system

Abstract

We provide the system. [Solution] Means for collecting biometric data and lifestyle data from users, A means for cleansing and analyzing the collected data, A means of generating individual care plans by applying a generative AI model, A means for delivering the generated care plan to the user device, A system including means for adjusting care plans using the aforementioned user feedback.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In response to the serious problem of the shortage of caregivers in an aging society, there is a problem that it is impossible to efficiently provide care suitable for each elderly person, and as a result, an excessive burden is imposed on family members and caregivers. In particular, it is difficult to generate and manage an individually suitable care plan, which reduces the quality of care and makes it difficult for the elderly to live safely and comfortably.

Means for Solving the Problems

[0005] This invention provides a system that collects biometric and lifestyle data from users, cleanses and analyzes this data, and generates individualized care plans based on a generated AI model. The generated care plans are delivered to the user's device and have a function to adjust the care plans based on user feedback. Furthermore, the generated AI model performs risk assessment and generates corresponding alerts, and the user device provides alarm and notification functions, thereby creating an environment in which elderly people can live with peace of mind and reducing the burden on family members and caregivers.

[0006] The term "user" refers to those who utilize the system, and is a concept that primarily includes elderly people and their families.

[0007] "Biometric data" refers to various measurements and sensor data that indicate the user's health status.

[0008] "Lifestyle data" refers to information about the user's daily behaviors and habits.

[0009] "Cleaning" refers to the process of removing invalid data and outliers from raw data, preparing it for analysis.

[0010] "Analysis" refers to the process of examining collected data in detail to reveal patterns and characteristics.

[0011] A "generative AI model" refers to an artificial intelligence framework that generates appropriate care plans by applying algorithms based on collected data.

[0012] A "care plan" refers to a plan or guideline regarding care and health management that is individually proposed to the user.

[0013] A "user device" refers to a terminal or digital device used by a user to input data or receive care plans.

[0014] "Feedback" refers to the reports and reactions of the user to the system after the implementation of the care plan.

[0015] "Risk assessment" refers to the process of diagnosing the user's health status based on the user's data and identifying and evaluating potential risks.

[0016] "Alert" refers to the notifications and messages sent to prompt the user's attention and warnings.

[0017] "Alarm and notification function" refers to the function of the user device to inform the user of specific information at the appropriate timing.

Brief Description of the Drawings

[0018] [Figure 1] It is a conceptual diagram showing an example of the configuration of the data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of the data processing device and the smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of the data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of the data processing device and the smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of the data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of the data processing device and the headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of the data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of the data processing device and the robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 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 Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a 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.

[0022] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] This invention is a system that provides care plans tailored to the user's daily life, enabling elderly people to live with peace of mind. An embodiment of this system is shown below.

[0040] 1. Data collection:

[0041] Users regularly input biometric and lifestyle data using a dedicated application. This data includes blood pressure, heart rate, weight, diet, exercise levels, and sleep duration. The device receives this data and transmits it to the server using secure communication methods.

[0042] 2. Data analysis and care plan generation:

[0043] The server cleanses and analyzes the data sent from the terminal. Using a generative AI model, it evaluates the user's health status and lifestyle habits and automatically generates an optimal care plan. This plan includes daily meal plans, recommended exercise, and key points for health management.

[0044] 3. Distribution and implementation of care plans:

[0045] The care plan generated from the server is sent to the device and displayed to the user in a visually easy-to-understand format. The user can then follow these instructions to manage their daily life. The device sends alarms and notifications as needed to help the user remember important tasks.

[0046] 4. Feedback and alerts:

[0047] The user continuously inputs the results of implementing the care plan into the terminal. The terminal sends the newly entered data to the server, where the effectiveness of the care plan is evaluated based on the feedback. If a risk is detected, the server generates an appropriate alert and notifies the user and their family.

[0048] Specific example:

[0049] For example, for a user at risk of hypertension, the server generates a care plan that includes a low-sodium diet and light aerobic exercise. Based on this plan, the terminal suggests a morning walk and a low-sodium breakfast to the user. After the user completes the plan, they input the details into the terminal, and the results are sent to the server. The server adjusts the care plan based on the results and sends the improved plan back to the terminal.

[0050] In this way, the invention provides continuous care tailored to individual needs, supporting healthier and more fulfilling lives for the elderly.

[0051] The following describes the processing flow.

[0052] Step 1:

[0053] Users input biometric data related to their health status into their device through a dedicated application. This includes information such as blood pressure, weight, diet, and exercise status.

[0054] Step 2:

[0055] The terminal receives data entered by the user and performs communication processing to send it to the server. At this time, it verifies that the data is accurate and complete before sending it.

[0056] Step 3:

[0057] The server receives data sent from the terminal and performs initial cleansing. It checks for data defects and abnormal values, and corrects or supplements the data if necessary.

[0058] Step 4:

[0059] The server applies a generative AI model to analyze the cleansed data. Based on the analysis results, it automatically generates an optimal care plan tailored to the user's health condition and lifestyle.

[0060] Step 5:

[0061] The server records the generated care plans in a database and manages them individually for each user. It then prepares to send the latest care plan to the terminal.

[0062] Step 6:

[0063] The terminal receives the care plan sent from the server and presents it to the user. It displays the plan in a simple and easy-to-understand format so that the user can visually confirm it.

[0064] Step 7:

[0065] The user performs daily activities according to the presented care plan and inputs the results as feedback into the device. This feedback information includes the success or failure of the activities and any observed changes in health status.

[0066] Step 8:

[0067] The device sends user feedback data to the server, providing additional information about the effectiveness of the care plan and any necessary adjustments.

[0068] Step 9:

[0069] The server re-analyzes the feedback data and evaluates the progress of the care plan and the user's health improvement. If necessary, it adjusts the care plan and generates an updated version.

[0070] Step 10:

[0071] The server generates necessary alerts and notifications based on updated care plans and new risks. These are sent to the user and their family to prompt appropriate action.

[0072] Through this series of processes, the system continuously provides users with optimal care and supports the maintenance of the health of the elderly.

[0073] (Example 1)

[0074] 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."

[0075] In health management systems for the elderly, there is a problem in efficiently providing personalized health support and proposing appropriate feedback and improvement measures based on the user's health condition. This is because there is a lack of mechanisms to accurately grasp an individual's lifestyle and health condition and respond quickly.

[0076] 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.

[0077] In this invention, the server includes means for recording biometric information and lifestyle information from the user, means for selecting and analyzing the recorded information, and means for generating an individualized support plan by applying a generative AI model. This enables the provision of individualized care plans tailored to each user's health condition, as well as continuous evaluation and improvement after implementation.

[0078] "User" refers to an individual who uses the system, and in particular includes elderly people who provide information on their health status and lifestyle and who receive individualized care plans.

[0079] "Biometric information" refers to data that indicates the user's physical condition, including measurable health indicators such as blood pressure, heart rate, and weight.

[0080] "Lifestyle information" refers to data about a user's daily activities and habits, including diet, exercise levels, and sleep duration.

[0081] "Selection" refers to the process of removing unnecessary information and extracting only useful data in the preliminary stages of data analysis.

[0082] "Analysis" refers to the process of evaluating a user's health status and lifestyle based on collected data, and deriving the results as data.

[0083] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to automatically generate appropriate care plans from data.

[0084] A "support plan" refers to a plan that includes specific instructions to promote diet, exercise, and health management optimized for each user's individual health condition, based on the analysis results.

[0085] A "user device" is a device used by a user to input information, receive generated care plans, or receive notifications, and includes smartphones and tablets.

[0086] "Feedback analysis" refers to the process of analysis performed by the system based on user feedback to evaluate the effectiveness of a support plan and revise that plan.

[0087] A "warning" is risk information generated based on the user's health status, which will be communicated to the user or their family if necessary.

[0088] This invention is a system for supporting the health management of the elderly, and in particular aims to provide personalized support plans based on biometric and lifestyle information. This system is implemented in the following form.

[0089] First, users input their biometric and lifestyle information using a device such as a smartphone or tablet. The information recorded by the user includes biometric data such as blood pressure, heart rate, and weight, as well as lifestyle information such as diet, exercise level, and sleep duration. The device sends this information to the server using a secure communication protocol (e.g., HTTPS).

[0090] The server sorts the received information, deleting or correcting unnecessary data to prepare it for analysis. Next, the server uses a generative AI model to evaluate each user's health status and lifestyle based on the cleansed data. The goal of the generative AI model is to generate an individualized support plan. For example, if a user shows a tendency towards high blood pressure, the generated support plan may include a low-sodium diet and light aerobic exercise. An example of a prompt would be to instruct the server, "Generate an optimal care plan based on the user's blood pressure data and past exercise history."

[0091] The server then sends the generated support plan to the user's device, which displays the plan on a visually easy-to-understand dashboard. The user can adjust their daily activities based on this support plan. The device also provides alarm and notification functions to help the user remember to follow the plan.

[0092] In addition, users can continue to input the results of their activities through their devices. The server analyzes the information received as feedback, modifies the support plan as needed, and sends the revised plan back to the user's device. This collaboration allows users to receive appropriate support tailored to their daily health condition.

[0093] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0094] Step 1:

[0095] Users input biometric and lifestyle information using a dedicated application on their device. This information includes blood pressure, heart rate, diet, exercise levels, and sleep duration. The device aggregates this information and transmits it to a server using a secure communication protocol. This process provides the foundational data needed to assess the user's health status.

[0096] Step 2:

[0097] The server receives information sent from the terminal and first sorts the data. During the sorting process, it detects outliers and inappropriate data, and then filters and corrects them to make them more manageable. Next, data analysis is performed, which provides insights into the user's current health status. The generative AI model receives this cleansed data as input and outputs the results of the analysis.

[0098] Step 3:

[0099] The server uses a generative AI model to generate a personalized support plan that takes into account the user's health status and lifestyle. During this process, the model is provided with prompts such as, "Generate an optimal care plan based on the user's blood pressure data and past exercise history." The generated support plan includes dietary recommendations, exercise suggestions, and health management tips. This output is personalized and tailored to the user's needs.

[0100] Step 4:

[0101] The server sends the generated support plan to the terminal, which displays the received plan in a visually easy-to-understand format on the user interface. The terminal provides alarms and reminders for the user to use when living according to the care plan, supporting its implementation. This allows the user to adjust their lifestyle to be healthier and encourages them to act in accordance with the plan.

[0102] Step 5:

[0103] The user re-enters the results of implementing the support plan into the terminal. The terminal sends this feedback to the server, which performs feedback analysis based on the received results. Based on the analysis results, the support plan is adjusted as needed, a newly optimized plan is generated, and redistributed to the terminal. This cycle makes it possible to continuously optimize the user's health.

[0104] (Application Example 1)

[0105] 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."

[0106] For elderly people to live independently and with peace of mind, it is essential to quickly detect abnormalities in their health and take appropriate action. However, conventional systems do not collect or analyze biometric data in real time, which can lead to delays in detecting abnormalities. Furthermore, warnings about abnormalities may not be adequately communicated, hindering prompt responses.

[0107] 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.

[0108] In this invention, the server includes means for collecting biometric data and lifestyle data from users, means for cleansing and analyzing the collected data, means for generating individual care plans by applying a generative AI model, means for acquiring the user's biometric data in real time using an integrated visualization device, and means for instantly detecting abnormalities and generating warnings based on the acquired biometric data. This enables elderly people to understand their health status in real time and respond quickly when abnormalities are detected.

[0109] "Biometric data from users" refers to indicators of an individual's health status, including information such as heart rate, blood pressure, and body temperature.

[0110] "Lifestyle data" refers to information about an individual's daily activities, including diet, exercise levels, and sleep duration.

[0111] "Means of collection" refers to methods and devices for acquiring and recording the target data, and includes sensors and software.

[0112] "Methods for cleansing and analyzing" refers to methods for removing unnecessary information and analyzing data in order to improve the accuracy of acquired data.

[0113] A "generative AI model" refers to an algorithm that uses artificial intelligence to learn specific patterns and trends, and then generates and evaluates data according to a specific purpose.

[0114] An "individualized care plan" is a set of specific action guidelines designed to improve or maintain the health of each individual user, based on their health condition and lifestyle.

[0115] A "visualization device" is a device that enables the visualization of data and presents information to users in real time, and includes displays and screens.

[0116] "Means for instantly detecting anomalies and generating warnings" refers to technologies or devices that monitor data in real time, immediately identify anomalies, and issue warnings to users.

[0117] This invention is a system to support the health management of the elderly. First, the user wears a dedicated real-time data collection device. This device has built-in sensors that measure heart rate and blood pressure, making it possible to continuously acquire the user's biometric data. This data is transmitted to a server via secure communication.

[0118] The server uses a backend program developed in Python and Flask to receive and manage data. This data is first cleansed to remove outliers and normalize the data. Then, it is analyzed by a generative AI model built using PyTorch to generate an optimal, personalized care plan for the user.

[0119] The generated care plan is delivered to the user's device in real time. The device is equipped with a visualization device that can show the user their daily health status and necessary actions. In addition, if an abnormality is detected, the system instantly generates a warning and sends an alarm or notification to the user. For example, if the user's heart rate exceeds the normal range, a warning such as "Your heart rate is elevated. Please take a break" will be displayed.

[0120] This system allows users to understand their health status in real time and take appropriate action. Furthermore, by inputting daily health information and the results of care performed as feedback from users, the generated AI model can continuously adjust its care plan, enabling it to provide a more precise plan.

[0121] An example of a prompt message is, "Design a system that monitors the health status of elderly people and issues warnings in real time."

[0122] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0123] Step 1:

[0124] The device acquires biometric data such as the user's heart rate, blood pressure, and body temperature in real time using sensors. The input is analog data from the sensors, which is converted to digital data and temporarily stored. This data is then transmitted to a server using a secure communication method. The output is biometric data in digital format.

[0125] Step 2:

[0126] The server receives biometric data transmitted from the terminal and performs a cleansing process. The input is biometric data from the terminal, and the output is clean data from which unnecessary information has been removed. At this stage, obviously incorrect values ​​are filtered out and the data is normalized.

[0127] Step 3:

[0128] The server analyzes the cleansed data using a generative AI model based on PyTorch. The input is the data processed in step 2. The model analyzes the data and generates a care plan tailored to each individual user. The output is an individualized care plan. This plan includes daily activity guidelines based on the user's health condition.

[0129] Step 4:

[0130] The server sends the generated care plan to the terminal. The terminal receives it and displays it to the user in a visually easy-to-understand format. The input is the care plan obtained from the generated AI model, and the output is what is displayed on the user's device. Specifically, the terminal uses a graphical interface to communicate the plan to the user.

[0131] Step 5:

[0132] The terminal provides an interface that allows users to input the results of their care plan implementation. Users input details of the activities performed, acquired heart rate data, and other information into the terminal. This input is user-provided feedback information and is stored as updated data that is sent to the server as output.

[0133] Step 6:

[0134] The server adjusts the care plan using a generative AI model based on feedback submitted by the user. The input is user feedback data. This data is analyzed, and the care plan is optimized as needed. The output is the newly adjusted care plan. The server then sends the updated plan back to the terminal, helping the user continue to adapt and take appropriate actions.

[0135] 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.

[0136] This invention is a system that integrates a user's biometric data, lifestyle data, and an emotion engine that recognizes their emotional state. This system is designed to facilitate the user's overall health management and to provide an appropriate care plan by simultaneously evaluating their physical and psychological health.

[0137] Data collection and emotion recognition:

[0138] Users input biometric and lifestyle data through dedicated terminals or wearable devices. The terminals are also equipped with cameras and microphones, which are used to analyze the user's facial expressions and voice, allowing the emotion engine to recognize the user's emotional state in real time.

[0139] Data analysis and care plan generation:

[0140] The server receives all data sent from the terminal, cleanses it, and then analyzes it based on the generated AI model. During this process, emotional data recognized by the emotion engine is also incorporated into the analysis, and a care plan that comprehensively considers the user's physical and emotional state is generated.

[0141] Distribution and implementation of care plans:

[0142] Care plans generated on the server are delivered to the terminal and presented to the user. Users are supported in taking actions based on the care plan in their daily lives. The terminal takes the user's emotional state into consideration and incorporates specific actions and suggestions into the plan.

[0143] Feedback and alerts:

[0144] Users input their experience and results as feedback into the device. The device sends this data to a server, which adjusts the care plan based on the feedback. In addition, if an abnormality is detected in the user's emotional state, the server generates suggestions for content aimed at stress reduction and relaxation, as well as alerts.

[0145] Specific example:

[0146] For example, in the case of a user who experiences daily stress, the server analyzes changes in heart rate in combination with the "anxiety" recognition by the emotion engine. As a result, it proposes a care plan of Nishishiki exercises, including specific breathing techniques for relaxation and light yoga sessions. The device notifies the user of when to perform the exercises and helps the user adhere to the plan. Based on the user's feedback on their performance, this care plan is adjusted as needed.

[0147] This system allows users to receive continuous support that helps maintain and improve not only their physical health but also their mental health.

[0148] The following describes the processing flow.

[0149] Step 1:

[0150] Users input biometric and lifestyle data related to their health status into the device. This includes information such as blood pressure, heart rate, weight, exercise records, and dietary information. Simultaneously, the device's camera and microphone are used to record emotional states in real time from voice and facial expressions.

[0151] Step 2:

[0152] The device collects biometric data, lifestyle data, and emotional data entered by the user and sends it to the server. This allows the server to store information about the user's overall health status.

[0153] Step 3:

[0154] The server cleanses the received data, identifying and correcting incomplete data and outliers. This creates a dataset suitable for analysis.

[0155] Step 4:

[0156] The server combines a generative AI model and an emotion engine to simultaneously analyze the user's health and emotional state. Based on the analysis results, it generates a personalized care plan optimized for the user. This plan includes recommendations for diet, exercise, and emotional improvement measures.

[0157] Step 5:

[0158] The server sends the generated care plan to the terminal. The terminal displays the received care plan to the user, providing it in a clear and easy-to-understand format.

[0159] Step 6:

[0160] Users act according to the care plan presented by the device in their daily lives. The device sends alarms and notifications when it is time to perform important tasks and make suggestions, helping users adhere to the plan.

[0161] Step 7:

[0162] Users input feedback on the results of their care plan implementation into a terminal, recording their emotions and physical changes. This allows for verification of the user's implementation status and its effectiveness.

[0163] Step 8:

[0164] The terminal sends the collected feedback data to the server. The server analyzes the feedback, evaluates the effectiveness of the care plan, and adjusts the plan as needed.

[0165] Step 9:

[0166] The server responds to the emotional state and increased stress identified by the emotion engine, and suggests appropriate relaxation content and stress reduction measures as alerts. It also sends these alerts to the user's device to encourage a quick response.

[0167] This processing flow allows the system to comprehensively support the user's physical and emotional health, providing a better quality of life.

[0168] (Example 2)

[0169] 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".

[0170] In modern society, personal health management is required to be comprehensive, encompassing not only physical health but also emotional health. However, conventional systems have struggled to centrally analyze an individual's biometric information and lifestyle patterns to provide personalized health management plans. Furthermore, there is a lack of technology to recognize a user's emotional state in real time and reflect it in health management.

[0171] 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.

[0172] In this invention, the server includes means for collecting biometric parameters and lifestyle pattern information from the user, means for purifying and analyzing the collected information, and means for applying a generative machine learning model to generate an individualized health management plan. This enables the provision of a health management plan optimized for the individual and comprehensive health maintenance that takes into account emotional state.

[0173] "Biometric parameters" are data that indicates the user's physical condition, including information such as heart rate and body temperature.

[0174] "Lifestyle pattern information" refers to data about the user's daily life, including habits such as sleep duration and exercise levels.

[0175] "Purification" is the process of preparing collected data into a format suitable for analysis, including removing noise and handling missing values.

[0176] A "generative machine learning model" is an algorithm that generates a health management plan tailored to the user based on collected data.

[0177] A "health management plan" is a plan that includes specific actions and indicators aimed at maintaining and improving the user's health.

[0178] "Emotional state" refers to the psychological state analyzed from the user's facial expressions and voice, and includes stress, happiness, and other similar feelings.

[0179] "Warning" is a function that provides advance notice of problems that are predicted to occur based on the user's health status.

[0180] This embodiment of the invention is a health management system that comprehensively collects and analyzes a user's biometric parameters and lifestyle pattern information. This system acquires user data through a dedicated terminal or wearable device. The terminal is equipped with a camera and microphone, which can analyze the user's facial expressions and voice to recognize their emotional state in real time. This provides information for maintaining mental health.

[0181] The server receives data sent from the terminal and processes noise and missing information using data cleansing algorithms. It then analyzes the data using generative machine learning models to generate an optimal health management plan for the user. This is done using an AI platform that operates on prompt-based instructions. For example, a prompt might say, "Assess the user's current health status and generate a recommended care plan."

[0182] The generated health management plan is delivered from the server to the device. The user receives notifications and can take specific actions based on the presented plan. The device provides support for the user to implement the plan and utilizes alarm and notification functions as needed.

[0183] Furthermore, user feedback is entered into the device and sent to the server. The server adjusts the health management plan based on the feedback, optimizing it for the user. For example, for a user who experiences daily stress, a plan including breathing exercises and other exercises is proposed based on an analysis combining changes in heart rate and emotional data. This allows the user to maintain overall physical and mental health.

[0184] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0185] Step 1:

[0186] Users collect biometric parameters and lifestyle pattern information using dedicated terminals or wearable devices. This input data includes heart rate, body temperature, sleep duration, and exercise level. This data collection provides specific basic data about the user's health status.

[0187] Step 2:

[0188] The device uses its built-in camera and microphone to analyze the user's facial expressions and voice in real time. The data acquired here becomes the input for emotion analysis. The device uses an emotion engine to recognize the user's emotional state and generates the result as output. This process provides information about the user's mental health.

[0189] Step 3:

[0190] The terminal transmits collected biometric parameters, lifestyle pattern information, and emotional state data to the server. The server receives this data and performs data cleansing. Data cleansing removes noise and imputes missing data, outputting data in an analyzable state. This enables accurate data analysis.

[0191] Step 4:

[0192] The server performs data analysis using a generated AI model based on the cleansed data. The analysis proceeds according to the prompt message, "Evaluate the user's current health status and generate a recommended health management plan." Based on the input data, the AI ​​model generates an optimal health management plan for the user and outputs the result.

[0193] Step 5:

[0194] The server delivers the generated health management plan to the terminal. The terminal notifies the user of this plan and suggests specific actionable steps. Examples include suggestions for relaxation breathing exercises or yoga sessions. This notification provides the user with guidance for implementing the plan in their daily life.

[0195] Step 6:

[0196] Users input feedback on the results and their experience using the health management plan into their device. This feedback is then sent from the device to the server. The server analyzes this feedback and adjusts the health management plan as needed. This enables the provision of more appropriate support to the user.

[0197] (Application Example 2)

[0198] 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".

[0199] In modern society, users are expected to receive personalized services while comprehensively managing their physical and mental health. However, understanding a user's emotional state in real time and providing appropriate services based on that understanding is not easy. Furthermore, optimizing individual customer experiences in stores requires effective emotion recognition technology and the use of user data, but conventional methods have limitations.

[0200] 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.

[0201] In this invention, the server includes means for collecting biometric and lifestyle information from the user, means for cleansing and analyzing the collected information, means for applying a generated AI model to generate individualized care guidelines, and means for recognizing the customer's emotional state using a smart device and providing recommended information based on purchase history. This makes it possible to simultaneously achieve user health management and the provision of individualized services, thereby improving the customer experience.

[0202] "Biometric information" refers to data obtained from within a user's body, and it represents information about their health status and physical characteristics.

[0203] "Lifestyle information" refers to information that includes data on the user's behavioral patterns and habits in their daily life.

[0204] "Cleansing" is a pre-processing step in data analysis that removes noise and inaccurate data.

[0205] A "generated AI model" is an artificial intelligence-powered model created based on data analysis, and serves as the foundation for generating user-optimized suggestions.

[0206] "Care guidelines" are recommended actions and plans created based on the user's health condition and lifestyle.

[0207] A "smart device" is a digital device that has the function of collecting and processing data using information and communication technology.

[0208] "Emotional state" refers to the user's psychological state and includes information obtained from facial expressions and voice.

[0209] "Recommended information" refers to suggestions about products and services presented based on the user's preferences and needs.

[0210] This system uses terminals to collect biometric and lifestyle information from users. These terminals, such as wearable devices and smartphones, acquire real-time data from users. Using emotion recognition functions with cameras and microphones, the terminals capture the user's emotional state.

[0211] The server receives the collected information and performs data cleansing. Specifically, it uses the Google® Cloud Vision API to perform emotion recognition and denoise the collected data, preparing it for analysis. Using a generative AI model, it generates care guidelines based on the user's health and emotional state. These care guidelines are sent to the user's smart device.

[0212] Smart devices provide recommendations that take into account the user's emotional state and purchase history. In particular, in stores, sales staff can use smart glasses to provide product suggestions optimized for the user.

[0213] For example, if a user visits a store and their emotional state is detected as "relaxed," the device will recommend products related to relaxation. As a result, the store can improve the user experience and provide personalized service.

[0214] An example of a prompt message would be, "Analyze the customer's facial expressions and generate optimal product suggestions based on their emotional state." This allows the generating AI model to efficiently provide personalized recommendations to the user.

[0215] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0216] Step 1:

[0217] Users input biometric and lifestyle information in real time using wearable devices or smartphones. The input data includes heart rate and activity levels. This data is immediately transmitted from the device to the server.

[0218] Step 2:

[0219] The server cleanses the received biometric and lifestyle information. Specifically, it removes noise data and prepares it for analysis. The input is raw data, and the output is refined data.

[0220] Step 3:

[0221] The server applies a generative AI model to the cleansed data. This model analyzes the data and generates care guidelines based on the user's health and emotional state. The input to the AI ​​model is formatted data, and the output is individual care guidelines.

[0222] Step 4:

[0223] The terminal receives care guidelines from the server and notifies the user. The notification includes recommended actions and product information. The input is care guidelines from the server, and the output is a visualized notification to the user.

[0224] Step 5:

[0225] The user's emotional state is recognized in real time through the device's camera and microphone. The device uses the Google Cloud Vision API to analyze facial expressions and voice to identify the emotional state. The input is real-time audio and video data, and the output is information about the emotional state.

[0226] Step 6:

[0227] Smart devices generate prompts that create recommendations based on emotional state and purchase history. Using the generated prompts, an AI model makes optimal product suggestions. The input is emotional state data and purchase history, and the output is the suggested products and services.

[0228] Step 7:

[0229] Users act based on the recommendations they receive. The device then sends the results of their actions and feedback back to the server. This feedback is used for further data analysis, and the entire system, as an ecosystem, continuously improves the user experience. The input is user feedback, and the output is information about system improvements based on that feedback.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] [Second Embodiment]

[0234] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0235] 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.

[0236] 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).

[0237] 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.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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".

[0246] This invention is a system that provides care plans tailored to the user's daily life, enabling elderly people to live with peace of mind. An embodiment of this system is shown below.

[0247] 1. Data collection:

[0248] Users regularly input biometric and lifestyle data using a dedicated application. This data includes blood pressure, heart rate, weight, diet, exercise levels, and sleep duration. The device receives this data and transmits it to the server using secure communication methods.

[0249] 2. Data analysis and care plan generation:

[0250] The server cleanses and analyzes the data sent from the terminal. Using a generative AI model, it evaluates the user's health status and lifestyle habits and automatically generates an optimal care plan. This plan includes daily meal plans, recommended exercise, and key points for health management.

[0251] 3. Distribution and implementation of care plans:

[0252] The care plan generated from the server is sent to the device and displayed to the user in a visually easy-to-understand format. The user can then follow these instructions to manage their daily life. The device sends alarms and notifications as needed to help the user remember important tasks.

[0253] 4. Feedback and alerts:

[0254] The user continuously inputs the results of implementing the care plan into the terminal. The terminal sends the newly entered data to the server, where the effectiveness of the care plan is evaluated based on the feedback. If a risk is detected, the server generates an appropriate alert and notifies the user and their family.

[0255] Specific example:

[0256] For example, for a user at risk of hypertension, the server generates a care plan that includes a low-sodium diet and light aerobic exercise. Based on this plan, the terminal suggests a morning walk and a low-sodium breakfast to the user. After the user completes the plan, they input the details into the terminal, and the results are sent to the server. The server adjusts the care plan based on the results and sends the improved plan back to the terminal.

[0257] In this way, the invention provides continuous care tailored to individual needs, supporting healthier and more fulfilling lives for the elderly.

[0258] The following describes the processing flow.

[0259] Step 1:

[0260] Users input biometric data related to their health status into their device through a dedicated application. This includes information such as blood pressure, weight, diet, and exercise status.

[0261] Step 2:

[0262] The terminal receives data entered by the user and performs communication processing to send it to the server. At this time, it verifies that the data is accurate and complete before sending it.

[0263] Step 3:

[0264] The server receives data sent from the terminal and performs initial cleansing. It checks for data defects and abnormal values, and corrects or supplements the data if necessary.

[0265] Step 4:

[0266] The server applies a generative AI model to analyze the cleansed data. Based on the analysis results, it automatically generates an optimal care plan tailored to the user's health condition and lifestyle.

[0267] Step 5:

[0268] The server records the generated care plans in a database and manages them individually for each user. It then prepares to send the latest care plan to the terminal.

[0269] Step 6:

[0270] The terminal receives the care plan sent from the server and presents it to the user. It displays the plan in a simple and easy-to-understand format so that the user can visually confirm it.

[0271] Step 7:

[0272] The user performs daily activities according to the presented care plan and inputs the results as feedback into the device. This feedback information includes the success or failure of the activities and any observed changes in health status.

[0273] Step 8:

[0274] The device sends user feedback data to the server, providing additional information about the effectiveness of the care plan and any necessary adjustments.

[0275] Step 9:

[0276] The server re-analyzes the feedback data and evaluates the progress of the care plan and the user's health improvement. If necessary, it adjusts the care plan and generates an updated version.

[0277] Step 10:

[0278] The server generates necessary alerts and notifications based on updated care plans and new risks. These are sent to the user and their family to prompt appropriate action.

[0279] Through this series of processes, the system continuously provides users with optimal care and supports the maintenance of the health of the elderly.

[0280] (Example 1)

[0281] 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."

[0282] In health management systems for the elderly, there is a problem in efficiently providing personalized health support and proposing appropriate feedback and improvement measures based on the user's health condition. This is because there is a lack of mechanisms to accurately grasp an individual's lifestyle and health condition and respond quickly.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0284] In this invention, the server includes means for recording biometric information and lifestyle information from a user, means for screening and analyzing the recorded information, and means for generating an individualized support plan by applying a generated AI model. As a result, it becomes possible to provide an individualized care plan according to the health status of each user and to continuously evaluate and improve it after its implementation.

[0285] The "user" refers to an individual who uses the system, and particularly includes the elderly who provide information on their health status and lifestyle and receive an individual care plan.

[0286] The "biometric information" is data indicating the physical state of the user and includes measurable health indicators such as blood pressure, heart rate, and weight.

[0287] The "lifestyle information" is data on the actions and habits in the daily life of the user and includes the content of meals, the amount of exercise, and the sleep time.

[0288] "Screening" refers to the process of removing unnecessary information in the pre-stage of data analysis and extracting only useful data.

[0289] "Analysis" refers to the process of evaluating the health status and lifestyle of the user based on the collected data and deriving the results as data.

[0290] The "generated AI model" refers to an algorithm for automatically generating an appropriate care plan from data using artificial intelligence technology.

[0291] The "support plan" refers to a plan including specific instructions for promoting meals, exercise, and health management optimized for the individual health status of the user based on the analysis results.

[0292] A "user device" is a device used by a user to input information, receive generated care plans, or receive notifications, and includes smartphones and tablets.

[0293] "Feedback analysis" refers to the process of analysis performed by the system based on user feedback to evaluate the effectiveness of a support plan and revise that plan.

[0294] A "warning" is risk information generated based on the user's health status, which will be communicated to the user or their family if necessary.

[0295] This invention is a system for supporting the health management of the elderly, and in particular aims to provide personalized support plans based on biometric and lifestyle information. This system is implemented in the following form.

[0296] First, users input their biometric and lifestyle information using a device such as a smartphone or tablet. The information recorded by the user includes biometric data such as blood pressure, heart rate, and weight, as well as lifestyle information such as diet, exercise level, and sleep duration. The device sends this information to the server using a secure communication protocol (e.g., HTTPS).

[0297] The server sorts the received information, deleting or correcting unnecessary data to prepare it for analysis. Next, the server uses a generative AI model to evaluate each user's health status and lifestyle based on the cleansed data. The goal of the generative AI model is to generate an individualized support plan. For example, if a user shows a tendency towards high blood pressure, the generated support plan may include a low-sodium diet and light aerobic exercise. An example of a prompt would be to instruct the server, "Generate an optimal care plan based on the user's blood pressure data and past exercise history."

[0298] The server then sends the generated support plan to the user's device, which displays the plan on a visually easy-to-understand dashboard. The user can adjust their daily activities based on this support plan. The device also provides alarm and notification functions to help the user remember to follow the plan.

[0299] In addition, users can continue to input the results of their activities through their devices. The server analyzes the information received as feedback, modifies the support plan as needed, and sends the revised plan back to the user's device. This collaboration allows users to receive appropriate support tailored to their daily health condition.

[0300] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0301] Step 1:

[0302] Users input biometric and lifestyle information using a dedicated application on their device. This information includes blood pressure, heart rate, diet, exercise levels, and sleep duration. The device aggregates this information and transmits it to a server using a secure communication protocol. This process provides the foundational data needed to assess the user's health status.

[0303] Step 2:

[0304] The server receives information sent from the terminal and first sorts the data. During the sorting process, it detects outliers and inappropriate data, and then filters and corrects them to make them more manageable. Next, data analysis is performed, which provides insights into the user's current health status. The generative AI model receives this cleansed data as input and outputs the results of the analysis.

[0305] Step 3:

[0306] The server uses a generative AI model to generate an individualized support plan based on the user's health status and lifestyle. In this process, a prompt sentence such as "Please generate an optimal care plan based on the user's blood pressure data and past exercise history" is provided to the model. The generated support plan includes dietary recommendations, exercise suggestions, health management points, etc. This output is individualized and tailored to the user's needs.

[0307] Step 4:

[0308] The server sends the generated support plan to the terminal, and the terminal displays the received plan on the user interface in a visually easy-to-view format. The terminal provides alarms and reminders for the user to use when living according to the care plan, and supports the implementation of the plan. This enables the user to adjust their lifestyle to be healthy and encourages actions according to the plan.

[0309] Step 5:

[0310] The user re-enters the results of implementing the support plan into the terminal. The terminal sends this feedback to the server, and the server performs feedback analysis based on the received results. Based on the analysis results, the support plan is adjusted as needed, a newly optimized plan is generated, and redistributed to the terminal. This cycle enables continuous optimization of the user's health.

[0311] (Application Example 1)

[0312] 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".

[0313] For elderly people to live independently and with peace of mind, it is essential to quickly detect abnormalities in their health and take appropriate action. However, conventional systems do not collect or analyze biometric data in real time, which can lead to delays in detecting abnormalities. Furthermore, warnings about abnormalities may not be adequately communicated, hindering prompt responses.

[0314] 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.

[0315] In this invention, the server includes means for collecting biometric data and lifestyle data from users, means for cleansing and analyzing the collected data, means for generating individual care plans by applying a generative AI model, means for acquiring the user's biometric data in real time using an integrated visualization device, and means for instantly detecting abnormalities and generating warnings based on the acquired biometric data. This enables elderly people to understand their health status in real time and respond quickly when abnormalities are detected.

[0316] "Biometric data from users" refers to indicators of an individual's health status, including information such as heart rate, blood pressure, and body temperature.

[0317] "Lifestyle data" refers to information about an individual's daily activities, including diet, exercise levels, and sleep duration.

[0318] "Means of collection" refers to methods and devices for acquiring and recording the target data, and includes sensors and software.

[0319] "Methods for cleansing and analyzing" refers to methods for removing unnecessary information and analyzing data in order to improve the accuracy of acquired data.

[0320] A "generative AI model" refers to an algorithm that uses artificial intelligence to learn specific patterns and trends, and then generates and evaluates data according to a specific purpose.

[0321] An "individualized care plan" is a set of specific action guidelines designed to improve or maintain the health of each individual user, based on their health condition and lifestyle.

[0322] A "visualization device" is a device that enables the visualization of data and presents information to users in real time, and includes displays and screens.

[0323] "Means for instantly detecting anomalies and generating warnings" refers to technologies or devices that monitor data in real time, immediately identify anomalies, and issue warnings to users.

[0324] This invention is a system to support the health management of the elderly. First, the user wears a dedicated real-time data collection device. This device has built-in sensors that measure heart rate and blood pressure, making it possible to continuously acquire the user's biometric data. This data is transmitted to a server via secure communication.

[0325] The server uses a backend program developed in Python and Flask to receive and manage data. This data is first cleansed to remove outliers and normalize the data. Then, it is analyzed by a generative AI model built using PyTorch to generate an optimal, personalized care plan for the user.

[0326] The generated care plan is delivered to the user's device in real time. The device is equipped with a visualization device that can show the user their daily health status and necessary actions. In addition, if an abnormality is detected, the system instantly generates a warning and sends an alarm or notification to the user. For example, if the user's heart rate exceeds the normal range, a warning such as "Your heart rate is elevated. Please take a break" will be displayed.

[0327] This system allows users to understand their health status in real time and take appropriate action. Furthermore, by inputting daily health information and the results of care performed as feedback from users, the generated AI model can continuously adjust its care plan, enabling it to provide a more precise plan.

[0328] An example of a prompt message is, "Design a system that monitors the health status of elderly people and issues warnings in real time."

[0329] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0330] Step 1:

[0331] The device acquires biometric data such as the user's heart rate, blood pressure, and body temperature in real time using sensors. The input is analog data from the sensors, which is converted to digital data and temporarily stored. This data is then transmitted to a server using a secure communication method. The output is biometric data in digital format.

[0332] Step 2:

[0333] The server receives biometric data transmitted from the terminal and performs a cleansing process. The input is biometric data from the terminal, and the output is clean data from which unnecessary information has been removed. At this stage, obviously incorrect values ​​are filtered out and the data is normalized.

[0334] Step 3:

[0335] The server analyzes the cleansed data using a generative AI model based on PyTorch. The input is the data processed in step 2. The model analyzes the data and generates a care plan tailored to each individual user. The output is an individualized care plan. This plan includes daily activity guidelines based on the user's health condition.

[0336] Step 4:

[0337] The server sends the generated care plan to the terminal. The terminal receives it and displays it to the user in a visually easy-to-understand format. The input is the care plan obtained from the generated AI model, and the output is what is displayed on the user's device. Specifically, the terminal uses a graphical interface to communicate the plan to the user.

[0338] Step 5:

[0339] The terminal provides an interface that allows users to input the results of their care plan implementation. Users input details of the activities performed, acquired heart rate data, and other information into the terminal. This input is user-provided feedback information and is stored as updated data that is sent to the server as output.

[0340] Step 6:

[0341] The server adjusts the care plan using a generative AI model based on feedback submitted by the user. The input is user feedback data. This data is analyzed, and the care plan is optimized as needed. The output is the newly adjusted care plan. The server then sends the updated plan back to the terminal, helping the user continue to adapt and take appropriate actions.

[0342] 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.

[0343] This invention is a system that integrates a user's biometric data, lifestyle data, and an emotion engine that recognizes their emotional state. This system is designed to facilitate the user's overall health management and to provide an appropriate care plan by simultaneously evaluating their physical and psychological health.

[0344] Data collection and emotion recognition:

[0345] Users input biometric and lifestyle data through dedicated terminals or wearable devices. The terminals are also equipped with cameras and microphones, which are used to analyze the user's facial expressions and voice, allowing the emotion engine to recognize the user's emotional state in real time.

[0346] Data analysis and care plan generation:

[0347] The server receives all data sent from the terminal, cleanses it, and then analyzes it based on the generated AI model. During this process, emotional data recognized by the emotion engine is also incorporated into the analysis, and a care plan that comprehensively considers the user's physical and emotional state is generated.

[0348] Distribution and implementation of care plans:

[0349] Care plans generated on the server are delivered to the terminal and presented to the user. Users are supported in taking actions based on the care plan in their daily lives. The terminal takes the user's emotional state into consideration and incorporates specific actions and suggestions into the plan.

[0350] Feedback and alerts:

[0351] Users input their experience and results as feedback into the device. The device sends this data to a server, which adjusts the care plan based on the feedback. In addition, if an abnormality is detected in the user's emotional state, the server generates suggestions for content aimed at stress reduction and relaxation, as well as alerts.

[0352] Specific example:

[0353] For example, in the case of a user who experiences daily stress, the server analyzes changes in heart rate in combination with the "anxiety" recognition by the emotion engine. As a result, it proposes a care plan of Nishishiki exercises, including specific breathing techniques for relaxation and light yoga sessions. The device notifies the user of when to perform the exercises and helps the user adhere to the plan. Based on the user's feedback on their performance, this care plan is adjusted as needed.

[0354] This system allows users to receive continuous support that helps maintain and improve not only their physical health but also their mental health.

[0355] The following describes the processing flow.

[0356] Step 1:

[0357] Users input biometric and lifestyle data related to their health status into the device. This includes information such as blood pressure, heart rate, weight, exercise records, and dietary information. Simultaneously, the device's camera and microphone are used to record emotional states in real time from voice and facial expressions.

[0358] Step 2:

[0359] The device collects biometric data, lifestyle data, and emotional data entered by the user and sends it to the server. This allows the server to store information about the user's overall health status.

[0360] Step 3:

[0361] The server cleanses the received data, identifying and correcting incomplete data and outliers. This creates a dataset suitable for analysis.

[0362] Step 4:

[0363] The server combines a generative AI model and an emotion engine to simultaneously analyze the user's health and emotional state. Based on the analysis results, it generates a personalized care plan optimized for the user. This plan includes recommendations for diet, exercise, and emotional improvement measures.

[0364] Step 5:

[0365] The server sends the generated care plan to the terminal. The terminal displays the received care plan to the user, providing it in a clear and easy-to-understand format.

[0366] Step 6:

[0367] Users act according to the care plan presented by the device in their daily lives. The device sends alarms and notifications when it is time to perform important tasks and make suggestions, helping users adhere to the plan.

[0368] Step 7:

[0369] Users input feedback on the results of their care plan implementation into a terminal, recording their emotions and physical changes. This allows for verification of the user's implementation status and its effectiveness.

[0370] Step 8:

[0371] The terminal sends the collected feedback data to the server. The server analyzes the feedback, evaluates the effectiveness of the care plan, and adjusts the plan as needed.

[0372] Step 9:

[0373] The server responds to the emotional state and increased stress identified by the emotion engine, and suggests appropriate relaxation content and stress reduction measures as alerts. It also sends these alerts to the user's device to encourage a quick response.

[0374] This processing flow allows the system to comprehensively support the user's physical and emotional health, providing a better quality of life.

[0375] (Example 2)

[0376] 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".

[0377] In modern society, personal health management is required to be comprehensive, encompassing not only physical health but also emotional health. However, conventional systems have struggled to centrally analyze an individual's biometric information and lifestyle patterns to provide personalized health management plans. Furthermore, there is a lack of technology to recognize a user's emotional state in real time and reflect it in health management.

[0378] 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.

[0379] In this invention, the server includes means for collecting biometric parameters and lifestyle pattern information from the user, means for purifying and analyzing the collected information, and means for applying a generative machine learning model to generate an individualized health management plan. This enables the provision of a health management plan optimized for the individual and comprehensive health maintenance that takes into account emotional state.

[0380] "Biometric parameters" are data that indicates the user's physical condition, including information such as heart rate and body temperature.

[0381] "Lifestyle pattern information" refers to data about the user's daily life, including habits such as sleep duration and exercise levels.

[0382] "Purification" is the process of preparing collected data into a format suitable for analysis, including removing noise and handling missing values.

[0383] A "generative machine learning model" is an algorithm that generates a health management plan tailored to the user based on collected data.

[0384] A "health management plan" is a plan that includes specific actions and indicators aimed at maintaining and improving the user's health.

[0385] "Emotional state" refers to the psychological state analyzed from the user's facial expressions and voice, and includes stress, happiness, and other similar feelings.

[0386] "Warning" is a function that provides advance notice of problems that are predicted to occur based on the user's health status.

[0387] This embodiment of the invention is a health management system that comprehensively collects and analyzes a user's biometric parameters and lifestyle pattern information. This system acquires user data through a dedicated terminal or wearable device. The terminal is equipped with a camera and microphone, which can analyze the user's facial expressions and voice to recognize their emotional state in real time. This provides information for maintaining mental health.

[0388] The server receives data sent from the terminal and processes noise and missing information using data cleansing algorithms. It then analyzes the data using generative machine learning models to generate an optimal health management plan for the user. This is done using an AI platform that operates on prompt-based instructions. For example, a prompt might say, "Assess the user's current health status and generate a recommended care plan."

[0389] The generated health management plan is delivered from the server to the device. The user receives notifications and can take specific actions based on the presented plan. The device provides support for the user to implement the plan and utilizes alarm and notification functions as needed.

[0390] Furthermore, user feedback is entered into the device and sent to the server. The server adjusts the health management plan based on the feedback, optimizing it for the user. For example, for a user who experiences daily stress, a plan including breathing exercises and other exercises is proposed based on an analysis combining changes in heart rate and emotional data. This allows the user to maintain overall physical and mental health.

[0391] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0392] Step 1:

[0393] Users collect biometric parameters and lifestyle pattern information using dedicated terminals or wearable devices. This input data includes heart rate, body temperature, sleep duration, and exercise level. This data collection provides specific basic data about the user's health status.

[0394] Step 2:

[0395] The device uses its built-in camera and microphone to analyze the user's facial expressions and voice in real time. The data acquired here becomes the input for emotion analysis. The device uses an emotion engine to recognize the user's emotional state and generates the result as output. This process provides information about the user's mental health.

[0396] Step 3:

[0397] The terminal transmits collected biometric parameters, lifestyle pattern information, and emotional state data to the server. The server receives this data and performs data cleansing. Data cleansing removes noise and imputes missing data, outputting data in an analyzable state. This enables accurate data analysis.

[0398] Step 4:

[0399] The server performs data analysis using a generated AI model based on the cleansed data. The analysis proceeds according to the prompt message, "Evaluate the user's current health status and generate a recommended health management plan." Based on the input data, the AI ​​model generates an optimal health management plan for the user and outputs the result.

[0400] Step 5:

[0401] The server delivers the generated health management plan to the terminal. The terminal notifies the user of this plan and suggests specific actionable steps. Examples include suggestions for relaxation breathing exercises or yoga sessions. This notification provides the user with guidance for implementing the plan in their daily life.

[0402] Step 6:

[0403] Users input feedback on the results and their experience using the health management plan into their device. This feedback is then sent from the device to the server. The server analyzes this feedback and adjusts the health management plan as needed. This enables the provision of more appropriate support to the user.

[0404] (Application Example 2)

[0405] 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."

[0406] In modern society, users are expected to receive personalized services while comprehensively managing their physical and mental health. However, understanding a user's emotional state in real time and providing appropriate services based on that understanding is not easy. Furthermore, optimizing individual customer experiences in stores requires effective emotion recognition technology and the use of user data, but conventional methods have limitations.

[0407] 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.

[0408] In this invention, the server includes means for collecting biometric and lifestyle information from the user, means for cleansing and analyzing the collected information, means for applying a generated AI model to generate individualized care guidelines, and means for recognizing the customer's emotional state using a smart device and providing recommended information based on purchase history. This makes it possible to simultaneously achieve user health management and the provision of individualized services, thereby improving the customer experience.

[0409] "Biometric information" refers to data obtained from within a user's body, and it represents information about their health status and physical characteristics.

[0410] "Lifestyle information" refers to information that includes data on the user's behavioral patterns and habits in their daily life.

[0411] "Cleansing" is a pre-processing step in data analysis that removes noise and inaccurate data.

[0412] A "generated AI model" is an artificial intelligence-powered model created based on data analysis, and serves as the foundation for generating user-optimized suggestions.

[0413] "Care guidelines" are recommended actions and plans created based on the user's health condition and lifestyle.

[0414] A "smart device" is a digital device that has the function of collecting and processing data using information and communication technology.

[0415] "Emotional state" refers to the user's psychological state and includes information obtained from facial expressions and voice.

[0416] "Recommended information" refers to suggestions about products and services presented based on the user's preferences and needs.

[0417] This system uses terminals to collect biometric and lifestyle information from users. These terminals, such as wearable devices and smartphones, acquire real-time data from users. Using emotion recognition functions with cameras and microphones, the terminals capture the user's emotional state.

[0418] The server receives the collected information and performs data cleansing. Specifically, it uses the Google Cloud Vision API to perform emotion recognition and denoise the collected data, preparing it for analysis. Using a generative AI model, it generates care guidelines based on the user's health and emotional state. These care guidelines are then sent to the user's smart device.

[0419] Smart devices provide recommendations that take into account the user's emotional state and purchase history. In particular, in stores, sales staff can use smart glasses to provide product suggestions optimized for the user.

[0420] For example, if a user visits a store and their emotional state is detected as "relaxed," the device will recommend products related to relaxation. As a result, the store can improve the user experience and provide personalized service.

[0421] An example of a prompt message would be, "Analyze the customer's facial expressions and generate optimal product suggestions based on their emotional state." This allows the generating AI model to efficiently provide personalized recommendations to the user.

[0422] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0423] Step 1:

[0424] Users input biometric and lifestyle information in real time using wearable devices or smartphones. The input data includes heart rate and activity levels. This data is immediately transmitted from the device to the server.

[0425] Step 2:

[0426] The server cleanses the received biometric and lifestyle information. Specifically, it removes noise data and prepares it for analysis. The input is raw data, and the output is refined data.

[0427] Step 3:

[0428] The server applies a generative AI model to the cleansed data. This model analyzes the data and generates care guidelines based on the user's health and emotional state. The input to the AI ​​model is formatted data, and the output is individual care guidelines.

[0429] Step 4:

[0430] The terminal receives care guidelines from the server and notifies the user. The notification includes recommended actions and product information. The input is care guidelines from the server, and the output is a visualized notification to the user.

[0431] Step 5:

[0432] The user's emotional state is recognized in real time through the device's camera and microphone. The device uses the Google Cloud Vision API to analyze facial expressions and voice to identify the emotional state. The input is real-time audio and video data, and the output is information about the emotional state.

[0433] Step 6:

[0434] Smart devices generate prompts that create recommendations based on emotional state and purchase history. Using the generated prompts, an AI model makes optimal product suggestions. The input is emotional state data and purchase history, and the output is the suggested products and services.

[0435] Step 7:

[0436] Users act based on the recommendations they receive. The device then sends the results of their actions and feedback back to the server. This feedback is used for further data analysis, and the entire system, as an ecosystem, continuously improves the user experience. The input is user feedback, and the output is information about system improvements based on that feedback.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] [Third Embodiment]

[0441] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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).

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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".

[0453] This invention is a system that provides care plans tailored to the user's daily life, enabling elderly people to live with peace of mind. An embodiment of this system is shown below.

[0454] 1. Data collection:

[0455] Users regularly input biometric and lifestyle data using a dedicated application. This data includes blood pressure, heart rate, weight, diet, exercise levels, and sleep duration. The device receives this data and transmits it to the server using secure communication methods.

[0456] 2. Data analysis and care plan generation:

[0457] The server cleanses and analyzes the data sent from the terminal. Using a generative AI model, it evaluates the user's health status and lifestyle habits and automatically generates an optimal care plan. This plan includes daily meal plans, recommended exercise, and key points for health management.

[0458] 3. Distribution and implementation of care plans:

[0459] The care plan generated from the server is sent to the device and displayed to the user in a visually easy-to-understand format. The user can then follow these instructions to manage their daily life. The device sends alarms and notifications as needed to help the user remember important tasks.

[0460] 4. Feedback and alerts:

[0461] The user continuously inputs the results of implementing the care plan into the terminal. The terminal sends the newly entered data to the server, where the effectiveness of the care plan is evaluated based on the feedback. If a risk is detected, the server generates an appropriate alert and notifies the user and their family.

[0462] Specific example:

[0463] For example, for a user at risk of hypertension, the server generates a care plan that includes a low-sodium diet and light aerobic exercise. Based on this plan, the terminal suggests a morning walk and a low-sodium breakfast to the user. After the user completes the plan, they input the details into the terminal, and the results are sent to the server. The server adjusts the care plan based on the results and sends the improved plan back to the terminal.

[0464] In this way, the invention provides continuous care tailored to individual needs, supporting healthier and more fulfilling lives for the elderly.

[0465] The following describes the processing flow.

[0466] Step 1:

[0467] Users input biometric data related to their health status into their device through a dedicated application. This includes information such as blood pressure, weight, diet, and exercise status.

[0468] Step 2:

[0469] The terminal receives data entered by the user and performs communication processing to send it to the server. At this time, it verifies that the data is accurate and complete before sending it.

[0470] Step 3:

[0471] The server receives data sent from the terminal and performs initial cleansing. It checks for data defects and abnormal values, and corrects or supplements the data if necessary.

[0472] Step 4:

[0473] The server applies a generative AI model to analyze the cleansed data. Based on the analysis results, it automatically generates an optimal care plan tailored to the user's health condition and lifestyle.

[0474] Step 5:

[0475] The server records the generated care plans in a database and manages them individually for each user. It then prepares to send the latest care plan to the terminal.

[0476] Step 6:

[0477] The terminal receives the care plan sent from the server and presents it to the user. It displays the plan in a simple and easy-to-understand format so that the user can visually confirm it.

[0478] Step 7:

[0479] The user performs daily activities according to the presented care plan and inputs the results as feedback into the device. This feedback information includes the success or failure of the activities and any observed changes in health status.

[0480] Step 8:

[0481] The device sends user feedback data to the server, providing additional information about the effectiveness of the care plan and any necessary adjustments.

[0482] Step 9:

[0483] The server re-analyzes the feedback data and evaluates the progress of the care plan and the user's health improvement. If necessary, it adjusts the care plan and generates an updated version.

[0484] Step 10:

[0485] The server generates necessary alerts and notifications based on updated care plans and new risks. These are sent to the user and their family to prompt appropriate action.

[0486] Through this series of processes, the system continuously provides users with optimal care and supports the maintenance of the health of the elderly.

[0487] (Example 1)

[0488] 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."

[0489] In health management systems for the elderly, there is a problem in efficiently providing personalized health support and proposing appropriate feedback and improvement measures based on the user's health condition. This is because there is a lack of mechanisms to accurately grasp an individual's lifestyle and health condition and respond quickly.

[0490] 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.

[0491] In this invention, the server includes means for recording biometric information and lifestyle information from the user, means for selecting and analyzing the recorded information, and means for generating an individualized support plan by applying a generative AI model. This enables the provision of individualized care plans tailored to each user's health condition, as well as continuous evaluation and improvement after implementation.

[0492] "User" refers to an individual who uses the system, and in particular includes elderly people who provide information on their health status and lifestyle and who receive individualized care plans.

[0493] "Biometric information" refers to data that indicates the user's physical condition, including measurable health indicators such as blood pressure, heart rate, and weight.

[0494] "Lifestyle information" refers to data about a user's daily activities and habits, including diet, exercise levels, and sleep duration.

[0495] "Selection" refers to the process of removing unnecessary information and extracting only useful data in the preliminary stages of data analysis.

[0496] "Analysis" refers to the process of evaluating a user's health status and lifestyle based on collected data, and deriving the results as data.

[0497] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to automatically generate appropriate care plans from data.

[0498] A "support plan" refers to a plan that includes specific instructions to promote diet, exercise, and health management optimized for each user's individual health condition, based on the analysis results.

[0499] A "user device" is a device used by a user to input information, receive generated care plans, or receive notifications, and includes smartphones and tablets.

[0500] "Feedback analysis" refers to the process of analysis performed by the system based on user feedback to evaluate the effectiveness of a support plan and revise that plan.

[0501] A "warning" is risk information generated based on the user's health status, which will be communicated to the user or their family if necessary.

[0502] This invention is a system for supporting the health management of the elderly, and in particular aims to provide personalized support plans based on biometric and lifestyle information. This system is implemented in the following form.

[0503] First, users input their biometric and lifestyle information using a device such as a smartphone or tablet. The information recorded by the user includes biometric data such as blood pressure, heart rate, and weight, as well as lifestyle information such as diet, exercise level, and sleep duration. The device sends this information to the server using a secure communication protocol (e.g., HTTPS).

[0504] The server sorts the received information, deleting or correcting unnecessary data to prepare it for analysis. Next, the server uses a generative AI model to evaluate each user's health status and lifestyle based on the cleansed data. The goal of the generative AI model is to generate an individualized support plan. For example, if a user shows a tendency towards high blood pressure, the generated support plan may include a low-sodium diet and light aerobic exercise. An example of a prompt would be to instruct the server, "Generate an optimal care plan based on the user's blood pressure data and past exercise history."

[0505] The server then sends the generated support plan to the user's device, which displays the plan on a visually easy-to-understand dashboard. The user can adjust their daily activities based on this support plan. The device also provides alarm and notification functions to help the user remember to follow the plan.

[0506] In addition, users can continue to input the results of their activities through their devices. The server analyzes the information received as feedback, modifies the support plan as needed, and sends the revised plan back to the user's device. This collaboration allows users to receive appropriate support tailored to their daily health condition.

[0507] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0508] Step 1:

[0509] Users input biometric and lifestyle information using a dedicated application on their device. This information includes blood pressure, heart rate, diet, exercise levels, and sleep duration. The device aggregates this information and transmits it to a server using a secure communication protocol. This process provides the foundational data needed to assess the user's health status.

[0510] Step 2:

[0511] The server receives information sent from the terminal and first sorts the data. During the sorting process, it detects outliers and inappropriate data, and then filters and corrects them to make them more manageable. Next, data analysis is performed, which provides insights into the user's current health status. The generative AI model receives this cleansed data as input and outputs the results of the analysis.

[0512] Step 3:

[0513] The server uses a generative AI model to generate a personalized support plan that takes into account the user's health status and lifestyle. During this process, the model is provided with prompts such as, "Generate an optimal care plan based on the user's blood pressure data and past exercise history." The generated support plan includes dietary recommendations, exercise suggestions, and health management tips. This output is personalized and tailored to the user's needs.

[0514] Step 4:

[0515] The server sends the generated support plan to the terminal, which displays the received plan in a visually easy-to-understand format on the user interface. The terminal provides alarms and reminders for the user to use when living according to the care plan, supporting its implementation. This allows the user to adjust their lifestyle to be healthier and encourages them to act in accordance with the plan.

[0516] Step 5:

[0517] The user re-enters the results of implementing the support plan into the terminal. The terminal sends this feedback to the server, which performs feedback analysis based on the received results. Based on the analysis results, the support plan is adjusted as needed, a newly optimized plan is generated, and redistributed to the terminal. This cycle makes it possible to continuously optimize the user's health.

[0518] (Application Example 1)

[0519] 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."

[0520] For elderly people to live independently and with peace of mind, it is essential to quickly detect abnormalities in their health and take appropriate action. However, conventional systems do not collect or analyze biometric data in real time, which can lead to delays in detecting abnormalities. Furthermore, warnings about abnormalities may not be adequately communicated, hindering prompt responses.

[0521] 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.

[0522] In this invention, the server includes means for collecting biometric data and lifestyle data from users, means for cleansing and analyzing the collected data, means for generating individual care plans by applying a generative AI model, means for acquiring the user's biometric data in real time using an integrated visualization device, and means for instantly detecting abnormalities and generating warnings based on the acquired biometric data. This enables elderly people to understand their health status in real time and respond quickly when abnormalities are detected.

[0523] "Biometric data from users" refers to indicators of an individual's health status, including information such as heart rate, blood pressure, and body temperature.

[0524] "Lifestyle data" refers to information about an individual's daily activities, including diet, exercise levels, and sleep duration.

[0525] "Means of collection" refers to methods and devices for acquiring and recording the target data, and includes sensors and software.

[0526] "Methods for cleansing and analyzing" refers to methods for removing unnecessary information and analyzing data in order to improve the accuracy of acquired data.

[0527] A "generative AI model" refers to an algorithm that uses artificial intelligence to learn specific patterns and trends, and then generates and evaluates data according to a specific purpose.

[0528] An "individualized care plan" is a set of specific action guidelines designed to improve or maintain the health of each individual user, based on their health condition and lifestyle.

[0529] A "visualization device" is a device that enables the visualization of data and presents information to users in real time, and includes displays and screens.

[0530] "Means for instantly detecting anomalies and generating warnings" refers to technologies or devices that monitor data in real time, immediately identify anomalies, and issue warnings to users.

[0531] This invention is a system to support the health management of the elderly. First, the user wears a dedicated real-time data collection device. This device has built-in sensors that measure heart rate and blood pressure, making it possible to continuously acquire the user's biometric data. This data is transmitted to a server via secure communication.

[0532] The server uses a backend program developed in Python and Flask to receive and manage data. This data is first cleansed to remove outliers and normalize the data. Then, it is analyzed by a generative AI model built using PyTorch to generate an optimal, personalized care plan for the user.

[0533] The generated care plan is delivered to the user's device in real time. The device is equipped with a visualization device that can show the user their daily health status and necessary actions. In addition, if an abnormality is detected, the system instantly generates a warning and sends an alarm or notification to the user. For example, if the user's heart rate exceeds the normal range, a warning such as "Your heart rate is elevated. Please take a break" will be displayed.

[0534] This system allows users to understand their health status in real time and take appropriate action. Furthermore, by inputting daily health information and the results of care performed as feedback from users, the generated AI model can continuously adjust its care plan, enabling it to provide a more precise plan.

[0535] An example of a prompt message is, "Design a system that monitors the health status of elderly people and issues warnings in real time."

[0536] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0537] Step 1:

[0538] The device acquires biometric data such as the user's heart rate, blood pressure, and body temperature in real time using sensors. The input is analog data from the sensors, which is converted to digital data and temporarily stored. This data is then transmitted to a server using a secure communication method. The output is biometric data in digital format.

[0539] Step 2:

[0540] The server receives biometric data transmitted from the terminal and performs a cleansing process. The input is biometric data from the terminal, and the output is clean data from which unnecessary information has been removed. At this stage, obviously incorrect values ​​are filtered out and the data is normalized.

[0541] Step 3:

[0542] The server analyzes the cleansed data using a generative AI model based on PyTorch. The input is the data processed in step 2. The model analyzes the data and generates a care plan tailored to each individual user. The output is an individualized care plan. This plan includes daily activity guidelines based on the user's health condition.

[0543] Step 4:

[0544] The server sends the generated care plan to the terminal. The terminal receives it and displays it to the user in a visually easy-to-understand format. The input is the care plan obtained from the generated AI model, and the output is what is displayed on the user's device. Specifically, the terminal uses a graphical interface to communicate the plan to the user.

[0545] Step 5:

[0546] The terminal provides an interface that allows users to input the results of their care plan implementation. Users input details of the activities performed, acquired heart rate data, and other information into the terminal. This input is user-provided feedback information and is stored as updated data that is sent to the server as output.

[0547] Step 6:

[0548] The server adjusts the care plan using a generative AI model based on feedback submitted by the user. The input is user feedback data. This data is analyzed, and the care plan is optimized as needed. The output is the newly adjusted care plan. The server then sends the updated plan back to the terminal, helping the user continue to adapt and take appropriate actions.

[0549] 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.

[0550] This invention is a system that integrates a user's biometric data, lifestyle data, and an emotion engine that recognizes their emotional state. This system is designed to facilitate the user's overall health management and to provide an appropriate care plan by simultaneously evaluating their physical and psychological health.

[0551] Data collection and emotion recognition:

[0552] Users input biometric and lifestyle data through dedicated terminals or wearable devices. The terminals are also equipped with cameras and microphones, which are used to analyze the user's facial expressions and voice, allowing the emotion engine to recognize the user's emotional state in real time.

[0553] Data analysis and care plan generation:

[0554] The server receives all data sent from the terminal, cleanses it, and then analyzes it based on the generated AI model. During this process, emotional data recognized by the emotion engine is also incorporated into the analysis, and a care plan that comprehensively considers the user's physical and emotional state is generated.

[0555] Distribution and implementation of care plans:

[0556] Care plans generated on the server are delivered to the terminal and presented to the user. Users are supported in taking actions based on the care plan in their daily lives. The terminal takes the user's emotional state into consideration and incorporates specific actions and suggestions into the plan.

[0557] Feedback and alerts:

[0558] Users input their experience and results as feedback into the device. The device sends this data to a server, which adjusts the care plan based on the feedback. In addition, if an abnormality is detected in the user's emotional state, the server generates suggestions for content aimed at stress reduction and relaxation, as well as alerts.

[0559] Specific example:

[0560] For example, in the case of a user who experiences daily stress, the server analyzes changes in heart rate in combination with the "anxiety" recognition by the emotion engine. As a result, it proposes a care plan of Nishishiki exercises, including specific breathing techniques for relaxation and light yoga sessions. The device notifies the user of when to perform the exercises and helps the user adhere to the plan. Based on the user's feedback on their performance, this care plan is adjusted as needed.

[0561] This system allows users to receive continuous support that helps maintain and improve not only their physical health but also their mental health.

[0562] The following describes the processing flow.

[0563] Step 1:

[0564] Users input biometric and lifestyle data related to their health status into the device. This includes information such as blood pressure, heart rate, weight, exercise records, and dietary information. Simultaneously, the device's camera and microphone are used to record emotional states in real time from voice and facial expressions.

[0565] Step 2:

[0566] The device collects biometric data, lifestyle data, and emotional data entered by the user and sends it to the server. This allows the server to store information about the user's overall health status.

[0567] Step 3:

[0568] The server cleanses the received data, identifying and correcting incomplete data and outliers. This creates a dataset suitable for analysis.

[0569] Step 4:

[0570] The server combines a generative AI model and an emotion engine to simultaneously analyze the user's health and emotional state. Based on the analysis results, it generates a personalized care plan optimized for the user. This plan includes recommendations for diet, exercise, and emotional improvement measures.

[0571] Step 5:

[0572] The server sends the generated care plan to the terminal. The terminal displays the received care plan to the user, providing it in a clear and easy-to-understand format.

[0573] Step 6:

[0574] Users act according to the care plan presented by the device in their daily lives. The device sends alarms and notifications when it is time to perform important tasks and make suggestions, helping users adhere to the plan.

[0575] Step 7:

[0576] Users input feedback on the results of their care plan implementation into a terminal, recording their emotions and physical changes. This allows for verification of the user's implementation status and its effectiveness.

[0577] Step 8:

[0578] The terminal sends the collected feedback data to the server. The server analyzes the feedback, evaluates the effectiveness of the care plan, and adjusts the plan as needed.

[0579] Step 9:

[0580] The server responds to the emotional state and increased stress identified by the emotion engine, and suggests appropriate relaxation content and stress reduction measures as alerts. It also sends these alerts to the user's device to encourage a quick response.

[0581] This processing flow allows the system to comprehensively support the user's physical and emotional health, providing a better quality of life.

[0582] (Example 2)

[0583] 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."

[0584] In modern society, personal health management is required to be comprehensive, encompassing not only physical health but also emotional health. However, conventional systems have struggled to centrally analyze an individual's biometric information and lifestyle patterns to provide personalized health management plans. Furthermore, there is a lack of technology to recognize a user's emotional state in real time and reflect it in health management.

[0585] 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.

[0586] In this invention, the server includes means for collecting biometric parameters and lifestyle pattern information from the user, means for purifying and analyzing the collected information, and means for applying a generative machine learning model to generate an individualized health management plan. This enables the provision of a health management plan optimized for the individual and comprehensive health maintenance that takes into account emotional state.

[0587] "Biometric parameters" are data that indicates the user's physical condition, including information such as heart rate and body temperature.

[0588] "Lifestyle pattern information" refers to data about the user's daily life, including habits such as sleep duration and exercise levels.

[0589] "Purification" is the process of preparing collected data into a format suitable for analysis, including removing noise and handling missing values.

[0590] A "generative machine learning model" is an algorithm that generates a health management plan tailored to the user based on collected data.

[0591] A "health management plan" is a plan that includes specific actions and indicators aimed at maintaining and improving the user's health.

[0592] "Emotional state" refers to the psychological state analyzed from the user's facial expressions and voice, and includes stress, happiness, and other similar feelings.

[0593] "Warning" is a function that provides advance notice of problems that are predicted to occur based on the user's health status.

[0594] This embodiment of the invention is a health management system that comprehensively collects and analyzes a user's biometric parameters and lifestyle pattern information. This system acquires user data through a dedicated terminal or wearable device. The terminal is equipped with a camera and microphone, which can analyze the user's facial expressions and voice to recognize their emotional state in real time. This provides information for maintaining mental health.

[0595] The server receives data sent from the terminal and processes noise and missing information using data cleansing algorithms. It then analyzes the data using generative machine learning models to generate an optimal health management plan for the user. This is done using an AI platform that operates on prompt-based instructions. For example, a prompt might say, "Assess the user's current health status and generate a recommended care plan."

[0596] The generated health management plan is delivered from the server to the device. The user receives notifications and can take specific actions based on the presented plan. The device provides support for the user to implement the plan and utilizes alarm and notification functions as needed.

[0597] Furthermore, user feedback is entered into the device and sent to the server. The server adjusts the health management plan based on the feedback, optimizing it for the user. For example, for a user who experiences daily stress, a plan including breathing exercises and other exercises is proposed based on an analysis combining changes in heart rate and emotional data. This allows the user to maintain overall physical and mental health.

[0598] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0599] Step 1:

[0600] Users collect biometric parameters and lifestyle pattern information using dedicated terminals or wearable devices. This input data includes heart rate, body temperature, sleep duration, and exercise level. This data collection provides specific basic data about the user's health status.

[0601] Step 2:

[0602] The device uses its built-in camera and microphone to analyze the user's facial expressions and voice in real time. The data acquired here becomes the input for emotion analysis. The device uses an emotion engine to recognize the user's emotional state and generates the result as output. This process provides information about the user's mental health.

[0603] Step 3:

[0604] The terminal transmits collected biometric parameters, lifestyle pattern information, and emotional state data to the server. The server receives this data and performs data cleansing. Data cleansing removes noise and imputes missing data, outputting data in an analyzable state. This enables accurate data analysis.

[0605] Step 4:

[0606] The server performs data analysis using a generated AI model based on the cleansed data. The analysis proceeds according to the prompt message, "Evaluate the user's current health status and generate a recommended health management plan." Based on the input data, the AI ​​model generates an optimal health management plan for the user and outputs the result.

[0607] Step 5:

[0608] The server delivers the generated health management plan to the terminal. The terminal notifies the user of this plan and suggests specific actionable steps. Examples include suggestions for relaxation breathing exercises or yoga sessions. This notification provides the user with guidance for implementing the plan in their daily life.

[0609] Step 6:

[0610] Users input feedback on the results and their experience using the health management plan into their device. This feedback is then sent from the device to the server. The server analyzes this feedback and adjusts the health management plan as needed. This enables the provision of more appropriate support to the user.

[0611] (Application Example 2)

[0612] 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."

[0613] In modern society, users are expected to receive personalized services while comprehensively managing their physical and mental health. However, understanding a user's emotional state in real time and providing appropriate services based on that understanding is not easy. Furthermore, optimizing individual customer experiences in stores requires effective emotion recognition technology and the use of user data, but conventional methods have limitations.

[0614] 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.

[0615] In this invention, the server includes means for collecting biometric and lifestyle information from the user, means for cleansing and analyzing the collected information, means for applying a generated AI model to generate individualized care guidelines, and means for recognizing the customer's emotional state using a smart device and providing recommended information based on purchase history. This makes it possible to simultaneously achieve user health management and the provision of individualized services, thereby improving the customer experience.

[0616] "Biometric information" refers to data obtained from within a user's body, and it represents information about their health status and physical characteristics.

[0617] "Lifestyle information" refers to information that includes data on the user's behavioral patterns and habits in their daily life.

[0618] "Cleansing" is a pre-processing step in data analysis that removes noise and inaccurate data.

[0619] A "generated AI model" is an artificial intelligence-powered model created based on data analysis, and serves as the foundation for generating user-optimized suggestions.

[0620] "Care guidelines" are recommended actions and plans created based on the user's health condition and lifestyle.

[0621] A "smart device" is a digital device that has the function of collecting and processing data using information and communication technology.

[0622] "Emotional state" refers to the user's psychological state and includes information obtained from facial expressions and voice.

[0623] "Recommended information" refers to suggestions about products and services presented based on the user's preferences and needs.

[0624] This system uses terminals to collect biometric and lifestyle information from users. These terminals, such as wearable devices and smartphones, acquire real-time data from users. Using emotion recognition functions with cameras and microphones, the terminals capture the user's emotional state.

[0625] The server receives the collected information and performs data cleansing. Specifically, it uses the Google Cloud Vision API to perform emotion recognition and denoise the collected data, preparing it for analysis. Using a generative AI model, it generates care guidelines based on the user's health and emotional state. These care guidelines are then sent to the user's smart device.

[0626] Smart devices provide recommendations that take into account the user's emotional state and purchase history. In particular, in stores, sales staff can use smart glasses to provide product suggestions optimized for the user.

[0627] For example, if a user visits a store and their emotional state is detected as "relaxed," the device will recommend products related to relaxation. As a result, the store can improve the user experience and provide personalized service.

[0628] An example of a prompt message would be, "Analyze the customer's facial expressions and generate optimal product suggestions based on their emotional state." This allows the generating AI model to efficiently provide personalized recommendations to the user.

[0629] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0630] Step 1:

[0631] Users input biometric and lifestyle information in real time using wearable devices or smartphones. The input data includes heart rate and activity levels. This data is immediately transmitted from the device to the server.

[0632] Step 2:

[0633] The server cleanses the received biometric and lifestyle information. Specifically, it removes noise data and prepares it for analysis. The input is raw data, and the output is refined data.

[0634] Step 3:

[0635] The server applies a generative AI model to the cleansed data. This model analyzes the data and generates care guidelines based on the user's health and emotional state. The input to the AI ​​model is formatted data, and the output is individual care guidelines.

[0636] Step 4:

[0637] The terminal receives care guidelines from the server and notifies the user. The notification includes recommended actions and product information. The input is care guidelines from the server, and the output is a visualized notification to the user.

[0638] Step 5:

[0639] The user's emotional state is recognized in real time through the device's camera and microphone. The device uses the Google Cloud Vision API to analyze facial expressions and voice to identify the emotional state. The input is real-time audio and video data, and the output is information about the emotional state.

[0640] Step 6:

[0641] Smart devices generate prompts that create recommendations based on emotional state and purchase history. Using the generated prompts, an AI model makes optimal product suggestions. The input is emotional state data and purchase history, and the output is the suggested products and services.

[0642] Step 7:

[0643] Users act based on the recommendations they receive. The device then sends the results of their actions and feedback back to the server. This feedback is used for further data analysis, and the entire system, as an ecosystem, continuously improves the user experience. The input is user feedback, and the output is information about system improvements based on that feedback.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] [Fourth Embodiment]

[0648] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0649] 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.

[0650] 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).

[0651] 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.

[0652] 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.

[0653] 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).

[0654] 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.

[0655] 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.

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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".

[0661] This invention is a system that provides care plans tailored to the user's daily life, enabling elderly people to live with peace of mind. An embodiment of this system is shown below.

[0662] 1. Data collection:

[0663] Users regularly input biometric and lifestyle data using a dedicated application. This data includes blood pressure, heart rate, weight, diet, exercise levels, and sleep duration. The device receives this data and transmits it to the server using secure communication methods.

[0664] 2. Data analysis and care plan generation:

[0665] The server cleanses and analyzes the data sent from the terminal. Using a generative AI model, it evaluates the user's health status and lifestyle habits and automatically generates an optimal care plan. This plan includes daily meal plans, recommended exercise, and key points for health management.

[0666] 3. Distribution and implementation of care plans:

[0667] The care plan generated from the server is sent to the device and displayed to the user in a visually easy-to-understand format. The user can then follow these instructions to manage their daily life. The device sends alarms and notifications as needed to help the user remember important tasks.

[0668] 4. Feedback and alerts:

[0669] The user continuously inputs the results of implementing the care plan into the terminal. The terminal sends the newly entered data to the server, where the effectiveness of the care plan is evaluated based on the feedback. If a risk is detected, the server generates an appropriate alert and notifies the user and their family.

[0670] Specific example:

[0671] For example, for a user at risk of hypertension, the server generates a care plan that includes a low-sodium diet and light aerobic exercise. Based on this plan, the terminal suggests a morning walk and a low-sodium breakfast to the user. After the user completes the plan, they input the details into the terminal, and the results are sent to the server. The server adjusts the care plan based on the results and sends the improved plan back to the terminal.

[0672] In this way, the invention provides continuous care tailored to individual needs, supporting healthier and more fulfilling lives for the elderly.

[0673] The following describes the processing flow.

[0674] Step 1:

[0675] Users input biometric data related to their health status into their device through a dedicated application. This includes information such as blood pressure, weight, diet, and exercise status.

[0676] Step 2:

[0677] The terminal receives data entered by the user and performs communication processing to send it to the server. At this time, it verifies that the data is accurate and complete before sending it.

[0678] Step 3:

[0679] The server receives data sent from the terminal and performs initial cleansing. It checks for data defects and abnormal values, and corrects or supplements the data if necessary.

[0680] Step 4:

[0681] The server applies a generative AI model to analyze the cleansed data. Based on the analysis results, it automatically generates an optimal care plan tailored to the user's health condition and lifestyle.

[0682] Step 5:

[0683] The server records the generated care plans in a database and manages them individually for each user. It then prepares to send the latest care plan to the terminal.

[0684] Step 6:

[0685] The terminal receives the care plan sent from the server and presents it to the user. It displays the plan in a simple and easy-to-understand format so that the user can visually confirm it.

[0686] Step 7:

[0687] The user performs daily activities according to the presented care plan and inputs the results as feedback into the device. This feedback information includes the success or failure of the activities and any observed changes in health status.

[0688] Step 8:

[0689] The device sends user feedback data to the server, providing additional information about the effectiveness of the care plan and any necessary adjustments.

[0690] Step 9:

[0691] The server re-analyzes the feedback data and evaluates the progress of the care plan and the user's health improvement. If necessary, it adjusts the care plan and generates an updated version.

[0692] Step 10:

[0693] The server generates necessary alerts and notifications based on updated care plans and new risks. These are sent to the user and their family to prompt appropriate action.

[0694] Through this series of processes, the system continuously provides users with optimal care and supports the maintenance of the health of the elderly.

[0695] (Example 1)

[0696] 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".

[0697] In health management systems for the elderly, there is a problem in efficiently providing personalized health support and proposing appropriate feedback and improvement measures based on the user's health condition. This is because there is a lack of mechanisms to accurately grasp an individual's lifestyle and health condition and respond quickly.

[0698] 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.

[0699] In this invention, the server includes means for recording biometric information and lifestyle information from the user, means for selecting and analyzing the recorded information, and means for generating an individualized support plan by applying a generative AI model. This enables the provision of individualized care plans tailored to each user's health condition, as well as continuous evaluation and improvement after implementation.

[0700] "User" refers to an individual who uses the system, and in particular includes elderly people who provide information on their health status and lifestyle and who receive individualized care plans.

[0701] "Biometric information" refers to data that indicates the user's physical condition, including measurable health indicators such as blood pressure, heart rate, and weight.

[0702] "Lifestyle information" refers to data about a user's daily activities and habits, including diet, exercise levels, and sleep duration.

[0703] "Selection" refers to the process of removing unnecessary information and extracting only useful data in the preliminary stages of data analysis.

[0704] "Analysis" refers to the process of evaluating a user's health status and lifestyle based on collected data, and deriving the results as data.

[0705] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to automatically generate appropriate care plans from data.

[0706] A "support plan" refers to a plan that includes specific instructions to promote diet, exercise, and health management optimized for each user's individual health condition, based on the analysis results.

[0707] A "user device" is a device used by a user to input information, receive generated care plans, or receive notifications, and includes smartphones and tablets.

[0708] "Feedback analysis" refers to the process of analysis performed by the system based on user feedback to evaluate the effectiveness of a support plan and revise that plan.

[0709] A "warning" is risk information generated based on the user's health status, which will be communicated to the user or their family if necessary.

[0710] This invention is a system for supporting the health management of the elderly, and in particular aims to provide personalized support plans based on biometric and lifestyle information. This system is implemented in the following form.

[0711] First, users input their biometric and lifestyle information using a device such as a smartphone or tablet. The information recorded by the user includes biometric data such as blood pressure, heart rate, and weight, as well as lifestyle information such as diet, exercise level, and sleep duration. The device sends this information to the server using a secure communication protocol (e.g., HTTPS).

[0712] The server sorts the received information, deleting or correcting unnecessary data to prepare it for analysis. Next, the server uses a generative AI model to evaluate each user's health status and lifestyle based on the cleansed data. The goal of the generative AI model is to generate an individualized support plan. For example, if a user shows a tendency towards high blood pressure, the generated support plan may include a low-sodium diet and light aerobic exercise. An example of a prompt would be to instruct the server, "Generate an optimal care plan based on the user's blood pressure data and past exercise history."

[0713] The server then sends the generated support plan to the user's device, which displays the plan on a visually easy-to-understand dashboard. The user can adjust their daily activities based on this support plan. The device also provides alarm and notification functions to help the user remember to follow the plan.

[0714] In addition, users can continue to input the results of their activities through their devices. The server analyzes the information received as feedback, modifies the support plan as needed, and sends the revised plan back to the user's device. This collaboration allows users to receive appropriate support tailored to their daily health condition.

[0715] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0716] Step 1:

[0717] Users input biometric and lifestyle information using a dedicated application on their device. This information includes blood pressure, heart rate, diet, exercise levels, and sleep duration. The device aggregates this information and transmits it to a server using a secure communication protocol. This process provides the foundational data needed to assess the user's health status.

[0718] Step 2:

[0719] The server receives information sent from the terminal and first sorts the data. During the sorting process, it detects outliers and inappropriate data, and then filters and corrects them to make them more manageable. Next, data analysis is performed, which provides insights into the user's current health status. The generative AI model receives this cleansed data as input and outputs the results of the analysis.

[0720] Step 3:

[0721] The server uses a generative AI model to generate a personalized support plan that takes into account the user's health status and lifestyle. During this process, the model is provided with prompts such as, "Generate an optimal care plan based on the user's blood pressure data and past exercise history." The generated support plan includes dietary recommendations, exercise suggestions, and health management tips. This output is personalized and tailored to the user's needs.

[0722] Step 4:

[0723] The server sends the generated support plan to the terminal, which displays the received plan in a visually easy-to-understand format on the user interface. The terminal provides alarms and reminders for the user to use when living according to the care plan, supporting its implementation. This allows the user to adjust their lifestyle to be healthier and encourages them to act in accordance with the plan.

[0724] Step 5:

[0725] The user re-enters the results of implementing the support plan into the terminal. The terminal sends this feedback to the server, which performs feedback analysis based on the received results. Based on the analysis results, the support plan is adjusted as needed, a newly optimized plan is generated, and redistributed to the terminal. This cycle makes it possible to continuously optimize the user's health.

[0726] (Application Example 1)

[0727] 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".

[0728] For elderly people to live independently and with peace of mind, it is essential to quickly detect abnormalities in their health and take appropriate action. However, conventional systems do not collect or analyze biometric data in real time, which can lead to delays in detecting abnormalities. Furthermore, warnings about abnormalities may not be adequately communicated, hindering prompt responses.

[0729] 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.

[0730] In this invention, the server includes means for collecting biometric data and lifestyle data from users, means for cleansing and analyzing the collected data, means for generating individual care plans by applying a generative AI model, means for acquiring the user's biometric data in real time using an integrated visualization device, and means for instantly detecting abnormalities and generating warnings based on the acquired biometric data. This enables elderly people to understand their health status in real time and respond quickly when abnormalities are detected.

[0731] "Biometric data from users" refers to indicators of an individual's health status, including information such as heart rate, blood pressure, and body temperature.

[0732] "Lifestyle data" refers to information about an individual's daily activities, including diet, exercise levels, and sleep duration.

[0733] "Means of collection" refers to methods and devices for acquiring and recording the target data, and includes sensors and software.

[0734] "Methods for cleansing and analyzing" refers to methods for removing unnecessary information and analyzing data in order to improve the accuracy of acquired data.

[0735] A "generative AI model" refers to an algorithm that uses artificial intelligence to learn specific patterns and trends, and then generates and evaluates data according to a specific purpose.

[0736] An "individualized care plan" is a set of specific action guidelines designed to improve or maintain the health of each individual user, based on their health condition and lifestyle.

[0737] A "visualization device" is a device that enables the visualization of data and presents information to users in real time, and includes displays and screens.

[0738] "Means for instantly detecting anomalies and generating warnings" refers to technologies or devices that monitor data in real time, immediately identify anomalies, and issue warnings to users.

[0739] This invention is a system to support the health management of the elderly. First, the user wears a dedicated real-time data collection device. This device has built-in sensors that measure heart rate and blood pressure, making it possible to continuously acquire the user's biometric data. This data is transmitted to a server via secure communication.

[0740] The server uses a backend program developed in Python and Flask to receive and manage data. This data is first cleansed to remove outliers and normalize the data. Then, it is analyzed by a generative AI model built using PyTorch to generate an optimal, personalized care plan for the user.

[0741] The generated care plan is delivered to the user's device in real time. The device is equipped with a visualization device that can show the user their daily health status and necessary actions. In addition, if an abnormality is detected, the system instantly generates a warning and sends an alarm or notification to the user. For example, if the user's heart rate exceeds the normal range, a warning such as "Your heart rate is elevated. Please take a break" will be displayed.

[0742] This system allows users to understand their health status in real time and take appropriate action. Furthermore, by inputting daily health information and the results of care performed as feedback from users, the generated AI model can continuously adjust its care plan, enabling it to provide a more precise plan.

[0743] An example of a prompt message is, "Design a system that monitors the health status of elderly people and issues warnings in real time."

[0744] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0745] Step 1:

[0746] The device acquires biometric data such as the user's heart rate, blood pressure, and body temperature in real time using sensors. The input is analog data from the sensors, which is converted to digital data and temporarily stored. This data is then transmitted to a server using a secure communication method. The output is biometric data in digital format.

[0747] Step 2:

[0748] The server receives biometric data transmitted from the terminal and performs a cleansing process. The input is biometric data from the terminal, and the output is clean data from which unnecessary information has been removed. At this stage, obviously incorrect values ​​are filtered out and the data is normalized.

[0749] Step 3:

[0750] The server analyzes the cleansed data using a generative AI model based on PyTorch. The input is the data processed in step 2. The model analyzes the data and generates a care plan tailored to each individual user. The output is an individualized care plan. This plan includes daily activity guidelines based on the user's health condition.

[0751] Step 4:

[0752] The server sends the generated care plan to the terminal. The terminal receives it and displays it to the user in a visually easy-to-understand format. The input is the care plan obtained from the generated AI model, and the output is what is displayed on the user's device. Specifically, the terminal uses a graphical interface to communicate the plan to the user.

[0753] Step 5:

[0754] The terminal provides an interface that allows users to input the results of their care plan implementation. Users input details of the activities performed, acquired heart rate data, and other information into the terminal. This input is user-provided feedback information and is stored as updated data that is sent to the server as output.

[0755] Step 6:

[0756] The server adjusts the care plan using a generative AI model based on feedback submitted by the user. The input is user feedback data. This data is analyzed, and the care plan is optimized as needed. The output is the newly adjusted care plan. The server then sends the updated plan back to the terminal, helping the user continue to adapt and take appropriate actions.

[0757] 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.

[0758] This invention is a system that integrates a user's biometric data, lifestyle data, and an emotion engine that recognizes their emotional state. This system is designed to facilitate the user's overall health management and to provide an appropriate care plan by simultaneously evaluating their physical and psychological health.

[0759] Data collection and emotion recognition:

[0760] Users input biometric and lifestyle data through dedicated terminals or wearable devices. The terminals are also equipped with cameras and microphones, which are used to analyze the user's facial expressions and voice, allowing the emotion engine to recognize the user's emotional state in real time.

[0761] Data analysis and care plan generation:

[0762] The server receives all data sent from the terminal, cleanses it, and then analyzes it based on the generated AI model. During this process, emotional data recognized by the emotion engine is also incorporated into the analysis, and a care plan that comprehensively considers the user's physical and emotional state is generated.

[0763] Distribution and implementation of care plans:

[0764] Care plans generated on the server are delivered to the terminal and presented to the user. Users are supported in taking actions based on the care plan in their daily lives. The terminal takes the user's emotional state into consideration and incorporates specific actions and suggestions into the plan.

[0765] Feedback and alerts:

[0766] Users input their experience and results as feedback into the device. The device sends this data to a server, which adjusts the care plan based on the feedback. In addition, if an abnormality is detected in the user's emotional state, the server generates suggestions for content aimed at stress reduction and relaxation, as well as alerts.

[0767] Specific example:

[0768] For example, in the case of a user who experiences daily stress, the server analyzes changes in heart rate in combination with the "anxiety" recognition by the emotion engine. As a result, it proposes a care plan of Nishishiki exercises, including specific breathing techniques for relaxation and light yoga sessions. The device notifies the user of when to perform the exercises and helps the user adhere to the plan. Based on the user's feedback on their performance, this care plan is adjusted as needed.

[0769] This system allows users to receive continuous support that helps maintain and improve not only their physical health but also their mental health.

[0770] The following describes the processing flow.

[0771] Step 1:

[0772] Users input biometric and lifestyle data related to their health status into the device. This includes information such as blood pressure, heart rate, weight, exercise records, and dietary information. Simultaneously, the device's camera and microphone are used to record emotional states in real time from voice and facial expressions.

[0773] Step 2:

[0774] The device collects biometric data, lifestyle data, and emotional data entered by the user and sends it to the server. This allows the server to store information about the user's overall health status.

[0775] Step 3:

[0776] The server cleanses the received data, identifying and correcting incomplete data and outliers. This creates a dataset suitable for analysis.

[0777] Step 4:

[0778] The server combines a generative AI model and an emotion engine to simultaneously analyze the user's health and emotional state. Based on the analysis results, it generates a personalized care plan optimized for the user. This plan includes recommendations for diet, exercise, and emotional improvement measures.

[0779] Step 5:

[0780] The server sends the generated care plan to the terminal. The terminal displays the received care plan to the user, providing it in a clear and easy-to-understand format.

[0781] Step 6:

[0782] Users act according to the care plan presented by the device in their daily lives. The device sends alarms and notifications when it is time to perform important tasks and make suggestions, helping users adhere to the plan.

[0783] Step 7:

[0784] Users input feedback on the results of their care plan implementation into a terminal, recording their emotions and physical changes. This allows for verification of the user's implementation status and its effectiveness.

[0785] Step 8:

[0786] The terminal sends the collected feedback data to the server. The server analyzes the feedback, evaluates the effectiveness of the care plan, and adjusts the plan as needed.

[0787] Step 9:

[0788] The server responds to the emotional state and increased stress identified by the emotion engine, and suggests appropriate relaxation content and stress reduction measures as alerts. It also sends these alerts to the user's device to encourage a quick response.

[0789] This processing flow allows the system to comprehensively support the user's physical and emotional health, providing a better quality of life.

[0790] (Example 2)

[0791] 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".

[0792] In modern society, personal health management is required to be comprehensive, encompassing not only physical health but also emotional health. However, conventional systems have struggled to centrally analyze an individual's biometric information and lifestyle patterns to provide personalized health management plans. Furthermore, there is a lack of technology to recognize a user's emotional state in real time and reflect it in health management.

[0793] 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.

[0794] In this invention, the server includes means for collecting biometric parameters and lifestyle pattern information from the user, means for purifying and analyzing the collected information, and means for applying a generative machine learning model to generate an individualized health management plan. This enables the provision of a health management plan optimized for the individual and comprehensive health maintenance that takes into account emotional state.

[0795] "Biometric parameters" are data that indicates the user's physical condition, including information such as heart rate and body temperature.

[0796] "Lifestyle pattern information" refers to data about the user's daily life, including habits such as sleep duration and exercise levels.

[0797] "Purification" is the process of preparing collected data into a format suitable for analysis, including removing noise and handling missing values.

[0798] A "generative machine learning model" is an algorithm that generates a health management plan tailored to the user based on collected data.

[0799] A "health management plan" is a plan that includes specific actions and indicators aimed at maintaining and improving the user's health.

[0800] "Emotional state" refers to the psychological state analyzed from the user's facial expressions and voice, and includes stress, happiness, and other similar feelings.

[0801] "Warning" is a function that provides advance notice of problems that are predicted to occur based on the user's health status.

[0802] This embodiment of the invention is a health management system that comprehensively collects and analyzes a user's biometric parameters and lifestyle pattern information. This system acquires user data through a dedicated terminal or wearable device. The terminal is equipped with a camera and microphone, which can analyze the user's facial expressions and voice to recognize their emotional state in real time. This provides information for maintaining mental health.

[0803] The server receives data sent from the terminal and processes noise and missing information using data cleansing algorithms. It then analyzes the data using generative machine learning models to generate an optimal health management plan for the user. This is done using an AI platform that operates on prompt-based instructions. For example, a prompt might say, "Assess the user's current health status and generate a recommended care plan."

[0804] The generated health management plan is delivered from the server to the device. The user receives notifications and can take specific actions based on the presented plan. The device provides support for the user to implement the plan and utilizes alarm and notification functions as needed.

[0805] Furthermore, user feedback is entered into the device and sent to the server. The server adjusts the health management plan based on the feedback, optimizing it for the user. For example, for a user who experiences daily stress, a plan including breathing exercises and other exercises is proposed based on an analysis combining changes in heart rate and emotional data. This allows the user to maintain overall physical and mental health.

[0806] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0807] Step 1:

[0808] Users collect biometric parameters and lifestyle pattern information using dedicated terminals or wearable devices. This input data includes heart rate, body temperature, sleep duration, and exercise level. This data collection provides specific basic data about the user's health status.

[0809] Step 2:

[0810] The device uses its built-in camera and microphone to analyze the user's facial expressions and voice in real time. The data acquired here becomes the input for emotion analysis. The device uses an emotion engine to recognize the user's emotional state and generates the result as output. This process provides information about the user's mental health.

[0811] Step 3:

[0812] The terminal transmits collected biometric parameters, lifestyle pattern information, and emotional state data to the server. The server receives this data and performs data cleansing. Data cleansing removes noise and imputes missing data, outputting data in an analyzable state. This enables accurate data analysis.

[0813] Step 4:

[0814] The server performs data analysis using a generated AI model based on the cleansed data. The analysis proceeds according to the prompt message, "Evaluate the user's current health status and generate a recommended health management plan." Based on the input data, the AI ​​model generates an optimal health management plan for the user and outputs the result.

[0815] Step 5:

[0816] The server delivers the generated health management plan to the terminal. The terminal notifies the user of this plan and suggests specific actionable steps. Examples include suggestions for relaxation breathing exercises or yoga sessions. This notification provides the user with guidance for implementing the plan in their daily life.

[0817] Step 6:

[0818] Users input feedback on the results and their experience using the health management plan into their device. This feedback is then sent from the device to the server. The server analyzes this feedback and adjusts the health management plan as needed. This enables the provision of more appropriate support to the user.

[0819] (Application Example 2)

[0820] 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".

[0821] In modern society, users are expected to receive personalized services while comprehensively managing their physical and mental health. However, understanding a user's emotional state in real time and providing appropriate services based on that understanding is not easy. Furthermore, optimizing individual customer experiences in stores requires effective emotion recognition technology and the use of user data, but conventional methods have limitations.

[0822] 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.

[0823] In this invention, the server includes means for collecting biometric and lifestyle information from the user, means for cleansing and analyzing the collected information, means for applying a generated AI model to generate individualized care guidelines, and means for recognizing the customer's emotional state using a smart device and providing recommended information based on purchase history. This makes it possible to simultaneously achieve user health management and the provision of individualized services, thereby improving the customer experience.

[0824] "Biometric information" refers to data obtained from within a user's body, and it represents information about their health status and physical characteristics.

[0825] "Lifestyle information" refers to information that includes data on the user's behavioral patterns and habits in their daily life.

[0826] "Cleansing" is a pre-processing step in data analysis that removes noise and inaccurate data.

[0827] A "generated AI model" is an artificial intelligence-powered model created based on data analysis, and serves as the foundation for generating user-optimized suggestions.

[0828] "Care guidelines" are recommended actions and plans created based on the user's health condition and lifestyle.

[0829] A "smart device" is a digital device that has the function of collecting and processing data using information and communication technology.

[0830] "Emotional state" refers to the user's psychological state and includes information obtained from facial expressions and voice.

[0831] "Recommended information" refers to suggestions about products and services presented based on the user's preferences and needs.

[0832] This system uses terminals to collect biometric and lifestyle information from users. These terminals, such as wearable devices and smartphones, acquire real-time data from users. Using emotion recognition functions with cameras and microphones, the terminals capture the user's emotional state.

[0833] The server receives the collected information and performs data cleansing. Specifically, it uses the Google Cloud Vision API to perform emotion recognition and denoise the collected data, preparing it for analysis. Using a generative AI model, it generates care guidelines based on the user's health and emotional state. These care guidelines are then sent to the user's smart device.

[0834] Smart devices provide recommendations that take into account the user's emotional state and purchase history. In particular, in stores, sales staff can use smart glasses to provide product suggestions optimized for the user.

[0835] For example, if a user visits a store and their emotional state is detected as "relaxed," the device will recommend products related to relaxation. As a result, the store can improve the user experience and provide personalized service.

[0836] An example of a prompt message would be, "Analyze the customer's facial expressions and generate optimal product suggestions based on their emotional state." This allows the generating AI model to efficiently provide personalized recommendations to the user.

[0837] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0838] Step 1:

[0839] Users input biometric and lifestyle information in real time using wearable devices or smartphones. The input data includes heart rate and activity levels. This data is immediately transmitted from the device to the server.

[0840] Step 2:

[0841] The server cleanses the received biometric and lifestyle information. Specifically, it removes noise data and prepares it for analysis. The input is raw data, and the output is refined data.

[0842] Step 3:

[0843] The server applies a generative AI model to the cleansed data. This model analyzes the data and generates care guidelines based on the user's health and emotional state. The input to the AI ​​model is formatted data, and the output is individual care guidelines.

[0844] Step 4:

[0845] The terminal receives care guidelines from the server and notifies the user. The notification includes recommended actions and product information. The input is care guidelines from the server, and the output is a visualized notification to the user.

[0846] Step 5:

[0847] The user's emotional state is recognized in real time through the device's camera and microphone. The device uses the Google Cloud Vision API to analyze facial expressions and voice to identify the emotional state. The input is real-time audio and video data, and the output is information about the emotional state.

[0848] Step 6:

[0849] Smart devices generate prompts that create recommendations based on emotional state and purchase history. Using the generated prompts, an AI model makes optimal product suggestions. The input is emotional state data and purchase history, and the output is the suggested products and services.

[0850] Step 7:

[0851] Users act based on the recommendations they receive. The device then sends the results of their actions and feedback back to the server. This feedback is used for further data analysis, and the entire system, as an ecosystem, continuously improves the user experience. The input is user feedback, and the output is information about system improvements based on that feedback.

[0852] 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.

[0853] 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.

[0854] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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."

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] The following is further disclosed regarding the embodiments described above.

[0874] (Claim 1)

[0875] Means for collecting biometric data and lifestyle data from users,

[0876] A means for cleansing and analyzing the collected data,

[0877] A means of generating individual care plans by applying a generative AI model,

[0878] A means for delivering the generated care plan to the user device,

[0879] A system including means for adjusting care plans using the aforementioned user feedback.

[0880] (Claim 2)

[0881] The system according to claim 1, wherein the generating AI model has means for performing a risk assessment based on the user's health status and lifestyle and generating a corresponding alert.

[0882] (Claim 3)

[0883] The system according to claim 1, wherein the user device is provided with means for providing alarm and notification functions to support the implementation of the care plan.

[0884] "Example 1"

[0885] (Claim 1)

[0886] A means for recording biometric information and lifestyle information from users,

[0887] means for selecting and analyzing the recorded information,

[0888] A means for generating personalized support plans by applying a generative AI model,

[0889] Means for transmitting the generated support plan to the user device,

[0890] A means for modifying the support plan using the results from the user,

[0891] A system including a feedback analysis means for evaluating the results and making appropriate modifications to the plan.

[0892] (Claim 2)

[0893] The system according to claim 1, wherein the generating AI model has means for performing a risk assessment based on the user's health condition and lifestyle and generating a corresponding warning.

[0894] (Claim 3)

[0895] The system according to claim 1, wherein the user device comprises means for providing alarm and notification functions to facilitate the implementation of the support plan.

[0896] "Application Example 1"

[0897] (Claim 1)

[0898] Means for collecting biometric data and lifestyle data from users,

[0899] A means for cleansing and analyzing the collected data,

[0900] A means of generating individual care plans by applying a generative AI model,

[0901] A means for delivering the generated care plan to the user device,

[0902] A means for adjusting the care plan using the user's feedback,

[0903] A method of using an integrated visualization device to acquire user biometric data in real time,

[0904] A means for instantly detecting anomalies and generating warnings based on the acquired biometric data,

[0905] A system that includes this.

[0906] (Claim 2)

[0907] The system according to claim 1, wherein the generating AI model has means for performing a risk assessment based on the user's health status and lifestyle and generating a corresponding alert.

[0908] (Claim 3)

[0909] The system according to claim 1, wherein the user device has means for providing alarm and notification functions and an interface for communicating warnings visually or audibly in order to support the implementation of the care plan.

[0910] "Example 2 of combining an emotion engine"

[0911] (Claim 1)

[0912] A means for collecting biometric parameters and lifestyle pattern information from users,

[0913] A means for purifying and analyzing the collected information,

[0914] A means of generating individual health management plans by applying a generative machine learning model,

[0915] A means for distributing the generated health management plan to the user terminal,

[0916] The aforementioned terminal recognizes the emotional state and incorporates a means for incorporating a specific action into the plan,

[0917] A system including means for adjusting a health management plan using the user experience described above.

[0918] (Claim 2)

[0919] The system according to claim 1, wherein the generative machine learning model performs a risk assessment based on the user's health status and daily life, and generates a corresponding warning.

[0920] (Claim 3)

[0921] The system according to claim 1, wherein the user terminal is provided with means for providing warning and notification functions to support the implementation of the health management plan.

[0922] "Application example 2 when combining with an emotional engine"

[0923] (Claim 1)

[0924] A means of collecting biometric and lifestyle information from users,

[0925] A means for cleansing and analyzing the collected information,

[0926] A means of generating individual care guidelines by applying the generated AI model,

[0927] A means for distributing the generated care guidelines to the user terminal,

[0928] A means for adjusting care guidelines using user feedback,

[0929] A means of recognizing a customer's emotional state using a smart device and providing recommendation information based on their purchase history,

[0930] A system that includes this.

[0931] (Claim 2)

[0932] The system according to claim 1, further comprising means for the generated AI model to perform a risk assessment based on the user's health status and lifestyle and generate a corresponding warning.

[0933] (Claim 3)

[0934] The system according to claim 1, wherein the user terminal is provided with means for providing a notification function to support the implementation of the care guidelines. [Explanation of symbols]

[0935] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for collecting biometric data and lifestyle data from users, A means for cleansing and analyzing the collected data, A means of generating individual care plans by applying a generative AI model, A means for delivering the generated care plan to the user device, A system including means for adjusting care plans using the aforementioned user feedback.

2. The system according to claim 1, wherein the generating AI model has means for performing a risk assessment based on the user's health status and lifestyle and generating a corresponding alert.

3. The system according to claim 1, wherein the user device is provided with means for providing alarm and notification functions to support the implementation of the care plan.

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