it with historical data. The output is formatted health data.

[0471] Step 2:

[0472] The server uses the acquired health-related information to evaluate the user's health status using an AI model. At this stage, it analyzes heart rate and past exercise data to assess the user's exercise load. The input is the data formatted in step 1, and the output is the evaluation result regarding the user's health status. Specifically, it checks whether the heart rate exceeds a certain threshold, for example.

[0473] Step 3:

[0474] Based on the evaluation results, the server uses a generation mechanism to formulate individual health improvement guidelines. At this stage, the generation AI model takes the prompt "Please suggest the optimal health improvement measures according to the user's health condition" as input and outputs detailed advice as if the user had received individual counseling. The results include information such as the need for rest and the recommended intensity of exercise.

[0475] Step 4:

[0476] The device displays the generated health improvement guidelines in real time on the smart glasses' display. The input is the health improvement guidelines provided by the server, and the output is health advice information that the user can visually confirm. Specifically, the information is overlaid in an easy-to-understand manner within the user's field of view.

[0477] Step 5:

[0478] Users receive advice presented via smart glasses and use it to manage their own health. The input is the advice information provided through the device, and the output is the user's actions to maintain and improve their health. Users take specific actions as needed, such as taking breaks or adjusting their exercise.

[0479] 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.

[0480] This invention incorporates an emotion engine into a system aimed at managing and improving health conditions, thereby comprehensively evaluating the user's health and emotional state and providing personalized improvement measures tailored to individual needs.

[0481] This system collects health-related information using the user's smartphone or wearable device. This includes heart rate, steps taken, sleep patterns, and emotional data such as the user's voice tone and facial expressions. This data is transmitted to a server in real time or at regular intervals.

[0482] The server centrally manages the received data and first cleans and formats it. This removes noise and missing data, making it suitable for analysis. Next, an AI model is applied to assess the user's health status and calculate a health score. In this process, the user's data is compared to the average for their age group to identify specific health risks and signs of presenteeism.

[0483] Furthermore, this system also uses an emotion engine to evaluate emotional states. The emotion engine utilizes voice analysis and facial recognition technology to grasp the user's emotional state in real time, and combines this with health assessments to provide a highly accurate overall evaluation of the user's condition.

[0484] The server generates personalized improvement plans for the user based on a comprehensive assessment of their health and emotional state. These plans include physical advice for maintaining health (e.g., exercise and dietary guidance) as well as mental health support for improving emotions (e.g., positive thinking and stress management techniques). The generated improvement plans are sent to the user's device, which then notifies the user in an intuitively understandable format. This may be presented as visually clear graphs or charts, or as voice notifications for guidance.

[0485] As a concrete example, consider a case where a user experiences anxiety on a daily basis. The emotion engine analyzes emotional data, and if it detects a high level of anxiety, the server suggests appropriate relaxation techniques and breathing exercises, which are then provided to the user as a guided view on their device. In this way, health and emotions are comprehensively managed, aiming to improve the user's quality of life.

[0486] The following describes the processing flow.

[0487] Step 1:

[0488] Users collect daily health data and emotional data through voice and facial expressions using smartphones and wearable devices. This data is transmitted from the device to a server.

[0489] Step 2:

[0490] The server receives the incoming data and performs data cleaning. This involves filling in missing data and removing outliers, preparing the data for accurate evaluation in the next step.

[0491] Step 3:

[0492] The server uses an AI model to analyze health data and calculate a user's health score. Based on this score, the user's health status is compared to the average of other people in the same age group.

[0493] Step 4:

[0494] The server uses an emotion engine to analyze emotional data. It evaluates the user's emotional state using voice tone and facial recognition to identify stress and anxiety levels.

[0495] Step 5:

[0496] The server comprehensively assesses both health and emotional states and generates health improvement strategies. These strategies include exercise guidance, dietary advice, and relaxation techniques.

[0497] Step 6:

[0498] The server sends the generated improvement suggestions to the user's device. The device displays the suggestions as a user interface or notification, making them easily accessible to the user.

[0499] Step 7:

[0500] Users can review the improvement suggestions received from the device and incorporate them into their daily lives. For example, they can practice relaxation techniques by following the device's guide.

[0501] Step 8:

[0502] The user inputs the results of the improvements they have made into their device as feedback and sends it back to the server. The server uses this to update the AI ​​model and improve the accuracy of personalized advice.

[0503] (Example 2)

[0504] 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."

[0505] In modern society, there is a need to comprehensively manage an individual's health and emotional state and provide personalized improvement measures. Conventional systems often focus on managing health status, lacking mechanisms for effectively evaluating and improving emotional state. As a result, there is a problem in that it is difficult to improve the overall quality of life of users.

[0506] 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.

[0507] In this invention, the server includes data collection means for collecting personal health-related information and emotional-related information; evaluation means for processing the health-related information and emotional-related information and comprehensively evaluating the health and emotional state; and generation means for generating individual health and emotional improvement measures based on the evaluation results of the health and emotional state obtained by the evaluation means. This makes it possible to comprehensively evaluate the user's health and emotional state and provide comprehensive improvement measures in a timely manner.

[0508] "Data collection means" refers to devices and systems used to acquire personal health-related and emotional-related information, including smartphones and wearable devices.

[0509] "Evaluation tools" refer to functions that process collected health-related and emotion-related information and comprehensively analyze an individual's health and emotional state.

[0510] "Generative means" refers to the function of creating individually tailored strategies to improve health and emotions based on evaluation results.

[0511] "Output means" refers to a mechanism for notifying the user of the generated improvement measures, and this includes visual display devices and voice notification devices.

[0512] An "emotion engine" refers to software or a system that uses voice analysis and facial recognition technology to evaluate an individual's emotional state in real time.

[0513] This invention is a system that comprehensively evaluates an individual's health and emotional state and provides solutions for improvement. Users collect health-related information such as heart rate, steps taken, and sleep patterns in their daily lives using smartphones and wearable devices. These devices also collect emotional data related to voice tone and facial expressions. This data is transmitted to a server by the device in real time or at regular intervals.

[0514] The server centrally manages the received data, performing data cleaning and noise reduction. An AI model is used to assess health status and calculate a health score. This AI model identifies potential health risks by comparing the user's data to the population average.

[0515] Simultaneously, the server uses an emotion engine to assess the user's emotional state. The emotion engine utilizes voice analysis and facial recognition technologies to analyze the user's emotions in real time. This assessment includes tone analysis from voice data and facial recognition using a camera.

[0516] Based on the assessment results of these health and emotional states, the server uses a generation AI module to generate personalized improvement plans. These plans include advice on exercise and diet, as well as stress management methods that focus on mental health. The generated improvement plans are sent to the terminal and notified to the user visually or audibly.

[0517] As a concrete example, consider a case where a user is experiencing chronic anxiety. When the emotion engine detects this anxiety, the server generates a guide on relaxation techniques and breathing exercises and presents it to the user on their device.

[0518] An example of a prompt message might be, "You appear to be feeling anxious. Please tell me some breathing techniques you can try to relax." In this way, the system comprehensively manages the user's health and emotions, improving their quality of life.

[0519] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0520] Step 1:

[0521] Users wear smartphones and wearable devices and collect health-related information while going about their daily lives. This includes heart rate, steps taken, and sleep patterns. The input is biometric data sensed by each device. The output is that these values ​​are temporarily stored in the device and ready to be transmitted later.

[0522] Step 2:

[0523] The device uses voice capture functionality and a camera to collect emotion-related information such as voice tone and facial expressions. Inputs include the user's voice data and video data of their facial expressions. Output is stored on the device as processed voice tone data and facial expression data.

[0524] Step 3:

[0525] The device transmits collected health-related and emotional data to the server. The input consists of all biometric and emotional data temporarily stored on the device. The data is encrypted using security technology, and the output is securely transmitted to the server.

[0526] Step 4:

[0527] The server cleans the received data, removing noise and filling in any missing data. The input is the raw data sent to the server. Data processing includes normalization and filtering, and the output is formatted data.

[0528] Step 5:

[0529] The server applies an AI model to assess health status using pre-processed data. The input is cleaned health-related data. This data is passed through a generating AI model, resulting in a health score as output. This score is compared to that of peers to analyze potential health risks.

[0530] Step 6:

[0531] The server uses an emotion engine to analyze voice and facial expression data and evaluate the user's emotional state. The input is cleaned emotion-related data. The output is the emotion evaluation result obtained from emotional tone and facial expression recognition.

[0532] Step 7:

[0533] The server generates improvement plans using generative AI technology based on assessments of health and emotional states. The inputs are the health score and emotional assessment results. The output is a set of specific improvement plans recommended to the user, which may manifest as suggestions for exercise, diet, or mental health guidance.

[0534] Step 8:

[0535] The terminal receives the generated improvement suggestions and notifies the user. The input is the improvement suggestion information sent from the server. The output is what is displayed or played to the user as a visual graph or audio notification.

[0536] (Application Example 2)

[0537] 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."

[0538] In modern society, comprehensively managing an individual's health and emotional state and providing appropriate improvement measures is becoming increasingly important. However, current systems tend to manage health data and emotional data separately, which can result in insufficient overall assessment and provision of improvement measures. Furthermore, there are few systems designed for use in retail environments, limiting opportunities for users to easily improve their health in their daily lives.

[0539] 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.

[0540] In this invention, the server includes information gathering means for collecting personal health and emotional information, evaluation means for processing the health and emotional information and evaluating the health and emotional state, generation means for generating individual health and emotional improvement measures based on the evaluation results obtained by the evaluation means, and display means for presenting improvement measures tailored to the individual through a display device in the store or a mobile terminal. This allows individual users to instantly grasp their own health and emotional state and easily take concrete actions to improve their health and emotional state in their daily lives.

[0541] "Information gathering means" refers to devices and methods for acquiring and storing information related to an individual's health and emotions.

[0542] "Evaluation means" refers to devices or methods that process collected health and emotional information and use that information to determine an individual's health and emotional state.

[0543] "Generative means" refers to methods and processes for creating individual health and emotional improvement measures from the results obtained through evaluation means.

[0544] "Output means" refers to devices or methods that inform the user of the generated improvement measures in the form of visuals, sounds, or other visual means.

[0545] "Display means" refers to devices or methods that visually show the generated improvement measures so that users can confirm them within the store.

[0546] "Methods of comparison" refer to methods or processes for evaluating an individual's health and emotional state by comparing their evaluation results with the average values ​​of other groups or specific criteria.

[0547] The system for implementing this invention is for the comprehensive management of an individual's health and emotional state. The server collects the user's health information (e.g., heart rate and steps) and emotional information (e.g., tone of voice and facial expressions) through information collection means. This information is obtained from smartphones and wearable devices and transmitted to the server in real time or at regular intervals.

[0548] Upon receiving this data, the server first cleans and formats it. It removes noise and missing data and converts it into a format suitable for analysis. Next, it uses evaluation tools to leverage AI models to assess the user's health and emotional state. This reveals where the user's condition stands compared to their peers and makes it possible to detect specific health risks and emotional anxieties.

[0549] The generation mechanism generates individual health and emotional improvement measures based on the evaluation results. These measures include physical actions (e.g., exercise guidance) and mental support (e.g., stress management techniques). The generated improvement measures are transmitted to the user's terminal via the output mechanism.

[0550] Depending on the display method, improvement measures are presented visually and audibly on in-store display devices or mobile terminals. A specific example is its use in a fitness gym. By having users in the gym wear heart rate sensors, their health status is evaluated in real time during exercise, and after certain exercises, suggestions for relaxation methods are displayed on the counter.

[0551] An example of a prompt for a generative AI model is: "Generate suggestions for the optimal relaxation method when the user's emotional state is unstable. Specific data will be provided in the form of voice tone and facial expression data." In this way, individual users can improve their health and emotional well-being in their daily lives by receiving appropriate health improvement measures.

[0552] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0553] Step 1:

[0554] The server acquires the user's health and emotional information through data collection methods. This information is obtained from smartphones and wearable devices and transmitted to the server in real time or periodically. Inputs include heart rate, step count, voice tone, and facial expression data, which are necessary for health and emotional assessment.

[0555] Step 2:

[0556] The server receives the raw data and performs data cleaning and formatting. It removes noise and missing data and converts it into a format suitable for algorithmic analysis. The cleaned data is then output, serving as the foundational data for subsequent health and emotional assessment processes.

[0557] Step 3:

[0558] The server analyzes cleaned data using evaluation tools and applies an AI model to assess health and emotional state. The input data is fed into the model, which outputs the user's health and emotional scores. By comparing these scores to the average for their age group, specific health risks and emotional anxieties are identified.

[0559] Step 4:

[0560] The server generates individual health and emotional improvement plans based on the evaluation results using a generation mechanism. These plans include specific advice that considers exercise guidance and stress management techniques. The generated improvement plans are output and become input data for subsequent notification processes.

[0561] Step 5:

[0562] The terminal displays improvement suggestions received from the server to the user using a display device. Advice is displayed visually or audibly on in-store display devices and mobile terminals to support users in easily understanding and implementing the suggestions.

[0563] Step 6:

[0564] Users choose actions based on the suggested improvement measures. For example, when receiving exercise instruction at a fitness gym, they can receive real-time feedback by wearing sensors. Based on this information, more effective health management can be achieved.

[0565] This system will enable users to monitor their health and emotional state on a daily basis and take appropriate action.

[0566] 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.

[0567] 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.

[0568] 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.

[0569] [Fourth Embodiment]

[0570] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0571] 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.

[0572] 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).

[0573] 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.

[0574] 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.

[0575] 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).

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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".

[0583] This invention is a system for managing and improving an individual's health status, and is configured as follows: The user collects daily health data using a smartphone or wearable device. This data includes heart rate, steps taken, sleep patterns, meal logs, exercise history, etc. The collected data is transmitted to a server in real time or at a fixed frequency (e.g., daily).

[0584] The server centrally manages the received data and first performs data formatting and cleaning. Next, it initiates an analysis process using the data, applying an AI model to assess the user's health status. This assessment is performed by comparing the user's data with the average values ​​for their age group. This process identifies specific risks and signs of presenteeism by clarifying health indicators tailored to each individual user.

[0585] Based on the evaluation results, the server generates personalized health improvement plans for the user. These plans include determining whether a visit to a medical institution is necessary, and providing guidance on stretches, meditation, appropriate exercise, and diet that can be done in daily life. The generated advice is sent to the user's device as a text message, and the device presents it to the user in an easy-to-understand visual format.

[0586] For example, if a user is experiencing chronic fatigue, the server will identify the cause based on sleep data and exercise levels. If it determines that the user is sleep-deprived, the server will notify the user's device of the importance of going to bed early and provide advice on creating a suitable sleep environment.

[0587] Users apply the provided advice to their daily lives to improve their health. The results and feedback from implementing the improvements are sent back to the server, ensuring that a flexible and personalized health management system continues to function.

[0588] The following describes the processing flow.

[0589] Step 1:

[0590] Users collect health data such as heart rate, steps taken, and sleep patterns using wearable devices and smartphones. This data is transmitted to a server via the device.

[0591] Step 2:

[0592] The server receives the transmitted health data and performs data formatting and cleaning. It removes incomplete data, standardizes values, and converts the data into the format required for evaluation.

[0593] Step 3:

[0594] The server applies an AI model to the formatted data and calculates an individual health score. This score indicates the user's health status and allows for comparison with others in the same age group.

[0595] Step 4:

[0596] The server uses the output of the AI ​​model to detect anomalies in the user's health status and signs of presenteeism. It generates an alert if specific health risks are detected.

[0597] Step 5:

[0598] Based on the detected results, the server generates health improvement measures tailored to the user. This includes determining whether lifestyle changes are necessary or if a visit to a medical institution is required.

[0599] Step 6:

[0600] The server sends the generated health improvement suggestions to the terminal, and the terminal notifies the user of this information. The suggestions are presented to the user using a visually easy-to-understand dashboard and notification messages.

[0601] Step 7:

[0602] Users review the suggestions they receive and incorporate health improvement measures into their daily lives. They can also send feedback on the results of their implementation to the server via their device.

[0603] Step 8:

[0604] The server analyzes user feedback and new health data to update the accuracy of the AI ​​model. This will enable it to provide more personalized advice in the future.

[0605] (Example 1)

[0606] 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".

[0607] Conventional health management systems struggle to accurately analyze individual health data and provide personalized health improvement strategies. Furthermore, the lack of comparisons using average data for the same age group can lead to insufficient identification of abnormalities and risks. Additionally, there are limited methods for presenting advice based on acquired health data in a way that users can implement in their daily lives. These problems need to be addressed.

[0608] 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.

[0609] In this invention, the server includes information acquisition means for collecting individual biometric data, information processing means for converting the biometric data into a unified format and correcting abnormal values, and state evaluation means for analyzing the biometric data and evaluating the health status using artificial intelligence. This makes it possible to perform precise data analysis to evaluate the health status of individuals and propose appropriate health improvement measures based on that evaluation.

[0610] "Information acquisition means" refers to a device or function for automatically collecting an individual's biometric data.

[0611] "Information processing means" refers to a function that converts acquired biometric data into a unified format and performs processing to correct abnormal values ​​within the data.

[0612] "Condition evaluation means" refers to a function that analyzes biometric data and uses artificial intelligence to evaluate an individual's health status.

[0613] The "proposal generation means" refers to a function that generates individually tailored health improvement measures based on the evaluation results from the condition evaluation means.

[0614] "Presentation means" refers to a function for displaying the generated health improvement measures on the user's communication terminal.

[0615] "Comparative processing" refers to a method of evaluating an individual by comparing their data with standard data from a group of people of the same age.

[0616] This invention is a system that provides personalized health improvement measures by collecting and analyzing biological data. This system mainly consists of a user terminal and a server, each with clearly defined roles. The following describes a specific embodiment of this system.

[0617] Users collect daily biometric data using smartphones and wearable devices. These devices have built-in heart rate sensors, accelerometers, GPS functionality, and other features to automatically record heart rate, steps, distance traveled, sleep patterns, and food intake. The data is transferred to the smartphone using Bluetooth or Wi-Fi.

[0618] The terminal provides an interface for sending collected data to the server. During this process, the data is encrypted and securely transmitted to the server. For example, it's possible to configure the system to send data in batches overnight, reducing the user's communication burden.

[0619] The server centrally manages and processes the received data. The software used includes a database management system, data cleaning tools, and generative AI models. Programming languages ​​such as Python and R are used for data shaping and cleaning, including the removal of outliers and data imputation. Generative AI models are applied to assess each user's health status and calculate risk by comparing it to average data for their age group.

[0620] Based on the evaluation results, the server generates individual health improvement measures using a suggestion generation mechanism. The submitted improvement measures are visually presented to the user on the terminal. The use of a graphical user interface makes the advice easy to understand intuitively.

[0621] For example, if a user inputs a prompt such as, "I've been feeling really tired lately, how can I improve this?" into the AI, the AI ​​will identify the cause of the fatigue from past sleep and heart rate data and provide specific advice for improvement.

[0622] This will enable users to make improvements that are more relevant to their daily lives, resulting in enhanced, personalized health management.

[0623] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0624] Step 1:

[0625] Users collect biometric data using smartphones and wearable devices. This data includes heart rate, steps taken, sleep patterns, meal logs, and exercise history. The data is transferred to the smartphone via Bluetooth or Wi-Fi. Input data is raw values ​​from sensors, while output is raw data stored on the smartphone. For example, if a user uses a pedometer throughout the day, the number of steps and data broken down by time of day will be collected.

[0626] Step 2:

[0627] The device sends the collected data to the server. Specifically, the smartphone securely transfers the data to the endpoint using an encryption protocol. The input is the raw data stored on the device, and the output is the unformatted data sent to the server. During this process, a process is carried out to prevent the accidental transmission of data. For example, depending on the communication environment, data transmission may be temporarily suspended and sent only when stable communication can be ensured.

[0628] Step 3:

[0629] The server formats and cleans the received data. Here, Python or R programs are used to detect and correct outliers and to impart missing data. The input is the raw data before formatting, and the output is the formatted and cleaned data. For example, if there are outliers in heart rate data, they are corrected as outliers exceeding a predefined range.

[0630] Step 4:

[0631] The server applies a generative AI model using the formatted data to evaluate the user's health status. Here, the data to be analyzed is input into the AI ​​model, and health status scoring and risk assessment are performed. The input is formatted data, and the output is a health assessment report. As a specific example, fluctuations in stress levels are reported based on the user's heart rate data over the past week.

[0632] Step 5:

[0633] The server generates health improvement measures based on the obtained health assessment. This involves a combination of data analysis results and user cases obtained through balance mining to generate suggestions. The input is the health assessment report, and the output is the generated improvement measures. For example, if a lack of exercise is detected, an individualized exercise plan is formulated.

[0634] Step 6:

[0635] The terminal visually presents the improvement suggestions received from the server to the user. Using a designed interface, it clearly displays the suggestions with animations and graphs. The input is the generated improvement suggestions, and the output is a visualization of those suggestions displayed to the user. For example, recommended stretches are presented with illustrations.

[0636] Step 7:

[0637] Users improve their lifestyle habits according to the provided advice and input the results as feedback into the device. The device then sends this data back to the server, where it is used for the next data analysis. The input is the user's feedback data, and the output is the new dataset sent to the server. For example, the system can record the results of a user's exercise plan, which will then be used to inform the next set of advice.

[0638] (Application Example 1)

[0639] 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".

[0640] In modern society, the importance of individual health management is increasing. However, traditional health management methods have difficulty providing personalized, real-time advice, and there is a particular challenge in obtaining health indicators and improvement measures that can be immediately used in exercise facilities and daily life.

[0641] 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.

[0642] In this invention, the server includes data acquisition means for collecting personal health-related information, evaluation means for processing the health-related information and evaluating the health status, generation means for providing individual health improvement guidelines based on the evaluation results obtained by the evaluation means, and presentation means for outputting the generated guidelines to a visual display device in real time. This makes it possible to provide real-time and personalized health advice based on the individual's health status.

[0643] "Health-related information" refers to data consisting of an individual's exercise history, heart rate, steps taken, sleep patterns, and dietary logs, and is used to evaluate an individual's health status.

[0644] "Data acquisition means" refers to a device or software that has the function of collecting personal health-related information.

[0645] "Evaluation means" refers to a device or software used for the purpose of processing collected health-related information and analyzing and evaluating an individual's health status.

[0646] "Generation means" refers to a device or software that provides individual health improvement guidelines based on evaluation results obtained by the evaluation means.

[0647] A "visual display device" is a device used to visually display generated health improvement guidelines to the user, and mainly includes smart glasses and screens.

[0648] "Real-time" refers to the ability to process information instantly and provide the results to users quickly, meaning that data is available in a timely manner without delay.

[0649] The server uses cloud-based data acquisition methods to obtain health-related information from smart glasses worn by individuals in fitness facilities and at home. This information includes heart rate, steps taken, sleep patterns, etc., and is collected in real time from various sensors. The server formats and cleans this data and analyzes the user's health status using a powerful AI model as an evaluation tool. Machine learning software such as TensorFlow is used for the analysis, learning data patterns and generating individualized health improvement guidelines. The generation tool formulates health improvement measures based on the evaluation results, and these measures are output in real time to the smart glasses, which are the visual display devices.

[0650] Based on the provided improvement guidelines, users can train efficiently and safely. For example, if their heart rate suddenly increases during training, the smart glasses will immediately display a warning to the user prompting them to take a break. Furthermore, it can suggest an optimal training plan for the user, taking into account their past exercise history and sleep data. This enables personalized health management.

[0651] An example of a prompt for a generating AI model might be, "Consider the user's recent exercise history and sleep quality, analyze their health status, and generate appropriate health improvement advice." Such a system would allow users to receive more personalized health guidance, which is expected to contribute to maintaining and improving their health.

[0652] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0653] Step 1:

[0654] The server acquires personal health-related information such as heart rate, steps taken, and sleep patterns from smart glasses. The input is real-time data from the sensors, which is stored in a database with high accuracy and preprocessed to link it with historical data. The output is formatted health data.

[0655] Step 2:

[0656] The server uses the acquired health-related information to evaluate the user's health status using an AI model. At this stage, it analyzes heart rate and past exercise data to assess the user's exercise load. The input is the data formatted in step 1, and the output is the evaluation result regarding the user's health status. Specifically, it checks whether the heart rate exceeds a certain threshold, for example.

[0657] Step 3:

[0658] Based on the evaluation results, the server uses a generation mechanism to formulate individual health improvement guidelines. At this stage, the generation AI model takes the prompt "Please suggest the optimal health improvement measures according to the user's health condition" as input and outputs detailed advice as if the user had received individual counseling. The results include information such as the need for rest and the recommended intensity of exercise.

[0659] Step 4:

[0660] The device displays the generated health improvement guidelines in real time on the smart glasses' display. The input is the health improvement guidelines provided by the server, and the output is health advice information that the user can visually confirm. Specifically, the information is overlaid in an easy-to-understand manner within the user's field of view.

[0661] Step 5:

[0662] Users receive advice presented via smart glasses and use it to manage their own health. The input is the advice information provided through the device, and the output is the user's actions to maintain and improve their health. Users take specific actions as needed, such as taking breaks or adjusting their exercise.

[0663] 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.

[0664] This invention incorporates an emotion engine into a system aimed at managing and improving health conditions, thereby comprehensively evaluating the user's health and emotional state and providing personalized improvement measures tailored to individual needs.

[0665] This system collects health-related information using the user's smartphone or wearable device. This includes heart rate, steps taken, sleep patterns, and emotional data such as the user's voice tone and facial expressions. This data is transmitted to a server in real time or at regular intervals.

[0666] The server centrally manages the received data and first cleans and formats it. This removes noise and missing data, making it suitable for analysis. Next, an AI model is applied to assess the user's health status and calculate a health score. In this process, the user's data is compared to the average for their age group to identify specific health risks and signs of presenteeism.

[0667] Furthermore, this system also uses an emotion engine to evaluate emotional states. The emotion engine utilizes voice analysis and facial recognition technology to grasp the user's emotional state in real time, and combines this with health assessments to provide a highly accurate overall evaluation of the user's condition.

[0668] The server generates personalized improvement plans for the user based on a comprehensive assessment of their health and emotional state. These plans include physical advice for maintaining health (e.g., exercise and dietary guidance) as well as mental health support for improving emotions (e.g., positive thinking and stress management techniques). The generated improvement plans are sent to the user's device, which then notifies the user in an intuitively understandable format. This may be presented as visually clear graphs or charts, or as voice notifications for guidance.

[0669] As a concrete example, consider a case where a user experiences anxiety on a daily basis. The emotion engine analyzes emotional data, and if it detects a high level of anxiety, the server suggests appropriate relaxation techniques and breathing exercises, which are then provided to the user as a guided view on their device. In this way, health and emotions are comprehensively managed, aiming to improve the user's quality of life.

[0670] The following describes the processing flow.

[0671] Step 1:

[0672] Users collect daily health data and emotional data through voice and facial expressions using smartphones and wearable devices. This data is transmitted from the device to a server.

[0673] Step 2:

[0674] The server receives the incoming data and performs data cleaning. This involves filling in missing data and removing outliers, preparing the data for accurate evaluation in the next step.

[0675] Step 3:

[0676] The server uses an AI model to analyze health data and calculate a user's health score. Based on this score, the user's health status is compared to the average of other people in the same age group.

[0677] Step 4:

[0678] The server uses an emotion engine to analyze emotional data. It evaluates the user's emotional state using voice tone and facial recognition to identify stress and anxiety levels.

[0679] Step 5:

[0680] The server comprehensively assesses both health and emotional states and generates health improvement strategies and emotional improvement strategies. These strategies include exercise guidance, dietary advice, and relaxation techniques.

[0681] Step 6:

[0682] The server sends the generated improvement suggestions to the user's device. The device displays the suggestions as a user interface or notification, making them easily accessible to the user.

[0683] Step 7:

[0684] Users can review the improvement suggestions received from the device and incorporate them into their daily lives. For example, they can practice relaxation techniques by following the device's guide.

[0685] Step 8:

[0686] The user inputs the results of the improvements they have made into their device as feedback and sends it back to the server. The server uses this to update the AI ​​model and improve the accuracy of personalized advice.

[0687] (Example 2)

[0688] 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".

[0689] In modern society, there is a need to comprehensively manage an individual's health and emotional state and provide personalized improvement measures. Conventional systems often focus on managing health status, lacking mechanisms for effectively evaluating and improving emotional state. As a result, there is a problem in that it is difficult to improve the overall quality of life of users.

[0690] 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.

[0691] In this invention, the server includes data collection means for collecting personal health-related information and emotional-related information; evaluation means for processing the health-related information and emotional-related information and comprehensively evaluating the health and emotional state; and generation means for generating individual health and emotional improvement measures based on the evaluation results of the health and emotional state obtained by the evaluation means. This makes it possible to comprehensively evaluate the user's health and emotional state and provide comprehensive improvement measures in a timely manner.

[0692] "Data collection means" refers to devices and systems used to acquire personal health-related and emotional-related information, including smartphones and wearable devices.

[0693] "Evaluation tools" refer to functions that process collected health-related and emotion-related information and comprehensively analyze an individual's health and emotional state.

[0694] "Generative means" refers to the function of creating individually tailored strategies to improve health and emotions based on evaluation results.

[0695] "Output means" refers to a mechanism for notifying the user of the generated improvement measures, and this includes visual display devices and voice notification devices.

[0696] An "emotion engine" refers to software or a system that uses voice analysis and facial recognition technology to evaluate an individual's emotional state in real time.

[0697] This invention is a system that comprehensively evaluates an individual's health and emotional state and provides solutions for improvement. Users collect health-related information such as heart rate, steps taken, and sleep patterns in their daily lives using smartphones and wearable devices. These devices also collect emotional data related to voice tone and facial expressions. This data is transmitted to a server by the device in real time or at regular intervals.

[0698] The server centrally manages the received data, performing data cleaning and noise reduction. An AI model is used to assess health status and calculate a health score. This AI model identifies potential health risks by comparing the user's data to the population average.

[0699] Simultaneously, the server uses an emotion engine to assess the user's emotional state. The emotion engine utilizes voice analysis and facial recognition technologies to analyze the user's emotions in real time. This assessment includes tone analysis from voice data and facial recognition using a camera.

[0700] Based on the assessment results of these health and emotional states, the server uses a generation AI module to generate personalized improvement plans. These plans include advice on exercise and diet, as well as stress management methods that focus on mental health. The generated improvement plans are sent to the terminal and notified to the user visually or audibly.

[0701] As a concrete example, consider a case where a user is experiencing chronic anxiety. When the emotion engine detects this anxiety, the server generates a guide on relaxation techniques and breathing exercises and presents it to the user on their device.

[0702] An example of a prompt message might be, "You appear to be feeling anxious. Please tell me some breathing exercises you can try to relax." In this way, the system comprehensively manages the user's health and emotions, improving their quality of life.

[0703] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0704] Step 1:

[0705] Users wear smartphones and wearable devices and collect health-related information while going about their daily lives. This includes heart rate, steps taken, and sleep patterns. The input is biometric data sensed by each device. The output is these values ​​being temporarily stored in the device and ready to be transmitted later.

[0706] Step 2:

[0707] The device uses voice capture functionality and a camera to collect emotion-related information such as voice tone and facial expressions. Inputs include the user's voice data and video data of their facial expressions. Output is stored on the device as processed voice tone data and facial expression data.

[0708] Step 3:

[0709] The device transmits collected health-related and emotional-related information to the server. The input consists of all biometric and emotional data temporarily stored on the device. The data is encrypted using security technology, and the output is securely transmitted to the server.

[0710] Step 4:

[0711] The server cleans the received data, removing noise and filling in any missing data. The input is the raw data sent to the server. Data processing includes normalization and filtering, and the output is formatted data.

[0712] Step 5:

[0713] The server applies an AI model to assess health status using pre-processed data. The input is cleaned health-related data. This data is passed through a generating AI model, resulting in a health score as output. This score is compared to that of peers to analyze potential health risks.

[0714] Step 6:

[0715] The server uses an emotion engine to analyze voice and facial expression data and evaluate the user's emotional state. The input is cleaned emotion-related data. The output is the emotion evaluation result obtained from emotional tone and facial expression recognition.

[0716] Step 7:

[0717] The server generates improvement plans using generative AI technology based on assessments of health and emotional states. The inputs are health scores and emotional assessment results. The output is a set of specific improvement plans recommended to the user, which may manifest as suggestions for exercise, diet, or mental health guidance.

[0718] Step 8:

[0719] The terminal receives the generated improvement suggestions and notifies the user. The input is the improvement suggestion information sent from the server. The output is what is displayed or played to the user as a visual graph or audio notification.

[0720] (Application Example 2)

[0721] 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".

[0722] In modern society, comprehensively managing an individual's health and emotional state and providing appropriate improvement measures is becoming increasingly important. However, current systems tend to manage health data and emotional data separately, which can result in insufficient overall assessment and provision of improvement measures. Furthermore, there are few systems designed for use in retail environments, limiting opportunities for users to easily improve their health in their daily lives.

[0723] 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.

[0724] In this invention, the server includes information gathering means for collecting personal health and emotional information, evaluation means for processing the health and emotional information and evaluating the health and emotional state, generation means for generating individual health and emotional improvement measures based on the evaluation results obtained by the evaluation means, and display means for presenting improvement measures tailored to the individual through a display device in the store or a mobile terminal. This allows individual users to instantly grasp their own health and emotional state and easily take concrete actions to improve their health and emotional state in their daily lives.

[0725] "Information gathering means" refers to devices and methods for acquiring and storing information related to an individual's health and emotions.

[0726] "Evaluation means" refers to devices or methods that process collected health and emotional information and use that information to determine an individual's health and emotional state.

[0727] "Generative means" refers to methods and processes for creating individual health and emotional improvement measures from the results obtained through evaluation means.

[0728] "Output means" refers to devices or methods that inform the user of the generated improvement measures in the form of visuals, sounds, or other visual means.

[0729] "Display means" refers to devices or methods that visually show the generated improvement measures so that users can confirm them within the store.

[0730] "Methods of comparison" refer to methods or processes for evaluating an individual's health and emotional state by comparing their evaluation results with the average values ​​of other groups or specific criteria.

[0731] The system for implementing this invention is for the comprehensive management of an individual's health and emotional state. The server collects the user's health information (e.g., heart rate and steps) and emotional information (e.g., tone of voice and facial expressions) through information collection means. This information is obtained from smartphones and wearable devices and transmitted to the server in real time or at regular intervals.

[0732] Upon receiving this data, the server first cleans and formats it. It removes noise and missing data and converts it into a format suitable for analysis. Next, it uses evaluation tools to leverage AI models to assess the user's health and emotional state. This reveals where the user's condition stands compared to their peers and makes it possible to detect specific health risks and emotional anxieties.

[0733] The generation mechanism generates individual health and emotional improvement measures based on the evaluation results. These measures include physical actions (e.g., exercise guidance) and mental support (e.g., stress management techniques). The generated improvement measures are transmitted to the user's terminal via the output mechanism.

[0734] Depending on the display method, improvement measures are presented visually and audibly on in-store display devices or mobile terminals. A specific example is its use in a fitness gym. By having users in the gym wear heart rate sensors, their health status is evaluated in real time during exercise, and after certain exercises, suggestions for relaxation methods are displayed on the counter.

[0735] An example of a prompt for a generative AI model is: "Generate suggestions for the optimal relaxation method when the user's emotional state is unstable. Specific data will be provided in the form of voice tone and facial expression data." In this way, individual users can improve their health and emotional well-being in their daily lives by receiving appropriate health improvement measures.

[0736] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0737] Step 1:

[0738] The server acquires the user's health and emotional information through data collection methods. This information is obtained from smartphones and wearable devices and transmitted to the server in real time or periodically. Inputs include heart rate, step count, voice tone, and facial expression data, which are necessary for health and emotional assessment.

[0739] Step 2:

[0740] The server receives the raw data and performs data cleaning and formatting. It removes noise and missing data and converts it into a format suitable for algorithmic analysis. The cleaned data is then output, serving as the foundational data for subsequent health and emotional assessment processes.

[0741] Step 3:

[0742] The server analyzes cleaned data using evaluation tools and applies an AI model to assess health and emotional state. The input data is fed into the model, which outputs the user's health and emotional scores. By comparing these scores to the average for their age group, specific health risks and emotional anxieties are identified.

[0743] Step 4:

[0744] The server generates individual health and emotional improvement plans based on the evaluation results using a generation mechanism. These plans include specific advice that considers exercise guidance and stress management techniques. The generated improvement plans are output and become input data for subsequent notification processes.

[0745] Step 5:

[0746] The terminal displays improvement suggestions received from the server to the user using a display device. Advice is displayed visually or audibly on in-store display devices and mobile terminals to support users in easily understanding and implementing the suggestions.

[0747] Step 6:

[0748] Users choose actions based on the suggested improvement measures. For example, when receiving exercise instruction at a fitness gym, they can receive real-time feedback by wearing sensors. Based on this information, more effective health management can be achieved.

[0749] This system will enable users to monitor their health and emotional state on a daily basis and take appropriate action.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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."

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] The following is further disclosed regarding the embodiments described above.

[0772] (Claim 1)

[0773] Data collection methods for collecting personal health-related information,

[0774] An evaluation means for processing the aforementioned health-related information and evaluating the health status,

[0775] A generation means for generating individual health improvement measures based on the evaluation results obtained by the evaluation means,

[0776] Output means for outputting the generated improvement measures,

[0777] A system that includes this.

[0778] (Claim 2)

[0779] The system according to claim 1, wherein the evaluation means includes a comparison means for evaluating an individual's health status by comparing it with other people of the same age.

[0780] (Claim 3)

[0781] The system according to claim 1, wherein the generation means generates health improvement measures including visits to medical institutions and relaxation methods.

[0782] "Example 1"

[0783] (Claim 1)

[0784] Information acquisition methods for collecting personal biometric data,

[0785] Information processing means for converting the aforementioned biological data into a unified format and correcting abnormal values,

[0786] A state evaluation means for analyzing the aforementioned biological data and evaluating the health status using artificial intelligence,

[0787] A proposal generation means that generates health improvement proposals based on the evaluation results of the condition evaluation means,

[0788] A presentation means for displaying the generated health improvement suggestions on an individual's communication terminal,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, wherein the state evaluation means includes a comparison process that evaluates the health status of each individual by comparing it with standard data of a group of people of the same age.

[0792] (Claim 3)

[0793] The system according to claim 1, wherein the proposal generation means generates proposals including guidance on consulting with medical facilities and methods for relieving mental and physical tension.

[0794] "Application Example 1"

[0795] (Claim 1)

[0796] A data acquisition method for collecting personal health-related information,

[0797] An evaluation means for processing the aforementioned health-related information and evaluating the health status,

[0798] A generation means that provides individual health improvement guidelines based on the evaluation results obtained by the evaluation means,

[0799] A presentation means that outputs the generated guidelines to a visual display device in real time,

[0800] A health management system that includes this.

[0801] (Claim 2)

[0802] The health management system according to claim 1, wherein the evaluation means has a comparison function for comparing an individual's health status with group data of the same age group.

[0803] (Claim 3)

[0804] The health management system according to claim 1, wherein the generating means creates health improvement guidelines, including visits to medical facilities and rest.

[0805] "Example 2 of combining an emotion engine"

[0806] (Claim 1)

[0807] A data collection method for collecting personal health-related and emotional information,

[0808] An evaluation means for processing the aforementioned health-related information and emotion-related information and comprehensively evaluating the health status and emotional status,

[0809] A generation means for generating individual health and emotional improvement measures based on the evaluation results of health and emotional states obtained by the evaluation means,

[0810] Output means for outputting the generated improvement measures in presentation format,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, wherein the evaluation means evaluates an individual's health status based on a comparison with that of their peers and evaluates their emotional state in real time using an emotion engine.

[0814] (Claim 3)

[0815] The system according to claim 1, wherein the generating means generates health and emotional improvement measures including exercise, dietary guidance, relaxation methods, and mental health support.

[0816] "Application example 2 when combining with an emotional engine"

[0817] (Claim 1)

[0818] Information gathering means for collecting personal health and emotional information,

[0819] An evaluation means for processing the aforementioned health and emotion-related information and evaluating the health and emotional state,

[0820] A generation means for generating individual health and emotional improvement measures based on the evaluation results obtained by the evaluation means,

[0821] Output means for outputting the generated improvement measures,

[0822] A display means that presents personalized improvement measures via a display device or mobile terminal within the store,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, wherein the evaluation means includes a comparison means for evaluating an individual's health status and emotional state by comparing them with other groups.

[0826] (Claim 3)

[0827] The system according to claim 1, wherein the generation means generates health and emotional improvement measures, including exercise guidance and stress management methods, and provides these as visual and audio guidance. [Explanation of symbols]

[0828] 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. Data collection methods for collecting personal health-related information, An evaluation means for processing the aforementioned health-related information and evaluating the health status, A generation means for generating individual health improvement measures based on the evaluation results obtained by the evaluation means, Output means for outputting the generated improvement measures, A system that includes this.

2. The system according to claim 1, wherein the evaluation means includes a comparison means for evaluating an individual's health status by comparing it with other people of the same age.

3. The system according to claim 1, wherein the generation means generates health improvement measures including visits to medical institutions and relaxation methods.

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