system

Abstract

Provide a system. 【Solution means】 Data input means for inputting the daily activity data of the user, Data collection means for collecting the data input through the data input means, Analysis means for analyzing the data received from the data collection means and detecting the pre-disease state, Care plan generation means for generating a care plan including traditional Chinese medicine and folk remedies based on the result of the analysis means, Plan presentation means for presenting the generated care plan to the user, Monitoring means for monitoring the progress data from the user, Data sharing means for sharing the data obtained by the monitoring means with the family based on the user's permission, A system including.

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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, the demand for individualized health management is increasing, and an effective method for this is sought. However, conventional health management systems have difficulty accurately grasping changes in users' lifestyles and physical conditions, predicting the pre-disease state, and performing appropriate interventions. In addition, there is a lack of means to provide support in cooperation with family members and to achieve safe and efficient health management. There is a need to develop a new system that exceeds the limits of the prior art, provides an optimized health plan for users, and strengthens the support cooperation of family members.

Means for Solving the Problems

[0005] This invention provides a system that collects users' daily activity data and analyzes their pre-disease state using AI technology to generate an appropriate care plan. This system includes data input, collection, analysis, care plan generation, plan presentation, monitoring, and data sharing means, enabling users to simultaneously receive personalized health management and support for family collaboration. Furthermore, it can provide voice instructions based on the generated care plan and accurately detect pre-disease states using machine learning algorithms. This creates a system that effectively supports the improvement of users' health and allows them to engage in real-time health management together with their families.

[0006] A "data input means" refers to a device or interface for users to input data on their daily activities and health-related information.

[0007] "Data collection means" refers to a device or program that has the function of collecting, recording, or transmitting data entered through data input means.

[0008] "Analysis means" refers to algorithms and programs used to analyze collected data and evaluate the user's health status and pre-disease state.

[0009] A "care plan generation means" is a device or program that has the function of creating a health management plan suitable for the user based on the results obtained by the analysis means.

[0010] A "care plan presentation means" refers to a device or interface for notifying or displaying a generated care plan to the user.

[0011] A "monitoring device" is a device or program used to track a user's daily behavior and recorded data, collect that information, and observe their progress.

[0012] "Data sharing means" refers to a device or program that has the function of providing a user's data to family members or others to the extent permitted by the user.

[0013] "Voice instruction generation means" refers to a device or program that has the function of generating voice instructions for the user based on the generated care plan.

[0014] A "machine learning algorithm" is an algorithm equipped with automatic learning capabilities used for data analysis. It is used to recognize patterns in data and make predictions or classifications. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

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

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

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

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

[0020] In the following embodiments, the 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.

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

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

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0036] This invention is a system designed to provide more effective and personalized health management for users in their daily lives. This system collects user data and analyzes it using AI technology to detect pre-disease states early and generate appropriate care plans.

[0037] First, users input daily activity data (e.g., steps taken, sleep quality, food and drink intake) via their smartphones or wearable devices. The device collects this data continuously and prepares to send it to the server as needed.

[0038] Next, the server receives the data sent from the terminal and automatically saves it to a database. This data is analyzed by an AI analysis model based on multiple health indicators. The purpose of the analysis is to identify pre-disease states that the user is unaware of. Machine learning algorithms extract patterns from this data and assess the potential health risks to the user.

[0039] Based on the analysis results, the server automatically generates a care plan, formulating specific health improvement measures, including herbal medicines and folk remedies, tailored to the user. The generated care plan is designed to be immediately applicable to daily life and is organized into actionable tasks.

[0040] Furthermore, this system supports users in incorporating the plan into their daily lives by using AI-generated voice instructions. Specifically, if the goal is to improve sleep, the device will provide simple voice notifications recommending things like going to bed at the same time every night or drinking a specific herbal tea.

[0041] Finally, the server monitors progress in real time and continuously updates the data. This allows users to manage their health more efficiently. Furthermore, family members can continuously monitor the user's health through the device and provide advice and support as needed.

[0042] Thus, the present invention realizes a new system that provides personalized health care and enables users and their families to work together on health management.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users input data about their daily activities using smartphones or wearable devices. This includes information such as steps taken, meals eaten, and sleep duration.

[0046] Step 2:

[0047] The terminal stores user-entered data locally and prepares to periodically send data to a server via the internet. During this process, data encryption and communication security are also ensured.

[0048] Step 3:

[0049] The server receives data sent from the terminal and stores it in a cloud database. After saving the data, it prepares the dataset for the analysis process.

[0050] Step 4:

[0051] The server inputs the stored data into an AI analysis model to evaluate the user's health status. This AI model uses machine learning algorithms to detect abnormal patterns and pre-disease states in the data.

[0052] Step 5:

[0053] The server automatically generates a care plan tailored to the user based on the analysis results. This care plan includes detailed information on health promotion measures, including types of herbal medicines and folk remedies.

[0054] Step 6:

[0055] The device notifies the user of the care plan received from the server and allows them to view the details within the app. It also facilitates improvements in daily life behaviors through generated voice instructions.

[0056] Step 7:

[0057] Users follow the care plan provided, take actions to improve their daily lives, and record their progress through the app.

[0058] Step 8:

[0059] The server regularly receives user feedback and progress updates, analyzes health data in real time, and adjusts care plans as needed.

[0060] Step 9:

[0061] The server updates the user's health status on a family dashboard based on the user's permission. This information can be accessed by family members through the app and used to support the user.

[0062] (Example 1)

[0063] 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."

[0064] In today's busy society, there is a problem in that it is difficult for individual users to be aware of their own health status and receive appropriate care according to that status. In particular, there is a need to detect pre-disease states early and take appropriate countermeasures, but there is a lack of means to provide this in an individualized and efficient manner.

[0065] 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.

[0066] In this invention, the server includes information input means, information collection means, and storage means. This enables precise tracking of the user's health status and the generation and execution of a personalized health plan.

[0067] "Information input means" refers to devices or methods for users to record their daily activity data and supply it to a system.

[0068] "Information gathering means" refers to devices or systems for collectively storing data collected through information input means.

[0069] "Storage means" refers to devices or systems for retaining collected data over the long term and making it accessible as needed.

[0070] "Information analysis means" refers to devices or systems that analyze stored data to detect pre-disease states and evaluate health.

[0071] A "health plan generation method" is a device or system for formulating an optimal health maintenance or improvement plan for a user based on the results of an information analysis method.

[0072] "Information presentation means" refers to a device or method for visually or audibly communicating a generated health plan to the user.

[0073] A "progress tracking device" is a device or system for monitoring progress and recording changes during the user's process of implementing a health plan.

[0074] "Information sharing means" refers to devices or methods for sharing collected progress information with third parties based on the user's consent.

[0075] A "voice instruction supply means" is a device or system for providing voice instructions and advice to users in order to facilitate the implementation of a health plan.

[0076] This invention is a personalized health management system that collects and analyzes a user's daily activity data to generate a plan for improving their health. The system uses smartphones and wearable devices to acquire data about the user's lifestyle activities. This allows the user to easily input information.

[0077] The device collects information provided by the user and transmits it to the server as needed. This includes smartphones and wearable devices that automatically acquire health data. HTTPS communication is used to ensure data is securely transferred and privacy is maintained.

[0078] The server receives activity data sent from the terminal and stores it in a dedicated database. The server runs a generative AI model using TENSORFLOW® or PyTorch to analyze the collected data. This analysis evaluates the user's health patterns and enables the detection of pre-disease states. Based on this information, health risks are identified and appropriate health plans are formulated. For example, if the data analysis determines that the stress level is high, health improvement measures to promote relaxation are suggested.

[0079] The generated health plan is presented to the user via the device. At this time, voice instructions are provided by a voice guidance system to help the user easily implement the plan. For example, specific instructions such as "Take five minutes of deep breathing every day" are communicated via the voice assistant.

[0080] The server tracks the user's progress in their health plan and continuously updates the progress data. This allows users to respond flexibly to changes in their health status. Furthermore, this progress information can be shared with family members with the user's consent.

[0081] As a concrete example, a user can receive health management suggestions by prompting the generated AI model with "Please tell me my health management plan for this month." In this way, the system aims to provide users with user-friendly and actionable health solutions.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] Users input data related to their daily activities through their smartphones or wearable devices. Specifically, this involves manually entering steps and weight into the app, or automatically measuring heart rate and sleep quality using wearable devices. This input data is temporarily stored in the device's internal storage.

[0085] Step 2:

[0086] The terminal collects activity data, compiles it, and sends it to the server at the appropriate time. The input data is organized by date and time and sent to the server in batch processing to ensure fault tolerance. HTTPS is used for communication to ensure data protection.

[0087] Step 3:

[0088] The server receives data sent from the terminal and stores it in the database. The received data is associated by user ID, and indexes are created for searching and analysis. The database uses transactional technology to maintain data integrity.

[0089] Step 4:

[0090] The server analyzes the stored data using a generative AI model. Specifically, it uses machine learning algorithms to extract patterns from the dataset and evaluate the user's health status. This analysis is performed using TensorFlow or PyTorch, and the extracted results are useful for the early detection of pre-disease states.

[0091] Step 5:

[0092] The server generates a personalized health improvement plan based on the output of the AI ​​analysis model. The resulting plan includes specific health improvement actions, such as recommendations like "take a 30-minute walk every day." This plan is customized to suit the user's daily routine.

[0093] Step 6:

[0094] The device presents the generated health plan to the user. It sets up specific notifications for the plan, including voice instructions, and notifies the user via the voice assistant with instructions such as, "Take your walk around your house today." This encourages the user to take actions in line with the health plan.

[0095] Step 7:

[0096] The server tracks and monitors the progress of the user's health plan. It continuously collects user feedback and implementation data to evaluate the plan's suitability. This progress data is used to adjust the plan as needed.

[0097] (Application Example 1)

[0098] 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."

[0099] In today's busy lifestyle, users often find it difficult to properly manage their own health, and in particular, they often fail to detect pre-disease conditions early and receive appropriate care. A system is needed to solve this problem and support health management in daily life. Furthermore, it is necessary to improve the efficiency of health management through the effective use of robots in the home.

[0100] 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.

[0101] In this invention, the server includes an information input means for inputting information about the user's daily activities, an information collection means for collecting information input via the information input means, and an analysis means for analyzing the information received from the information collection means and detecting a pre-disease state. This enables the user to efficiently detect a pre-disease state and receive an appropriate care plan while utilizing a robot in their home.

[0102] "Information input means" refers to devices or interfaces that allow users to input information about their daily activities into a system.

[0103] "Information gathering means" refers to a function that collects daily activity information entered through information input means and stores it as data in a format that allows for necessary processing.

[0104] "Analysis means" refers to systems and algorithms that perform data analysis to detect a user's pre-disease state based on collected information.

[0105] The "care plan generation means" is a function that automatically creates a care plan, including health improvement measures suitable for the user, based on the results of the analysis means.

[0106] A "care plan presentation tool" refers to an interface or device that effectively communicates the generated care plan to the user.

[0107] "Monitoring measures" refer to functions for continuously tracking and monitoring the user's health status and the implementation status of their care plan.

[0108] "Information sharing means" refers to a system for sharing a user's health information with third parties, such as relatives, based on their permission.

[0109] A "robot instruction means" is a function that utilizes a robot in the home to provide instructions to the user based on a generated care plan.

[0110] This invention is a system that provides more effective and personalized health management for users in their daily lives. The system utilizes the following hardware and software.

[0111] First, users input daily activity information such as steps taken, sleep patterns, and dietary details using smart devices or wearable devices. This information is then collected by an information collection system via the information input device. The device then transmits this information to a server.

[0112] The server processes the received information using analysis tools to detect pre-disease states. Specifically, it uses machine learning algorithms to identify patterns related to the user's health status from the information. Based on the analysis results, the care plan generation tool creates a specific health improvement plan, including alternative medicines and traditional treatments.

[0113] The generated care plan is presented to the user through a plan presentation system, and the user aims to improve their health in their daily life based on this plan. In addition, home devices such as robots are used, and voice instructions and visual information are provided to the user through a robot instruction system.

[0114] User activity is tracked through monitoring mechanisms, and progress information can be shared with third parties, such as family members, using information sharing tools. This allows relatives to understand the user's health status and provide support and advice as needed.

[0115] For example, a robot could provide instructions every night before bedtime, such as, "Go to bed by 10 PM to ensure you get enough sleep." It could also suggest in the morning, "Why not take a morning walk?"

[0116] An example of a prompt statement can be shown as follows:

[0117] "Analyze the user's sleep data from last night and suggest an appropriate amount of sleep. Provide voice prompts, such as a notification of what time they should go to bed."

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] Users input daily activity information using smart devices or wearable devices. This includes steps taken, sleep patterns, and dietary information. This information is collected by the terminal through the information input method and prepared as input data for subsequent processing.

[0121] Step 2:

[0122] The device transmits collected user activity information to the server. This transmission occurs at regular intervals, and the information is accumulated by the data collection method. Since the input data is sent directly to the server, real-time data from the user's device is ensured.

[0123] Step 3:

[0124] The server processes the received information using analytical tools to detect pre-disease states. The received data is input into a machine learning algorithm to identify patterns related to the user's health status. This analysis then outputs specific health risks.

[0125] Step 4:

[0126] The server uses a care plan generation tool based on the analysis results to create a health improvement plan. This plan includes alternative medicines and traditional treatments. A generation AI model is used to create a plan tailored to the user's condition, and specific health improvement measures are provided as output.

[0127] Step 5:

[0128] The generated care plan is presented to the user through a plan presentation system. Using a terminal or a home robot, the plan is communicated to the user visually and audibly. The outputted plan information is presented in a format that is easily understandable to the user.

[0129] Step 6:

[0130] Users follow and implement the care plan provided in their daily lives. Monitoring devices track the user's actions and continuously collect progress information. This allows new activity information to be accumulated as additional input data.

[0131] Step 7:

[0132] The monitored progress information is shared with relatives using information sharing tools. If necessary, an AI model generated from the server receives prompts and creates appropriate advice and follow-up plans. Based on this information, the user and relatives can jointly manage their health.

[0133] 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.

[0134] This invention provides effective and personalized health management for users in their daily lives, and is a system that supports health monitoring and improvement, particularly through care plans that take into account the user's emotional state. This system analyzes the user's input data and emotional data, and presents a generated care plan to achieve more accurate health support.

[0135] First, users input data on their daily activities and emotional information (e.g., health feedback and mood reports) from their smartphones or wearable devices. This data is collected by the device, encrypted, securely stored, and then prepared for transmission to the server.

[0136] Next, the server receives the data sent from the terminal and stores it in a database. This stored data includes the user's health indicators as well as emotional data analyzed by the emotion engine. Then, an AI analysis model is used to simultaneously evaluate the user's health status and emotional changes.

[0137] Based on the analysis results, the server generates a care plan that incorporates emotional data into conventional health data. This plan dynamically adapts to the user's health and emotional state, resulting in more personalized suggestions. For example, if the user is experiencing stress, the server can suggest relaxation-enhancing folk remedies or herbal medicines to promote calmness.

[0138] The care plan and emotion-based guidance described above are communicated to the user via the device and are designed to facilitate their implementation in daily life. In addition, the generated instructions are provided via voice to support the smooth implementation of the plan on a daily basis.

[0139] Finally, the server monitors the user's progress and emotional data in real time and adjusts the plan as needed. This information can also be shared with family members with the user's permission, allowing them to understand the user's health, including their emotional state.

[0140] Thus, this invention allows users to enjoy personalized health care and receive support that takes their emotional needs into consideration. This system improves the user's quality of life and provides more effective health management.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] Users input data about their daily activities via smartphones or wearable devices. This includes not only exercise levels, diet, and sleep duration, but also simple emotional questionnaires and daily moods.

[0144] Step 2:

[0145] The terminal collects and stores the entered data. To ensure data security, it encrypts the data and prepares it for secure transmission to the server.

[0146] Step 3:

[0147] The server receives data from the terminal and stores it in a cloud-based database. This includes data on the user's lifestyle and emotional state.

[0148] Step 4:

[0149] The server uses an emotion engine to analyze emotional data. This quantifies the user's emotional state, and evaluates stress levels and well-being.

[0150] Step 5:

[0151] The server uses an AI analysis model to comprehensively analyze the collected health and emotional data to evaluate the user's health status and pre-disease state.

[0152] Step 6:

[0153] The server generates a care plan based on the analysis results. This plan outlines specific action guidelines necessary to maintain and improve the user's health, and includes content that takes stress management and mental health into consideration.

[0154] Step 7:

[0155] The device notifies the user of the generated care plan. The user can then view the details in the app and incorporate the proposed plan into their daily life.

[0156] Step 8:

[0157] The device uses a voice instruction generation function to provide users with voice guidance to support the implementation of their care plan. This voice guidance is customized according to the user's situation.

[0158] Step 9:

[0159] Users provide feedback on their activities within the app and record their progress. This record can be easily managed and viewed within the app.

[0160] Step 10:

[0161] The server monitors progress and user feedback in real time and modifies the care plan as needed based on the data obtained. If necessary, it shares information with the family and establishes an appropriate support system.

[0162] (Example 2)

[0163] 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".

[0164] Traditional health management systems have struggled to provide personalized care plans that take into account users' emotional states. Furthermore, conventional methods have limitations in achieving dynamic health support that aligns with users' emotions. Additionally, privacy protection when sharing generated care plans with stakeholders is insufficient. A system that addresses these challenges and allows users to use it with peace of mind is needed.

[0165] 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.

[0166] In this invention, the server includes data input means for inputting user behavior information and emotional information, emotion analysis means for analyzing encrypted data and evaluating the emotional state, and AI suggestion generation means for generating an individualized care plan using an AI model. This enables dynamic and individualized health support that takes into account the user's emotional state.

[0167] A "user" refers to an individual who uses a service or system, and is the entity that inputs information about their lifestyle and emotions.

[0168] "Behavioral information" refers to activity data related to the user's daily life, including exercise levels, sleep duration, and dietary content.

[0169] "Emotional information" refers to data about the user's psychological state, including reports on changes in mood and emotions.

[0170] "Data input means" refers to a device or system used by a user to input behavioral information or emotional information.

[0171] A "data collection method" refers to a device or mechanism that collects information entered by a user, encrypts it as needed, and transmits it to a system.

[0172] "Encrypted data" refers to information that has been transformed in a way that prevents its content from being deciphered by a third party, and is a format used to ensure secure communication.

[0173] "Analysis means" refers to mechanical or computer program-based methods for analyzing collected data and identifying specific patterns or states.

[0174] "Emotional analysis means" refers to a technology or combination of technologies used to determine a user's emotional state based on emotional information.

[0175] A "care plan" is a collection of measures and suggestions provided according to the user's health condition and emotional state, with the aim of maintaining and improving the user's health.

[0176] The "AI proposal generation method" is a system that uses artificial intelligence technology to automatically generate the optimal care plan from user data.

[0177] "Plan presentation means" refers to technologies for notifying and presenting the generated care plan to the user, and includes digital terminals and voice guidance.

[0178] A "monitoring and adjustment mechanism" is a system that monitors the user's progress and emotional information in real time and modifies or adjusts the care plan as needed.

[0179] "Information sharing methods" refer to means of sharing data with relevant individuals with the user's permission, enabling safe and effective information transmission.

[0180] This invention is a system that personalizes users' health management and presents care plans that take their emotional state into consideration. Specifically, the user, terminal, and server work together to implement this system.

[0181] Users input their behavioral and emotional information using smartphones or wearable devices. Behavioral information includes details of daily activities such as exercise, diet, and sleep, while emotional information includes reports on mood and emotional changes. This data is received by the device, encrypted, and stored collectively. Advanced encryption technology, AES-256, is used for encryption.

[0182] The collected data is securely transmitted from the terminal to the server via the HTTPS protocol. The server receives the data and stores it in a database. The database uses an SQL-based system, such as MySQL®. The server analyzes the stored data and performs sentiment analysis using a machine learning model incorporating natural language processing techniques, such as TensorFlow.

[0183] The server then generates a personalized care plan using a generative AI model based on the analyzed emotional data and the user's health indicators. The generative AI model generates specific suggestions for relaxation based on prompts, for example, in response to the question, "Generate specific suggestions for the user to relax."

[0184] The generated care plan is communicated to the user via their device. Notifications are delivered via push notifications and voice prompts, designed to facilitate user action. Furthermore, the server monitors the user's care plan progress and emotional state in real time, adjusting the plan as needed. The monitored data is securely shared with relevant parties with the user's permission. In this way, the system provides dynamic health management tailored to the user's individual needs.

[0185] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0186] Step 1:

[0187] Users input behavioral and emotional information using smartphones or wearable devices. This input includes exercise time, meal content, sleep duration, and current mood. This information is collected by the device. The device first stores the collected data locally and then encrypts it using the AES-256 encryption algorithm. The output is encrypted data, which is then prepared for transmission to the server.

[0188] Step 2:

[0189] The device uses the HTTPS protocol to securely send encrypted data to the server. The input is encrypted data, and the output is the completion of the data transfer to the server. This data processing is essential to protect user privacy.

[0190] Step 3:

[0191] The server receives data sent from the terminal and stores it in the database. The input is encrypted data from the terminal, and the output is decrypted data stored in the database. A high-performance SQL system is used for the database, ensuring efficient data management.

[0192] Step 4:

[0193] The server performs sentiment analysis using stored data. The input consists of user behavior information and decoded sentiment information stored in a database. Specifically, it uses a machine learning model (such as TensorFlow) incorporating natural language processing techniques to determine sentiment categories such as positive or negative. The output is the analyzed sentiment data.

[0194] Step 5:

[0195] The server generates personalized care plans using an AI model based on the results of emotion analysis and health indicator data. The inputs are emotion data and health indicators. By providing prompts to the generating AI model, such as "Generate specific suggestions for the user to relax," personalized suggestions are generated. The output is a personalized care plan.

[0196] Step 6:

[0197] The generated care plan is notified to the user via the device. The input is the care plan from the server, and the output is the notification to the user. Specifically, the care plan is delivered to the user using push notifications or voice guidance.

[0198] Step 7:

[0199] The server monitors user progress data and emotional information in real time. Input is the user's daily input data. The care plan is dynamically adjusted as needed, and the updated care plan is sent to the terminal as output. This ensures continuous and appropriate support for the user.

[0200] (Application Example 2)

[0201] 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".

[0202] Existing health management systems have struggled to simultaneously consider an individual's emotional state and physical condition, making it difficult to provide personalized care plans. Furthermore, there was a lack of systems capable of monitoring the health status of the elderly in real time and providing appropriate care plans based on their emotional state. This resulted in a problem where the elderly were not receiving adequate health care.

[0203] 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.

[0204] In this invention, the server includes input means for inputting user activity and emotional information, aggregation means for aggregating the information input via the input means, and processing means for processing the data received from the aggregation means and simultaneously evaluating the health and emotional state. This makes it possible to generate personalized health care plans that take into account an individual's health and emotional state.

[0205] "Input means" refers to devices or interfaces that collect user activity and emotional information.

[0206] "Aggregation methods" refer to processes or devices that integrate input information and compile it into meaningful datasets.

[0207] "Processing means" refers to technologies and algorithms used to analyze aggregated data and evaluate the user's health and emotional state.

[0208] "Plan generation means" refers to a device or program that creates an individualized health care plan based on the analysis results from the processing means.

[0209] "Communication means" refers to devices and methods for communicating the generated care plan to the user.

[0210] "Tracking means" refers to devices and methods for continuously monitoring and recording user progress information.

[0211] "Information sharing methods" refer to technologies and processes for sharing information obtained through tracking methods with relevant parties with the user's consent.

[0212] "Voice generation means" refers to technology or devices that create and provide voice instructions to the user based on a generated plan.

[0213] An "artificial intelligence algorithm" refers to a computer program or technical method used to analyze data and simultaneously assess health and emotional states.

[0214] The system of the present invention is designed to provide a health care plan that effectively manages the activity and emotional information of users, including the elderly. This system inputs data obtained from the user's daily activities via a terminal such as a smartphone or smart glasses and transmits it to a server. The server aggregates the input data and uses artificial intelligence algorithms to simultaneously evaluate the user's health and emotional state. Based on the evaluation results, a personalized health care plan is generated and communicated to the user via the terminal.

[0215] Specifically, the device collects information about the user's activity level and mood through sensors and the user interface. This data is encrypted and sent to a server for secure storage. The server analyzes the data using machine learning frameworks such as TensorFlow and analyzes the user's health and emotional state through a generative AI model. Based on this analysis, a health care plan tailored to the user's needs is generated.

[0216] For example, if a user is determined to be emotionally unstable, voice instructions will be provided suggesting relaxation exercises or meals. Furthermore, by allowing users to record their progress, the system can adapt its plan to the new data, enabling dynamic and flexible responses.

[0217] As part of this system, the server can provide the generating AI model with a prompt message such as, "Evaluate the user's mood and activity data, and suggest appropriate relaxation methods and health plans," enabling more innovative care plans. Furthermore, information can be shared with family members, and collaboration with others to support the user's health management is also considered.

[0218] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0219] Step 1:

[0220] The device collects user activity and emotional information through sensors and the user interface. Inputs include step count, heart rate, and user-submitted emotional state assessments. This information is encrypted and processed into a transmittable format for subsequent analysis.

[0221] Step 2:

[0222] The terminal sends the collected data to the server. The server stores the received data in a database for accurate storage. The input is encrypted user data, and the output is a securely stored dataset. The server adds the data to the database while verifying data integrity and security.

[0223] Step 3:

[0224] The server retrieves stored data and performs analysis using artificial intelligence algorithms. The input is user information from the database, and the output is analysis results indicating health and emotional status. This analysis uses machine learning frameworks such as TensorFlow to analyze correlations between variables and evaluate pre-symptomatic states and emotional changes.

[0225] Step 4:

[0226] The server generates a personalized health care plan based on the analysis results. The input is the analysis results, and the output is the personalized plan. The generating AI model uses the prompt "Evaluate the user's mood and activity data and suggest appropriate relaxation methods and a health plan" to create an effective care plan.

[0227] Step 5:

[0228] The server sends the generated care plan to the terminal and notifies the user. The input is the generated care plan, and the output is the notification to the user. The notification is made audibly and is designed to be easy for the user to understand and act upon.

[0229] Step 6:

[0230] The terminal monitors the user's progress and reports the results to the server. The input is user execution data, and the output is updated progress data. This allows the server to evaluate the effectiveness of the plan and make adjustments as needed.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] [Second Embodiment]

[0235] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0236] 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.

[0237] 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).

[0238] 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.

[0239] 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.

[0240] 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).

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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".

[0247] This invention is a system designed to provide more effective and personalized health management for users in their daily lives. This system collects user data and analyzes it using AI technology to detect pre-disease states early and generate appropriate care plans.

[0248] First, users input daily activity data (e.g., steps taken, sleep quality, food and drink intake) via their smartphones or wearable devices. The device collects this data continuously and prepares to send it to the server as needed.

[0249] Next, the server receives the data sent from the terminal and automatically saves it to a database. This data is analyzed by an AI analysis model based on multiple health indicators. The purpose of the analysis is to identify pre-disease states that the user is unaware of. Machine learning algorithms extract patterns from this data and assess the potential health risks to the user.

[0250] Based on the analysis results, the server automatically generates a care plan, formulating specific health improvement measures, including herbal medicines and folk remedies, tailored to the user. The generated care plan is designed to be immediately applicable to daily life and is organized into actionable tasks.

[0251] Furthermore, this system supports users in incorporating the plan into their daily lives by using AI-generated voice instructions. Specifically, if the goal is to improve sleep, the device will provide simple voice notifications recommending things like going to bed at the same time every night or drinking a specific herbal tea.

[0252] Finally, the server monitors progress in real time and continuously updates the data. This allows users to manage their health more efficiently. Furthermore, family members can continuously monitor the user's health through the device and provide advice and support as needed.

[0253] Thus, the present invention realizes a new system that provides personalized health care and enables users and their families to work together on health management.

[0254] The following describes the processing flow.

[0255] Step 1:

[0256] Users input data about their daily activities using smartphones or wearable devices. This includes information such as steps taken, meals eaten, and sleep duration.

[0257] Step 2:

[0258] The terminal stores user-entered data locally and prepares to periodically send data to a server via the internet. During this process, data encryption and communication security are also ensured.

[0259] Step 3:

[0260] The server receives data sent from the terminal and stores it in a cloud database. After saving the data, it prepares the dataset for the analysis process.

[0261] Step 4:

[0262] The server inputs the stored data into an AI analysis model to evaluate the user's health status. This AI model uses machine learning algorithms to detect abnormal patterns and pre-disease states in the data.

[0263] Step 5:

[0264] The server automatically generates a care plan tailored to the user based on the analysis results. This care plan includes detailed information on health promotion measures, including types of herbal medicines and folk remedies.

[0265] Step 6:

[0266] The device notifies the user of the care plan received from the server and allows them to view the details within the app. It also facilitates improvements in daily life behaviors through generated voice instructions.

[0267] Step 7:

[0268] Users follow the care plan provided, take actions to improve their daily lives, and record their progress through the app.

[0269] Step 8:

[0270] The server regularly receives user feedback and progress updates, analyzes health data in real time, and adjusts care plans as needed.

[0271] Step 9:

[0272] The server updates the user's health status on a family dashboard based on the user's permission. This information can be accessed by family members through the app and used to support the user.

[0273] (Example 1)

[0274] 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."

[0275] In today's busy society, there is a problem in that it is difficult for individual users to be aware of their own health status and receive appropriate care according to that status. In particular, there is a need to detect pre-disease states early and take appropriate countermeasures, but there is a lack of means to provide this in an individualized and efficient manner.

[0276] 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.

[0277] In this invention, the server includes information input means, information collection means, and storage means. This enables precise tracking of the user's health status and the generation and execution of a personalized health plan.

[0278] "Information input means" refers to devices or methods for users to record their daily activity data and supply it to a system.

[0279] "Information gathering means" refers to devices or systems for collectively storing data collected through information input means.

[0280] The "storage means" is a device or method for storing the collected data in the long term and making it accessible as needed.

[0281] The "information analysis means" is a device or method for analyzing the stored data and detecting the pre-disease state or evaluating health.

[0282] The "health plan generation means" is a device or method for formulating a plan for optimal health maintenance and improvement for the user based on the results of the information analysis means.

[0283] The "guidance presentation means" is a device or method for visually or auditorily transmitting the generated health plan to the user.

[0284] The "progress tracking means" is a device or method for monitoring the progress and recording changes in the process of the user implementing the health plan.

[0285] The "information sharing means" is a device or method for sharing the collected progress information with a third party based on the user's consent.

[0286] The "voice instruction supply means" is a device or method for providing instructions and advice to the user by voice to promote the implementation of the health plan.

[0287] This invention is an individualized health management system that collects and analyzes the user's daily activity data to generate a plan for health improvement. The system uses a smartphone or a wearable device to acquire data related to the user's life activities. As a result, the user can easily input information.

[0288] The terminal plays a role of collecting the information provided by the user and transmitting it to the server as needed. The terminal includes a smartphone and a wearable device that automatically acquires health data. As a result, the data is safely transferred, and HTTPS communication is used to maintain privacy.

[0289] The server receives activity data sent from the terminal and stores it in a dedicated database. The server runs a generative AI model using TensorFlow or PyTorch to analyze the collected data. This analysis evaluates the user's health patterns and enables the detection of pre-disease states. Based on this information, health risks are identified and appropriate health plans are formulated. For example, if the data analysis determines that the stress level is high, health improvement measures to promote relaxation are suggested.

[0290] The generated health plan is presented to the user via the device. At this time, voice instructions are provided by a voice guidance system to help the user easily implement the plan. For example, specific instructions such as "Take five minutes of deep breathing every day" are communicated via the voice assistant.

[0291] The server tracks the user's progress in their health plan and continuously updates the progress data. This allows users to respond flexibly to changes in their health status. Furthermore, this progress information can be shared with family members with the user's consent.

[0292] As a concrete example, a user can receive health management suggestions by prompting the generated AI model with "Please tell me my health management plan for this month." In this way, the system aims to provide users with user-friendly and actionable health solutions.

[0293] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0294] Step 1:

[0295] Users input data related to their daily activities through their smartphones or wearable devices. Specifically, this involves manually entering steps and weight into the app, or automatically measuring heart rate and sleep quality using wearable devices. This input data is temporarily stored in the device's internal storage.

[0296] Step 2:

[0297] The terminal collects activity data, compiles it, and sends it to the server at the appropriate time. The input data is organized by date and time and sent to the server in batch processing to ensure fault tolerance. HTTPS is used for communication to ensure data protection.

[0298] Step 3:

[0299] The server receives data sent from the terminal and stores it in the database. The received data is associated by user ID, and indexes are created for searching and analysis. The database uses transactional technology to maintain data integrity.

[0300] Step 4:

[0301] The server analyzes the stored data using a generative AI model. Specifically, it uses machine learning algorithms to extract patterns from the dataset and evaluate the user's health status. This analysis is performed using TensorFlow or PyTorch, and the extracted results are useful for the early detection of pre-disease states.

[0302] Step 5:

[0303] The server generates a personalized health improvement plan based on the output of the AI ​​analysis model. The resulting plan includes specific health improvement actions, such as recommendations like "take a 30-minute walk every day." This plan is customized to suit the user's daily routine.

[0304] Step 6:

[0305] The terminal presents the generated health plan to the user. Set specific notifications for the plan including voice instructions, and notify instructions such as "Please take a walk around your house today" via the voice assistant. This prompts the user to take actions in line with the health plan.

[0306] Step 7:

[0307] The server tracks the progress of the user's health plan and monitors the progress. Continuously collect feedback from the user and data on the implementation status, and evaluate the compliance of the plan. This progress data is utilized for adjusting the plan as needed.

[0308] (Application Example 1)

[0309] 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".

[0310] In the modern busy living environment, it is difficult for users to appropriately manage their own health, especially in the early detection of the sub-healthy state and the inability to receive appropriate care in many cases. There is a need for a system that solves this problem and supports health management in daily life. Furthermore, it is necessary to improve the efficiency of health management through the effective utilization of robots within the home.

[0311] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0312] In this invention, the server includes an information input means for inputting information about the user's daily activities, an information collection means for collecting information input via the information input means, and an analysis means for analyzing the information received from the information collection means and detecting a pre-disease state. This enables the user to efficiently detect a pre-disease state and receive an appropriate care plan while utilizing a robot in their home.

[0313] "Information input means" refers to devices or interfaces that allow users to input information about their daily activities into a system.

[0314] "Information gathering means" refers to a function that collects daily activity information entered through information input means and stores it as data in a format that allows for necessary processing.

[0315] "Analysis means" refers to systems and algorithms that perform data analysis to detect a user's pre-disease state based on collected information.

[0316] The "care plan generation means" is a function that automatically creates a care plan, including health improvement measures suitable for the user, based on the results of the analysis means.

[0317] A "care plan presentation tool" refers to an interface or device that effectively communicates the generated care plan to the user.

[0318] "Monitoring measures" refer to functions for continuously tracking and monitoring the user's health status and the implementation status of their care plan.

[0319] "Information sharing means" refers to a system for sharing a user's health information with third parties, such as relatives, based on their permission.

[0320] A "robot instruction means" is a function that utilizes a robot in the home to provide instructions to the user based on a generated care plan.

[0321] This invention is a system that provides more effective and personalized health management for users in their daily lives. The system utilizes the following hardware and software.

[0322] First, users input daily activity information such as steps taken, sleep patterns, and dietary details using smart devices or wearable devices. This information is then collected by an information collection system via the information input device. The device then transmits this information to a server.

[0323] The server processes the received information using analysis tools to detect pre-disease states. Specifically, it uses machine learning algorithms to identify patterns related to the user's health status from the information. Based on the analysis results, the care plan generation tool creates a specific health improvement plan, including alternative medicines and traditional treatments.

[0324] The generated care plan is presented to the user through a plan presentation system, and the user aims to improve their health in their daily life based on this plan. In addition, home devices such as robots are used, and voice instructions and visual information are provided to the user through a robot instruction system.

[0325] User activity is tracked through monitoring mechanisms, and progress information can be shared with third parties, such as family members, using information sharing tools. This allows relatives to understand the user's health status and provide support and advice as needed.

[0326] For example, a robot could provide instructions every night before bedtime, such as, "Go to bed by 10 PM to ensure you get enough sleep." It could also suggest in the morning, "Why not take a morning walk?"

[0327] An example of a prompt statement can be shown as follows:

[0328] "Analyze the user's sleep data from last night and suggest an appropriate amount of sleep. Provide voice prompts, such as a notification of what time they should go to bed."

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

[0330] Step 1:

[0331] Users input daily activity information using smart devices or wearable devices. This includes steps taken, sleep patterns, and dietary information. This information is collected by the terminal through the information input method and prepared as input data for subsequent processing.

[0332] Step 2:

[0333] The device transmits collected user activity information to the server. This transmission occurs at regular intervals, and the information is accumulated by the data collection method. Since the input data is sent directly to the server, real-time data from the user's device is ensured.

[0334] Step 3:

[0335] The server processes the received information using analytical tools to detect pre-disease states. The received data is input into a machine learning algorithm to identify patterns related to the user's health status. This analysis then outputs specific health risks.

[0336] Step 4:

[0337] The server uses a care plan generation tool based on the analysis results to create a health improvement plan. This plan includes alternative medicines and traditional treatments. A generation AI model is used to create a plan tailored to the user's condition, and specific health improvement measures are provided as output.

[0338] Step 5:

[0339] The generated care plan is presented to the user through a plan presentation system. Using a terminal or a home robot, the plan is communicated to the user visually and audibly. The outputted plan information is presented in a format that is easily understandable to the user.

[0340] Step 6:

[0341] Users follow and implement the care plan provided in their daily lives. Monitoring devices track the user's actions and continuously collect progress information. This allows new activity information to be accumulated as additional input data.

[0342] Step 7:

[0343] The monitored progress information is shared with relatives using information sharing tools. If necessary, an AI model generated from the server receives prompts and creates appropriate advice and follow-up plans. Based on this information, the user and relatives can jointly manage their health.

[0344] 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.

[0345] This invention provides effective and personalized health management for users in their daily lives, and is a system that supports health monitoring and improvement, particularly through care plans that take into account the user's emotional state. This system analyzes the user's input data and emotional data, and presents a generated care plan to achieve more accurate health support.

[0346] First, users input data on their daily activities and emotional information (e.g., health feedback and mood reports) from their smartphones or wearable devices. This data is collected by the device, encrypted, securely stored, and then prepared for transmission to the server.

[0347] Next, the server receives the data sent from the terminal and stores it in a database. This stored data includes the user's health indicators as well as emotional data analyzed by the emotion engine. Then, an AI analysis model is used to simultaneously evaluate the user's health status and emotional changes.

[0348] Based on the analysis results, the server generates a care plan that incorporates emotional data into conventional health data. This plan dynamically adapts to the user's health and emotional state, resulting in more personalized suggestions. For example, if the user is experiencing stress, the server can suggest relaxation-enhancing folk remedies or herbal medicines to promote calmness.

[0349] The care plan and emotion-based guidance described above are communicated to the user via the device and are designed to facilitate their implementation in daily life. In addition, the generated instructions are provided via voice to support the smooth implementation of the plan on a daily basis.

[0350] Finally, the server monitors the user's progress and emotional data in real time and adjusts the plan as needed. This information can also be shared with family members with the user's permission, allowing them to understand the user's health, including their emotional state.

[0351] Thus, this invention allows users to enjoy personalized health care and receive support that takes their emotional needs into consideration. This system improves the user's quality of life and provides more effective health management.

[0352] The following describes the processing flow.

[0353] Step 1:

[0354] Users input data about their daily activities via smartphones or wearable devices. This includes not only exercise levels, diet, and sleep duration, but also simple emotional questionnaires and daily moods.

[0355] Step 2:

[0356] The terminal collects and stores the entered data. To ensure data security, it encrypts the data and prepares it for secure transmission to the server.

[0357] Step 3:

[0358] The server receives data from the terminal and stores it in a cloud-based database. This includes data on the user's lifestyle and emotional state.

[0359] Step 4:

[0360] The server uses an emotion engine to analyze emotional data. This quantifies the user's emotional state, and evaluates stress levels and well-being.

[0361] Step 5:

[0362] The server uses an AI analysis model to comprehensively analyze the collected health and emotional data to evaluate the user's health status and pre-disease state.

[0363] Step 6:

[0364] The server generates a care plan based on the analysis results. This plan outlines specific action guidelines necessary to maintain and improve the user's health, and includes content that takes stress management and mental health into consideration.

[0365] Step 7:

[0366] The device notifies the user of the generated care plan. The user can then view the details in the app and incorporate the proposed plan into their daily life.

[0367] Step 8:

[0368] The device uses a voice instruction generation function to provide users with voice guidance to support the implementation of their care plan. This voice guidance is customized according to the user's situation.

[0369] Step 9:

[0370] Users provide feedback on their activities within the app and record their progress. This record can be easily managed and viewed within the app.

[0371] Step 10:

[0372] The server monitors progress and user feedback in real time and modifies the care plan as needed based on the data obtained. If necessary, it shares information with the family and establishes an appropriate support system.

[0373] (Example 2)

[0374] 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".

[0375] Traditional health management systems have struggled to provide personalized care plans that take into account users' emotional states. Furthermore, conventional methods have limitations in achieving dynamic health support that aligns with users' emotions. Additionally, privacy protection when sharing generated care plans with stakeholders is insufficient. A system that addresses these challenges and allows users to use it with peace of mind is needed.

[0376] 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.

[0377] In this invention, the server includes data input means for inputting user behavior information and emotional information, emotion analysis means for analyzing encrypted data and evaluating the emotional state, and AI suggestion generation means for generating an individualized care plan using an AI model. This enables dynamic and individualized health support that takes into account the user's emotional state.

[0378] A "user" refers to an individual who uses a service or system, and is the entity that inputs information about their lifestyle and emotions.

[0379] "Behavioral information" refers to activity data related to the user's daily life, including exercise levels, sleep duration, and dietary content.

[0380] "Emotional information" refers to data about the user's psychological state, including reports on changes in mood and emotions.

[0381] "Data input means" refers to a device or system used by a user to input behavioral information or emotional information.

[0382] A "data collection method" refers to a device or mechanism that collects information entered by a user, encrypts it as needed, and transmits it to a system.

[0383] "Encrypted data" refers to information that has been transformed in a way that prevents its content from being deciphered by a third party, and is a format used to ensure secure communication.

[0384] "Analysis means" refers to mechanical or computer program-based methods for analyzing collected data and identifying specific patterns or states.

[0385] "Emotional analysis means" refers to a technology or combination of technologies used to determine a user's emotional state based on emotional information.

[0386] A "care plan" is a collection of measures and suggestions provided according to the user's health condition and emotional state, with the aim of maintaining and improving the user's health.

[0387] The "AI proposal generation method" is a system that uses artificial intelligence technology to automatically generate the optimal care plan from user data.

[0388] "Plan presentation means" refers to technologies for notifying and presenting the generated care plan to the user, and includes digital terminals and voice guidance.

[0389] A "monitoring and adjustment mechanism" is a system that monitors the user's progress and emotional information in real time and modifies or adjusts the care plan as needed.

[0390] "Information sharing methods" refer to means of sharing data with relevant individuals with the user's permission, enabling safe and effective information transmission.

[0391] This invention is a system that personalizes users' health management and presents care plans that take their emotional state into consideration. Specifically, the user, terminal, and server work together to implement this system.

[0392] Users input their behavioral and emotional information using smartphones or wearable devices. Behavioral information includes details of daily activities such as exercise, diet, and sleep, while emotional information includes reports on mood and emotional changes. This data is received by the device, encrypted, and stored collectively. Advanced encryption technology, AES-256, is used for encryption.

[0393] The collected data is securely transmitted from the terminal to the server via the HTTPS protocol. The server receives the data and stores it in a database. A SQL-based system, such as MySQL, is used for the database. The server analyzes the stored data and performs sentiment analysis using a machine learning model incorporating natural language processing techniques, such as TensorFlow.

[0394] The server then generates a personalized care plan using a generative AI model based on the analyzed emotional data and the user's health indicators. The generative AI model generates specific suggestions for relaxation based on prompts, for example, in response to the question, "Generate specific suggestions for the user to relax."

[0395] The generated care plan is communicated to the user via their device. Notifications are delivered via push notifications and voice prompts, designed to facilitate user action. Furthermore, the server monitors the user's care plan progress and emotional state in real time, adjusting the plan as needed. The monitored data is securely shared with relevant parties with the user's permission. In this way, the system provides dynamic health management tailored to the user's individual needs.

[0396] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0397] Step 1:

[0398] Users input behavioral and emotional information using smartphones or wearable devices. This input includes exercise time, meal content, sleep duration, and current mood. This information is collected by the device. The device first stores the collected data locally and then encrypts it using the AES-256 encryption algorithm. The output is encrypted data, which is then prepared for transmission to the server.

[0399] Step 2:

[0400] The device uses the HTTPS protocol to securely send encrypted data to the server. The input is encrypted data, and the output is the completion of the data transfer to the server. This data processing is essential to protect user privacy.

[0401] Step 3:

[0402] The server receives data sent from the terminal and stores it in the database. The input is encrypted data from the terminal, and the output is decrypted data stored in the database. A high-performance SQL system is used for the database, ensuring efficient data management.

[0403] Step 4:

[0404] The server performs sentiment analysis using stored data. The input consists of user behavior information and decoded sentiment information stored in a database. Specifically, it uses a machine learning model (such as TensorFlow) incorporating natural language processing techniques to determine sentiment categories such as positive or negative. The output is the analyzed sentiment data.

[0405] Step 5:

[0406] The server generates personalized care plans using an AI model based on the results of emotion analysis and health indicator data. The inputs are emotion data and health indicators. By providing prompts to the generating AI model, such as "Generate specific suggestions for the user to relax," personalized suggestions are generated. The output is a personalized care plan.

[0407] Step 6:

[0408] The generated care plan is notified to the user via the device. The input is the care plan from the server, and the output is the notification to the user. Specifically, the care plan is delivered to the user using push notifications or voice guidance.

[0409] Step 7:

[0410] The server monitors user progress data and emotional information in real time. Input is the user's daily input data. The care plan is dynamically adjusted as needed, and the updated care plan is sent to the terminal as output. This ensures continuous and appropriate support for the user.

[0411] (Application Example 2)

[0412] 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."

[0413] Existing health management systems have struggled to simultaneously consider an individual's emotional state and physical condition, making it difficult to provide personalized care plans. Furthermore, there was a lack of systems capable of monitoring the health status of the elderly in real time and providing appropriate care plans based on their emotional state. This resulted in a problem where the elderly were not receiving adequate health care.

[0414] 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.

[0415] In this invention, the server includes input means for inputting user activity and emotional information, aggregation means for aggregating the information input via the input means, and processing means for processing the data received from the aggregation means and simultaneously evaluating the health and emotional state. This makes it possible to generate personalized health care plans that take into account an individual's health and emotional state.

[0416] "Input means" refers to devices or interfaces that collect user activity and emotional information.

[0417] "Aggregation methods" refer to processes or devices that integrate input information and compile it into meaningful datasets.

[0418] "Processing means" refers to technologies and algorithms used to analyze aggregated data and evaluate the user's health and emotional state.

[0419] "Plan generation means" refers to a device or program that creates an individualized health care plan based on the analysis results from the processing means.

[0420] "Communication means" refers to devices and methods for communicating the generated care plan to the user.

[0421] "Tracking means" refers to devices and methods for continuously monitoring and recording user progress information.

[0422] "Information sharing methods" refer to technologies and processes for sharing information obtained through tracking methods with relevant parties with the user's consent.

[0423] "Voice generation means" refers to technology or devices that create and provide voice instructions to the user based on a generated plan.

[0424] An "artificial intelligence algorithm" refers to a computer program or technical method used to analyze data and simultaneously assess health and emotional states.

[0425] The system of the present invention is designed to provide a health care plan that effectively manages the activity and emotional information of users, including the elderly. This system inputs data obtained from the user's daily activities via a terminal such as a smartphone or smart glasses and transmits it to a server. The server aggregates the input data and uses artificial intelligence algorithms to simultaneously evaluate the user's health and emotional state. Based on the evaluation results, a personalized health care plan is generated and communicated to the user via the terminal.

[0426] Specifically, the device collects information about the user's activity level and mood through sensors and the user interface. This data is encrypted and sent to a server for secure storage. The server analyzes the data using machine learning frameworks such as TensorFlow and analyzes the user's health and emotional state through a generative AI model. Based on this analysis, a health care plan tailored to the user's needs is generated.

[0427] For example, if a user is determined to be emotionally unstable, voice instructions will be provided suggesting relaxation exercises or meals. Furthermore, by allowing users to record their progress, the system can adapt its plan to the new data, enabling dynamic and flexible responses.

[0428] As part of this system, the server can provide the generating AI model with a prompt message such as, "Evaluate the user's mood and activity data, and suggest appropriate relaxation methods and health plans," enabling more innovative care plans. Furthermore, information can be shared with family members, and collaboration with others to support the user's health management is also considered.

[0429] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0430] Step 1:

[0431] The device collects user activity and emotional information through sensors and the user interface. Inputs include step count, heart rate, and user-submitted emotional state assessments. This information is encrypted and processed into a transmittable format for subsequent analysis.

[0432] Step 2:

[0433] The terminal sends the collected data to the server. The server stores the received data in a database for accurate storage. The input is encrypted user data, and the output is a securely stored dataset. The server adds the data to the database while verifying data integrity and security.

[0434] Step 3:

[0435] The server retrieves stored data and performs analysis using artificial intelligence algorithms. The input is user information from the database, and the output is analysis results indicating health and emotional status. This analysis uses machine learning frameworks such as TensorFlow to analyze correlations between variables and evaluate pre-symptomatic states and emotional changes.

[0436] Step 4:

[0437] The server generates a personalized health care plan based on the analysis results. The input is the analysis results, and the output is the personalized plan. The generating AI model uses the prompt "Evaluate the user's mood and activity data and suggest appropriate relaxation methods and a health plan" to create an effective care plan.

[0438] Step 5:

[0439] The server sends the generated care plan to the terminal and notifies the user. The input is the generated care plan, and the output is the notification to the user. The notification is made audibly and is designed to be easy for the user to understand and act upon.

[0440] Step 6:

[0441] The terminal monitors the user's progress and reports the results to the server. The input is user execution data, and the output is updated progress data. This allows the server to evaluate the effectiveness of the plan and make adjustments as needed.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] [Third Embodiment]

[0446] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0447] 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.

[0448] 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).

[0449] 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.

[0450] 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.

[0451] 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).

[0452] 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.

[0453] 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.

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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".

[0458] This invention is a system designed to provide more effective and personalized health management for users in their daily lives. This system collects user data and analyzes it using AI technology to detect pre-disease states early and generate appropriate care plans.

[0459] First, users input daily activity data (e.g., steps taken, sleep quality, food and drink intake) via their smartphones or wearable devices. The device collects this data continuously and prepares to send it to the server as needed.

[0460] Next, the server receives the data sent from the terminal and automatically saves it to a database. This data is analyzed by an AI analysis model based on multiple health indicators. The purpose of the analysis is to identify pre-disease states that the user is unaware of. Machine learning algorithms extract patterns from this data and assess the potential health risks to the user.

[0461] Based on the analysis results, the server automatically generates a care plan, formulating specific health improvement measures, including herbal medicines and folk remedies, tailored to the user. The generated care plan is designed to be immediately applicable to daily life and is organized into actionable tasks.

[0462] Furthermore, this system supports users in incorporating the plan into their daily lives by using AI-generated voice instructions. Specifically, if the goal is to improve sleep, the device will provide simple voice notifications recommending things like going to bed at the same time every night or drinking a specific herbal tea.

[0463] Finally, the server monitors progress in real time and continuously updates the data. This allows users to manage their health more efficiently. Furthermore, family members can continuously monitor the user's health through the device and provide advice and support as needed.

[0464] Thus, the present invention realizes a new system that provides personalized health care and enables users and their families to work together on health management.

[0465] The following describes the processing flow.

[0466] Step 1:

[0467] Users input data about their daily activities using smartphones or wearable devices. This includes information such as steps taken, meals eaten, and sleep duration.

[0468] Step 2:

[0469] The terminal stores user-entered data locally and prepares to periodically send data to a server via the internet. During this process, data encryption and communication security are also ensured.

[0470] Step 3:

[0471] The server receives data sent from the terminal and stores it in a cloud database. After saving the data, it prepares the dataset for the analysis process.

[0472] Step 4:

[0473] The server inputs the stored data into an AI analysis model to evaluate the user's health status. This AI model uses machine learning algorithms to detect abnormal patterns and pre-disease states in the data.

[0474] Step 5:

[0475] The server automatically generates a care plan tailored to the user based on the analysis results. This care plan includes detailed information on health promotion measures, including types of herbal medicines and folk remedies.

[0476] Step 6:

[0477] The device notifies the user of the care plan received from the server and allows them to view the details within the app. It also facilitates improvements in daily life behaviors through generated voice instructions.

[0478] Step 7:

[0479] Users follow the care plan provided, take actions to improve their daily lives, and record their progress through the app.

[0480] Step 8:

[0481] The server regularly receives user feedback and progress updates, analyzes health data in real time, and adjusts care plans as needed.

[0482] Step 9:

[0483] The server updates the user's health status on a family dashboard based on the user's permission. This information can be accessed by family members through the app and used to support the user.

[0484] (Example 1)

[0485] 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."

[0486] In today's busy society, there is a problem in that it is difficult for individual users to be aware of their own health status and receive appropriate care according to that status. In particular, there is a need to detect pre-disease states early and take appropriate countermeasures, but there is a lack of means to provide this in an individualized and efficient manner.

[0487] 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.

[0488] In this invention, the server includes information input means, information collection means, and storage means. This enables precise tracking of the user's health status and the generation and execution of a personalized health plan.

[0489] "Information input means" refers to devices or methods for users to record their daily activity data and supply it to a system.

[0490] "Information gathering means" refers to devices or systems for collectively storing data collected through information input means.

[0491] "Storage means" refers to devices or systems for retaining collected data over the long term and making it accessible as needed.

[0492] "Information analysis means" refers to devices or systems that analyze stored data to detect pre-disease states and evaluate health.

[0493] A "health plan generation method" is a device or system for formulating an optimal health maintenance or improvement plan for a user based on the results of an information analysis method.

[0494] "Information presentation means" refers to a device or method for visually or audibly communicating a generated health plan to the user.

[0495] A "progress tracking device" is a device or system for monitoring progress and recording changes during the user's process of implementing a health plan.

[0496] "Information sharing means" refers to devices or methods for sharing collected progress information with third parties based on the user's consent.

[0497] A "voice instruction supply means" is a device or system for providing voice instructions and advice to users in order to facilitate the implementation of a health plan.

[0498] This invention is a personalized health management system that collects and analyzes a user's daily activity data to generate a plan for improving their health. The system uses smartphones and wearable devices to acquire data about the user's lifestyle activities. This allows the user to easily input information.

[0499] The device collects information provided by the user and transmits it to the server as needed. This includes smartphones and wearable devices that automatically acquire health data. HTTPS communication is used to ensure data is securely transferred and privacy is maintained.

[0500] The server receives activity data sent from the terminal and stores it in a dedicated database. The server runs a generative AI model using TensorFlow or PyTorch to analyze the collected data. This analysis evaluates the user's health patterns and enables the detection of pre-disease states. Based on this information, health risks are identified and appropriate health plans are formulated. For example, if the data analysis determines that the stress level is high, health improvement measures to promote relaxation are suggested.

[0501] The generated health plan is presented to the user via the device. At this time, voice instructions are provided by a voice guidance system to help the user easily implement the plan. For example, specific instructions such as "Take five minutes of deep breathing every day" are communicated via the voice assistant.

[0502] The server tracks the user's progress in their health plan and continuously updates the progress data. This allows users to respond flexibly to changes in their health status. Furthermore, this progress information can be shared with family members with the user's consent.

[0503] As a concrete example, a user can receive health management suggestions by prompting the generated AI model with "Please tell me my health management plan for this month." In this way, the system aims to provide users with user-friendly and actionable health solutions.

[0504] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0505] Step 1:

[0506] Users input data related to their daily activities through their smartphones or wearable devices. Specifically, this involves manually entering steps and weight into the app, or automatically measuring heart rate and sleep quality using wearable devices. This input data is temporarily stored in the device's internal storage.

[0507] Step 2:

[0508] The terminal collects activity data, compiles it, and sends it to the server at the appropriate time. The input data is organized by date and time and sent to the server in batch processing to ensure fault tolerance. HTTPS is used for communication to ensure data protection.

[0509] Step 3:

[0510] The server receives data sent from the terminal and stores it in the database. The received data is associated by user ID, and indexes are created for searching and analysis. The database uses transactional technology to maintain data integrity.

[0511] Step 4:

[0512] The server analyzes the stored data using a generative AI model. Specifically, it uses machine learning algorithms to extract patterns from the dataset and evaluate the user's health status. This analysis is performed using TensorFlow or PyTorch, and the extracted results are useful for the early detection of pre-disease states.

[0513] Step 5:

[0514] The server generates a personalized health improvement plan based on the output of the AI ​​analysis model. The resulting plan includes specific health improvement actions, such as recommendations like "take a 30-minute walk every day." This plan is customized to suit the user's daily routine.

[0515] Step 6:

[0516] The device presents the generated health plan to the user. It sets up specific notifications for the plan, including voice instructions, and notifies the user via the voice assistant with instructions such as, "Take your walk around your house today." This encourages the user to take actions in line with the health plan.

[0517] Step 7:

[0518] The server tracks and monitors the progress of the user's health plan. It continuously collects user feedback and implementation data to evaluate the plan's suitability. This progress data is used to adjust the plan as needed.

[0519] (Application Example 1)

[0520] 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."

[0521] In today's busy lifestyle, users often find it difficult to properly manage their own health, and in particular, they often fail to detect pre-disease conditions early and receive appropriate care. A system is needed to solve this problem and support health management in daily life. Furthermore, it is necessary to improve the efficiency of health management through the effective use of robots in the home.

[0522] 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.

[0523] In this invention, the server includes an information input means for inputting information about the user's daily activities, an information collection means for collecting information input via the information input means, and an analysis means for analyzing the information received from the information collection means and detecting a pre-disease state. This enables the user to efficiently detect a pre-disease state and receive an appropriate care plan while utilizing a robot in their home.

[0524] "Information input means" refers to devices or interfaces that allow users to input information about their daily activities into a system.

[0525] "Information gathering means" refers to a function that collects daily activity information entered through information input means and stores it as data in a format that allows for necessary processing.

[0526] "Analysis means" refers to systems and algorithms that perform data analysis to detect a user's pre-disease state based on collected information.

[0527] The "care plan generation means" is a function that automatically creates a care plan, including health improvement measures suitable for the user, based on the results of the analysis means.

[0528] A "care plan presentation tool" refers to an interface or device that effectively communicates the generated care plan to the user.

[0529] "Monitoring measures" refer to functions for continuously tracking and monitoring the user's health status and the implementation status of their care plan.

[0530] "Information sharing means" refers to a system for sharing a user's health information with third parties, such as relatives, based on their permission.

[0531] A "robot instruction means" is a function that utilizes a robot in the home to provide instructions to the user based on a generated care plan.

[0532] This invention is a system that provides more effective and personalized health management for users in their daily lives. The system utilizes the following hardware and software.

[0533] First, users input daily activity information such as steps taken, sleep patterns, and dietary details using smart devices or wearable devices. This information is then collected by an information collection system via the information input device. The device then transmits this information to a server.

[0534] The server processes the received information using analysis tools to detect pre-disease states. Specifically, it uses machine learning algorithms to identify patterns related to the user's health status from the information. Based on the analysis results, the care plan generation tool creates a specific health improvement plan, including alternative medicines and traditional treatments.

[0535] The generated care plan is presented to the user through a plan presentation system, and the user aims to improve their health in their daily life based on this plan. In addition, home devices such as robots are used, and voice instructions and visual information are provided to the user through a robot instruction system.

[0536] User activity is tracked through monitoring mechanisms, and progress information can be shared with third parties, such as family members, using information sharing tools. This allows relatives to understand the user's health status and provide support and advice as needed.

[0537] For example, a robot could provide instructions every night before bedtime, such as, "Go to bed by 10 PM to ensure you get enough sleep." It could also suggest in the morning, "Why not take a morning walk?"

[0538] An example of a prompt statement can be shown as follows:

[0539] "Analyze the user's sleep data from last night and suggest an appropriate amount of sleep. Provide voice prompts, such as a notification of what time they should go to bed."

[0540] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0541] Step 1:

[0542] Users input daily activity information using smart devices or wearable devices. This includes steps taken, sleep patterns, and dietary information. This information is collected by the terminal through the information input method and prepared as input data for subsequent processing.

[0543] Step 2:

[0544] The device transmits collected user activity information to the server. This transmission occurs at regular intervals, and the information is accumulated by the data collection method. Since the input data is sent directly to the server, real-time data from the user's device is ensured.

[0545] Step 3:

[0546] The server processes the received information using analytical tools to detect pre-disease states. The received data is input into a machine learning algorithm to identify patterns related to the user's health status. This analysis then outputs specific health risks.

[0547] Step 4:

[0548] The server uses a care plan generation tool based on the analysis results to create a health improvement plan. This plan includes alternative medicines and traditional treatments. A generation AI model is used to create a plan tailored to the user's condition, and specific health improvement measures are provided as output.

[0549] Step 5:

[0550] The generated care plan is presented to the user through a plan presentation system. Using a terminal or a home robot, the plan is communicated to the user visually and audibly. The outputted plan information is presented in a format that is easily understandable to the user.

[0551] Step 6:

[0552] Users follow and implement the care plan provided in their daily lives. Monitoring devices track the user's actions and continuously collect progress information. This allows new activity information to be accumulated as additional input data.

[0553] Step 7:

[0554] The monitored progress information is shared with relatives using information sharing tools. If necessary, an AI model generated from the server receives prompts and creates appropriate advice and follow-up plans. Based on this information, the user and relatives can jointly manage their health.

[0555] 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.

[0556] This invention provides effective and personalized health management for users in their daily lives, and is a system that supports health monitoring and improvement, particularly through care plans that take into account the user's emotional state. This system analyzes the user's input data and emotional data, and presents a generated care plan to achieve more accurate health support.

[0557] First, users input data on their daily activities and emotional information (e.g., health feedback and mood reports) from their smartphones or wearable devices. This data is collected by the device, encrypted, securely stored, and then prepared for transmission to the server.

[0558] Next, the server receives the data sent from the terminal and stores it in a database. This stored data includes the user's health indicators as well as emotional data analyzed by the emotion engine. Then, an AI analysis model is used to simultaneously evaluate the user's health status and emotional changes.

[0559] Based on the analysis results, the server generates a care plan that incorporates emotional data into conventional health data. This plan dynamically adapts to the user's health and emotional state, resulting in more personalized suggestions. For example, if the user is experiencing stress, the server can suggest relaxation-enhancing folk remedies or herbal medicines to promote calmness.

[0560] The care plan and emotion-based guidance described above are communicated to the user via the device and are designed to facilitate their implementation in daily life. In addition, the generated instructions are provided via voice to support the smooth implementation of the plan on a daily basis.

[0561] Finally, the server monitors the user's progress and emotional data in real time and adjusts the plan as needed. This information can also be shared with family members with the user's permission, allowing them to understand the user's health, including their emotional state.

[0562] Thus, this invention allows users to enjoy personalized health care and receive support that takes their emotional needs into consideration. This system improves the user's quality of life and provides more effective health management.

[0563] The following describes the processing flow.

[0564] Step 1:

[0565] Users input data about their daily activities via smartphones or wearable devices. This includes not only exercise levels, diet, and sleep duration, but also simple emotional questionnaires and daily moods.

[0566] Step 2:

[0567] The terminal collects and stores the entered data. To ensure data security, it encrypts the data and prepares it for secure transmission to the server.

[0568] Step 3:

[0569] The server receives data from the terminal and stores it in a cloud-based database. This includes data on the user's lifestyle and emotional state.

[0570] Step 4:

[0571] The server uses an emotion engine to analyze emotional data. This quantifies the user's emotional state, and evaluates stress levels and well-being.

[0572] Step 5:

[0573] The server uses an AI analysis model to comprehensively analyze the collected health and emotional data to evaluate the user's health status and pre-disease state.

[0574] Step 6:

[0575] The server generates a care plan based on the analysis results. This plan outlines specific action guidelines necessary to maintain and improve the user's health, and includes content that takes stress management and mental health into consideration.

[0576] Step 7:

[0577] The device notifies the user of the generated care plan. The user can then view the details in the app and incorporate the proposed plan into their daily life.

[0578] Step 8:

[0579] The device uses a voice instruction generation function to provide users with voice guidance to support the implementation of their care plan. This voice guidance is customized according to the user's situation.

[0580] Step 9:

[0581] Users provide feedback on their activities within the app and record their progress. This record can be easily managed and viewed within the app.

[0582] Step 10:

[0583] The server monitors progress and user feedback in real time and modifies the care plan as needed based on the data obtained. If necessary, it shares information with the family and establishes an appropriate support system.

[0584] (Example 2)

[0585] 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."

[0586] Traditional health management systems have struggled to provide personalized care plans that take into account users' emotional states. Furthermore, conventional methods have limitations in achieving dynamic health support that aligns with users' emotions. Additionally, privacy protection when sharing generated care plans with stakeholders is insufficient. A system that addresses these challenges and allows users to use it with peace of mind is needed.

[0587] 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.

[0588] In this invention, the server includes data input means for inputting user behavior information and emotional information, emotion analysis means for analyzing encrypted data and evaluating the emotional state, and AI suggestion generation means for generating an individualized care plan using an AI model. This enables dynamic and individualized health support that takes into account the user's emotional state.

[0589] A "user" refers to an individual who uses a service or system, and is the entity that inputs information about their lifestyle and emotions.

[0590] "Behavioral information" refers to activity data related to the user's daily life, including exercise levels, sleep duration, and dietary content.

[0591] "Emotional information" refers to data about the user's psychological state, including reports on changes in mood and emotions.

[0592] "Data input means" refers to a device or system used by a user to input behavioral information or emotional information.

[0593] A "data collection method" refers to a device or mechanism that collects information entered by a user, encrypts it as needed, and transmits it to a system.

[0594] "Encrypted data" refers to information that has been transformed in a way that prevents its content from being deciphered by a third party, and is a format used to ensure secure communication.

[0595] "Analysis means" refers to mechanical or computer program-based methods for analyzing collected data and identifying specific patterns or states.

[0596] "Emotional analysis means" refers to a technology or combination of technologies used to determine a user's emotional state based on emotional information.

[0597] A "care plan" is a collection of measures and suggestions provided according to the user's health condition and emotional state, with the aim of maintaining and improving the user's health.

[0598] The "AI proposal generation method" is a system that uses artificial intelligence technology to automatically generate the optimal care plan from user data.

[0599] "Plan presentation means" refers to technologies for notifying and presenting the generated care plan to the user, and includes digital terminals and voice guidance.

[0600] A "monitoring and adjustment mechanism" is a system that monitors the user's progress and emotional information in real time and modifies or adjusts the care plan as needed.

[0601] "Information sharing methods" refer to means of sharing data with relevant individuals with the user's permission, enabling safe and effective information transmission.

[0602] This invention is a system that personalizes users' health management and presents care plans that take their emotional state into consideration. Specifically, the user, terminal, and server work together to implement this system.

[0603] Users input their behavioral and emotional information using smartphones or wearable devices. Behavioral information includes details of daily activities such as exercise, diet, and sleep, while emotional information includes reports on mood and emotional changes. This data is received by the device, encrypted, and stored collectively. Advanced encryption technology, AES-256, is used for encryption.

[0604] The collected data is securely transmitted from the terminal to the server via the HTTPS protocol. The server receives the data and stores it in a database. A SQL-based system, such as MySQL, is used for the database. The server analyzes the stored data and performs sentiment analysis using a machine learning model incorporating natural language processing techniques, such as TensorFlow.

[0605] The server then generates a personalized care plan using a generative AI model based on the analyzed emotional data and the user's health indicators. The generative AI model generates specific suggestions for relaxation based on prompts, for example, in response to the question, "Generate specific suggestions for the user to relax."

[0606] The generated care plan is communicated to the user via their device. Notifications are delivered via push notifications and voice prompts, designed to facilitate user action. Furthermore, the server monitors the user's care plan progress and emotional state in real time, adjusting the plan as needed. The monitored data is securely shared with relevant parties with the user's permission. In this way, the system provides dynamic health management tailored to the user's individual needs.

[0607] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0608] Step 1:

[0609] Users input behavioral and emotional information using smartphones or wearable devices. This input includes exercise time, meal content, sleep duration, and current mood. This information is collected by the device. The device first stores the collected data locally and then encrypts it using the AES-256 encryption algorithm. The output is encrypted data, which is then prepared for transmission to the server.

[0610] Step 2:

[0611] The device uses the HTTPS protocol to securely send encrypted data to the server. The input is encrypted data, and the output is the completion of the data transfer to the server. This data processing is essential to protect user privacy.

[0612] Step 3:

[0613] The server receives data sent from the terminal and stores it in the database. The input is encrypted data from the terminal, and the output is decrypted data stored in the database. A high-performance SQL system is used for the database, ensuring efficient data management.

[0614] Step 4:

[0615] The server performs sentiment analysis using stored data. The input consists of user behavior information and decoded sentiment information stored in a database. Specifically, it uses a machine learning model (such as TensorFlow) incorporating natural language processing techniques to determine sentiment categories such as positive or negative. The output is the analyzed sentiment data.

[0616] Step 5:

[0617] The server generates personalized care plans using an AI model based on the results of emotion analysis and health indicator data. The inputs are emotion data and health indicators. By providing prompts to the generating AI model, such as "Generate specific suggestions for the user to relax," personalized suggestions are generated. The output is a personalized care plan.

[0618] Step 6:

[0619] The generated care plan is notified to the user via the device. The input is the care plan from the server, and the output is the notification to the user. Specifically, the care plan is delivered to the user using push notifications or voice guidance.

[0620] Step 7:

[0621] The server monitors user progress data and emotional information in real time. Input is the user's daily input data. The care plan is dynamically adjusted as needed, and the updated care plan is sent to the terminal as output. This ensures continuous and appropriate support for the user.

[0622] (Application Example 2)

[0623] 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."

[0624] Existing health management systems have struggled to simultaneously consider an individual's emotional state and physical condition, making it difficult to provide personalized care plans. Furthermore, there was a lack of systems capable of monitoring the health status of the elderly in real time and providing appropriate care plans based on their emotional state. This resulted in a problem where the elderly were not receiving adequate health care.

[0625] 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.

[0626] In this invention, the server includes input means for inputting user activity and emotional information, aggregation means for aggregating the information input via the input means, and processing means for processing the data received from the aggregation means and simultaneously evaluating the health and emotional state. This makes it possible to generate personalized health care plans that take into account an individual's health and emotional state.

[0627] "Input means" refers to devices or interfaces that collect user activity and emotional information.

[0628] "Aggregation methods" refer to processes or devices that integrate input information and compile it into meaningful datasets.

[0629] "Processing means" refers to technologies and algorithms used to analyze aggregated data and evaluate the user's health and emotional state.

[0630] "Plan generation means" refers to a device or program that creates an individualized health care plan based on the analysis results from the processing means.

[0631] "Communication means" refers to devices and methods for communicating the generated care plan to the user.

[0632] "Tracking means" refers to devices and methods for continuously monitoring and recording user progress information.

[0633] "Information sharing methods" refer to technologies and processes for sharing information obtained through tracking methods with relevant parties with the user's consent.

[0634] "Voice generation means" refers to technology or devices that create and provide voice instructions to the user based on a generated plan.

[0635] An "artificial intelligence algorithm" refers to a computer program or technical method used to analyze data and simultaneously assess health and emotional states.

[0636] The system of the present invention is designed to provide a health care plan that effectively manages the activity and emotional information of users, including the elderly. This system inputs data obtained from the user's daily activities via a terminal such as a smartphone or smart glasses and transmits it to a server. The server aggregates the input data and uses artificial intelligence algorithms to simultaneously evaluate the user's health and emotional state. Based on the evaluation results, a personalized health care plan is generated and communicated to the user via the terminal.

[0637] Specifically, the device collects information about the user's activity level and mood through sensors and the user interface. This data is encrypted and sent to a server for secure storage. The server analyzes the data using machine learning frameworks such as TensorFlow and analyzes the user's health and emotional state through a generative AI model. Based on this analysis, a health care plan tailored to the user's needs is generated.

[0638] For example, if a user is determined to be emotionally unstable, voice instructions will be provided suggesting relaxation exercises or meals. Furthermore, by allowing users to record their progress, the system can adapt its plan to the new data, enabling dynamic and flexible responses.

[0639] As part of this system, the server can provide the generating AI model with a prompt message such as, "Evaluate the user's mood and activity data, and suggest appropriate relaxation methods and health plans," enabling more innovative care plans. Furthermore, information can be shared with family members, and collaboration with others to support the user's health management is also considered.

[0640] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0641] Step 1:

[0642] The device collects user activity and emotional information through sensors and the user interface. Inputs include step count, heart rate, and user-submitted emotional state assessments. This information is encrypted and processed into a transmittable format for subsequent analysis.

[0643] Step 2:

[0644] The terminal sends the collected data to the server. The server stores the received data in a database for accurate storage. The input is encrypted user data, and the output is a securely stored dataset. The server adds the data to the database while verifying data integrity and security.

[0645] Step 3:

[0646] The server retrieves stored data and performs analysis using artificial intelligence algorithms. The input is user information from the database, and the output is analysis results indicating health and emotional status. This analysis uses machine learning frameworks such as TensorFlow to analyze correlations between variables and evaluate pre-symptomatic states and emotional changes.

[0647] Step 4:

[0648] The server generates a personalized health care plan based on the analysis results. The input is the analysis results, and the output is the personalized plan. The generating AI model uses the prompt "Evaluate the user's mood and activity data and suggest appropriate relaxation methods and a health plan" to create an effective care plan.

[0649] Step 5:

[0650] The server sends the generated care plan to the terminal and notifies the user. The input is the generated care plan, and the output is the notification to the user. The notification is made audibly and is designed to be easy for the user to understand and act upon.

[0651] Step 6:

[0652] The terminal monitors the user's progress and reports the results to the server. The input is user execution data, and the output is updated progress data. This allows the server to evaluate the effectiveness of the plan and make adjustments as needed.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] [Fourth Embodiment]

[0657] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0658] 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.

[0659] 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).

[0660] 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.

[0661] 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.

[0662] 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).

[0663] 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.

[0664] 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.

[0665] 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.

[0666] 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.

[0667] 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.

[0668] 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.

[0669] 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".

[0670] This invention is a system designed to provide more effective and personalized health management for users in their daily lives. This system collects user data and analyzes it using AI technology to detect pre-disease states early and generate appropriate care plans.

[0671] First, users input daily activity data (e.g., steps taken, sleep quality, food and drink intake) via their smartphones or wearable devices. The device collects this data continuously and prepares to send it to the server as needed.

[0672] Next, the server receives the data sent from the terminal and automatically saves it to a database. This data is analyzed by an AI analysis model based on multiple health indicators. The purpose of the analysis is to identify pre-disease states that the user is unaware of. Machine learning algorithms extract patterns from this data and assess the potential health risks to the user.

[0673] Based on the analysis results, the server automatically generates a care plan, formulating specific health improvement measures, including herbal medicines and folk remedies, tailored to the user. The generated care plan is designed to be immediately applicable to daily life and is organized into actionable tasks.

[0674] Furthermore, this system supports users in incorporating the plan into their daily lives by using AI-generated voice instructions. Specifically, if the goal is to improve sleep, the device will provide simple voice notifications recommending things like going to bed at the same time every night or drinking a specific herbal tea.

[0675] Finally, the server monitors progress in real time and continuously updates the data. This allows users to manage their health more efficiently. Furthermore, family members can continuously monitor the user's health through the device and provide advice and support as needed.

[0676] Thus, the present invention realizes a new system that provides personalized health care and enables users and their families to work together on health management.

[0677] The following describes the processing flow.

[0678] Step 1:

[0679] Users input data about their daily activities using smartphones or wearable devices. This includes information such as steps taken, meals eaten, and sleep duration.

[0680] Step 2:

[0681] The terminal stores user-entered data locally and prepares to periodically send data to a server via the internet. During this process, data encryption and communication security are also ensured.

[0682] Step 3:

[0683] The server receives data sent from the terminal and stores it in a cloud database. After saving the data, it prepares the dataset for the analysis process.

[0684] Step 4:

[0685] The server inputs the stored data into an AI analysis model to evaluate the user's health status. This AI model uses machine learning algorithms to detect abnormal patterns and pre-disease states in the data.

[0686] Step 5:

[0687] The server automatically generates a care plan tailored to the user based on the analysis results. This care plan includes detailed information on health promotion measures, including types of herbal medicines and folk remedies.

[0688] Step 6:

[0689] The device notifies the user of the care plan received from the server and allows them to view the details within the app. It also facilitates improvements in daily life behaviors through generated voice instructions.

[0690] Step 7:

[0691] Users follow the care plan provided, take actions to improve their daily lives, and record their progress through the app.

[0692] Step 8:

[0693] The server regularly receives user feedback and progress updates, analyzes health data in real time, and adjusts care plans as needed.

[0694] Step 9:

[0695] The server updates the user's health status on a family dashboard based on the user's permission. This information can be accessed by family members through the app and used to support the user.

[0696] (Example 1)

[0697] 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".

[0698] In today's busy society, there is a problem in that it is difficult for individual users to be aware of their own health status and receive appropriate care according to that status. In particular, there is a need to detect pre-disease states early and take appropriate countermeasures, but there is a lack of means to provide this in an individualized and efficient manner.

[0699] 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.

[0700] In this invention, the server includes information input means, information collection means, and storage means. This enables precise tracking of the user's health status and the generation and execution of a personalized health plan.

[0701] "Information input means" refers to devices or methods for users to record their daily activity data and supply it to a system.

[0702] "Information gathering means" refers to devices or systems for collectively storing data collected through information input means.

[0703] "Storage means" refers to devices or systems for retaining collected data over the long term and making it accessible as needed.

[0704] "Information analysis means" refers to devices or systems that analyze stored data to detect pre-disease states and evaluate health.

[0705] A "health plan generation method" is a device or system for formulating an optimal health maintenance or improvement plan for a user based on the results of an information analysis method.

[0706] "Information presentation means" refers to a device or method for visually or audibly communicating a generated health plan to the user.

[0707] A "progress tracking device" is a device or system for monitoring progress and recording changes during the user's process of implementing a health plan.

[0708] "Information sharing means" refers to devices or methods for sharing collected progress information with third parties based on the user's consent.

[0709] A "voice instruction supply means" is a device or system for providing voice instructions and advice to users in order to facilitate the implementation of a health plan.

[0710] This invention is a personalized health management system that collects and analyzes a user's daily activity data to generate a plan for improving their health. The system uses smartphones and wearable devices to acquire data about the user's lifestyle activities. This allows the user to easily input information.

[0711] The device collects information provided by the user and transmits it to the server as needed. This includes smartphones and wearable devices that automatically acquire health data. HTTPS communication is used to ensure data is securely transferred and privacy is maintained.

[0712] The server receives activity data sent from the terminal and stores it in a dedicated database. The server runs a generative AI model using TensorFlow or PyTorch to analyze the collected data. This analysis evaluates the user's health patterns and enables the detection of pre-disease states. Based on this information, health risks are identified and appropriate health plans are formulated. For example, if the data analysis determines that the stress level is high, health improvement measures to promote relaxation are suggested.

[0713] The generated health plan is presented to the user via the device. At this time, voice instructions are provided by a voice guidance system to help the user easily implement the plan. For example, specific instructions such as "Take five minutes of deep breathing every day" are communicated via the voice assistant.

[0714] The server tracks the user's progress in their health plan and continuously updates the progress data. This allows users to respond flexibly to changes in their health status. Furthermore, this progress information can be shared with family members with the user's consent.

[0715] As a concrete example, a user can receive health management suggestions by prompting the generated AI model with "Please tell me my health management plan for this month." In this way, the system aims to provide users with user-friendly and actionable health solutions.

[0716] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0717] Step 1:

[0718] Users input data related to their daily activities through their smartphones or wearable devices. Specifically, this involves manually entering steps and weight into the app, or automatically measuring heart rate and sleep quality using wearable devices. This input data is temporarily stored in the device's internal storage.

[0719] Step 2:

[0720] The terminal collects activity data, compiles it, and sends it to the server at the appropriate time. The input data is organized by date and time and sent to the server in batch processing to ensure fault tolerance. HTTPS is used for communication to ensure data protection.

[0721] Step 3:

[0722] The server receives data sent from the terminal and stores it in the database. The received data is associated by user ID, and indexes are created for searching and analysis. The database uses transactional technology to maintain data integrity.

[0723] Step 4:

[0724] The server analyzes the stored data using a generative AI model. Specifically, it uses machine learning algorithms to extract patterns from the dataset and evaluate the user's health status. This analysis is performed using TensorFlow or PyTorch, and the extracted results are useful for the early detection of pre-disease states.

[0725] Step 5:

[0726] The server generates a personalized health improvement plan based on the output of the AI ​​analysis model. The resulting plan includes specific health improvement actions, such as recommendations like "take a 30-minute walk every day." This plan is customized to suit the user's daily routine.

[0727] Step 6:

[0728] The device presents the generated health plan to the user. It sets up specific notifications for the plan, including voice instructions, and notifies the user via the voice assistant with instructions such as, "Take your walk around your house today." This encourages the user to take actions in line with the health plan.

[0729] Step 7:

[0730] The server tracks and monitors the progress of the user's health plan. It continuously collects user feedback and implementation data to evaluate the plan's suitability. This progress data is used to adjust the plan as needed.

[0731] (Application Example 1)

[0732] 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".

[0733] In today's busy lifestyle, users often find it difficult to properly manage their own health, and in particular, they often fail to detect pre-disease conditions early and receive appropriate care. A system is needed to solve this problem and support health management in daily life. Furthermore, it is necessary to improve the efficiency of health management through the effective use of robots in the home.

[0734] 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.

[0735] In this invention, the server includes an information input means for inputting information about the user's daily activities, an information collection means for collecting information input via the information input means, and an analysis means for analyzing the information received from the information collection means and detecting a pre-disease state. This enables the user to efficiently detect a pre-disease state and receive an appropriate care plan while utilizing a robot in their home.

[0736] "Information input means" refers to devices or interfaces that allow users to input information about their daily activities into a system.

[0737] "Information gathering means" refers to a function that collects daily activity information entered through information input means and stores it as data in a format that allows for necessary processing.

[0738] "Analysis means" refers to systems and algorithms that perform data analysis to detect a user's pre-disease state based on collected information.

[0739] The "care plan generation means" is a function that automatically creates a care plan, including health improvement measures suitable for the user, based on the results of the analysis means.

[0740] A "care plan presentation tool" refers to an interface or device that effectively communicates the generated care plan to the user.

[0741] "Monitoring measures" refer to functions for continuously tracking and monitoring the user's health status and the implementation status of their care plan.

[0742] "Information sharing means" refers to a system for sharing a user's health information with third parties, such as relatives, based on their permission.

[0743] A "robot instruction means" is a function that utilizes a robot in the home to provide instructions to the user based on a generated care plan.

[0744] This invention is a system that provides more effective and personalized health management for users in their daily lives. The system utilizes the following hardware and software.

[0745] First, users input daily activity information such as steps taken, sleep patterns, and dietary details using smart devices or wearable devices. This information is then collected by an information collection system via the information input device. The device then transmits this information to a server.

[0746] The server processes the received information using analysis tools to detect pre-disease states. Specifically, it uses machine learning algorithms to identify patterns related to the user's health status from the information. Based on the analysis results, the care plan generation tool creates a specific health improvement plan, including alternative medicines and traditional treatments.

[0747] The generated care plan is presented to the user through a plan presentation system, and the user aims to improve their health in their daily life based on this plan. In addition, home devices such as robots are used, and voice instructions and visual information are provided to the user through a robot instruction system.

[0748] User activity is tracked through monitoring mechanisms, and progress information can be shared with third parties, such as family members, using information sharing tools. This allows relatives to understand the user's health status and provide support and advice as needed.

[0749] For example, a robot could provide instructions every night before bedtime, such as, "Go to bed by 10 PM to ensure you get enough sleep." It could also suggest in the morning, "Why not take a morning walk?"

[0750] An example of a prompt statement can be shown as follows:

[0751] "Analyze the user's sleep data from last night and suggest an appropriate amount of sleep. Provide voice prompts, such as a notification of what time they should go to bed."

[0752] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0753] Step 1:

[0754] Users input daily activity information using smart devices or wearable devices. This includes steps taken, sleep patterns, and dietary information. This information is collected by the terminal through the information input method and prepared as input data for subsequent processing.

[0755] Step 2:

[0756] The device transmits collected user activity information to the server. This transmission occurs at regular intervals, and the information is accumulated by the data collection method. Since the input data is sent directly to the server, real-time data from the user's device is ensured.

[0757] Step 3:

[0758] The server processes the received information using analytical tools to detect pre-disease states. The received data is input into a machine learning algorithm to identify patterns related to the user's health status. This analysis then outputs specific health risks.

[0759] Step 4:

[0760] The server uses a care plan generation tool based on the analysis results to create a health improvement plan. This plan includes alternative medicines and traditional treatments. A generation AI model is used to create a plan tailored to the user's condition, and specific health improvement measures are provided as output.

[0761] Step 5:

[0762] The generated care plan is presented to the user through a plan presentation system. Using a terminal or a home robot, the plan is communicated to the user visually and audibly. The outputted plan information is presented in a format that is easily understandable to the user.

[0763] Step 6:

[0764] Users follow and implement the care plan provided in their daily lives. Monitoring devices track the user's actions and continuously collect progress information. This allows new activity information to be accumulated as additional input data.

[0765] Step 7:

[0766] The monitored progress information is shared with relatives using information sharing tools. If necessary, an AI model generated from the server receives prompts and creates appropriate advice and follow-up plans. Based on this information, the user and relatives can jointly manage their health.

[0767] 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.

[0768] This invention provides effective and personalized health management for users in their daily lives, and is a system that supports health monitoring and improvement, particularly through care plans that take into account the user's emotional state. This system analyzes the user's input data and emotional data, and presents a generated care plan to achieve more accurate health support.

[0769] First, users input data on their daily activities and emotional information (e.g., health feedback and mood reports) from their smartphones or wearable devices. This data is collected by the device, encrypted, securely stored, and then prepared for transmission to the server.

[0770] Next, the server receives the data sent from the terminal and stores it in a database. This stored data includes the user's health indicators as well as emotional data analyzed by the emotion engine. Then, an AI analysis model is used to simultaneously evaluate the user's health status and emotional changes.

[0771] Based on the analysis results, the server generates a care plan that incorporates emotional data into conventional health data. This plan dynamically adapts to the user's health and emotional state, resulting in more personalized suggestions. For example, if the user is experiencing stress, the server can suggest relaxation-enhancing folk remedies or herbal medicines to promote calmness.

[0772] The care plan and emotion-based guidance described above are communicated to the user via the device and are designed to facilitate their implementation in daily life. In addition, the generated instructions are provided via voice to support the smooth implementation of the plan on a daily basis.

[0773] Finally, the server monitors the user's progress and emotional data in real time and adjusts the plan as needed. This information can also be shared with family members with the user's permission, allowing them to understand the user's health, including their emotional state.

[0774] Thus, this invention allows users to enjoy personalized health care and receive support that takes their emotional needs into consideration. This system improves the user's quality of life and provides more effective health management.

[0775] The following describes the processing flow.

[0776] Step 1:

[0777] Users input data about their daily activities via smartphones or wearable devices. This includes not only exercise levels, diet, and sleep duration, but also simple emotional questionnaires and daily moods.

[0778] Step 2:

[0779] The terminal collects and stores the entered data. To ensure data security, it encrypts the data and prepares it for secure transmission to the server.

[0780] Step 3:

[0781] The server receives data from the terminal and stores it in a cloud-based database. This includes data on the user's lifestyle and emotional state.

[0782] Step 4:

[0783] The server uses an emotion engine to analyze emotional data. This quantifies the user's emotional state, and evaluates stress levels and well-being.

[0784] Step 5:

[0785] The server uses an AI analysis model to comprehensively analyze the collected health and emotional data to evaluate the user's health status and pre-disease state.

[0786] Step 6:

[0787] The server generates a care plan based on the analysis results. This plan outlines specific action guidelines necessary to maintain and improve the user's health, and includes content that takes stress management and mental health into consideration.

[0788] Step 7:

[0789] The device notifies the user of the generated care plan. The user can then view the details in the app and incorporate the proposed plan into their daily life.

[0790] Step 8:

[0791] The device uses a voice instruction generation function to provide users with voice guidance to support the implementation of their care plan. This voice guidance is customized according to the user's situation.

[0792] Step 9:

[0793] Users provide feedback on their activities within the app and record their progress. This record can be easily managed and viewed within the app.

[0794] Step 10:

[0795] The server monitors progress and user feedback in real time and modifies the care plan as needed based on the data obtained. If necessary, it shares information with the family and establishes an appropriate support system.

[0796] (Example 2)

[0797] 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".

[0798] Traditional health management systems have struggled to provide personalized care plans that take into account users' emotional states. Furthermore, conventional methods have limitations in achieving dynamic health support that aligns with users' emotions. Additionally, privacy protection when sharing generated care plans with stakeholders is insufficient. A system that addresses these challenges and allows users to use it with peace of mind is needed.

[0799] 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.

[0800] In this invention, the server includes data input means for inputting user behavior information and emotional information, emotion analysis means for analyzing encrypted data and evaluating the emotional state, and AI suggestion generation means for generating an individualized care plan using an AI model. This enables dynamic and individualized health support that takes into account the user's emotional state.

[0801] A "user" refers to an individual who uses a service or system, and is the entity that inputs information about their lifestyle and emotions.

[0802] "Behavioral information" refers to activity data related to the user's daily life, including exercise levels, sleep duration, and dietary content.

[0803] "Emotional information" refers to data about the user's psychological state, including reports on changes in mood and emotions.

[0804] "Data input means" refers to a device or system used by a user to input behavioral information or emotional information.

[0805] A "data collection method" refers to a device or mechanism that collects information entered by a user, encrypts it as needed, and transmits it to a system.

[0806] "Encrypted data" refers to information that has been transformed in a way that prevents its content from being deciphered by a third party, and is a format used to ensure secure communication.

[0807] "Analysis means" refers to mechanical or computer program-based methods for analyzing collected data and identifying specific patterns or states.

[0808] "Emotional analysis means" refers to a technology or combination of technologies used to determine a user's emotional state based on emotional information.

[0809] A "care plan" is a collection of measures and suggestions provided according to the user's health condition and emotional state, with the aim of maintaining and improving the user's health.

[0810] The "AI proposal generation method" is a system that uses artificial intelligence technology to automatically generate the optimal care plan from user data.

[0811] "Plan presentation means" refers to technologies for notifying and presenting the generated care plan to the user, and includes digital terminals and voice guidance.

[0812] A "monitoring and adjustment mechanism" is a system that monitors the user's progress and emotional information in real time and modifies or adjusts the care plan as needed.

[0813] "Information sharing methods" refer to means of sharing data with relevant individuals with the user's permission, enabling safe and effective information transmission.

[0814] This invention is a system that personalizes users' health management and presents care plans that take their emotional state into consideration. Specifically, the user, terminal, and server work together to implement this system.

[0815] Users input their behavioral and emotional information using smartphones or wearable devices. Behavioral information includes details of daily activities such as exercise, diet, and sleep, while emotional information includes reports on mood and emotional changes. This data is received by the device, encrypted, and stored collectively. Advanced encryption technology, AES-256, is used for encryption.

[0816] The collected data is securely transmitted from the terminal to the server via the HTTPS protocol. The server receives the data and stores it in a database. A SQL-based system, such as MySQL, is used for the database. The server analyzes the stored data and performs sentiment analysis using a machine learning model incorporating natural language processing techniques, such as TensorFlow.

[0817] The server then generates a personalized care plan using a generative AI model based on the analyzed emotional data and the user's health indicators. The generative AI model generates specific suggestions for relaxation based on prompts, for example, in response to the question, "Generate specific suggestions for the user to relax."

[0818] The generated care plan is communicated to the user via their device. Notifications are delivered via push notifications and voice prompts, designed to facilitate user action. Furthermore, the server monitors the user's care plan progress and emotional state in real time, adjusting the plan as needed. The monitored data is securely shared with relevant parties with the user's permission. In this way, the system provides dynamic health management tailored to the user's individual needs.

[0819] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0820] Step 1:

[0821] Users input behavioral and emotional information using smartphones or wearable devices. This input includes exercise time, meal content, sleep duration, and current mood. This information is collected by the device. The device first stores the collected data locally and then encrypts it using the AES-256 encryption algorithm. The output is encrypted data, which is then prepared for transmission to the server.

[0822] Step 2:

[0823] The device uses the HTTPS protocol to securely send encrypted data to the server. The input is encrypted data, and the output is the completion of the data transfer to the server. This data processing is essential to protect user privacy.

[0824] Step 3:

[0825] The server receives data sent from the terminal and stores it in the database. The input is encrypted data from the terminal, and the output is decrypted data stored in the database. A high-performance SQL system is used for the database, ensuring efficient data management.

[0826] Step 4:

[0827] The server performs sentiment analysis using stored data. The input consists of user behavior information and decoded sentiment information stored in a database. Specifically, it uses a machine learning model (such as TensorFlow) incorporating natural language processing techniques to determine sentiment categories such as positive or negative. The output is the analyzed sentiment data.

[0828] Step 5:

[0829] The server generates personalized care plans using an AI model based on the results of emotion analysis and health indicator data. The inputs are emotion data and health indicators. By providing prompts to the generating AI model, such as "Generate specific suggestions for the user to relax," personalized suggestions are generated. The output is a personalized care plan.

[0830] Step 6:

[0831] The generated care plan is notified to the user via the device. The input is the care plan from the server, and the output is the notification to the user. Specifically, the care plan is delivered to the user using push notifications or voice guidance.

[0832] Step 7:

[0833] The server monitors user progress data and emotional information in real time. Input is the user's daily input data. The care plan is dynamically adjusted as needed, and the updated care plan is sent to the terminal as output. This ensures continuous and appropriate support for the user.

[0834] (Application Example 2)

[0835] 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".

[0836] Existing health management systems have struggled to simultaneously consider an individual's emotional state and physical condition, making it difficult to provide personalized care plans. Furthermore, there was a lack of systems capable of monitoring the health status of the elderly in real time and providing appropriate care plans based on their emotional state. This resulted in a problem where the elderly were not receiving adequate health care.

[0837] 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.

[0838] In this invention, the server includes input means for inputting user activity and emotional information, aggregation means for aggregating the information input via the input means, and processing means for processing the data received from the aggregation means and simultaneously evaluating the health and emotional state. This makes it possible to generate personalized health care plans that take into account an individual's health and emotional state.

[0839] "Input means" refers to devices or interfaces that collect user activity and emotional information.

[0840] "Aggregation methods" refer to processes or devices that integrate input information and compile it into meaningful datasets.

[0841] "Processing means" refers to technologies and algorithms used to analyze aggregated data and evaluate the user's health and emotional state.

[0842] "Plan generation means" refers to a device or program that creates an individualized health care plan based on the analysis results from the processing means.

[0843] "Communication means" refers to devices and methods for communicating the generated care plan to the user.

[0844] "Tracking means" refers to devices and methods for continuously monitoring and recording user progress information.

[0845] "Information sharing methods" refer to technologies and processes for sharing information obtained through tracking methods with relevant parties with the user's consent.

[0846] "Voice generation means" refers to technology or devices that create and provide voice instructions to the user based on a generated plan.

[0847] An "artificial intelligence algorithm" refers to a computer program or technical method used to analyze data and simultaneously assess health and emotional states.

[0848] The system of the present invention is designed to provide a health care plan that effectively manages the activity and emotional information of users, including the elderly. This system inputs data obtained from the user's daily activities via a terminal such as a smartphone or smart glasses and transmits it to a server. The server aggregates the input data and uses artificial intelligence algorithms to simultaneously evaluate the user's health and emotional state. Based on the evaluation results, a personalized health care plan is generated and communicated to the user via the terminal.

[0849] Specifically, the device collects information about the user's activity level and mood through sensors and the user interface. This data is encrypted and sent to a server for secure storage. The server analyzes the data using machine learning frameworks such as TensorFlow and analyzes the user's health and emotional state through a generative AI model. Based on this analysis, a health care plan tailored to the user's needs is generated.

[0850] For example, if a user is determined to be emotionally unstable, voice instructions will be provided suggesting relaxation exercises or meals. Furthermore, by allowing users to record their progress, the system can adapt its plan to the new data, enabling dynamic and flexible responses.

[0851] As part of this system, the server can provide the generating AI model with a prompt message such as, "Evaluate the user's mood and activity data, and suggest appropriate relaxation methods and health plans," enabling more innovative care plans. Furthermore, information can be shared with family members, and collaboration with others to support the user's health management is also considered.

[0852] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0853] Step 1:

[0854] The device collects user activity and emotional information through sensors and the user interface. Inputs include step count, heart rate, and user-submitted emotional state assessments. This information is encrypted and processed into a transmittable format for subsequent analysis.

[0855] Step 2:

[0856] The terminal sends the collected data to the server. The server stores the received data in a database for accurate storage. The input is encrypted user data, and the output is a securely stored dataset. The server adds the data to the database while verifying data integrity and security.

[0857] Step 3:

[0858] The server retrieves stored data and performs analysis using artificial intelligence algorithms. The input is user information from the database, and the output is analysis results indicating health and emotional status. This analysis uses machine learning frameworks such as TensorFlow to analyze correlations between variables and evaluate pre-symptomatic states and emotional changes.

[0859] Step 4:

[0860] The server generates a personalized health care plan based on the analysis results. The input is the analysis results, and the output is the personalized plan. The generating AI model uses the prompt "Evaluate the user's mood and activity data and suggest appropriate relaxation methods and a health plan" to create an effective care plan.

[0861] Step 5:

[0862] The server sends the generated care plan to the terminal and notifies the user. The input is the generated care plan, and the output is the notification to the user. The notification is made audibly and is designed to be easy for the user to understand and act upon.

[0863] Step 6:

[0864] The terminal monitors the user's progress and reports the results to the server. The input is user execution data, and the output is updated progress data. This allows the server to evaluate the effectiveness of the plan and make adjustments as needed.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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."

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] The following is further disclosed regarding the embodiments described above.

[0887] (Claim 1)

[0888] A data entry method for inputting the user's daily activity data,

[0889] A data collection means for collecting data input via the aforementioned data input means,

[0890] An analysis means for analyzing data received from the aforementioned data collection means and detecting a pre-disease state,

[0891] Based on the results of the analysis means, a care plan generation means for generating a care plan that includes herbal medicine and folk remedies,

[0892] A plan presentation method for presenting the generated care plan to the user,

[0893] A monitoring method for monitoring progress data from users,

[0894] A data sharing means for sharing the data obtained by the monitoring means with family members based on the user's permission,

[0895] A system that includes this.

[0896] (Claim 2)

[0897] The system according to claim 1, further comprising a voice instruction generation means for providing voice instructions to the user based on the generated care plan.

[0898] (Claim 3)

[0899] The system according to claim 1, wherein the analysis means detects a pre-disease state using a machine learning algorithm.

[0900] "Example 1"

[0901] (Claim 1)

[0902] A means of inputting information for entering the user's daily activity data,

[0903] Information collection means for collecting information input via the aforementioned information input means,

[0904] A storage means for storing information received from the aforementioned information gathering means,

[0905] Information analysis means for detecting a pre-disease state by analyzing the information stored in the storage means using a machine learning algorithm,

[0906] Based on the results of the information analysis means, a health plan generation means for generating a care plan that includes naturally derived treatments,

[0907] A means of presenting the generated health plan to the user in an adaptable format,

[0908] A progress tracking mechanism for tracking progress information from users,

[0909] Information sharing means for sharing information obtained by the progress tracking means with family members based on the user's consent,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, further comprising a voice instruction supply means for providing voice instructions to the user based on the generated health plan.

[0913] (Claim 3)

[0914] The system according to claim 1, wherein the information analysis means detects a pre-disease state using a machine learning algorithm.

[0915] "Application Example 1"

[0916] (Claim 1)

[0917] An information input method for inputting the user's daily activity information,

[0918] Information collection means for collecting information input via the aforementioned information input means,

[0919] An analysis means for analyzing information received from the aforementioned information gathering means and detecting a pre-disease state,

[0920] Based on the results of the analysis means, a care plan generation means for generating a care plan that includes alternative medicines and traditional treatments,

[0921] A plan presentation method for presenting the generated care plan to the user,

[0922] A monitoring method for monitoring progress information from users,

[0923] Information sharing means for sharing information obtained by the monitoring means with relatives based on the user's permission,

[0924] A robot instruction means for providing instructions to the user by a device including a robot, based on the generated care plan,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, further comprising a voice instruction generation means for providing voice instructions to the user based on the generated care plan.

[0928] (Claim 3)

[0929] The system according to claim 1, wherein the analysis means detects a pre-disease state using a learning algorithm.

[0930] "Example 2 of combining an emotion engine"

[0931] (Claim 1)

[0932] A data input method for inputting user behavior information and emotional information,

[0933] A data collection means for collecting and encrypting data input via the aforementioned data input means,

[0934] An analysis means for storing and analyzing encrypted data received from the aforementioned data collection means,

[0935] An emotion analysis means for evaluating the emotional state of a user using the aforementioned analysis means,

[0936] A care plan generation means for generating an individualized care plan based on the aforementioned emotion analysis means and health indicator data,

[0937] An AI proposal generation means for generating care suggestions based on prompt sentences using a generative AI model,

[0938] A plan presentation means for notifying the user of the generated care plan and proposals,

[0939] A monitoring and adjustment mechanism for monitoring progress and sentiment information from users and adjusting the plan as needed,

[0940] Information sharing means for sharing data obtained by the monitoring and adjustment means with relevant people based on the user's permission,

[0941] A system that includes this.

[0942] (Claim 2)

[0943] The system according to claim 1, further comprising a voice guidance generation means for providing voice guidance to a user based on a generated care plan.

[0944] (Claim 3)

[0945] The system according to claim 1, wherein the analysis means evaluates the emotional state using a machine learning algorithm.

[0946] "Application example 2 when combining with an emotional engine"

[0947] (Claim 1)

[0948] An input means for inputting user activity and emotional information,

[0949] A means for aggregating the information input via the input means,

[0950] A processing means for processing data received from the aforementioned aggregation means and simultaneously evaluating health status and emotional status,

[0951] A plan generation means for generating an individualized health care plan based on the results of the processing means,

[0952] A means of communicating the generated plan to the user,

[0953] Tracking methods for tracking user progress,

[0954] Information sharing means for sharing information obtained by the aforementioned tracking means with relevant parties based on user approval,

[0955] A system that includes this.

[0956] (Claim 2)

[0957] The system according to claim 1, further comprising voice generation means for providing voice instructions to a user based on the generated plan.

[0958] (Claim 3)

[0959] The system according to claim 1, wherein the processing means simultaneously evaluates health status and emotional status using an artificial intelligence algorithm. [Explanation of symbols]

[0960] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data entry method for inputting the user's daily activity data, A data collection means for collecting data input via the aforementioned data input means, An analysis means for analyzing data received from the aforementioned data collection means and detecting a pre-disease state, Based on the results of the analysis means, a care plan generation means for generating a care plan that includes herbal medicine and folk remedies, A plan presentation method for presenting the generated care plan to the user, A monitoring method for monitoring progress data from users, A data sharing means for sharing the data obtained by the monitoring means with family members based on the user's permission, A system that includes this.

2. The system according to claim 1, further comprising a voice instruction generation means for providing voice instructions to the user based on the generated care plan.

3. The system according to claim 1, wherein the analysis means detects a pre-disease state using a machine learning algorithm.

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