system

Abstract

We provide the system. [Solution] A data acquisition method for obtaining biometric data from a user, A data processing method for integrating, standardizing, and anonymizing acquired biometric data, Analytical methods for analyzing anonymized data and assessing health risks, A plan creation method for creating individually optimized health management plans based on evaluation results, A user interface means for presenting the created plan to the user, 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 as a 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] There is a current situation where the early detection and prevention of health risks, particularly frailty, in the elderly and adults are not adequately carried out. Also, there is a need for a method to provide an individually optimized care plan while safely handling personal health data. In the face of strict requirements for privacy protection, there is a problem of performing highly accurate health information analysis without identifying individuals while utilizing data.

Means for Solving the Problems

[0005] This invention provides a means for acquiring biometric data, integrating and standardizing it, and anonymizing it. Furthermore, by incorporating analytical means for evaluating health risks based on the anonymized data, it enables highly accurate risk assessment while protecting individual privacy. The invention also includes means for creating individually optimized health management plans based on the analysis results, and by presenting these plans to the user, provides a system that allows users to effectively manage their health from home.

[0006] "Data acquisition means" refers to functions or devices for acquiring biometric data from users.

[0007] "Data processing means" refers to functions and devices for integrating and standardizing acquired biometric data and anonymizing it for privacy protection.

[0008] "Analysis means" refers to a technology or device for analyzing anonymized data and assessing the health risks of a user.

[0009] "Plan creation means" refers to functions or devices for creating individually optimized health management plans based on the results of a health risk assessment.

[0010] "User interface means" refers to technology or devices for presenting the created health management plan and related information to the user.

[0011] "Update means" refers to functions or devices for continuously revising and updating health management plans based on collected biometric data.

[0012] "Anonymization" refers to processing data in a way that makes it impossible to identify individuals. [Brief explanation of the drawing]

[0013] [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]It is a conceptual diagram showing an example of the main 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 multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple 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.

MODE FOR CARRYING OUT THE INVENTION

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] 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.

[0017] 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.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] 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), etc.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] The present invention provides a system that assesses health risks through individual users' biometric data and creates individually optimized health management plans. This system mainly consists of data acquisition means, data processing means, analysis means, plan creation means, and user interface means.

[0035] First, users collect their biometric data (e.g., steps, heart rate, sleep data, etc.) using smartphones or wearable devices. This data is measured in real time through various sensors. The data is stored directly on the user's device and prepared for transfer to a server via data acquisition methods.

[0036] The device transmits this biometric data to the server at appropriate intervals. During this process, corrections and standardization based on user input are performed as needed to ensure the accuracy and consistency of the data.

[0037] The server aggregates and integrates the received biometric data. This ensures that information collected from different data sources is presented in a consistent format. Subsequently, the information is anonymized by data processing tools to protect privacy, and processing proceeds without identifying individuals.

[0038] Next, the server analyzes the anonymized data using a generative AI model. Multiple indicators for assessing frailty risk are considered and converted into a numerical risk score representing each user's health status. These indicators include daily activity levels and sleep quality.

[0039] Furthermore, the server generates a customized care plan that reflects the individual user's health condition based on the analysis results. This plan includes specific action suggestions (e.g., ensuring a certain amount of exercise per week, dietary improvement plan, etc.) and provides guidance aimed at improving health.

[0040] Ultimately, users review the care plan presented through their device and effectively manage their health at home. The results and progress are recorded on the device and used to update future plans. In this way, users can continuously manage their health and receive better care through a feedback cycle.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users sync their smartphones or wearable devices with the app. This prepares the app to collect health-related information such as steps, heart rate, and sleep data.

[0044] Step 2:

[0045] The device collects the user's biometric data in real time and automatically stores it in temporary memory. It can be configured to send this data to a server at specific times (e.g., late at night every day) or trigger events (e.g., when Wi-Fi is connected).

[0046] Step 3:

[0047] The server receives data sent from terminals and aggregates it into a database. Data in different formats is integrated and converted into a standard format suitable for analysis.

[0048] Step 4:

[0049] The server anonymizes the data using data processing tools to ensure privacy. This prepares the data for analysis without identifying personal information.

[0050] Step 5:

[0051] The generative AI model assesses the user's health status using anonymized data. It calculates a frailty risk score using parameters such as steps taken, heart rate, and sleep quality.

[0052] Step 6:

[0053] The server creates a personalized care plan for each user based on the evaluation results. The plan includes exercise programs and dietary improvements tailored to the user's lifestyle.

[0054] Step 7:

[0055] Users receive care plans presented through their devices and incorporate them into their daily lives. The devices track health maintenance activities based on the plan and record progress.

[0056] Step 8:

[0057] The server analyzes the feedback data resubmitted by the user and updates the care plan as needed. This cycle is repeated to support continuous health management.

[0058] (Example 1)

[0059] 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."

[0060] While there is a growing need for personalized and efficient health management, conventional systems have failed to protect user privacy during the collection and analysis of biometric data, making it difficult to generate effective health management plans based on the analysis results. Furthermore, real-time data updates and continuous tracking of health status are insufficient, resulting in delays in providing users with feedback on their health management.

[0061] 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.

[0062] In this invention, the server includes input means, processing means, protection means, evaluation means, planning means, and display means. This makes it possible to analyze anonymized biometric data using a generative artificial intelligence model while protecting user privacy, and to generate and present a personalized health management plan in real time. As a result, users can continuously monitor their health status and implement appropriate health management.

[0063] An "input means" is a mechanism for acquiring biometric data from a user.

[0064] A "processing means" is a mechanism that integrates acquired biological data and converts it into a standardized format.

[0065] "Protective measures" refer to mechanisms that protect privacy by anonymizing processed data.

[0066] The "evaluation method" is a mechanism that uses anonymized data to analyze the risk of health conditions using a generative artificial intelligence model.

[0067] "Planning method" refers to a mechanism that generates individually optimized health management plans based on evaluation results.

[0068] A "display means" is a mechanism for presenting the generated plan to the user.

[0069] This invention utilizes a combination of specific hardware and software to support the health management of individual users. Specifically, users collect biometric data such as heart rate, steps taken, and sleep data using input means such as smartphones or wearable devices. This data is measured by sensors within the device, such as accelerometers and heart rate monitors.

[0070] Next, the terminal standardizes the collected data using processing tools and prepares it for transmission to the server. This process includes data format conversion and correction of inconsistencies. The terminal then uses encrypted communication methods to securely transfer the data to the server.

[0071] The server anonymizes the received data using protective measures, organizing it into a consistent format while preserving privacy. This anonymized data is then analyzed using an evaluation method that utilizes a generative artificial intelligence model. This procedure uses prompts such as, "Create a health management plan suitable for a user who is a man in his 50s, takes an average of 6,000 steps per day, and sleeps an average of 7 hours," and calculates a risk score for the user's health status.

[0072] Furthermore, the server uses planning tools based on the analysis results to create a health management plan optimized for each user. This plan includes specific examples, such as "150 minutes of aerobic exercise per week is recommended." The plan is presented to the user on their device through a display tool, allowing them to review its contents and use it to manage their daily health.

[0073] This entire system seamlessly handles everything from data acquisition and analysis to plan generation and presentation, enabling personalized and optimized health management for each user.

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

[0075] Step 1:

[0076] Users collect biometric data using smartphones or wearable devices. Inputs include information such as heart rate, steps taken, and sleep data. Sensors within the device (e.g., accelerometer, heart rate monitor) measure the data in real time, and it is stored on the device as an initial dataset.

[0077] Step 2:

[0078] The terminal verifies the collected data and uses processing tools to standardize it. Input is in the raw biometric data format, and output is in a unified format that can be sent to the server. During this process, inconsistencies are corrected and timestamps are added.

[0079] Step 3:

[0080] The terminal sends data to the server. This transmission process is encrypted to ensure the security of the communication. The input is standardized biometric data, and the output is encrypted data received by the server.

[0081] Step 4:

[0082] The server anonymizes the received data using protective measures and securely stores it in a database. The input is encrypted biometric data, and the output is an anonymized dataset. This anonymization includes operations to remove personally identifiable information in order to maintain privacy.

[0083] Step 5:

[0084] The server uses evaluation tools and a generative AI model to analyze anonymized data. This process utilizes prompts (e.g., "Assess this user's health risk"). The input is anonymized data, and the output is a health risk score for each user.

[0085] Step 6:

[0086] The server uses planning tools to generate an individually optimized health management plan based on a health risk score. The input is the health risk score, and the output is a health management plan that includes specific action suggestions. This plan might include, for example, "a 30-minute walk every day is recommended."

[0087] Step 7:

[0088] The server sends the generated plan to the terminal, and the terminal presents the plan to the user through a display device. The input is the health management plan, and the output is information presented visually to the user. The user reviews the presented information and uses it to manage their daily health.

[0089] (Application Example 1)

[0090] 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."

[0091] While assessing health risks and providing appropriate care plans based on those assessments is crucial for maintaining users' health, it is difficult to address individual needs in practice. Furthermore, updating plans in real time to reflect changes in health status and recommending appropriate products for daily life are also challenges. Against this backdrop, there is a need for a system that creates individually optimized health and care plans based on health risks and provides product recommendations in real time and effectively.

[0092] 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.

[0093] In this invention, the server includes data acquisition means for obtaining biological information from the user; data processing means for integrating, standardizing, and anonymizing the acquired biological information; analysis means for analyzing the anonymized information and evaluating the risk of the user's health condition; plan creation means for creating an individually optimized health care plan based on the evaluation results; user interface means for presenting the created plan to the user; recommendation means for recommending products based on the user's health risk; and adjustment means for readjusting the plan based on the user's purchase history and progress. This makes it possible to provide an individually optimized care plan based on the user's health risk and to recommend appropriate products in a timely manner.

[0094] A "user" is an individual who provides biometric information and is the target of this system's services.

[0095] "Biological information" refers to data that forms the basis for assessing health status, and includes measurements such as heart rate and step count.

[0096] "Data acquisition means" refers to a method or device for collecting biological information from users and incorporating it into a system.

[0097] "Data processing means" refers to a method or apparatus for integrating, standardizing, and anonymizing collected biological information.

[0098] "Analysis means" refers to a method or apparatus for evaluating the risk of a health condition using anonymized information.

[0099] "Plan creation means" refers to a method or apparatus for designing an individually optimized health care plan based on an assessed health condition.

[0100] "User interface means" refers to a method or device for presenting a created plan to a user visually or tactilely.

[0101] "Recommendation method" refers to a method or device for showing a user appropriate products based on their health risks.

[0102] "Adjustment means" refers to a method or device for resetting an existing plan based on the user's purchase history and progress.

[0103] The system that realizes this application consists of various elements that work together to provide an optimal care plan and product recommendations based on the user's health condition.

[0104] Users collect their own biometric information using smartphones and wearable devices. These devices include heart rate sensors and accelerometers, and collect data through them on a daily basis.

[0105] The device sends the collected biological information to the server. The transmitted data is integrated, standardized, and anonymized using software such as Python libraries (e.g., NumPy, Pandas).

[0106] The server uses a generative AI model (e.g., TENSORFLOW®) to assess health risks on anonymized data. Based on the assessment results, a specific health care plan is automatically created.

[0107] Furthermore, the server analyzes the user's health risks and recommends appropriate products based on these risks. To do this, it selects and recommends the most suitable products from the product database and the user's purchase history.

[0108] The user interface presents the generated plan and product recommendations to the user through a smartphone application. This allows the user to obtain guidance for health management and easily purchase the most suitable products.

[0109] As a concrete example, the server analyzes the user's heart rate data and recommends products such as, "Your heart rate has been a little high recently, so we recommend purchasing a relaxing tea."

[0110] An example of a specific prompt message is: "Recommend suitable health products based on user health risk scores and enable seamless in-app purchasing to maintain user health effectively."

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] Users collect biometric information using smartphones and wearable devices. This includes steps taken, heart rate, and sleep data. This data is temporarily stored within the device.

[0114] Step 2:

[0115] The terminal transmits the collected biological information to the server. After transmission, the data is integrated and standardized using NumPy and Pandas libraries. Some manual input correction is also performed to improve the accuracy of the data. As a result, standardized data in a format suitable for analysis is obtained.

[0116] Step 3:

[0117] The server anonymizes the received data and transforms it into a form that does not identify individuals. The data processing means processes the data so that it can be analyzed while protecting personal information. The output is anonymized, integrated data.

[0118] Step 4:

[0119] The server analyzes anonymized data using a generative AI model based on TensorFlow. It quantifies health status based on heart rate and activity data, and calculates a health risk score for each user. The output here is the risk score for each user.

[0120] Step 5:

[0121] The server automatically generates a health care plan based on the risk score assessment results. Using the plan creation tool, it provides suggestions for exercise and dietary improvements tailored to the user's condition. The generated output is an individually optimized care plan.

[0122] Step 6:

[0123] The server recommends appropriate products based on the user's health risks. It refers to a product database to identify and recommend products that contribute to risk reduction. As a result, a list of recommended products is generated.

[0124] Step 7:

[0125] The user interface presents plans and product recommendations to the user via a smartphone app. Users can then make decisions regarding health management and product purchases based on the displayed information. The output of this step is the health information and product recommendations available to the user.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] This invention combines an emotion engine with a system that manages a user's health status and provides an individually optimized health management plan, thereby enabling comprehensive health management that also takes into account the user's emotional state. This system consists of data acquisition means, data processing means, analysis means, emotion engine, plan creation means, and user interface means.

[0128] Users install a dedicated app on their smartphone or wearable device to begin collecting biometric data. This biometric data includes heart rate, steps taken, and body temperature, and is stored on the user's device. The emotion engine also analyzes the user's facial expressions, voice tone, and entered text messages to collect emotional data.

[0129] The device periodically transmits this biometric and emotional data to a server. The transmitted data is aggregated on the server and standardized and anonymized using data processing tools. This prepares the data for analysis while protecting user privacy.

[0130] The server analyzes this data using a generative AI model. Biometric data is used to assess the user's health risks, while emotional data obtained by the emotion engine is used to evaluate the user's daily stress levels and emotional state.

[0131] Based on the evaluated data, the server uses a plan creation mechanism to create a personalized health management plan for each user. This plan takes into account both the user's physical and emotional state and may include suggestions for appropriate exercise and relaxation. For example, if emotional data indicates high stress levels, relaxation exercises or meditation may be suggested.

[0132] Users can receive these health management plans through their devices and incorporate them into their daily lives. The user interface provides a more personalized experience by selecting interaction methods that respond to the user's emotions and adjusting the tone of messages and notifications.

[0133] For example, if a user has low activity levels during the day and the emotional engine detects stress, the server will incorporate light yoga or deep breathing exercises into their health plan. The user interface will also display encouraging messages in a gentle tone to promote positive behavior. Through this process, users can manage their health in a way that supports both their physical and mental well-being.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] Users install the app on their smartphone or wearable device and synchronize it with the device. This initiates the collection of biometric data such as heart rate, steps taken, and body temperature.

[0137] Step 2:

[0138] The device captures the user's facial expressions and voice, and analyzes emotional data through an emotion engine. This emotional data includes the user's mood and stress level, and is quantified.

[0139] Step 3:

[0140] The device transmits biometric and emotional data to the server at regular intervals. During this transmission, the data is transmitted while maintaining accuracy and consistency.

[0141] Step 4:

[0142] The server stores the data received from the terminal in a database and performs standardization and anonymization using data processing methods. This process ensures the protection of personal information.

[0143] Step 5:

[0144] The server uses a generative AI model to analyze anonymized biometric and emotional data, and calculates a health risk score and emotional state score for each user.

[0145] Step 6:

[0146] Based on these evaluation results, the server utilizes planning tools to create an individually optimized health management plan. This plan includes recommendations for appropriate exercise and relaxation, and incorporates stress reduction programs based on emotional data.

[0147] Step 7:

[0148] Users review health management plans sent from the system via their devices and incorporate suggested activities into their daily lives. The devices continue to track and record the activities performed.

[0149] Step 8:

[0150] The server receives user feedback, new biometric data, and emotional data, and updates the plan in real time. This process is repeated, allowing users to continuously manage their health optimally.

[0151] (Example 2)

[0152] 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".

[0153] In modern society, many people are expected to maintain not only physical health but also mental health. However, existing health management systems are limited to evaluating health status based on users' biometric data, and there is a problem in that they do not adequately consider mental factors such as emotions and stress. Therefore, there is a need for a system that enables comprehensive health management, including emotional state.

[0154] 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.

[0155] In this invention, the server includes data acquisition means for acquiring biometric and emotional data from users, data processing means for integrating, standardizing, and anonymizing the acquired biometric and emotional data, and analysis means for analyzing the anonymized data using a generating AI model to evaluate the risks to health and emotional states. This makes it possible to provide individually optimized health management plans that take into account both the physical and mental health of the user.

[0156] "Data acquisition means" refers to a device or method for acquiring biometric data and emotional data from a user.

[0157] "Data processing means" refers to a device or method for integrating, standardizing, and anonymizing acquired biometric data and emotional data.

[0158] A "generative AI model" is an artificial intelligence model used to appropriately analyze collected data.

[0159] "Analysis means" refers to an apparatus or method for analyzing anonymized data and assessing the risks to a user's health and emotional state.

[0160] "Plan creation means" refers to a device or method for creating a health management plan that is individually optimized for each user based on evaluation results.

[0161] "User interface means" refers to a device or method that adjusts and presents a created health management plan according to the user's emotions.

[0162] "Update means" refers to a device or method that tracks changes based on biometric data and emotional data and updates the plan in real time.

[0163] This health management system aims to comprehensively manage the user's biological and emotional state and provide an individually optimized health plan. The system consists of data acquisition means, data processing means, analysis means, plan creation means, user interface means, and update means.

[0164] Users install a dedicated application on their smartphone or wearable device to begin collecting biometric and emotional data. Biometric data includes heart rate, steps taken, and body temperature, while emotional data includes facial expressions, tone of voice, and text messages. This data is collected by the user's device.

[0165] The terminal periodically transmits this data to the server via wireless communication or an internet connection. The transmitted data is then aggregated on the server.

[0166] The server uses data processing tools to integrate, standardize, and anonymize the received biometric and emotional data. This makes the data analyzable while protecting user privacy. The server then analyzes the data using a generative AI model. This analysis enables the assessment of the user's health risk and emotional state.

[0167] Based on the evaluated data, the server uses a plan creation mechanism to generate a personalized health management plan for each user. This plan takes into account not only the user's physical condition but also their daily emotional state, and includes suggestions for appropriate exercise and relaxation methods.

[0168] Users receive the plan via their device and integrate it into their daily lives. The user interface presents the plan in an emotionally relatable way, enabling effective interaction. Furthermore, the system includes update mechanisms that continuously track changes in the user's biometric and emotional data, updating the plan in real time as needed.

[0169] For example, if a user is not getting enough exercise, the server can suggest a 20-minute walk every day and send an encouraging message. An example of a prompt to the generative AI model would be a question like, "What is the best exercise for a user who is not getting enough exercise?" This would enable the model to suggest a specific plan.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] Users launch a dedicated application and collect biometric and emotional data using their smartphones or wearable devices. The data collected includes heart rate, steps taken, body temperature, facial expressions, voice tone, and text messages. This data is stored in the device's temporary storage. The data collection process is automated; users simply wear the device.

[0173] Step 2:

[0174] The device periodically transmits collected biometric and emotional data to a server via wireless communication. The input data is previously collected data, and the output data is sent to the server. Specifically, the application on the device packages, encodes, and encrypts the data at regular intervals before transmitting it.

[0175] Step 3:

[0176] The server integrates, standardizes, and anonymizes the received data using data processing tools. The data received as input is formatted and anonymized for privacy protection. This results in anonymized data in a format suitable for analysis as output. Specific operations include removing outliers and formatting the data.

[0177] Step 4:

[0178] The server analyzes standardized data using a generative AI model. Using anonymized data as input, it assesses health risk and emotional state. The output includes risk assessment results and emotional assessment results. Specific operations include running the AI ​​model and applying the assessment algorithm.

[0179] Step 5:

[0180] The server creates an individually optimized health management plan using a plan creation mechanism based on the analysis results. Based on the evaluation results as input, it generates a user-specific health plan as output. Specifically, it compares the evaluation results with past data and creates a plan that includes optimal action suggestions.

[0181] Step 6:

[0182] Users receive a health management plan through a user interface on their device and use this information to improve their daily lives. The plan content is displayed in a way that suits the user as input, and the output is designed to elicit user action. Specifically, this involves notifying users of the plan as alerts or messages to encourage user interaction.

[0183] (Application Example 2)

[0184] 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".

[0185] In recent years, managing the health of workers and improving productivity in factories and production sites has become increasingly important. However, there is a lack of systems that continuously monitor the operating status of equipment and robots used in these sites and make appropriate adjustments based on that monitoring. In particular, since the operating status of equipment can affect productivity and safety, there is a need for a function that can accurately evaluate this and notify operators as needed.

[0186] 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.

[0187] In this invention, the server includes data acquisition means for acquiring biometric data from a user, data processing means for integrating, standardizing, and anonymizing the acquired biometric data, and monitoring means for monitoring the operating status of the equipment and evaluating its operating status. This enables appropriate adjustments and notifications based on the operating status.

[0188] "Data acquisition means" refers to devices and methods for collecting information such as biometric data and operating status from users or equipment.

[0189] "Data processing means" refers to the process of integrating, standardizing, and anonymizing acquired biometric data, thereby enabling data handling while protecting individual privacy.

[0190] "Analysis methods" refer to systems that evaluate health risks and equipment operating conditions by analyzing anonymized data.

[0191] The "plan creation method" is a mechanism for formulating health management plans and equipment operation adjustment plans optimized for the user based on evaluation results.

[0192] "User interface means" refers to a display device or method for presenting created plans and notification information to the user in an easily understandable manner.

[0193] "Monitoring means" refers to devices or systems for continuously observing the operating status of equipment and evaluating the smoothness of operation and noise levels.

[0194] A "notification system" is a mechanism that transmits appropriate information to the operator when adjustments are needed based on the operating status of the equipment.

[0195] In this invention, a series of processes are carried out through the collaboration of a server, terminals, and users to realize a health management system in factories and production sites. The server is responsible for receiving data transmitted from users and devices and performing data processing and analysis based on that data. Specifically, the invention is implemented in the following form.

[0196] The server receives biometric data and device operation data collected from users and devices through various sensors using data acquisition means. This data is then standardized and anonymized using data processing means to protect individual privacy. Next, a generative AI model is used to analyze the data and evaluate health risks and device operation status. This evaluation includes operational conditions such as device vibration and noise levels.

[0197] Based on the evaluation results, the plan creation system generates individually optimized health management plans and equipment operation improvement plans. These plans are presented to the user via a user interface, allowing the user to review the plan and implement self-care and equipment adjustments.

[0198] For example, if a robot in a production facility starts exhibiting behavior different from its normal work pattern, the server can analyze the robot's vibration data and notify the operator that abnormal noise is increasing. Based on this result, the server can also propose a robot maintenance schedule.

[0199] For example, a prompt message could include a notification to the operator, such as, "Based on this robot's recent operational data, have its work efficiency or driving smoothness decreased?"

[0200] This allows users to comprehensively evaluate their health status and the operating status of their equipment, enabling them to take quick and effective action.

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The terminal collects biometric and operational status data from the user and the device. This data includes the user's heart rate and body temperature, as well as the device's vibration and temperature. The terminal collects this data and prepares it to be transmitted to the server.

[0204] Step 2:

[0205] The server receives biometric and driving status data transmitted from the terminal. The server uses data processing equipment to standardize and anonymize the data. In this process, the collected data is converted into a common format. For example, the unit of heart rate is standardized to bpm and vibration to Hz.

[0206] Step 3:

[0207] The server inputs anonymized data into a generative AI model for data analysis. The generative AI model assesses health risk from biometric data and simultaneously detects abnormal patterns in equipment from operating status data. The output includes health status assessments and probabilities of abnormal conditions.

[0208] Step 4:

[0209] The server planning mechanism creates an optimized plan based on the results of health and operating status assessments. For example, if high stress is detected, it will include stretching suggestions, and if abnormal vibrations are detected, it will recommend inspection today.

[0210] Step 5:

[0211] The server notifies the user of the generated plan through a user interface. The information is presented in a user-friendly interface and in a way that is easy to incorporate into daily activities. For example, a prompt might ask, "Should I inspect the robot's operation?"

[0212] Step 6:

[0213] Users check notifications from the server via their terminals and take action on health management suggestions and equipment adjustment instructions. The results of these actions are also entered into the terminal, providing feedback data that the system uses to further optimize future suggestions.

[0214] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0215] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0216] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0217] [Second Embodiment]

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

[0219] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0220] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0221] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0222] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0223] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0224] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0225] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0226] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0227] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0228] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0229] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0230] The present invention provides a system that assesses health risks through individual users' biometric data and creates individually optimized health management plans. This system mainly consists of data acquisition means, data processing means, analysis means, plan creation means, and user interface means.

[0231] First, users collect their biometric data (e.g., steps, heart rate, sleep data, etc.) using smartphones or wearable devices. This data is measured in real time through various sensors. The data is stored directly on the user's device and prepared for transfer to a server via data acquisition methods.

[0232] The device transmits this biometric data to the server at appropriate intervals. During this process, corrections and standardization based on user input are performed as needed to ensure the accuracy and consistency of the data.

[0233] The server aggregates and integrates the received biometric data. This ensures that information collected from different data sources is presented in a consistent format. Subsequently, the information is anonymized by data processing tools to protect privacy, and processing proceeds without identifying individuals.

[0234] Next, the server analyzes the anonymized data using a generative AI model. Multiple indicators for assessing frailty risk are considered and converted into a numerical risk score representing each user's health status. These indicators include daily activity levels and sleep quality.

[0235] Furthermore, the server generates a customized care plan that reflects the individual user's health condition based on the analysis results. This plan includes specific action suggestions (e.g., ensuring a certain amount of exercise per week, dietary improvement plan, etc.) and provides guidance aimed at improving health.

[0236] Ultimately, users review the care plan presented through their device and effectively manage their health at home. The results and progress are recorded on the device and used to update future plans. In this way, users can continuously manage their health and receive better care through a feedback cycle.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] Users sync their smartphones or wearable devices with the app. This prepares the app to collect health-related information such as steps, heart rate, and sleep data.

[0240] Step 2:

[0241] The device collects the user's biometric data in real time and automatically stores it in temporary memory. It can be configured to send this data to a server at specific times (e.g., late at night every day) or trigger events (e.g., when Wi-Fi is connected).

[0242] Step 3:

[0243] The server receives data sent from terminals and aggregates it into a database. Data in different formats is integrated and converted into a standard format suitable for analysis.

[0244] Step 4:

[0245] The server anonymizes the data using data processing tools to ensure privacy. This prepares the data for analysis without identifying personal information.

[0246] Step 5:

[0247] The generative AI model assesses the user's health status using anonymized data. It calculates a frailty risk score using parameters such as steps taken, heart rate, and sleep quality.

[0248] Step 6:

[0249] The server creates a personalized care plan for each user based on the evaluation results. The plan includes exercise programs and dietary improvements tailored to the user's lifestyle.

[0250] Step 7:

[0251] Users receive care plans presented through their devices and incorporate them into their daily lives. The devices track health maintenance activities based on the plan and record progress.

[0252] Step 8:

[0253] The server analyzes the feedback data resubmitted by the user and updates the care plan as needed. This cycle is repeated to support continuous health management.

[0254] (Example 1)

[0255] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0256] While there is a growing need for personalized and efficient health management, conventional systems have failed to protect user privacy during the collection and analysis of biometric data, making it difficult to generate effective health management plans based on the analysis results. Furthermore, real-time data updates and continuous tracking of health status are insufficient, resulting in delays in providing users with feedback on their health management.

[0257] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0258] In this invention, the server includes input means, processing means, protection means, evaluation means, planning means, and display means. This makes it possible to analyze anonymized biometric data using a generative artificial intelligence model while protecting user privacy, and to generate and present a personalized health management plan in real time. As a result, users can continuously monitor their health status and implement appropriate health management.

[0259] An "input means" is a mechanism for acquiring biometric data from a user.

[0260] A "processing means" is a mechanism that integrates acquired biological data and converts it into a standardized format.

[0261] "Protective measures" refer to mechanisms that protect privacy by anonymizing processed data.

[0262] The "evaluation method" is a mechanism that uses anonymized data to analyze the risk of health conditions using a generative artificial intelligence model.

[0263] "Planning method" refers to a mechanism that generates individually optimized health management plans based on evaluation results.

[0264] A "display means" is a mechanism for presenting the generated plan to the user.

[0265] This invention utilizes a combination of specific hardware and software to support the health management of individual users. Specifically, users collect biometric data such as heart rate, steps taken, and sleep data using input means such as smartphones or wearable devices. This data is measured by sensors within the device, such as accelerometers and heart rate monitors.

[0266] Next, the terminal standardizes the collected data using processing tools and prepares it for transmission to the server. This process includes data format conversion and correction of inconsistencies. The terminal then uses encrypted communication methods to securely transfer the data to the server.

[0267] The server anonymizes the received data using protective measures, organizing it into a consistent format while preserving privacy. This anonymized data is then analyzed using an evaluation method that utilizes a generative artificial intelligence model. This procedure uses prompts such as, "Create a health management plan suitable for a user who is a man in his 50s, takes an average of 6,000 steps per day, and sleeps an average of 7 hours," and calculates a risk score for the user's health status.

[0268] Furthermore, the server uses planning tools based on the analysis results to create a health management plan optimized for each user. This plan includes specific examples, such as "150 minutes of aerobic exercise per week is recommended." The plan is presented to the user on their device through a display tool, allowing them to review its contents and use it to manage their daily health.

[0269] This entire system seamlessly handles everything from data acquisition and analysis to plan generation and presentation, enabling personalized and optimized health management for each user.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] Users collect biometric data using smartphones or wearable devices. Inputs include information such as heart rate, steps taken, and sleep data. Sensors within the device (e.g., accelerometer, heart rate monitor) measure the data in real time, and it is stored on the device as an initial dataset.

[0273] Step 2:

[0274] The terminal verifies the collected data and uses processing tools to standardize it. Input is in the raw biometric data format, and output is in a unified format that can be sent to the server. During this process, inconsistencies are corrected and timestamps are added.

[0275] Step 3:

[0276] The terminal sends data to the server. This transmission process is encrypted to ensure the security of the communication. The input is standardized biometric data, and the output is encrypted data received by the server.

[0277] Step 4:

[0278] The server anonymizes the received data with protection means and securely stores it in the database. The input is encrypted biometric data, and the output is an anonymized dataset. This anonymization includes operations to remove personal identification information to maintain privacy.

[0279] Step 5:

[0280] The server uses evaluation means to analyze the anonymized data using the generated AI model. A prompt sentence (e.g., "Evaluate the health risk of this user") is utilized in this process. The input is anonymized data, and the output is a health risk score for each user.

[0281] Step 6:

[0282] The server uses planning means to generate an individually optimized health management plan based on the health risk score. The input is the health risk score, and the output is a health management plan that includes specific action plans. This plan includes, for example, "Recommend taking a 30-minute walk every day."

[0283] Step 7:

[0284] The server sends the generated plan to the terminal, and the terminal presents the plan to the user through display means. The input is the health management plan, and the output is information visually presented to the user. The user checks the presented content and uses it for daily health management.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server," and the smart glasses 214 are referred to as the "terminal."

[0287] While assessing health risks and providing appropriate care plans based on those assessments is crucial for maintaining users' health, it is difficult to address individual needs in practice. Furthermore, updating plans in real time to reflect changes in health status and recommending appropriate products for daily life are also challenges. Against this backdrop, there is a need for a system that creates individually optimized health and care plans based on health risks and provides product recommendations in real time and effectively.

[0288] 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.

[0289] In this invention, the server includes data acquisition means for obtaining biological information from the user; data processing means for integrating, standardizing, and anonymizing the acquired biological information; analysis means for analyzing the anonymized information and evaluating the risk of the user's health condition; plan creation means for creating an individually optimized health care plan based on the evaluation results; user interface means for presenting the created plan to the user; recommendation means for recommending products based on the user's health risk; and adjustment means for readjusting the plan based on the user's purchase history and progress. This makes it possible to provide an individually optimized care plan based on the user's health risk and to recommend appropriate products in a timely manner.

[0290] A "user" is an individual who provides biometric information and is the target of this system's services.

[0291] "Biological information" refers to data that forms the basis for assessing health status, and includes measurements such as heart rate and step count.

[0292] "Data acquisition means" refers to a method or device for collecting biological information from users and incorporating it into a system.

[0293] "Data processing means" refers to a method or apparatus for integrating, standardizing, and anonymizing collected biological information.

[0294] "Analysis means" refers to a method or apparatus for evaluating the risk of a health condition using anonymized information.

[0295] "Plan creation means" refers to a method or apparatus for designing an individually optimized health care plan based on an assessed health condition.

[0296] "User interface means" refers to a method or device for presenting a created plan to a user visually or tactilely.

[0297] "Recommendation method" refers to a method or device for showing a user appropriate products based on their health risks.

[0298] "Adjustment means" refers to a method or device for resetting an existing plan based on the user's purchase history and progress.

[0299] The system that realizes this application consists of various elements that work together to provide an optimal care plan and product recommendations based on the user's health condition.

[0300] Users collect their own biometric information using smartphones and wearable devices. These devices include heart rate sensors and accelerometers, and collect data through them on a daily basis.

[0301] The device sends the collected biological information to the server. The transmitted data is integrated, standardized, and anonymized using software such as Python libraries (e.g., NumPy, Pandas).

[0302] The server uses a generative AI model (e.g., TensorFlow) to assess health risks on anonymized data. Based on the assessment results, a specific health care plan is automatically created.

[0303] In addition, the server analyzes the user's health risks and makes appropriate product recommendations based on this. To do this, it selects and recommends the optimal products from the product database and the user's purchase history.

[0304] The user interface presents the generated plan and product recommendations to the user through a smartphone application. As a result, the user can obtain guidance on health management and easily purchase the optimal products.

[0305] As a specific example, the server analyzes the user's heart rate data and recommends products such as "Since your recent heart rate has been relatively high, we recommend purchasing tea with a relaxing effect."

[0306] Examples of specific prompt texts are as follows: "Recommend suitable health products based on user health risk scores and enable seamless in-app purchasing to maintain user health effectively."

[0307] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The user collects biometric information using a smartphone or wearable device. This includes the number of steps, heart rate, sleep data, etc. These data are temporarily stored in the device.

[0310] Step 2:

[0311] The terminal transmits the collected biometric information to the server. After transmission, the data is integrated and standardized using libraries such as NumPy and Pandas. To improve the accuracy of the data, some manual input corrections are also made. As a result, standardized data in a form suitable for analysis is obtained.

[0312] Step 3:

[0313] The server anonymizes the received data and transforms it into a form that does not identify individuals. The data processing means processes the data so that it can be analyzed while protecting personal information. The output is anonymized, integrated data.

[0314] Step 4:

[0315] The server analyzes anonymized data using a generative AI model based on TensorFlow. It quantifies health status based on heart rate and activity data, and calculates a health risk score for each user. The output here is the risk score for each user.

[0316] Step 5:

[0317] The server automatically generates a health care plan based on the risk score assessment results. Using the plan creation tool, it provides suggestions for exercise and dietary improvements tailored to the user's condition. The generated output is an individually optimized care plan.

[0318] Step 6:

[0319] The server recommends appropriate products based on the user's health risks. It refers to a product database to identify and recommend products that contribute to risk reduction. As a result, a list of recommended products is generated.

[0320] Step 7:

[0321] The user interface presents plans and product recommendations to the user via a smartphone app. Users can then make decisions regarding health management and product purchases based on the displayed information. The output of this step is the health information and product recommendations available to the user.

[0322] 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.

[0323] This invention combines an emotion engine with a system that manages a user's health status and provides an individually optimized health management plan, thereby enabling comprehensive health management that also takes into account the user's emotional state. This system consists of data acquisition means, data processing means, analysis means, emotion engine, plan creation means, and user interface means.

[0324] Users install a dedicated app on their smartphone or wearable device to begin collecting biometric data. This biometric data includes heart rate, steps taken, and body temperature, and is stored on the user's device. The emotion engine also analyzes the user's facial expressions, voice tone, and entered text messages to collect emotional data.

[0325] The device periodically transmits this biometric and emotional data to a server. The transmitted data is aggregated on the server and standardized and anonymized using data processing tools. This prepares the data for analysis while protecting user privacy.

[0326] The server analyzes this data using a generative AI model. Biometric data is used to assess the user's health risks, while emotional data obtained by the emotion engine is used to evaluate the user's daily stress levels and emotional state.

[0327] Based on the evaluated data, the server uses a plan creation mechanism to create a personalized health management plan for each user. This plan takes into account both the user's physical and emotional state and may include suggestions for appropriate exercise and relaxation. For example, if emotional data indicates high stress levels, relaxation exercises or meditation may be suggested.

[0328] Users can receive these health management plans through their devices and incorporate them into their daily lives. The user interface provides a more personalized experience by selecting interaction methods that respond to the user's emotions and adjusting the tone of messages and notifications.

[0329] For example, if a user has low activity levels during the day and the emotional engine detects stress, the server will incorporate light yoga or deep breathing exercises into their health plan. The user interface will also display encouraging messages in a gentle tone to promote positive behavior. Through this process, users can manage their health in a way that supports both their physical and mental well-being.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] Users install the app on their smartphone or wearable device and synchronize it with the device. This initiates the collection of biometric data such as heart rate, steps taken, and body temperature.

[0333] Step 2:

[0334] The device captures the user's facial expressions and voice, and analyzes emotional data through an emotion engine. This emotional data includes the user's mood and stress level, and is quantified.

[0335] Step 3:

[0336] The device transmits biometric and emotional data to the server at regular intervals. During this transmission, the data is transmitted while maintaining accuracy and consistency.

[0337] Step 4:

[0338] The server stores the data received from the terminal in a database and performs standardization and anonymization using data processing methods. This process ensures the protection of personal information.

[0339] Step 5:

[0340] The server uses a generative AI model to analyze anonymized biometric and emotional data, and calculates a health risk score and emotional state score for each user.

[0341] Step 6:

[0342] Based on these evaluation results, the server utilizes planning tools to create an individually optimized health management plan. This plan includes recommendations for appropriate exercise and relaxation, and incorporates stress reduction programs based on emotional data.

[0343] Step 7:

[0344] Users review health management plans sent from the system via their devices and incorporate suggested activities into their daily lives. The devices continue to track and record the activities performed.

[0345] Step 8:

[0346] The server receives user feedback, new biometric data, and emotional data, and updates the plan in real time. This process is repeated, allowing users to continuously manage their health optimally.

[0347] (Example 2)

[0348] 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".

[0349] In modern society, many people are expected to maintain not only physical health but also mental health. However, existing health management systems are limited to evaluating health status based on users' biometric data, and there is a problem in that they do not adequately consider mental factors such as emotions and stress. Therefore, there is a need for a system that enables comprehensive health management, including emotional state.

[0350] 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.

[0351] In this invention, the server includes data acquisition means for acquiring biometric and emotional data from users, data processing means for integrating, standardizing, and anonymizing the acquired biometric and emotional data, and analysis means for analyzing the anonymized data using a generating AI model to evaluate the risks to health and emotional states. This makes it possible to provide individually optimized health management plans that take into account both the physical and mental health of the user.

[0352] "Data acquisition means" refers to a device or method for acquiring biometric data and emotional data from a user.

[0353] "Data processing means" refers to a device or method for integrating, standardizing, and anonymizing acquired biometric data and emotional data.

[0354] A "generative AI model" is an artificial intelligence model used to appropriately analyze collected data.

[0355] "Analysis means" refers to an apparatus or method for analyzing anonymized data and assessing the risks to a user's health and emotional state.

[0356] "Plan creation means" refers to a device or method for creating a health management plan that is individually optimized for each user based on evaluation results.

[0357] "User interface means" refers to a device or method that adjusts and presents a created health management plan according to the user's emotions.

[0358] "Update means" refers to a device or method that tracks changes based on biometric data and emotional data and updates the plan in real time.

[0359] This health management system aims to comprehensively manage the user's biological and emotional state and provide an individually optimized health plan. The system consists of data acquisition means, data processing means, analysis means, plan creation means, user interface means, and update means.

[0360] Users install a dedicated application on their smartphone or wearable device to begin collecting biometric and emotional data. Biometric data includes heart rate, steps taken, and body temperature, while emotional data includes facial expressions, tone of voice, and text messages. This data is collected by the user's device.

[0361] The terminal periodically transmits this data to the server via wireless communication or an internet connection. The transmitted data is then aggregated on the server.

[0362] The server uses data processing tools to integrate, standardize, and anonymize the received biometric and emotional data. This makes the data analyzable while protecting user privacy. The server then analyzes the data using a generative AI model. This analysis enables the assessment of the user's health risk and emotional state.

[0363] Based on the evaluated data, the server uses a plan creation mechanism to generate a personalized health management plan for each user. This plan takes into account not only the user's physical condition but also their daily emotional state, and includes suggestions for appropriate exercise and relaxation methods.

[0364] Users receive the plan via their device and integrate it into their daily lives. The user interface presents the plan in an emotionally relatable way, enabling effective interaction. Furthermore, the system includes update mechanisms that continuously track changes in the user's biometric and emotional data, updating the plan in real time as needed.

[0365] For example, if a user is not getting enough exercise, the server can suggest a 20-minute walk every day and send an encouraging message. An example of a prompt to the generative AI model would be a question like, "What is the best exercise for a user who is not getting enough exercise?" This would enable the model to suggest a specific plan.

[0366] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0367] Step 1:

[0368] Users launch a dedicated application and collect biometric and emotional data using their smartphones or wearable devices. The data collected includes heart rate, steps taken, body temperature, facial expressions, voice tone, and text messages. This data is stored in the device's temporary storage. The data collection process is automated; users simply wear the device.

[0369] Step 2:

[0370] The device periodically transmits collected biometric and emotional data to a server via wireless communication. The input data is previously collected data, and the output data is sent to the server. Specifically, the application on the device packages, encodes, and encrypts the data at regular intervals before transmitting it.

[0371] Step 3:

[0372] The server integrates, standardizes, and anonymizes the received data using data processing tools. The data received as input is formatted and anonymized for privacy protection. This results in anonymized data in a format suitable for analysis as output. Specific operations include removing outliers and formatting the data.

[0373] Step 4:

[0374] The server analyzes standardized data using a generative AI model. Using anonymized data as input, it assesses health risk and emotional state. The output includes risk assessment results and emotional assessment results. Specific operations include running the AI ​​model and applying the assessment algorithm.

[0375] Step 5:

[0376] The server creates an individually optimized health management plan using a plan creation mechanism based on the analysis results. Based on the evaluation results as input, it generates a user-specific health plan as output. Specifically, it compares the evaluation results with past data and creates a plan that includes optimal action suggestions.

[0377] Step 6:

[0378] Users receive a health management plan through a user interface on their device and use this information to improve their daily lives. The plan content is displayed in a way that suits the user as input, and the output is designed to elicit user action. Specifically, this involves notifying users of the plan as alerts or messages to encourage user interaction.

[0379] (Application Example 2)

[0380] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0381] In recent years, managing the health of workers and improving productivity in factories and production sites has become increasingly important. However, there is a lack of systems that continuously monitor the operating status of equipment and robots used in these sites and make appropriate adjustments based on that monitoring. In particular, since the operating status of equipment can affect productivity and safety, there is a need for a function that can accurately evaluate this and notify operators as needed.

[0382] 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.

[0383] In this invention, the server includes data acquisition means for acquiring biometric data from a user, data processing means for integrating, standardizing, and anonymizing the acquired biometric data, and monitoring means for monitoring the operating status of the equipment and evaluating its operating status. This enables appropriate adjustments and notifications based on the operating status.

[0384] "Data acquisition means" refers to devices and methods for collecting information such as biometric data and operating status from users or equipment.

[0385] "Data processing means" refers to the process of integrating, standardizing, and anonymizing acquired biometric data, thereby enabling data handling while protecting individual privacy.

[0386] "Analysis methods" refer to systems that evaluate health risks and equipment operating conditions by analyzing anonymized data.

[0387] The "plan creation method" is a mechanism for formulating health management plans and equipment operation adjustment plans optimized for the user based on evaluation results.

[0388] "User interface means" refers to a display device or method for presenting created plans and notification information to the user in an easily understandable manner.

[0389] "Monitoring means" refers to devices or systems for continuously observing the operating status of equipment and evaluating the smoothness of operation and noise levels.

[0390] A "notification system" is a mechanism that transmits appropriate information to the operator when adjustments are needed based on the operating status of the equipment.

[0391] In this invention, a series of processes are carried out through the collaboration of a server, terminals, and users to realize a health management system in factories and production sites. The server is responsible for receiving data transmitted from users and devices and performing data processing and analysis based on that data. Specifically, the invention is implemented in the following form.

[0392] The server receives biometric data and device operation data collected from users and devices through various sensors using data acquisition means. This data is then standardized and anonymized using data processing means to protect individual privacy. Next, a generative AI model is used to analyze the data and evaluate health risks and device operation status. This evaluation includes operational conditions such as device vibration and noise levels.

[0393] Based on the evaluation results, the plan creation system generates individually optimized health management plans and equipment operation improvement plans. These plans are presented to the user via a user interface, allowing the user to review the plan and implement self-care and equipment adjustments.

[0394] For example, if a robot in a production facility starts exhibiting behavior different from its normal work pattern, the server can analyze the robot's vibration data and notify the operator that abnormal noise is increasing. Based on this result, the server can also propose a robot maintenance schedule.

[0395] For example, a prompt message could include a notification to the operator, such as, "Based on this robot's recent operational data, have its work efficiency or driving smoothness decreased?"

[0396] This allows users to comprehensively evaluate their health status and the operating status of their equipment, enabling them to take quick and effective action.

[0397] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0398] Step 1:

[0399] The terminal collects biometric and operational status data from the user and the device. This data includes the user's heart rate and body temperature, as well as the device's vibration and temperature. The terminal collects this data and prepares it to be transmitted to the server.

[0400] Step 2:

[0401] The server receives biometric and driving status data transmitted from the terminal. The server uses data processing equipment to standardize and anonymize the data. In this process, the collected data is converted into a common format. For example, the unit of heart rate is standardized to bpm and vibration to Hz.

[0402] Step 3:

[0403] The server inputs anonymized data into a generative AI model for data analysis. The generative AI model assesses health risk from biometric data and simultaneously detects abnormal patterns in equipment from operating status data. The output includes health status assessments and probabilities of abnormal conditions.

[0404] Step 4:

[0405] The server planning mechanism creates an optimized plan based on the results of health and operating status assessments. For example, if high stress is detected, it will include stretching suggestions, and if abnormal vibrations are detected, it will recommend inspection today.

[0406] Step 5:

[0407] The server notifies the user of the generated plan through a user interface. The information is presented in a user-friendly interface and in a way that is easy to incorporate into daily activities. For example, a prompt might ask, "Should I inspect the robot's operation?"

[0408] Step 6:

[0409] Users check notifications from the server via their terminals and take action on health management suggestions and equipment adjustment instructions. The results of these actions are also entered into the terminal, providing feedback data that the system uses to further optimize future suggestions.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] [Third Embodiment]

[0414] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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).

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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".

[0426] The present invention provides a system that assesses health risks through individual users' biometric data and creates individually optimized health management plans. This system mainly consists of data acquisition means, data processing means, analysis means, plan creation means, and user interface means.

[0427] First, users collect their biometric data (e.g., steps, heart rate, sleep data, etc.) using smartphones or wearable devices. This data is measured in real time through various sensors. The data is stored directly on the user's device and prepared for transfer to a server via data acquisition methods.

[0428] The device transmits this biometric data to the server at appropriate intervals. During this process, corrections and standardization based on user input are performed as needed to ensure the accuracy and consistency of the data.

[0429] The server aggregates and integrates the received biometric data. This ensures that information collected from different data sources is presented in a consistent format. Subsequently, the information is anonymized by data processing tools to protect privacy, and processing proceeds without identifying individuals.

[0430] Next, the server analyzes the anonymized data using a generative AI model. Multiple indicators for assessing frailty risk are considered and converted into a numerical risk score representing each user's health status. These indicators include daily activity levels and sleep quality.

[0431] Furthermore, the server generates a customized care plan that reflects the individual user's health condition based on the analysis results. This plan includes specific action suggestions (e.g., ensuring a certain amount of exercise per week, dietary improvement plan, etc.) and provides guidance aimed at improving health.

[0432] Ultimately, users review the care plan presented through their device and effectively manage their health at home. The results and progress are recorded on the device and used to update future plans. In this way, users can continuously manage their health and receive better care through a feedback cycle.

[0433] The following describes the processing flow.

[0434] Step 1:

[0435] Users sync their smartphones or wearable devices with the app. This prepares the app to collect health-related information such as steps, heart rate, and sleep data.

[0436] Step 2:

[0437] The device collects the user's biometric data in real time and automatically stores it in temporary memory. It can be configured to send this data to a server at specific times (e.g., late at night every day) or trigger events (e.g., when Wi-Fi is connected).

[0438] Step 3:

[0439] The server receives data sent from terminals and aggregates it into a database. Data in different formats is integrated and converted into a standard format suitable for analysis.

[0440] Step 4:

[0441] The server anonymizes the data using data processing tools to ensure privacy. This prepares the data for analysis without identifying personal information.

[0442] Step 5:

[0443] The generative AI model assesses the user's health status using anonymized data. It calculates a frailty risk score using parameters such as steps taken, heart rate, and sleep quality.

[0444] Step 6:

[0445] The server creates a personalized care plan for each user based on the evaluation results. The plan includes exercise programs and dietary improvements tailored to the user's lifestyle.

[0446] Step 7:

[0447] Users receive care plans presented through their devices and incorporate them into their daily lives. The devices track health maintenance activities based on the plan and record progress.

[0448] Step 8:

[0449] The server analyzes the feedback data resubmitted by the user and updates the care plan as needed. This cycle is repeated to support continuous health management.

[0450] (Example 1)

[0451] 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."

[0452] While there is a growing need for personalized and efficient health management, conventional systems have failed to protect user privacy during the collection and analysis of biometric data, making it difficult to generate effective health management plans based on the analysis results. Furthermore, real-time data updates and continuous tracking of health status are insufficient, resulting in delays in providing users with feedback on their health management.

[0453] 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.

[0454] In this invention, the server includes input means, processing means, protection means, evaluation means, planning means, and display means. This makes it possible to analyze anonymized biometric data using a generative artificial intelligence model while protecting user privacy, and to generate and present a personalized health management plan in real time. As a result, users can continuously monitor their health status and implement appropriate health management.

[0455] An "input means" is a mechanism for acquiring biometric data from a user.

[0456] A "processing means" is a mechanism that integrates acquired biological data and converts it into a standardized format.

[0457] A "protective measure" is a mechanism that protects privacy by anonymizing processed data.

[0458] The "evaluation method" is a mechanism that uses anonymized data to analyze the risk of health conditions using a generative artificial intelligence model.

[0459] "Planning method" refers to a mechanism that generates individually optimized health management plans based on evaluation results.

[0460] A "display means" is a mechanism for presenting the generated plan to the user.

[0461] This invention utilizes a combination of specific hardware and software to support the health management of individual users. Specifically, users collect biometric data such as heart rate, steps taken, and sleep data using input means such as smartphones or wearable devices. This data is measured by sensors within the device, such as accelerometers and heart rate monitors.

[0462] Next, the terminal standardizes the collected data using processing tools and prepares it for transmission to the server. This process includes data format conversion and correction of inconsistencies. The terminal then uses encrypted communication methods to securely transfer the data to the server.

[0463] The server anonymizes the received data using protective measures, organizing it into a consistent format while preserving privacy. This anonymized data is then analyzed using an evaluation method that utilizes a generative artificial intelligence model. This procedure uses prompts such as, "Create a health management plan suitable for a user who is a man in his 50s, takes an average of 6,000 steps per day, and sleeps an average of 7 hours," and calculates a risk score for the user's health status.

[0464] Furthermore, the server uses planning tools based on the analysis results to create a health management plan optimized for each user. This plan includes specific examples, such as "150 minutes of aerobic exercise per week is recommended." The plan is presented to the user on their device through a display tool, allowing them to review its contents and use it to manage their daily health.

[0465] This entire system seamlessly handles everything from data acquisition and analysis to plan generation and presentation, enabling personalized and optimized health management for each user.

[0466] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0467] Step 1:

[0468] Users collect biometric data using smartphones or wearable devices. Inputs include information such as heart rate, steps taken, and sleep data. Sensors within the device (e.g., accelerometer, heart rate monitor) measure the data in real time, and it is stored on the device as an initial dataset.

[0469] Step 2:

[0470] The terminal verifies the collected data and uses processing tools to standardize it. Input is in the raw biometric data format, and output is in a unified format that can be sent to the server. During this process, inconsistencies are corrected and timestamps are added.

[0471] Step 3:

[0472] The terminal sends data to the server. This transmission process is encrypted to ensure the security of the communication. The input is standardized biometric data, and the output is encrypted data received by the server.

[0473] Step 4:

[0474] The server anonymizes the received data using protective measures and securely stores it in a database. The input is encrypted biometric data, and the output is an anonymized dataset. This anonymization includes operations to remove personally identifiable information in order to maintain privacy.

[0475] Step 5:

[0476] The server uses evaluation tools and a generative AI model to analyze anonymized data. This process utilizes prompts (e.g., "Assess this user's health risk"). The input is anonymized data, and the output is a health risk score for each user.

[0477] Step 6:

[0478] The server uses planning tools to generate an individually optimized health management plan based on a health risk score. The input is the health risk score, and the output is a health management plan that includes specific action suggestions. This plan might include, for example, "a 30-minute walk every day is recommended."

[0479] Step 7:

[0480] The server sends the generated plan to the terminal, and the terminal presents the plan to the user through a display device. The input is the health management plan, and the output is information presented visually to the user. The user reviews the presented information and uses it to manage their daily health.

[0481] (Application Example 1)

[0482] 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."

[0483] While assessing health risks and providing appropriate care plans based on those assessments is crucial for maintaining users' health, it is difficult to address individual needs in practice. Furthermore, updating plans in real time to reflect changes in health status and recommending appropriate products for daily life are also challenges. Against this backdrop, there is a need for a system that creates individually optimized health and care plans based on health risks and provides product recommendations in real time and effectively.

[0484] 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.

[0485] In this invention, the server includes data acquisition means for obtaining biological information from the user; data processing means for integrating, standardizing, and anonymizing the acquired biological information; analysis means for analyzing the anonymized information and evaluating the risk of the user's health condition; plan creation means for creating an individually optimized health care plan based on the evaluation results; user interface means for presenting the created plan to the user; recommendation means for recommending products based on the user's health risk; and adjustment means for readjusting the plan based on the user's purchase history and progress. This makes it possible to provide an individually optimized care plan based on the user's health risk and to recommend appropriate products in a timely manner.

[0486] A "user" is an individual who provides biometric information and is the target of this system's services.

[0487] "Biological information" refers to data that forms the basis for assessing health status, and includes measurements such as heart rate and step count.

[0488] "Data acquisition means" refers to a method or device for collecting biological information from users and incorporating it into a system.

[0489] "Data processing means" refers to a method or apparatus for integrating, standardizing, and anonymizing collected biological information.

[0490] "Analysis means" refers to a method or apparatus for evaluating the risk of a health condition using anonymized information.

[0491] "Plan creation means" refers to a method or apparatus for designing an individually optimized health care plan based on an assessed health condition.

[0492] "User interface means" refers to a method or device for presenting a created plan to a user visually or tactilely.

[0493] "Recommendation method" refers to a method or device for showing a user appropriate products based on their health risks.

[0494] "Adjustment means" refers to a method or device for resetting an existing plan based on the user's purchase history and progress.

[0495] The system that realizes this application consists of various elements that work together to provide an optimal care plan and product recommendations based on the user's health condition.

[0496] Users collect their own biometric information using smartphones and wearable devices. These devices include heart rate sensors and accelerometers, and collect data through them on a daily basis.

[0497] The device sends the collected biological information to the server. The transmitted data is integrated, standardized, and anonymized using software such as Python libraries (e.g., NumPy, Pandas).

[0498] The server uses a generative AI model (e.g., TensorFlow) to assess health risks on anonymized data. Based on the assessment results, a specific health care plan is automatically created.

[0499] Furthermore, the server analyzes the user's health risks and recommends appropriate products based on these risks. To do this, it selects and recommends the most suitable products from the product database and the user's purchase history.

[0500] The user interface presents the generated plan and product recommendations to the user through a smartphone application. This allows the user to obtain guidance for health management and easily purchase the most suitable products.

[0501] As a concrete example, the server analyzes the user's heart rate data and recommends products such as, "Your heart rate has been a little high recently, so we recommend purchasing a relaxing tea."

[0502] An example of a specific prompt message is: "Recommend suitable health products based on user health risk scores and enable seamless in-app purchasing to maintain user health effectively."

[0503] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0504] Step 1:

[0505] Users collect biometric information using smartphones and wearable devices. This includes steps taken, heart rate, and sleep data. This data is temporarily stored within the device.

[0506] Step 2:

[0507] The terminal transmits the collected biological information to the server. After transmission, the data is integrated and standardized using NumPy and Pandas libraries. Some manual input correction is also performed to improve the accuracy of the data. As a result, standardized data in a format suitable for analysis is obtained.

[0508] Step 3:

[0509] The server anonymizes the received data and transforms it into a form that does not identify individuals. The data processing means processes the data so that it can be analyzed while protecting personal information. The output is anonymized, integrated data.

[0510] Step 4:

[0511] The server analyzes anonymized data using a generative AI model based on TensorFlow. It quantifies health status based on heart rate and activity data, and calculates a health risk score for each user. The output here is the risk score for each user.

[0512] Step 5:

[0513] The server automatically generates a health care plan based on the risk score assessment results. Using the plan creation tool, it provides suggestions for exercise and dietary improvements tailored to the user's condition. The generated output is an individually optimized care plan.

[0514] Step 6:

[0515] The server recommends appropriate products based on the user's health risks. It refers to a product database to identify and recommend products that contribute to risk reduction. As a result, a list of recommended products is generated.

[0516] Step 7:

[0517] The user interface presents plans and product recommendations to the user via a smartphone app. Users can then make decisions regarding health management and product purchases based on the displayed information. The output of this step is the health information and product recommendations available to the user.

[0518] 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.

[0519] This invention combines an emotion engine with a system that manages a user's health status and provides an individually optimized health management plan, thereby enabling comprehensive health management that also takes into account the user's emotional state. This system consists of data acquisition means, data processing means, analysis means, emotion engine, plan creation means, and user interface means.

[0520] Users install a dedicated app on their smartphone or wearable device to begin collecting biometric data. This biometric data includes heart rate, steps taken, and body temperature, and is stored on the user's device. The emotion engine also analyzes the user's facial expressions, voice tone, and entered text messages to collect emotional data.

[0521] The device periodically transmits this biometric and emotional data to a server. The transmitted data is aggregated on the server and standardized and anonymized using data processing tools. This prepares the data for analysis while protecting user privacy.

[0522] The server analyzes this data using a generative AI model. Biometric data is used to assess the user's health risks, while emotional data obtained by the emotion engine is used to evaluate the user's daily stress levels and emotional state.

[0523] Based on the evaluated data, the server uses a plan creation mechanism to create a personalized health management plan for each user. This plan takes into account both the user's physical and emotional state and may include suggestions for appropriate exercise and relaxation. For example, if emotional data indicates high stress levels, relaxation exercises or meditation may be suggested.

[0524] Users can receive these health management plans through their devices and incorporate them into their daily lives. The user interface provides a more personalized experience by selecting interaction methods that respond to the user's emotions and adjusting the tone of messages and notifications.

[0525] For example, if a user has low activity levels during the day and the emotional engine detects stress, the server will incorporate light yoga or deep breathing exercises into their health plan. The user interface will also display encouraging messages in a gentle tone to promote positive behavior. Through this process, users can manage their health in a way that supports both their physical and mental well-being.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] Users install the app on their smartphone or wearable device and synchronize it with the device. This initiates the collection of biometric data such as heart rate, steps taken, and body temperature.

[0529] Step 2:

[0530] The device captures the user's facial expressions and voice, and analyzes emotional data through an emotion engine. This emotional data includes the user's mood and stress level, and is quantified.

[0531] Step 3:

[0532] The device transmits biometric and emotional data to the server at regular intervals. During this transmission, the data is transmitted while maintaining accuracy and consistency.

[0533] Step 4:

[0534] The server stores the data received from the terminal in a database and performs standardization and anonymization using data processing methods. This process ensures the protection of personal information.

[0535] Step 5:

[0536] The server uses a generative AI model to analyze anonymized biometric and emotional data, and calculates a health risk score and emotional state score for each user.

[0537] Step 6:

[0538] Based on these evaluation results, the server utilizes planning tools to create an individually optimized health management plan. This plan includes recommendations for appropriate exercise and relaxation, and incorporates stress reduction programs based on emotional data.

[0539] Step 7:

[0540] Users review health management plans sent from the system via their devices and incorporate suggested activities into their daily lives. The devices continue to track and record the activities performed.

[0541] Step 8:

[0542] The server receives user feedback, new biometric data, and emotional data, and updates the plan in real time. This process is repeated, allowing users to continuously manage their health optimally.

[0543] (Example 2)

[0544] 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."

[0545] In modern society, many people are expected to maintain not only physical health but also mental health. However, existing health management systems are limited to evaluating health status based on users' biometric data, and there is a problem in that they do not adequately consider mental factors such as emotions and stress. Therefore, there is a need for a system that enables comprehensive health management, including emotional state.

[0546] 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.

[0547] In this invention, the server includes data acquisition means for acquiring biometric and emotional data from users, data processing means for integrating, standardizing, and anonymizing the acquired biometric and emotional data, and analysis means for analyzing the anonymized data using a generating AI model to evaluate the risks to health and emotional states. This makes it possible to provide individually optimized health management plans that take into account both the physical and mental health of the user.

[0548] "Data acquisition means" refers to a device or method for acquiring biometric data and emotional data from a user.

[0549] "Data processing means" refers to a device or method for integrating, standardizing, and anonymizing acquired biometric data and emotional data.

[0550] A "generative AI model" is an artificial intelligence model used to appropriately analyze collected data.

[0551] "Analysis means" refers to an apparatus or method for analyzing anonymized data and assessing the risks to a user's health and emotional state.

[0552] "Plan creation means" refers to a device or method for creating a health management plan that is individually optimized for each user based on evaluation results.

[0553] "User interface means" refers to a device or method that adjusts and presents a created health management plan according to the user's emotions.

[0554] "Update means" refers to a device or method that tracks changes based on biometric data and emotional data and updates the plan in real time.

[0555] This health management system aims to comprehensively manage the user's biological and emotional state and provide an individually optimized health plan. The system consists of data acquisition means, data processing means, analysis means, plan creation means, user interface means, and update means.

[0556] Users install a dedicated application on their smartphone or wearable device to begin collecting biometric and emotional data. Biometric data includes heart rate, steps taken, and body temperature, while emotional data includes facial expressions, tone of voice, and text messages. This data is collected by the user's device.

[0557] The terminal periodically transmits this data to the server via wireless communication or an internet connection. The transmitted data is then aggregated on the server.

[0558] The server uses data processing tools to integrate, standardize, and anonymize the received biometric and emotional data. This makes the data analyzable while protecting user privacy. The server then analyzes the data using a generative AI model. This analysis enables the assessment of the user's health risk and emotional state.

[0559] Based on the evaluated data, the server uses a plan creation mechanism to generate a personalized health management plan for each user. This plan takes into account not only the user's physical condition but also their daily emotional state, and includes suggestions for appropriate exercise and relaxation methods.

[0560] Users receive the plan via their device and integrate it into their daily lives. The user interface presents the plan in an emotionally relatable way, enabling effective interaction. Furthermore, the system includes update mechanisms that continuously track changes in the user's biometric and emotional data, updating the plan in real time as needed.

[0561] For example, if a user is not getting enough exercise, the server can suggest a 20-minute walk every day and send an encouraging message. An example of a prompt to the generative AI model would be a question like, "What is the best exercise for a user who is not getting enough exercise?" This would enable the model to suggest a specific plan.

[0562] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0563] Step 1:

[0564] Users launch a dedicated application and collect biometric and emotional data using their smartphones or wearable devices. The data collected includes heart rate, steps taken, body temperature, facial expressions, voice tone, and text messages. This data is stored in the device's temporary storage. The data collection process is automated; users simply wear the device.

[0565] Step 2:

[0566] The device periodically transmits collected biometric and emotional data to a server via wireless communication. The input data is previously collected data, and the output data is sent to the server. Specifically, the application on the device packages, encodes, and encrypts the data at regular intervals before transmitting it.

[0567] Step 3:

[0568] The server integrates, standardizes, and anonymizes the received data using data processing tools. The data received as input is formatted and anonymized for privacy protection. This results in anonymized data in a format suitable for analysis as output. Specific operations include removing outliers and formatting the data.

[0569] Step 4:

[0570] The server analyzes standardized data using a generative AI model. Using anonymized data as input, it assesses health risk and emotional state. The output includes risk assessment results and emotional assessment results. Specific operations include running the AI ​​model and applying the assessment algorithm.

[0571] Step 5:

[0572] The server creates an individually optimized health management plan using a plan creation mechanism based on the analysis results. Based on the evaluation results as input, it generates a user-specific health plan as output. Specifically, it compares the evaluation results with past data and creates a plan that includes optimal action suggestions.

[0573] Step 6:

[0574] Users receive a health management plan through a user interface on their device and use this information to improve their daily lives. The plan content is displayed in a way that suits the user as input, and the output is designed to elicit user action. Specifically, this involves notifying users of the plan as alerts or messages to encourage user interaction.

[0575] (Application Example 2)

[0576] 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."

[0577] In recent years, managing the health of workers and improving productivity in factories and production sites has become increasingly important. However, there is a lack of systems that continuously monitor the operating status of equipment and robots used in these sites and make appropriate adjustments based on that monitoring. In particular, since the operating status of equipment can affect productivity and safety, there is a need for a function that can accurately evaluate this and notify operators as needed.

[0578] 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.

[0579] In this invention, the server includes data acquisition means for acquiring biometric data from a user, data processing means for integrating, standardizing, and anonymizing the acquired biometric data, and monitoring means for monitoring the operating status of the equipment and evaluating its operating status. This enables appropriate adjustments and notifications based on the operating status.

[0580] "Data acquisition means" refers to devices and methods for collecting information such as biometric data and operating status from users or equipment.

[0581] "Data processing means" refers to the process of integrating, standardizing, and anonymizing acquired biometric data, thereby enabling data handling while protecting individual privacy.

[0582] "Analysis methods" refer to systems that evaluate health risks and equipment operating conditions by analyzing anonymized data.

[0583] The "plan creation method" is a mechanism for formulating health management plans and equipment operation adjustment plans optimized for the user based on evaluation results.

[0584] "User interface means" refers to a display device or method for presenting created plans and notification information to the user in an easily understandable manner.

[0585] "Monitoring means" refers to devices or systems for continuously observing the operating status of equipment and evaluating the smoothness of operation and noise levels.

[0586] A "notification system" is a mechanism that transmits appropriate information to the operator when adjustments are needed based on the operating status of the equipment.

[0587] In this invention, a series of processes are carried out through the collaboration of a server, terminals, and users to realize a health management system in factories and production sites. The server is responsible for receiving data transmitted from users and devices and performing data processing and analysis based on that data. Specifically, the invention is implemented in the following form.

[0588] The server receives biometric data and device operation data collected from users and devices through various sensors using data acquisition means. This data is then standardized and anonymized using data processing means to protect individual privacy. Next, a generative AI model is used to analyze the data and evaluate health risks and device operation status. This evaluation includes operational conditions such as device vibration and noise levels.

[0589] Based on the evaluation results, the plan creation system generates individually optimized health management plans and equipment operation improvement plans. These plans are presented to the user via a user interface, allowing the user to review the plan and implement self-care and equipment adjustments.

[0590] For example, if a robot in a production facility starts exhibiting behavior different from its normal work pattern, the server can analyze the robot's vibration data and notify the operator that abnormal noise is increasing. Based on this result, the server can also propose a robot maintenance schedule.

[0591] For example, a prompt message could include a notification to the operator, such as, "Based on this robot's recent operational data, have its work efficiency or driving smoothness decreased?"

[0592] This allows users to comprehensively evaluate their health status and the operating status of their equipment, enabling them to take quick and effective action.

[0593] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0594] Step 1:

[0595] The terminal collects biometric and operational status data from the user and the device. This data includes the user's heart rate and body temperature, as well as the device's vibration and temperature. The terminal collects this data and prepares it to be transmitted to the server.

[0596] Step 2:

[0597] The server receives biometric and driving status data transmitted from the terminal. The server uses data processing equipment to standardize and anonymize the data. In this process, the collected data is converted into a common format. For example, the unit of heart rate is standardized to bpm and vibration to Hz.

[0598] Step 3:

[0599] The server inputs anonymized data into a generative AI model for data analysis. The generative AI model assesses health risk from biometric data and simultaneously detects abnormal patterns in equipment from operating status data. The output includes health status assessments and probabilities of abnormal conditions.

[0600] Step 4:

[0601] The server planning mechanism creates an optimized plan based on the results of health and operating status assessments. For example, if high stress is detected, it will include stretching suggestions, and if abnormal vibrations are detected, it will recommend inspection today.

[0602] Step 5:

[0603] The server notifies the user of the generated plan through a user interface. The information is presented in a user-friendly interface and in a way that is easy to incorporate into daily activities. For example, a prompt might ask, "Should I inspect the robot's operation?"

[0604] Step 6:

[0605] Users check notifications from the server via their terminals and take action on health management suggestions and equipment adjustment instructions. The results of these actions are also entered into the terminal, providing feedback data that the system uses to further optimize future suggestions.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] [Fourth Embodiment]

[0610] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0611] 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.

[0612] 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).

[0613] 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.

[0614] 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.

[0615] 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).

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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".

[0623] The present invention provides a system that assesses health risks through individual users' biometric data and creates individually optimized health management plans. This system mainly consists of data acquisition means, data processing means, analysis means, plan creation means, and user interface means.

[0624] First, users collect their biometric data (e.g., steps, heart rate, sleep data, etc.) using smartphones or wearable devices. This data is measured in real time through various sensors. The data is stored directly on the user's device and prepared for transfer to a server via data acquisition methods.

[0625] The device transmits this biometric data to the server at appropriate intervals. During this process, corrections and standardization based on user input are performed as needed to ensure the accuracy and consistency of the data.

[0626] The server aggregates and integrates the received biometric data. This ensures that information collected from different data sources is presented in a consistent format. Subsequently, the information is anonymized by data processing tools to protect privacy, and processing proceeds without identifying individuals.

[0627] Next, the server analyzes the anonymized data using a generative AI model. Multiple indicators for assessing frailty risk are considered and converted into a numerical risk score representing each user's health status. These indicators include daily activity levels and sleep quality.

[0628] Furthermore, the server generates a customized care plan that reflects the individual user's health condition based on the analysis results. This plan includes specific action suggestions (e.g., ensuring a certain amount of exercise per week, dietary improvement plan, etc.) and provides guidance aimed at improving health.

[0629] Ultimately, users review the care plan presented through their device and effectively manage their health at home. The results and progress are recorded on the device and used to update future plans. In this way, users can continuously manage their health and receive better care through a feedback cycle.

[0630] The following describes the processing flow.

[0631] Step 1:

[0632] Users sync their smartphones or wearable devices with the app. This prepares the app to collect health-related information such as steps, heart rate, and sleep data.

[0633] Step 2:

[0634] The device collects the user's biometric data in real time and automatically stores it in temporary memory. It can be configured to send this data to a server at specific times (e.g., late at night every day) or trigger events (e.g., when Wi-Fi is connected).

[0635] Step 3:

[0636] The server receives data sent from terminals and aggregates it into a database. Data in different formats is integrated and converted into a standard format suitable for analysis.

[0637] Step 4:

[0638] The server anonymizes the data using data processing tools to ensure privacy. This prepares the data for analysis without identifying personal information.

[0639] Step 5:

[0640] The generative AI model assesses the user's health status using anonymized data. It calculates a frailty risk score using parameters such as steps taken, heart rate, and sleep quality.

[0641] Step 6:

[0642] The server creates a personalized care plan for each user based on the evaluation results. The plan includes exercise programs and dietary improvements tailored to the user's lifestyle.

[0643] Step 7:

[0644] Users receive care plans presented through their devices and incorporate them into their daily lives. The devices track health maintenance activities based on the plan and record progress.

[0645] Step 8:

[0646] The server analyzes the feedback data resubmitted by the user and updates the care plan as needed. This cycle is repeated to support continuous health management.

[0647] (Example 1)

[0648] 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".

[0649] While there is a growing need for personalized and efficient health management, conventional systems have failed to protect user privacy during the collection and analysis of biometric data, making it difficult to generate effective health management plans based on the analysis results. Furthermore, real-time data updates and continuous tracking of health status are insufficient, resulting in delays in providing users with feedback on their health management.

[0650] 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.

[0651] In this invention, the server includes input means, processing means, protection means, evaluation means, planning means, and display means. This makes it possible to analyze anonymized biometric data using a generative artificial intelligence model while protecting user privacy, and to generate and present a personalized health management plan in real time. As a result, users can continuously monitor their health status and implement appropriate health management.

[0652] An "input means" is a mechanism for acquiring biometric data from a user.

[0653] A "processing means" is a mechanism that integrates acquired biological data and converts it into a standardized format.

[0654] A "protective measure" is a mechanism that protects privacy by anonymizing processed data.

[0655] The "evaluation method" is a mechanism that uses anonymized data to analyze the risk of health conditions using a generative artificial intelligence model.

[0656] "Planning method" refers to a mechanism that generates individually optimized health management plans based on evaluation results.

[0657] A "display means" is a mechanism for presenting the generated plan to the user.

[0658] This invention utilizes a combination of specific hardware and software to support the health management of individual users. Specifically, users collect biometric data such as heart rate, steps taken, and sleep data using input means such as smartphones or wearable devices. This data is measured by sensors within the device, such as accelerometers and heart rate monitors.

[0659] Next, the terminal standardizes the collected data using processing tools and prepares it for transmission to the server. This process includes data format conversion and correction of inconsistencies. The terminal then uses encrypted communication methods to securely transfer the data to the server.

[0660] The server anonymizes the received data using protective measures, organizing it into a consistent format while preserving privacy. This anonymized data is then analyzed using an evaluation method that utilizes a generative artificial intelligence model. This procedure uses prompts such as, "Create a health management plan suitable for a user who is a man in his 50s, takes an average of 6,000 steps per day, and sleeps an average of 7 hours," and calculates a risk score for the user's health status.

[0661] Furthermore, the server uses planning tools based on the analysis results to create a health management plan optimized for each user. This plan includes specific examples, such as "150 minutes of aerobic exercise per week is recommended." The plan is presented to the user on their device through a display tool, allowing them to review its contents and use it to manage their daily health.

[0662] This entire system seamlessly handles everything from data acquisition and analysis to plan generation and presentation, enabling personalized and optimized health management for each user.

[0663] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0664] Step 1:

[0665] Users collect biometric data using smartphones or wearable devices. Inputs include information such as heart rate, steps taken, and sleep data. Sensors within the device (e.g., accelerometer, heart rate monitor) measure the data in real time, and it is stored on the device as an initial dataset.

[0666] Step 2:

[0667] The terminal verifies the collected data and uses processing tools to standardize it. Input is in the raw biometric data format, and output is in a unified format that can be sent to the server. During this process, inconsistencies are corrected and timestamps are added.

[0668] Step 3:

[0669] The terminal sends data to the server. This transmission process is encrypted to ensure the security of the communication. The input is standardized biometric data, and the output is encrypted data received by the server.

[0670] Step 4:

[0671] The server anonymizes the received data using protective measures and securely stores it in a database. The input is encrypted biometric data, and the output is an anonymized dataset. This anonymization includes operations to remove personally identifiable information in order to maintain privacy.

[0672] Step 5:

[0673] The server uses evaluation tools and a generative AI model to analyze anonymized data. This process utilizes prompts (e.g., "Assess this user's health risk"). The input is anonymized data, and the output is a health risk score for each user.

[0674] Step 6:

[0675] The server uses planning tools to generate an individually optimized health management plan based on a health risk score. The input is the health risk score, and the output is a health management plan that includes specific action suggestions. This plan might include, for example, "a 30-minute walk every day is recommended."

[0676] Step 7:

[0677] The server sends the generated plan to the terminal, and the terminal presents the plan to the user through a display device. The input is the health management plan, and the output is information presented visually to the user. The user reviews the presented information and uses it to manage their daily health.

[0678] (Application Example 1)

[0679] 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".

[0680] While assessing health risks and providing appropriate care plans based on those assessments is crucial for maintaining users' health, it is difficult to address individual needs in practice. Furthermore, updating plans in real time to reflect changes in health status and recommending appropriate products for daily life are also challenges. Against this backdrop, there is a need for a system that creates individually optimized health and care plans based on health risks and provides product recommendations in real time and effectively.

[0681] 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.

[0682] In this invention, the server includes data acquisition means for obtaining biological information from the user; data processing means for integrating, standardizing, and anonymizing the acquired biological information; analysis means for analyzing the anonymized information and evaluating the risk of the user's health condition; plan creation means for creating an individually optimized health care plan based on the evaluation results; user interface means for presenting the created plan to the user; recommendation means for recommending products based on the user's health risk; and adjustment means for readjusting the plan based on the user's purchase history and progress. This makes it possible to provide an individually optimized care plan based on the user's health risk and to recommend appropriate products in a timely manner.

[0683] A "user" is an individual who provides biometric information and is the target of this system's services.

[0684] "Biological information" refers to data that forms the basis for assessing health status, and includes measurements such as heart rate and step count.

[0685] "Data acquisition means" refers to a method or device for collecting biological information from users and incorporating it into a system.

[0686] "Data processing means" refers to a method or apparatus for integrating, standardizing, and anonymizing collected biological information.

[0687] "Analysis means" refers to a method or apparatus for evaluating the risk of a health condition using anonymized information.

[0688] "Plan creation means" refers to a method or apparatus for designing an individually optimized health care plan based on an assessed health condition.

[0689] "User interface means" refers to a method or device for presenting a created plan to a user visually or tactilely.

[0690] "Recommendation method" refers to a method or device for showing a user appropriate products based on their health risks.

[0691] "Adjustment means" refers to a method or device for resetting an existing plan based on the user's purchase history and progress.

[0692] The system that realizes this application consists of various elements that work together to provide an optimal care plan and product recommendations based on the user's health condition.

[0693] Users collect their own biometric information using smartphones and wearable devices. These devices include heart rate sensors and accelerometers, and collect data through them on a daily basis.

[0694] The device sends the collected biological information to the server. The transmitted data is integrated, standardized, and anonymized using software such as Python libraries (e.g., NumPy, Pandas).

[0695] The server uses a generative AI model (e.g., TensorFlow) to assess health risks on anonymized data. Based on the assessment results, a specific health care plan is automatically created.

[0696] Furthermore, the server analyzes the user's health risks and recommends appropriate products based on these risks. To do this, it selects and recommends the most suitable products from the product database and the user's purchase history.

[0697] The user interface presents the generated plan and product recommendations to the user through a smartphone application. This allows the user to obtain guidance for health management and easily purchase the most suitable products.

[0698] As a concrete example, the server analyzes the user's heart rate data and recommends products such as, "Your heart rate has been a little high recently, so we recommend purchasing a relaxing tea."

[0699] An example of a specific prompt message is: "Recommend suitable health products based on user health risk scores and enable seamless in-app purchasing to maintain user health effectively."

[0700] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0701] Step 1:

[0702] Users collect biometric information using smartphones and wearable devices. This includes steps taken, heart rate, and sleep data. This data is temporarily stored within the device.

[0703] Step 2:

[0704] The terminal transmits the collected biological information to the server. After transmission, the data is integrated and standardized using NumPy and Pandas libraries. Some manual input correction is also performed to improve the accuracy of the data. As a result, standardized data in a format suitable for analysis is obtained.

[0705] Step 3:

[0706] The server anonymizes the received data and transforms it into a form that does not identify individuals. The data processing means processes the data so that it can be analyzed while protecting personal information. The output is anonymized, integrated data.

[0707] Step 4:

[0708] The server analyzes anonymized data using a generative AI model based on TensorFlow. It quantifies health status based on heart rate and activity data, and calculates a health risk score for each user. The output here is the risk score for each user.

[0709] Step 5:

[0710] The server automatically generates a health care plan based on the risk score assessment results. Using the plan creation tool, it provides suggestions for exercise and dietary improvements tailored to the user's condition. The generated output is an individually optimized care plan.

[0711] Step 6:

[0712] The server recommends appropriate products based on the user's health risks. It refers to a product database to identify and recommend products that contribute to risk reduction. As a result, a list of recommended products is generated.

[0713] Step 7:

[0714] The user interface presents plans and product recommendations to the user via a smartphone app. Users can then make decisions regarding health management and product purchases based on the displayed information. The output of this step is the health information and product recommendations available to the user.

[0715] 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.

[0716] This invention combines an emotion engine with a system that manages a user's health status and provides an individually optimized health management plan, thereby enabling comprehensive health management that also takes into account the user's emotional state. This system consists of data acquisition means, data processing means, analysis means, emotion engine, plan creation means, and user interface means.

[0717] Users install a dedicated app on their smartphone or wearable device to begin collecting biometric data. This biometric data includes heart rate, steps taken, and body temperature, and is stored on the user's device. The emotion engine also analyzes the user's facial expressions, voice tone, and entered text messages to collect emotional data.

[0718] The device periodically transmits this biometric and emotional data to a server. The transmitted data is aggregated on the server and standardized and anonymized using data processing tools. This prepares the data for analysis while protecting user privacy.

[0719] The server analyzes this data using a generative AI model. Biometric data is used to assess the user's health risks, while emotional data obtained by the emotion engine is used to evaluate the user's daily stress levels and emotional state.

[0720] Based on the evaluated data, the server uses a plan creation mechanism to create a personalized health management plan for each user. This plan takes into account both the user's physical and emotional state and may include suggestions for appropriate exercise and relaxation. For example, if emotional data indicates high stress levels, relaxation exercises or meditation may be suggested.

[0721] Users can receive these health management plans through their devices and incorporate them into their daily lives. The user interface provides a more personalized experience by selecting interaction methods that respond to the user's emotions and adjusting the tone of messages and notifications.

[0722] For example, if a user has low activity levels during the day and the emotional engine detects stress, the server will incorporate light yoga or deep breathing exercises into their health plan. The user interface will also display encouraging messages in a gentle tone to promote positive behavior. Through this process, users can manage their health in a way that supports both their physical and mental well-being.

[0723] The following describes the processing flow.

[0724] Step 1:

[0725] Users install the app on their smartphone or wearable device and synchronize it with the device. This initiates the collection of biometric data such as heart rate, steps taken, and body temperature.

[0726] Step 2:

[0727] The device captures the user's facial expressions and voice, and analyzes emotional data through an emotion engine. This emotional data includes the user's mood and stress level, and is quantified.

[0728] Step 3:

[0729] The device transmits biometric and emotional data to the server at regular intervals. During this transmission, the data is transmitted while maintaining accuracy and consistency.

[0730] Step 4:

[0731] The server stores the data received from the terminal in a database and performs standardization and anonymization using data processing methods. This process ensures the protection of personal information.

[0732] Step 5:

[0733] The server uses a generative AI model to analyze anonymized biometric and emotional data, and calculates a health risk score and emotional state score for each user.

[0734] Step 6:

[0735] Based on these evaluation results, the server utilizes planning tools to create an individually optimized health management plan. This plan includes recommendations for appropriate exercise and relaxation, and incorporates stress reduction programs based on emotional data.

[0736] Step 7:

[0737] Users review health management plans sent from the system via their devices and incorporate suggested activities into their daily lives. The devices continue to track and record the activities performed.

[0738] Step 8:

[0739] The server receives user feedback, new biometric data, and emotional data, and updates the plan in real time. This process is repeated, allowing users to continuously manage their health optimally.

[0740] (Example 2)

[0741] 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".

[0742] In modern society, many people are expected to maintain not only physical health but also mental health. However, existing health management systems are limited to evaluating health status based on users' biometric data, and there is a problem in that they do not adequately consider mental factors such as emotions and stress. Therefore, there is a need for a system that enables comprehensive health management, including emotional state.

[0743] 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.

[0744] In this invention, the server includes data acquisition means for acquiring biometric and emotional data from users, data processing means for integrating, standardizing, and anonymizing the acquired biometric and emotional data, and analysis means for analyzing the anonymized data using a generating AI model to evaluate the risks to health and emotional states. This makes it possible to provide individually optimized health management plans that take into account both the physical and mental health of the user.

[0745] "Data acquisition means" refers to a device or method for acquiring biometric data and emotional data from a user.

[0746] "Data processing means" refers to a device or method for integrating, standardizing, and anonymizing acquired biometric data and emotional data.

[0747] A "generative AI model" is an artificial intelligence model used to appropriately analyze collected data.

[0748] "Analysis means" refers to an apparatus or method for analyzing anonymized data and assessing the risks to a user's health and emotional state.

[0749] "Plan creation means" refers to a device or method for creating a health management plan that is individually optimized for each user based on evaluation results.

[0750] "User interface means" refers to a device or method that adjusts and presents a created health management plan according to the user's emotions.

[0751] "Update means" refers to a device or method that tracks changes based on biometric data and emotional data and updates the plan in real time.

[0752] This health management system aims to comprehensively manage the user's biological and emotional state and provide an individually optimized health plan. The system consists of data acquisition means, data processing means, analysis means, plan creation means, user interface means, and update means.

[0753] Users install a dedicated application on their smartphone or wearable device to begin collecting biometric and emotional data. Biometric data includes heart rate, steps taken, and body temperature, while emotional data includes facial expressions, tone of voice, and text messages. This data is collected by the user's device.

[0754] The terminal periodically transmits this data to the server via wireless communication or an internet connection. The transmitted data is then aggregated on the server.

[0755] The server uses data processing tools to integrate, standardize, and anonymize the received biometric and emotional data. This makes the data analyzable while protecting user privacy. The server then analyzes the data using a generative AI model. This analysis enables the assessment of the user's health risk and emotional state.

[0756] Based on the evaluated data, the server uses a plan creation mechanism to generate a personalized health management plan for each user. This plan takes into account not only the user's physical condition but also their daily emotional state, and includes suggestions for appropriate exercise and relaxation methods.

[0757] Users receive the plan via their device and integrate it into their daily lives. The user interface presents the plan in an emotionally relatable way, enabling effective interaction. Furthermore, the system includes update mechanisms that continuously track changes in the user's biometric and emotional data, updating the plan in real time as needed.

[0758] For example, if a user is not getting enough exercise, the server can suggest a 20-minute walk every day and send an encouraging message. An example of a prompt to the generative AI model would be a question like, "What is the best exercise for a user who is not getting enough exercise?" This would enable the model to suggest a specific plan.

[0759] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0760] Step 1:

[0761] Users launch a dedicated application and collect biometric and emotional data using their smartphones or wearable devices. The data collected includes heart rate, steps taken, body temperature, facial expressions, voice tone, and text messages. This data is stored in the device's temporary storage. The data collection process is automated; users simply wear the device.

[0762] Step 2:

[0763] The device periodically transmits collected biometric and emotional data to a server via wireless communication. The input data is previously collected data, and the output data is sent to the server. Specifically, the application on the device packages, encodes, and encrypts the data at regular intervals before transmitting it.

[0764] Step 3:

[0765] The server integrates, standardizes, and anonymizes the received data using data processing tools. The data received as input is formatted and anonymized for privacy protection. This results in anonymized data in a format suitable for analysis as output. Specific operations include removing outliers and formatting the data.

[0766] Step 4:

[0767] The server analyzes standardized data using a generative AI model. Using anonymized data as input, it assesses health risk and emotional state. The output includes risk assessment results and emotional assessment results. Specific operations include running the AI ​​model and applying the assessment algorithm.

[0768] Step 5:

[0769] The server creates an individually optimized health management plan using a plan creation mechanism based on the analysis results. Based on the evaluation results as input, it generates a user-specific health plan as output. Specifically, it compares the evaluation results with past data and creates a plan that includes optimal action suggestions.

[0770] Step 6:

[0771] Users receive a health management plan through a user interface on their device and use this information to improve their daily lives. The plan content is displayed in a way that suits the user as input, and the output is designed to elicit user action. Specifically, this involves notifying users of the plan as alerts or messages to encourage user interaction.

[0772] (Application Example 2)

[0773] 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".

[0774] In recent years, managing the health of workers and improving productivity in factories and production sites has become increasingly important. However, there is a lack of systems that continuously monitor the operating status of equipment and robots used in these sites and make appropriate adjustments based on that monitoring. In particular, since the operating status of equipment can affect productivity and safety, there is a need for a function that can accurately evaluate this and notify operators as needed.

[0775] 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.

[0776] In this invention, the server includes data acquisition means for acquiring biometric data from a user, data processing means for integrating, standardizing, and anonymizing the acquired biometric data, and monitoring means for monitoring the operating status of the equipment and evaluating its operating status. This enables appropriate adjustments and notifications based on the operating status.

[0777] "Data acquisition means" refers to devices and methods for collecting information such as biometric data and operating status from users or equipment.

[0778] "Data processing means" refers to the process of integrating, standardizing, and anonymizing acquired biometric data, thereby enabling data handling while protecting individual privacy.

[0779] "Analysis methods" refer to systems that evaluate health risks and equipment operating conditions by analyzing anonymized data.

[0780] The "plan creation method" is a mechanism for formulating health management plans and equipment operation adjustment plans optimized for the user based on evaluation results.

[0781] "User interface means" refers to a display device or method for presenting created plans and notification information to the user in an easily understandable manner.

[0782] "Monitoring means" refers to devices or systems for continuously observing the operating status of equipment and evaluating the smoothness of operation and noise levels.

[0783] A "notification system" is a mechanism that transmits appropriate information to the operator when adjustments are needed based on the operating status of the equipment.

[0784] In this invention, a series of processes are carried out through the collaboration of a server, terminals, and users to realize a health management system in factories and production sites. The server is responsible for receiving data transmitted from users and devices and performing data processing and analysis based on that data. Specifically, the invention is implemented in the following form.

[0785] The server receives biometric data and device operation data collected from users and devices through various sensors using data acquisition means. This data is then standardized and anonymized using data processing means to protect individual privacy. Next, a generative AI model is used to analyze the data and evaluate health risks and device operation status. This evaluation includes operational conditions such as device vibration and noise levels.

[0786] Based on the evaluation results, the plan creation system generates individually optimized health management plans and equipment operation improvement plans. These plans are presented to the user via a user interface, allowing the user to review the plan and implement self-care and equipment adjustments.

[0787] For example, if a robot in a production facility starts exhibiting behavior different from its normal work pattern, the server can analyze the robot's vibration data and notify the operator that abnormal noise is increasing. Based on this result, the server can also propose a robot maintenance schedule.

[0788] For example, a prompt message could include a notification to the operator, such as, "Based on this robot's recent operational data, have its work efficiency or driving smoothness decreased?"

[0789] This allows users to comprehensively evaluate their health status and the operating status of their equipment, enabling them to take quick and effective action.

[0790] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0791] Step 1:

[0792] The terminal collects biometric and operational status data from the user and the device. This data includes the user's heart rate and body temperature, as well as the device's vibration and temperature. The terminal collects this data and prepares it to be transmitted to the server.

[0793] Step 2:

[0794] The server receives biometric and driving status data transmitted from the terminal. The server uses data processing equipment to standardize and anonymize the data. In this process, the collected data is converted into a common format. For example, the unit of heart rate is standardized to bpm and vibration to Hz.

[0795] Step 3:

[0796] The server inputs anonymized data into a generative AI model for data analysis. The generative AI model assesses health risk from biometric data and simultaneously detects abnormal patterns in equipment from operating status data. The output includes health status assessments and probabilities of abnormal conditions.

[0797] Step 4:

[0798] The server planning mechanism creates an optimized plan based on the results of health and operating status assessments. For example, if high stress is detected, it will include stretching suggestions, and if abnormal vibrations are detected, it will recommend inspection today.

[0799] Step 5:

[0800] The server notifies the user of the generated plan through a user interface. The information is presented in a user-friendly interface and in a way that is easy to incorporate into daily activities. For example, a prompt might ask, "Should I inspect the robot's operation?"

[0801] Step 6:

[0802] Users check notifications from the server via their terminals and take action on health management suggestions and equipment adjustment instructions. The results of these actions are also entered into the terminal, providing feedback data that the system uses to further optimize future suggestions.

[0803] 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.

[0804] 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.

[0805] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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."

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] The following is further disclosed regarding the embodiments described above.

[0825] (Claim 1)

[0826] A data acquisition method for obtaining biometric data from a user,

[0827] A data processing method for integrating, standardizing, and anonymizing acquired biometric data,

[0828] Analytical methods for analyzing anonymized data and assessing health risks,

[0829] A plan creation method for creating individually optimized health management plans based on evaluation results,

[0830] A user interface means for presenting the created plan to the user,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, comprising an update means for continuously tracking changes in health status based on biometric data and updating the plan in real time.

[0834] (Claim 3)

[0835] The system according to claim 1, wherein the analysis is performed using anonymized data in order to protect user privacy.

[0836] "Example 1"

[0837] (Claim 1)

[0838] An input means for acquiring biometric data from the user,

[0839] A processing means for integrating acquired biometric data and converting it into a standardized format,

[0840] A protective measure to anonymize the processed data,

[0841] An evaluation method that uses anonymized data to analyze the risk of health status using a generative artificial intelligence model,

[0842] A planning method for generating individually optimized health management plans based on evaluation results,

[0843] A display means for presenting the generated plan to the user,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, comprising tracking means for continuously tracking health status and updating plans based thereon in real time.

[0847] (Claim 3)

[0848] The system according to claim 1, which uses a generative artificial intelligence model to perform data analysis through prompt sentences.

[0849] "Application Example 1"

[0850] (Claim 1)

[0851] A data acquisition method for obtaining biological information from users,

[0852] A data processing method for integrating, standardizing, and anonymizing acquired biological information,

[0853] An analytical method for analyzing anonymized information and assessing the risk to health status,

[0854] A plan creation method for creating individually optimized health care plans based on evaluation results,

[0855] A user interface means for presenting the created plan to the user,

[0856] A recommendation method that recommends products based on the user's health risks,

[0857] A means of readjusting the plan based on the user's purchase history and progress,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, comprising an update means for continuously tracking changes in health status based on biological information and updating the plan in real time.

[0861] (Claim 3)

[0862] The system according to claim 1, which performs analysis using anonymized information in order to protect the user's personal information.

[0863] "Example 2 of combining an emotion engine"

[0864] (Claim 1)

[0865] A data acquisition means for obtaining biometric data and emotional data from users,

[0866] A data processing means for integrating, standardizing, and anonymizing acquired biometric and emotional data,

[0867] An analytical method that uses anonymized data to generate and analyze AI models to assess the risks of health and emotional states,

[0868] Based on the evaluation results, an individually optimized health management plan is created, and a plan creation method that includes content that takes into account emotional state is provided.

[0869] A user interface means that adjusts and presents the created plan according to the user's emotions,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, comprising update means for continuously tracking changes in health and emotional status based on biometric data and emotional data, and updating the plan in real time.

[0873] (Claim 3)

[0874] The system according to claim 1, which uses anonymized data to perform analysis using a generative AI model in order to protect user privacy.

[0875] "Application example 2 when combining with an emotional engine"

[0876] (Claim 1)

[0877] A data acquisition method for obtaining biometric data from a user,

[0878] A data processing method for integrating, standardizing, and anonymizing acquired biometric data,

[0879] Analytical methods for analyzing anonymized data and assessing health risks,

[0880] A plan creation method for creating individually optimized health management plans based on evaluation results,

[0881] A user interface means for presenting the created plan to the user,

[0882] A monitoring means for monitoring the operating status of equipment and evaluating its operating status,

[0883] A notification system that determines the need for adjustment based on the operating status and notifies the user,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, comprising an update means for continuously tracking changes in health status based on biometric data and updating the plan in real time.

[0887] (Claim 3)

[0888] The system according to claim 1, wherein the operating data of the equipment used is anonymized and analyzed in a way that protects privacy. [Explanation of Symbols]

[0889] 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. A data acquisition method for obtaining biometric data from a user, A data processing method for integrating, standardizing, and anonymizing acquired biometric data, Analytical methods for analyzing anonymized data and assessing health risks, A plan creation method for creating individually optimized health management plans based on evaluation results, A user interface means for presenting the created plan to the user, A system that includes this.

2. The system according to claim 1, comprising an update means for continuously tracking changes in health status based on biometric data and updating the plan in real time.

3. The system according to claim 1, wherein the analysis is performed using anonymized data in order to protect user privacy.

Images