system

Abstract

We provide the system. [Solution] A means of collecting diverse health-related data from a device that acquires user data, Means for generating and integrating the user's past and present data sets, A means of applying a data analysis method for immediate evaluation of progress, A means for generating individually optimized health management plans based on analysis results, A means of providing an interface for presenting a proposed plan to the user, A means of collecting user responses and using them to generate the next plan, A system that includes means for home robots to support users' daily lives and provide health data via voice.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, it is important to timely and comprehensively grasp an individual's health status. However, the health data obtained from smart devices and wearable terminals are diverse, and there is not a sufficient system for effectively integrating and analyzing them. Therefore, it is difficult for users to optimally manage their own health status and take appropriate preventive measures and life improvements. The purpose of the present invention is to solve the above problems and provide a personalized health management program in real time for individual users.

Means for Solving the Problems

[0005] This invention is characterized by securely transmitting multiple types of health-related data acquired from a user device to a server and integrating that data to generate a dataset. The generated dataset is analyzed in real time using machine learning technology. Based on the analysis results, a health management program optimized for each user's needs is automatically generated and notified to the user. Furthermore, the program is continuously optimized by utilizing feedback provided by the user in program generation. In this way, this invention provides a system that improves the accuracy of understanding and managing the user's health status.

[0006] "User data" refers to information related to a user's health status, such as personal identification information, activity level, vital signs, and location information.

[0007] "Device" refers to terminals such as smartphones and wearable devices used to collect health-related data from users.

[0008] A "dataset" refers to a user-specific collection of information constructed by integrating different types of health-related data.

[0009] "Machine learning technology" refers to a type of artificial intelligence technology used to analyze user data and detect patterns and trends.

[0010] A "health management program" refers to a set of behavioral guidelines, such as exercise, diet, and stress management, that are automatically generated based on analyzed user data.

[0011] An "interface" refers to a visual or interactive means on a device for a user to receive generated programs and feedback.

[0012] "Feedback" refers to the results and opinions that users provide regarding the proposed health management program. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0016] In the following embodiments, a tagged processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

[0019] In the following embodiments, the 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).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] Embodiments of the present invention are systems for providing specific health management programs to individual users, and are carried out by the following process.

[0035] First, the device collects health-related data from the user through their smartphone or wearable device. This includes, for example, steps taken, heart rate, sleep duration, and location information. The device then transmits this data to a server using a secure communication protocol.

[0036] Next, the server integrates the received data for each user to generate a comprehensive dataset. This makes it possible to get a chronological overview of the user's health status. After data cleansing and preprocessing, the generated dataset is analyzed by machine learning algorithms.

[0037] The server analyzes and evaluates the user's current health status. Based on this, it generates a health management program tailored to each user's needs. The generated program includes a wide range of content, such as exercise recommendations, dietary advice, and relaxation methods. This program is written in natural language in a format that the user can immediately execute, and notifications are sent to the device.

[0038] Furthermore, users can provide feedback when they perform the provided program. This feedback, such as the frequency of activity and perceived effectiveness, is sent to the server via the device.

[0039] Ultimately, the server receives feedback and incorporates that information into the next health management program generated. This cycle enables the service to be continuously optimized for the user. This embodiment allows users to manage their own health more effectively and improve their quality of life.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device collects health-related data via the user's smartphone or wearable device. This data includes steps taken, heart rate, location information, and sleep patterns. The collected data is temporarily stored on the device.

[0043] Step 2:

[0044] The device prepares the collected data for transmission to the server. The data is encrypted to protect privacy and sent to the server using a secure communication protocol.

[0045] Step 3:

[0046] The server integrates the received health data for analysis. It combines data from multiple data sources into time-series datasets for each user. This process includes noise reduction and outlier removal.

[0047] Step 4:

[0048] The server analyzes the integrated dataset using machine learning techniques. The goal of the analysis is to assess the user's health status in real time and identify important patterns and trends.

[0049] Step 5:

[0050] Based on the analysis results, the server generates a health management program optimized for the user's individual needs. This program may include, for example, exercise recommendations, dietary advice, and mental health care suggestions.

[0051] Step 6:

[0052] The server converts the generated health management program into natural language and notifies the user's device in an easy-to-understand format. This allows users to smoothly implement specific health improvement activities in their daily lives.

[0053] Step 7:

[0054] The user completes the provided health management program and enters feedback, including results and impressions, into the device. The device then sends this feedback back to the server.

[0055] Step 8:

[0056] The server adjusts its analysis algorithm based on the feedback it receives, improving the accuracy of the next health management program it generates. This, in turn, improves the quality of ongoing service provided to the user.

[0057] (Example 1)

[0058] 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."

[0059] In modern society, health management is increasingly important, but finding effective, personalized health management methods is difficult. Traditional methods fail to provide accurate suggestions based on the user's health condition, making efficient health maintenance challenging. Furthermore, there is a need to utilize collected data to continuously provide users with optimal advice.

[0060] 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.

[0061] In this invention, the server includes means for analyzing the user's behavior patterns, means for evaluating the user's data in real time, and means for creating a health management plan using machine learning technology. This enables personalized health management tailored to each user.

[0062] "User" refers to an individual who uses this system for health management.

[0063] "Device" refers to a device used to collect health-related information and transmit it to a server.

[0064] "Information" refers to all data related to the user's health status, specifically including steps taken, heart rate, sleep duration, and location information.

[0065] "Analysis methods" refer to the methods and techniques used to process collected information and to evaluate the health status and analyze behavioral patterns of users.

[0066] A "health management plan" refers to a series of plans that include various activities and behavioral guidelines proposed to improve the health status of users.

[0067] "Information display means" refers to an interface for displaying health management plans and notifications to users.

[0068] "Evaluation" refers to the assessments and opinions that users provide regarding the implementation of their health management plan.

[0069] "Transmission means" refers to the technologies and protocols used to securely transmit information from a device to a server.

[0070] A "generative AI model" refers to an algorithm or model that uses machine learning to predict a user's health status and propose an optimal health management plan.

[0071] One embodiment of the present invention is a system that provides personalized health management. This system aims to support the user's health maintenance by collecting and analyzing the user's health-related information and generating an optimal health management plan.

[0072] The terminal collects data related to the user's health using devices such as smartphones and wearable devices. This data includes steps taken, heart rate, sleep duration, and location information. The terminal securely transmits this data to a central server using Bluetooth or Wi-Fi.

[0073] The server uses machine learning libraries such as Scikit-learn and TENSORFLOW® to integrate and analyze received data for each target. Specifically, it performs data cleansing and preprocessing to create a model for predicting the user's health status. Using the generated model, it constructs a health management plan optimized for each user. This plan includes exercise recommendations, suggestions for moderate eating, and guidance on relaxation techniques.

[0074] Users receive a health management plan provided through their device and actually perform activities based on that plan. Users can send feedback on their impressions and areas for improvement, which can then be incorporated into the next plan. This feedback is processed again on the server and contributes to the optimization of the entire system.

[0075] As a concrete example, the prompt message to the generating AI model might be set as follows: "Based on User A's health data for this week, please generate a recommended exercise plan for next week." Using such prompts enables detailed health support that matches the user's needs.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The device collects health data using the user's smartphone or wearable device. This data includes steps taken, heart rate, sleep duration, and location information. The device formats this data and transmits it to a server via Bluetooth or Wi-Fi. The output is encrypted data packets using a secure protocol.

[0079] Step 2:

[0080] The server integrates the data received from the terminals. The input consists of various data formats from multiple users. The server cleanses the data, including imputing missing values ​​and removing outliers. The formatted data is output as a consistent dataset for each user.

[0081] Step 3:

[0082] The server inputs the dataset into a machine learning algorithm and performs the analysis. Specifically, it uses Scikit-learn and TensorFlow to build time series analysis and classification models. The output of this step is a prediction of each user's health status.

[0083] Step 4:

[0084] The server generates a personalized health management plan based on the analysis results. The input is the prediction results obtained through the analysis, which are used to create a plan that includes specific exercise suggestions and dietary recommendations, utilizing a powerful AI model. The output is the health management plan in content format, which is then provided to the user.

[0085] Step 5:

[0086] The terminal notifies the user of the health management plan sent from the server. The input here is plan data from the server, and the terminal visually displays the information through an application. The output is a list of tasks and guidelines that the user can execute.

[0087] Step 6:

[0088] Users perform activities based on the notified health management plan and provide feedback. Input consists of the user's thoughts and evaluations of the completed plan and its results. This information is sent to the server via the device and becomes feedback data that is incorporated into the next plan.

[0089] Step 7:

[0090] The server receives user feedback and incorporates it into generating the next health management plan. The input is user feedback data, which is analyzed and used to identify areas for improvement, which are then fed back into the model. The output is a more optimized health management plan for the next session.

[0091] (Application Example 1)

[0092] 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."

[0093] In modern society, it is becoming increasingly important for individual users to manage their own health. However, conventional health management systems often offer uniform suggestions and fail to adequately address individual needs. Furthermore, there are limited ways to provide continuous health support within the home, and coordination with family members can be difficult.

[0094] 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.

[0095] In this invention, the server includes means for collecting diverse health-related data from a device that acquires user data; means for generating and integrating sets of past and present user data; means for applying a data analysis method for real-time evaluation of progress; means for generating individually optimized health management plans based on the analysis results; means for providing an interface for presenting the proposed plan to the user; means for collecting responses from the user and utilizing them for generating the next plan; and means for a home robot to support the user's life and provide health data via voice. This enables optimal health management for each individual user and allows for continuous health support within the home.

[0096] A "device for acquiring user data" refers to a terminal, such as a smartphone or wearable device, that collects health-related information from users.

[0097] "Diverse health-related data" refers to a collection of information necessary to evaluate a user's health status, such as steps taken, heart rate, sleep duration, and location information.

[0098] "Means for generating and integrating data sets" refers to the process of organizing multiple types of collected user data chronologically and combining them into a single, comprehensive dataset.

[0099] "Data analysis methods for immediate progress evaluation" refers to technologies that process collected data in real time and instantly analyze the user's health status.

[0100] A "personally optimized health management plan" is a routine and advice designed to maintain and improve the health of each user, based on their individual health status and lifestyle.

[0101] "Means of providing an interface" refers to a point of contact with the user to effectively notify them of the generated health management plan and encourage them to implement it.

[0102] "Means for collecting responses and using them to generate the next plan" refers to a system that obtains user feedback and uses it to inform the next health management plan.

[0103] A "household robot" is an autonomous device installed in a home that provides various forms of support to its inhabitants.

[0104] "A means of providing health data via voice" refers to a voice output function that can convey analysis results and advice to users in voice.

[0105] This invention is built around a home robot as a system to support health management within the home. The server collects and manages various health-related data from devices that acquire user data. This generates and integrates a set of data for evaluating the user's health status, such as the user's heart rate, steps taken, and sleep duration.

[0106] The server processes this data in real time and applies machine learning algorithms using software such as Python and TensorFlow. This allows for immediate evaluation of the user's progress. Based on the analysis results, it also generates a health management plan optimized for each user and forms suggestions for maintaining and improving health that meet the user's needs.

[0107] Through its interface, the home robot notifies the user via voice about the generated health management plan. This robot uses voice recognition technology similar to Google Assistant, enabling it to receive user instructions and feedback.

[0108] Users can provide feedback to the robot, sharing their opinions and feelings, and this response is sent to the server. The server uses this feedback to inform the next health management plan, enabling more effective health management by continuously optimizing the service for the user.

[0109] For example, if a user uses a caregiving robot on a daily basis, in the morning the robot will notify the user based on their sleep data and activity data from the previous day, saying something like, "Your condition this morning is good. We recommend a 30-minute walk today." If the user then tells the robot that they would like to exercise a little more, the server can incorporate that information into the next suggestion.

[0110] An example of a prompt to a generative AI model is, "Based on recent data, suggest the best health management program for this user." This prompt prompts the server to use machine learning to generate a personalized health program.

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

[0112] Step 1:

[0113] The device collects health-related data through the user's smartphone or wearable device. Inputs include steps taken, heart rate, sleep duration, and location information, which the device sends to a server in batches. Outputs are formalized data packets.

[0114] Step 2:

[0115] The server analyzes the received data and generates and integrates data sets for each user. The input is data packets sent from the terminal. The server cleanses the data, filters out abnormal values, and then arranges the data in chronological order to obtain an integrated dataset as output.

[0116] Step 3:

[0117] The server uses machine learning algorithms to analyze health status based on collected data. The input is an integrated dataset, and model analysis is performed using TensorFlow. The output is an indicator showing the user's health status and its fluctuations.

[0118] Step 4:

[0119] The server generates an individually optimized health management program based on the analysis results. The input is an indicator of health status, and the output is a specific health program tailored to the user's needs, including exercise, diet, and relaxation. The server constructs this program using a generative AI model through natural language processing.

[0120] Step 5:

[0121] The server notifies the user of the generated health management program via an interface. The input is the health management program, and the output is a voice or text notification to the user. The user is notified by a home robot reading the program aloud.

[0122] Step 6:

[0123] After completing the proposed health management program, the user provides feedback to the home robot. The input is the user's execution status and impressions, and the output is feedback data sent to the server via the robot.

[0124] Step 7:

[0125] The server analyzes user feedback and incorporates it into subsequent health management programs. The input is feedback data, and the output is an optimized health management program. This allows for continuous optimization for each user, enabling more effective health management.

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

[0127] Embodiments of the present invention are systems that provide a comprehensive health management program that takes into account not only the user's physical condition but also their emotional state.

[0128] First, the device collects health-related data such as steps taken, heart rate, and location information from the user's smartphone or wearable device. In addition, an emotion engine acquires the user's voice and facial expression data and estimates their emotions in real time. For example, by analyzing the tone of the user's voice when they speak and changes in facial expressions detected by the camera, the emotion engine estimates the user's stress level and emotional state.

[0129] Next, the server integrates health-related data and emotional data obtained from the user. Based on this integrated data, a machine learning algorithm is used to evaluate the user's current health and emotional state and generate a personalized health management program. In this process, the program content is dynamically adjusted; for example, if the system determines that the user is experiencing stress, it will proactively suggest relaxing activities and advice to alleviate stress.

[0130] The generated health management program is translated into natural language and notified to the user via their device. This notification is presented in a way that users can easily implement in their daily lives, and includes, for example, relaxation advice. This approach allows users to receive support not only for their physical health but also for their emotional well-being.

[0131] Furthermore, users provide feedback on the results of the health programs they have completed and changes in their emotions, which is used to generate future programs. The server analyzes this feedback to optimize the overall system algorithm. This loop improves the accuracy and relevance of suggestions to users over time.

[0132] Thus, by using the system of the present invention, users can comprehensively manage both their physical and emotional states and improve their quality of life.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The device collects user health-related and emotional data via smartphones and wearable devices. Health-related data includes steps taken, heart rate, and location information, while emotional data includes voice tone and facial expressions. The device utilizes voice recognition and a camera to transmit the user's voice and facial expressions to the emotion engine.

[0136] Step 2:

[0137] The device encrypts the collected health and emotional data and transmits it to the server via a secure network. This data is tagged with a user ID and timestamp to facilitate data organization during subsequent integration processing.

[0138] Step 3:

[0139] The server integrates the received data and creates detailed datasets for each user. It then checks the consistency of health and emotional data included in the integrated dataset, preparing it for accurate data analysis.

[0140] Step 4:

[0141] The server applies machine learning algorithms to the integrated dataset to assess the user's health and emotional state in real time. This analysis places particular emphasis on the user's stress levels and well-being, identifying areas for improvement.

[0142] Step 5:

[0143] Based on the analysis results, the server generates a health management program optimized for the user. If the user's emotional state is unstable, it suggests activities such as relaxation and stress reduction; if their health is good, it provides lifestyle advice to maintain their current state.

[0144] Step 6:

[0145] The server translates the generated program into natural language and notifies the user's device in a format that is intuitively understandable. The notification includes specific action suggestions, designed to be easily incorporated into the user's daily routine.

[0146] Step 7:

[0147] Users perform the suggested program and provide feedback on the results, their experiences, and any changes in their physical condition. This feedback, including the effectiveness of the activity and their wishes for the next time, is submitted to the server via their device.

[0148] Step 8:

[0149] The server updates its analysis algorithm based on user feedback, improving the accuracy of subsequent health management programs. This iterative process ensures that users continue to receive the most optimal service over time.

[0150] (Example 2)

[0151] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0152] In modern society, it is difficult for individuals to effectively manage their physical and emotional states simultaneously. Traditional methods often analyze health and emotional data separately, lacking integrated support. Furthermore, there is a need for efficient systems to generate personalized health management programs.

[0153] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0154] In this invention, the server includes means for integrating health-related data with voice and facial expression data to generate a data profile, means for analyzing the data using a generative AI model to generate a personalized health management program, and means for incorporating a learning algorithm to collect user feedback and utilize it for system optimization. This makes it possible to comprehensively manage both an individual's health and emotions and improve their quality of life.

[0155] A "user device" is a portable computing device used by an individual, including smartphones and wearable technology. This makes it possible to acquire the user's physiological and emotional data.

[0156] "Physiological data" refers to information that indicates an individual's physical condition, and includes things like step count, heart rate, and location information.

[0157] "Emotional data" refers to information used to evaluate an individual's mental state, and is data related to emotions estimated based on changes in voice and facial expressions.

[0158] A "generative AI model" is an algorithm that performs machine learning based on a large amount of data, and its role is to analyze the user's data profile and generate a personalized health management program.

[0159] A "data profile" is a general term for information that integrates health data and emotional data about a user, and is used to evaluate the individual user's state.

[0160] A "health management program" is a set of activities and advice suggested based on the user's health and emotional state, and is characterized by its individualized nature.

[0161] "Natural language processing technology" is a technology that enables computers to understand and generate natural human language, and it is used when communicating health management programs to users.

[0162] A "learning algorithm" is a computational method that learns from feedback and aims to improve the performance of a system, and is incorporated with the purpose of improving the accuracy of program proposals.

[0163] Embodiments of the present invention are advanced health management systems that integrate and manage a user's physical and emotional state. This system operates using a user device, a server, and a generative AI model.

[0164] First, the terminal uses a smartphone or wearable device as the user device to collect physiological and emotional data from the user. Physiological data includes steps taken and heart rate, which are acquired by built-in sensors. Emotional data is acquired via the device's camera and microphone and estimated based on changes in voice tone and facial expressions.

[0165] Next, the server receives this data and integrates the health and emotional data together. This integrated data profile is used to comprehensively assess the user's health and emotional state. A generative AI model running on the server analyzes the data profile using machine learning algorithms and generates a health management program optimized for each user.

[0166] The generated program is communicated to the user via the terminal in natural language. This communication may include suggestions for specific breathing techniques or exercises for relaxation. This allows the user to receive practical advice for improving their health.

[0167] Furthermore, after users complete the provided health management program, they provide feedback on the results and changes in their emotions. The server analyzes this feedback and optimizes the algorithm of the generated AI model. This feedback loop improves the accuracy and relevance of the proposed program.

[0168] As a concrete example, here are some prompt statements that can be input to a generative AI model:

[0169] "Suggest ways for users to relax."

[0170] "Estimate stress levels from heart rate and facial expression data."

[0171] In this way, this system comprehensively addresses both the user's health and emotions, providing concrete support to improve their quality of life.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The terminal collects physiological and emotional data from the user's device. Specifically, it uses built-in sensors to acquire physiological data such as steps and heart rate, and cameras and microphones to acquire voice and facial expression data. Inputs include numerical data and voice / image data obtained from each sensor. This data is then converted into a digital format and prepared for transmission to the server.

[0175] Step 2:

[0176] The server receives physiological and emotional data transmitted from the terminal and integrates them to generate a data profile. The input data consists of numerical data as physiological data and audio / image data as emotional data. By analyzing this data, the server understands the user's current health and mental state and integrates it as a data profile. This data profile forms the basis for subsequent processing steps.

[0177] Step 3:

[0178] The server uses a generative AI model to analyze data profiles and generate personalized health management programs. The input to this process is an integrated data profile, and the output is a user-specific health management program. The generative AI model compares past data with the user's current state and provides specific suggestions for health improvement. This determines the optimal advice and exercise program tailored to the user's needs.

[0179] Step 4:

[0180] The device translates the health management program sent from the server into natural language and notifies the user. The input is the generated health management program, and the output is a notification in natural language format that is easy for the user to understand. The device provides the notification visually and aurally, or audibly, to help the user take immediate action.

[0181] Step 5:

[0182] Users complete the provided health management program and provide feedback on the results via a terminal. The input consists of changes in their health status and emotions after completing the program, which are returned to the feedback system as qualitative or quantitative data. This feedback is used to improve the accuracy of future program generation.

[0183] Step 6:

[0184] The server analyzes user feedback and modifies and optimizes the generated AI model. The input is user-provided feedback information, and the output is the improved program generation algorithm. This optimization improves the system's suggestion accuracy over time, enabling it to provide health management programs that are more tailored to the user.

[0185] (Application Example 2)

[0186] 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".

[0187] In modern times, health management and emotional management are closely related, but conventional systems have been unable to comprehensively evaluate them and provide individually optimized programs. Furthermore, there is a demand for real-time monitoring of health and emotional states and the provision of personalized management plans, but the technology to adequately address this has not yet been established.

[0188] 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.

[0189] In this invention, the server includes means for collecting health-related information from a data acquisition device, means for generating and integrating a set of past and present information of the user, and means for performing voice analysis and facial expression analysis to estimate the emotional state. This makes it possible to comprehensively evaluate the health and emotional state of each individual user and provide them with an individually optimized management plan in real time.

[0190] A "data acquisition device" is a device that collects health-related information from users, and includes wearable devices and mobile terminals.

[0191] An "information set" is a collection of data that integrates a user's past and present health and emotional data.

[0192] "Voice analysis" is a technology that estimates a user's emotional state by analyzing the tone and manner of speaking of their voice.

[0193] "Facial expression analysis" is a technology that recognizes and analyzes a user's facial expressions to estimate their emotional state.

[0194] A "server" is a computing device responsible for data collection and analysis, processing large amounts of data and generating individually optimized management plans.

[0195] A "management plan" is a plan that includes individually optimized health and emotional management suggestions based on the user's health and emotional state.

[0196] To implement this invention, it is necessary to collect health-related data and emotional data using a wearable device worn by the user or a mobile device used (e.g., a smartphone). The collected data, including heart rate, steps taken, voice, and facial expressions, is transmitted to a server to evaluate the user's health and emotional state.

[0197] The server receives this data and estimates the emotional state using voice and facial expression analysis. Furthermore, it integrates past and present health data and uses machine learning algorithms to evaluate the user's health and emotional state. Based on this evaluation, the server creates an individually optimized health and emotional management plan. This allows the server to provide users with real-time suggestions for optimal health and emotional management.

[0198] For example, if a user exhibits a high stress level and a higher-than-normal heart rate, the server generates a program recommending relaxation exercises and notifies the user via the terminal. This system can support both the user's physical and emotional state, thereby improving the quality of care.

[0199] For example, if the system detects that resident A in a nursing care facility is experiencing stress, it will suggest activities to promote relaxation.

[0200] Examples of prompts to input into a generative AI model are as follows:

[0201] "We are developing an application that monitors the health and emotional state of residents in a nursing home and proposes the optimal care program. Please tell us how to acquire data from wearable devices and how to use machine learning to generate the program."

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The device acquires health data (heart rate, steps, etc.), voice data, and facial expression data from wearable devices and mobile terminals. This data is collected through the device's sensors, microphone, and camera. Health data and emotional data are inputs, and these data are aggregated as outputs.

[0205] Step 2:

[0206] The terminal sends the collected data to the server. A secure communication protocol is used for transmission. In this step, the terminal receives the aggregated data as input and sends that data to the server as output. The data is encrypted and transmitted to the server over the internet.

[0207] Step 3:

[0208] The server analyzes the received data, performing speech analysis on the audio data and facial expression analysis on the facial expression data. The input is the audio and facial expression data sent to the server, and the output is the estimated emotional state. A speech analysis engine and a facial recognition algorithm are used here.

[0209] Step 4:

[0210] The server integrates analyzed health data and estimated emotional states, and uses machine learning algorithms to evaluate the user's health and emotional state. The input is the integrated health and emotional data, and the output is the evaluated user state. A machine learning model based on historical datasets is used to evaluate the data.

[0211] Step 5:

[0212] The server generates individually optimized health and emotional management plans based on the assessment. The input is the user's health and emotional assessment results, and the output is the optimized management plan. This plan includes specific action plans tailored to the user's condition.

[0213] Step 6:

[0214] The generated management plan is notified to the user via the terminal. The input is the generated management plan, and the output is the notification message to the user. It is displayed in a format that is easy for the user to understand.

[0215] Step 7:

[0216] The user acts according to the provided plan and inputs the results of their actions and changes in their emotions as feedback into the terminal. The input is user feedback data, and the output is that same feedback data. This data is sent to the server as feedback so that it can be used to generate the next program.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] Embodiments of the present invention are systems for providing specific health management programs to individual users, and are carried out by the following process.

[0234] First, the device collects health-related data from the user through their smartphone or wearable device. This includes, for example, steps taken, heart rate, sleep duration, and location information. The device then transmits this data to a server using a secure communication protocol.

[0235] Next, the server integrates the received data for each user to generate a comprehensive dataset. This makes it possible to get a chronological overview of the user's health status. After data cleansing and preprocessing, the generated dataset is analyzed by machine learning algorithms.

[0236] The server analyzes and evaluates the user's current health status. Based on this, it generates a health management program tailored to each user's needs. The generated program includes a wide range of content, such as exercise recommendations, dietary advice, and relaxation methods. This program is written in natural language in a format that the user can immediately execute, and notifications are sent to the device.

[0237] Furthermore, users can provide feedback when they perform the provided program. This feedback, such as the frequency of activity and perceived effectiveness, is sent to the server via the device.

[0238] Ultimately, the server receives feedback and incorporates that information into the next health management program generated. This cycle enables the service to be continuously optimized for the user. This embodiment allows users to manage their own health more effectively and improve their quality of life.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The device collects health-related data via the user's smartphone or wearable device. This data includes steps taken, heart rate, location information, and sleep patterns. The collected data is temporarily stored on the device.

[0242] Step 2:

[0243] The device prepares the collected data for transmission to the server. The data is encrypted to protect privacy and sent to the server using a secure communication protocol.

[0244] Step 3:

[0245] The server integrates the received health data for analysis. It combines data from multiple data sources into time-series datasets for each user. This process includes noise reduction and outlier removal.

[0246] Step 4:

[0247] The server analyzes the integrated dataset using machine learning techniques. The goal of the analysis is to assess the user's health status in real time and identify important patterns and trends.

[0248] Step 5:

[0249] Based on the analysis results, the server generates a health management program optimized for the user's individual needs. This program may include, for example, exercise recommendations, dietary advice, and mental health care suggestions.

[0250] Step 6:

[0251] The server converts the generated health management program into natural language and notifies the user's device in an easy-to-understand format. This allows users to smoothly implement specific health improvement activities in their daily lives.

[0252] Step 7:

[0253] The user completes the provided health management program and enters feedback, including results and impressions, into the device. The device then sends this feedback back to the server.

[0254] Step 8:

[0255] The server adjusts its analysis algorithm based on the feedback it receives, improving the accuracy of the next health management program it generates. This, in turn, improves the quality of ongoing service provided to the user.

[0256] (Example 1)

[0257] 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."

[0258] In modern society, health management is increasingly important, but finding effective, personalized health management methods is difficult. Traditional methods fail to provide accurate suggestions based on the user's health condition, making efficient health maintenance challenging. Furthermore, there is a need to utilize collected data to continuously provide users with optimal advice.

[0259] 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.

[0260] In this invention, the server includes means for analyzing the user's behavior patterns, means for evaluating the user's data in real time, and means for creating a health management plan using machine learning technology. This enables personalized health management tailored to each user.

[0261] "User" refers to an individual who uses this system for health management.

[0262] "Device" refers to a device used to collect health-related information and transmit it to a server.

[0263] "Information" refers to all data related to the user's health status, specifically including steps taken, heart rate, sleep duration, and location information.

[0264] "Analysis methods" refer to the methods and techniques used to process collected information and to evaluate the health status and analyze behavioral patterns of users.

[0265] A "health management plan" refers to a series of plans that include various activities and behavioral guidelines proposed to improve the health status of users.

[0266] "Information display means" refers to an interface for displaying health management plans and notifications to users.

[0267] "Evaluation" refers to the assessments and opinions that users provide regarding the implementation of their health management plan.

[0268] "Transmission means" refers to the technologies and protocols used to securely transmit information from a device to a server.

[0269] A "generative AI model" refers to an algorithm or model that uses machine learning to predict a user's health status and propose an optimal health management plan.

[0270] One embodiment of the present invention is a system that provides personalized health management. This system aims to support the user's health maintenance by collecting and analyzing the user's health-related information and generating an optimal health management plan.

[0271] The terminal collects data related to the user's health using devices such as smartphones and wearable devices. This data includes steps taken, heart rate, sleep duration, and location information. The terminal securely transmits this data to a central server using Bluetooth or Wi-Fi.

[0272] The server uses machine learning libraries such as Scikit-learn and TensorFlow to integrate and analyze received data for each target. Specifically, it performs data cleansing and preprocessing to create a model for predicting the user's health status. Using the generated model, it builds a health management plan optimized for each user. This plan includes exercise recommendations, suggestions for moderate eating, and guidance on relaxation techniques.

[0273] Users receive a health management plan provided through their device and actually perform activities based on that plan. Users can send feedback on their impressions and areas for improvement, which can then be incorporated into the next plan. This feedback is processed again on the server and contributes to the optimization of the entire system.

[0274] As a concrete example, the prompt message to the generating AI model might be set as follows: "Based on User A's health data for this week, please generate a recommended exercise plan for next week." Using such prompts enables detailed health support that matches the user's needs.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The device collects health data using the user's smartphone or wearable device. This data includes steps taken, heart rate, sleep duration, and location information. The device formats this data and transmits it to a server via Bluetooth or Wi-Fi. The output is encrypted data packets using a secure protocol.

[0278] Step 2:

[0279] The server integrates the data received from the terminal. The input is data in various formats from multiple users. The server performs data cleansing, such as filling in missing values and removing outliers. The formatted data is output as a dataset with consistency for each user.

[0280] Step 3:

[0281] The server inputs the dataset into a machine learning algorithm for analysis. Specifically, it uses Scikit - learn or TensorFlow to build time - series analysis and classification models. The output at this step is the prediction results indicating the health status of each user.

[0282] Step 4:

[0283] Based on the analysis results, the server generates an optimized health management plan for each user. The input is the prediction results obtained from the analysis, and it uses the generated AI model to create a plan including specific exercise suggestions and diet recommendations. The output is a health management plan in the form of content provided to the user.

[0284] Step 5:

[0285] The terminal notifies the user of the health management plan sent from the server. The input here is the plan data from the server, and the terminal visually displays the information through an application. The output is a task list and guidelines that the user can execute.

[0286] Step 6:

[0287] The user carries out activities based on the notified health management plan and provides feedback. The input is the user's feelings and evaluations regarding the implemented plan and its results. This is sent to the server through the terminal and becomes feedback data reflected in the next plan as the output.

[0288] Step 7:

[0289] The server receives user feedback and incorporates it into generating the next health management plan. The input is user feedback data, which is analyzed and used to identify areas for improvement, which are then fed back into the model. The output is a more optimized health management plan for the next session.

[0290] (Application Example 1)

[0291] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0292] In modern society, it is becoming increasingly important for individual users to manage their own health. However, conventional health management systems often offer uniform suggestions and fail to adequately address individual needs. Furthermore, there are limited ways to provide continuous health support within the home, and coordination with family members can be difficult.

[0293] 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.

[0294] In this invention, the server includes means for collecting diverse health-related data from a device that acquires user data; means for generating and integrating sets of past and present user data; means for applying a data analysis method for real-time evaluation of progress; means for generating individually optimized health management plans based on the analysis results; means for providing an interface for presenting the proposed plan to the user; means for collecting responses from the user and utilizing them for generating the next plan; and means for a home robot to support the user's life and provide health data via voice. This enables optimal health management for each individual user and allows for continuous health support within the home.

[0295] A "device for acquiring user data" refers to a terminal, such as a smartphone or wearable device, that collects health-related information from users.

[0296] "Diverse health-related data" refers to a collection of information necessary to evaluate a user's health status, such as steps taken, heart rate, sleep duration, and location information.

[0297] "Means for generating and integrating data sets" refers to the process of organizing multiple types of collected user data chronologically and combining them into a single, comprehensive dataset.

[0298] "Data analysis methods for immediate progress evaluation" refers to technologies that process collected data in real time and instantly analyze the user's health status.

[0299] A "personally optimized health management plan" is a routine and advice designed to maintain and improve the health of each user, based on their individual health status and lifestyle.

[0300] "Means of providing an interface" refers to a point of contact with the user to effectively notify them of the generated health management plan and encourage them to implement it.

[0301] "Means for collecting responses and using them to generate the next plan" refers to a system that obtains user feedback and uses it to inform the next health management plan.

[0302] A "household robot" is an autonomous device installed in a home that provides various forms of support to its inhabitants.

[0303] "A means of providing health data via voice" refers to a voice output function that can convey analysis results and advice to users in voice.

[0304] This invention is constructed around a household robot as a system for supporting health management within the home. The server collects various health-related data from devices that acquire user data and manages it. Thereby, a group of data for evaluating the user's health state, such as the user's heart rate, number of steps, sleep time, etc., is generated and integrated.

[0305] The server processes these data in real time and applies machine learning algorithms using software such as Python and TensorFlow. Thereby, an immediate evaluation of the user's progress is carried out. Also, based on the results of the analysis, a health management plan optimized individually for each user is generated, and proposals for maintaining and improving health according to the user's needs are formed.

[0306] Through the interface, the household robot notifies the user of the generated health management plan by voice. This robot uses voice recognition technology such as Google Assistant and can receive the user's instructions and feedback.

[0307] The user can provide feedback to the robot about their opinions and feelings, and the response is sent to the server. The server utilizes this feedback in the next health management plan and provides continuously optimized services to the user, enabling more effective health management.

[0308] As a specific example, when a user uses a care robot daily, in the morning, based on the user's sleep data and the previous day's activity data, the robot gives a notification such as "You are in good condition this morning. I recommend 30 minutes of walking today." At this time, if the user tells the robot "I want to exercise a little more," the server can reflect that information in the next proposal.

[0309] An example of a prompt to a generative AI model is, "Based on recent data, suggest the best health management program for this user." This prompt prompts the server to use machine learning to generate a personalized health program.

[0310] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0311] Step 1:

[0312] The device collects health-related data through the user's smartphone or wearable device. Inputs include steps taken, heart rate, sleep duration, and location information, which the device sends to a server in batches. Outputs are formalized data packets.

[0313] Step 2:

[0314] The server analyzes the received data and generates and integrates data sets for each user. The input is data packets sent from the terminal. The server cleanses the data, filters out abnormal values, and then arranges the data in chronological order to obtain an integrated dataset as output.

[0315] Step 3:

[0316] The server uses machine learning algorithms to analyze health status based on collected data. The input is an integrated dataset, and model analysis is performed using TensorFlow. The output is an indicator showing the user's health status and its fluctuations.

[0317] Step 4:

[0318] The server generates an individually optimized health management program based on the analysis results. The input is an indicator of health status, and the output is a specific health program tailored to the user's needs, including exercise, diet, and relaxation. The server constructs this program using a generative AI model through natural language processing.

[0319] Step 5:

[0320] The server notifies the user of the generated health management program via an interface. The input is the health management program, and the output is a voice or text notification to the user. The user is notified by a home robot reading the program aloud.

[0321] Step 6:

[0322] After completing the proposed health management program, the user provides feedback to the home robot. The input is the user's execution status and impressions, and the output is feedback data sent to the server via the robot.

[0323] Step 7:

[0324] The server analyzes user feedback and incorporates it into subsequent health management programs. The input is feedback data, and the output is an optimized health management program. This allows for continuous optimization for each user, enabling more effective health management.

[0325] 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.

[0326] Embodiments of the present invention are systems that provide a comprehensive health management program that takes into account not only the user's physical condition but also their emotional state.

[0327] First, the device collects health-related data such as steps taken, heart rate, and location information from the user's smartphone or wearable device. In addition, an emotion engine acquires the user's voice and facial expression data and estimates their emotions in real time. For example, by analyzing the tone of the user's voice when they speak and changes in facial expressions detected by the camera, the emotion engine estimates the user's stress level and emotional state.

[0328] Next, the server integrates health-related data and emotional data obtained from the user. Based on this integrated data, a machine learning algorithm is used to evaluate the user's current health and emotional state and generate a personalized health management program. In this process, the program content is dynamically adjusted; for example, if the system determines that the user is experiencing stress, it will proactively suggest relaxing activities and advice to alleviate stress.

[0329] The generated health management program is translated into natural language and notified to the user via their device. This notification is presented in a way that users can easily implement in their daily lives, and includes, for example, relaxation advice. This approach allows users to receive support not only for their physical health but also for their emotional well-being.

[0330] Furthermore, users provide feedback on the results of the health programs they have completed and changes in their emotions, which is used to generate future programs. The server analyzes this feedback to optimize the overall system algorithm. This loop improves the accuracy and relevance of suggestions to users over time.

[0331] Thus, by using the system of the present invention, users can comprehensively manage both their physical and emotional states and improve their quality of life.

[0332] The following describes the processing flow.

[0333] Step 1:

[0334] The device collects user health-related and emotional data via smartphones and wearable devices. Health-related data includes steps taken, heart rate, and location information, while emotional data includes voice tone and facial expressions. The device utilizes voice recognition and a camera to transmit the user's voice and facial expressions to the emotion engine.

[0335] Step 2:

[0336] The device encrypts the collected health and emotional data and transmits it to the server via a secure network. This data is tagged with a user ID and timestamp to facilitate data organization during subsequent integration processing.

[0337] Step 3:

[0338] The server integrates the received data and creates detailed datasets for each user. It then checks the consistency of health and emotional data included in the integrated dataset, preparing it for accurate data analysis.

[0339] Step 4:

[0340] The server applies machine learning algorithms to the integrated dataset to assess the user's health and emotional state in real time. This analysis places particular emphasis on the user's stress levels and well-being, identifying areas for improvement.

[0341] Step 5:

[0342] Based on the analysis results, the server generates a health management program optimized for the user. If the user's emotional state is unstable, it suggests activities such as relaxation and stress reduction; if their health is good, it provides lifestyle advice to maintain their current state.

[0343] Step 6:

[0344] The server translates the generated program into natural language and notifies the user's device in a format that is intuitively understandable. The notification includes specific action suggestions, designed to be easily incorporated into the user's daily routine.

[0345] Step 7:

[0346] Users perform the suggested program and provide feedback on the results, their experiences, and any changes in their physical condition. This feedback, including the effectiveness of the activity and their wishes for the next time, is submitted to the server via their device.

[0347] Step 8:

[0348] The server updates its analysis algorithm based on user feedback, improving the accuracy of subsequent health management programs. This iterative process ensures that users continue to receive the most optimal service over time.

[0349] (Example 2)

[0350] 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".

[0351] In modern society, it is difficult for individuals to effectively manage their physical and emotional states simultaneously. Traditional methods often analyze health and emotional data separately, lacking integrated support. Furthermore, there is a need for efficient systems to generate personalized health management programs.

[0352] 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.

[0353] In this invention, the server includes means for integrating health-related data with voice and facial expression data to generate a data profile, means for analyzing the data using a generative AI model to generate a personalized health management program, and means for incorporating a learning algorithm to collect user feedback and utilize it for system optimization. This makes it possible to comprehensively manage both an individual's health and emotions and improve their quality of life.

[0354] A "user device" is a portable computing device used by an individual, including smartphones and wearable technology. This makes it possible to acquire the user's physiological and emotional data.

[0355] "Physiological data" refers to information that indicates an individual's physical condition, and includes things like step count, heart rate, and location information.

[0356] "Emotional data" refers to information used to evaluate an individual's mental state, and is data related to emotions estimated based on changes in voice and facial expressions.

[0357] A "generative AI model" is an algorithm that performs machine learning based on a large amount of data, and its role is to analyze the user's data profile and generate a personalized health management program.

[0358] A "data profile" is a general term for information that integrates health data and emotional data about a user, and is used to evaluate the individual user's state.

[0359] A "health management program" is a set of activities and advice suggested based on the user's health and emotional state, and is characterized by its individualized nature.

[0360] "Natural language processing technology" is a technology that enables computers to understand and generate natural human language, and it is used when communicating health management programs to users.

[0361] A "learning algorithm" is a computational method that learns from feedback and aims to improve the performance of a system, and is incorporated with the purpose of improving the accuracy of program proposals.

[0362] Embodiments of the present invention are advanced health management systems that integrate and manage a user's physical and emotional state. This system operates using a user device, a server, and a generative AI model.

[0363] First, the terminal uses a smartphone or wearable device as the user device to collect physiological and emotional data from the user. Physiological data includes steps taken and heart rate, which are acquired by built-in sensors. Emotional data is acquired via the device's camera and microphone and estimated based on changes in voice tone and facial expressions.

[0364] Next, the server receives this data and integrates the health and emotional data together. This integrated data profile is used to comprehensively assess the user's health and emotional state. A generative AI model running on the server analyzes the data profile using machine learning algorithms and generates a health management program optimized for each user.

[0365] The generated program is communicated to the user via the terminal in natural language. This communication may include suggestions for specific breathing techniques or exercises for relaxation. This allows the user to receive practical advice for improving their health.

[0366] Furthermore, after users complete the provided health management program, they provide feedback on the results and changes in their emotions. The server analyzes this feedback and optimizes the algorithm of the generated AI model. This feedback loop improves the accuracy and relevance of the proposed program.

[0367] As a concrete example, here are some prompt statements that can be input to a generative AI model:

[0368] "Suggest ways for users to relax."

[0369] "Estimate stress levels from heart rate and facial expression data."

[0370] In this way, this system comprehensively addresses both the user's health and emotions, providing concrete support to improve their quality of life.

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] The terminal collects physiological and emotional data from the user's device. Specifically, it uses built-in sensors to acquire physiological data such as steps and heart rate, and cameras and microphones to acquire voice and facial expression data. Inputs include numerical data and voice / image data obtained from each sensor. This data is then converted into a digital format and prepared for transmission to the server.

[0374] Step 2:

[0375] The server receives physiological and emotional data transmitted from the terminal and integrates them to generate a data profile. The input data consists of numerical data as physiological data and audio / image data as emotional data. By analyzing this data, the server understands the user's current health and mental state and integrates it as a data profile. This data profile forms the basis for subsequent processing steps.

[0376] Step 3:

[0377] The server uses a generative AI model to analyze data profiles and generate personalized health management programs. The input to this process is an integrated data profile, and the output is a user-specific health management program. The generative AI model compares past data with the user's current state and provides specific suggestions for health improvement. This determines the optimal advice and exercise program tailored to the user's needs.

[0378] Step 4:

[0379] The device translates the health management program sent from the server into natural language and notifies the user. The input is the generated health management program, and the output is a notification in natural language format that is easy for the user to understand. The device provides the notification visually and aurally, or audibly, to help the user take immediate action.

[0380] Step 5:

[0381] Users complete the provided health management program and provide feedback on the results via a terminal. The input consists of changes in their health status and emotions after completing the program, which are returned to the feedback system as qualitative or quantitative data. This feedback is used to improve the accuracy of future program generation.

[0382] Step 6:

[0383] The server analyzes user feedback and modifies and optimizes the generated AI model. The input is user-provided feedback information, and the output is the improved program generation algorithm. This optimization improves the system's suggestion accuracy over time, enabling it to provide health management programs that are more tailored to the user.

[0384] (Application Example 2)

[0385] 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."

[0386] In modern times, health management and emotional management are closely related, but conventional systems have been unable to comprehensively evaluate them and provide individually optimized programs. Furthermore, there is a demand for real-time monitoring of health and emotional states and the provision of personalized management plans, but the technology to adequately address this has not yet been established.

[0387] 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.

[0388] In this invention, the server includes means for collecting health-related information from a data acquisition device, means for generating and integrating a set of past and present information of the user, and means for performing voice analysis and facial expression analysis to estimate the emotional state. This makes it possible to comprehensively evaluate the health and emotional state of each individual user and provide them with an individually optimized management plan in real time.

[0389] A "data acquisition device" is a device that collects health-related information from users, and includes wearable devices and mobile terminals.

[0390] An "information set" is a collection of data that integrates a user's past and present health and emotional data.

[0391] "Voice analysis" is a technology that estimates a user's emotional state by analyzing the tone and manner of speaking of their voice.

[0392] "Facial expression analysis" is a technology that recognizes and analyzes a user's facial expressions to estimate their emotional state.

[0393] A "server" is a computing device responsible for data collection and analysis, processing large amounts of data and generating individually optimized management plans.

[0394] A "management plan" is a plan that includes individually optimized health and emotional management suggestions based on the user's health and emotional state.

[0395] To implement this invention, it is necessary to collect health-related data and emotional data using a wearable device worn by the user or a mobile device used (e.g., a smartphone). The collected data, including heart rate, steps taken, voice, and facial expressions, is transmitted to a server to evaluate the user's health and emotional state.

[0396] The server receives this data and estimates the emotional state using voice and facial expression analysis. Furthermore, it integrates past and present health data and uses machine learning algorithms to evaluate the user's health and emotional state. Based on this evaluation, the server creates an individually optimized health and emotional management plan. This allows the server to provide users with real-time suggestions for optimal health and emotional management.

[0397] For example, if a user exhibits a high stress level and a higher-than-normal heart rate, the server generates a program recommending relaxation exercises and notifies the user via the terminal. This system can support both the user's physical and emotional state, thereby improving the quality of care.

[0398] For example, if the system detects that resident A in a nursing care facility is experiencing stress, it will suggest activities to promote relaxation.

[0399] Examples of prompts to input into a generative AI model are as follows:

[0400] "We are developing an application that monitors the health and emotional state of residents in a nursing home and proposes the optimal care program. Please tell us how to acquire data from wearable devices and how to use machine learning to generate the program."

[0401] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0402] Step 1:

[0403] The device acquires health data (heart rate, steps, etc.), voice data, and facial expression data from wearable devices and mobile terminals. This data is collected through the device's sensors, microphone, and camera. Health data and emotional data are inputs, and these data are aggregated as outputs.

[0404] Step 2:

[0405] The terminal sends the collected data to the server. A secure communication protocol is used for transmission. In this step, the terminal receives the aggregated data as input and sends that data to the server as output. The data is encrypted and transmitted to the server over the internet.

[0406] Step 3:

[0407] The server analyzes the received data, performing speech analysis on the audio data and facial expression analysis on the facial expression data. The input is the audio and facial expression data sent to the server, and the output is the estimated emotional state. A speech analysis engine and a facial recognition algorithm are used here.

[0408] Step 4:

[0409] The server integrates analyzed health data and estimated emotional states, and uses machine learning algorithms to evaluate the user's health and emotional state. The input is the integrated health and emotional data, and the output is the evaluated user state. A machine learning model based on historical datasets is used to evaluate the data.

[0410] Step 5:

[0411] The server generates individually optimized health and emotional management plans based on the assessment. The input is the user's health and emotional assessment results, and the output is the optimized management plan. This plan includes specific action plans tailored to the user's condition.

[0412] Step 6:

[0413] The generated management plan is notified to the user via the terminal. The input is the generated management plan, and the output is the notification message to the user. It is displayed in a format that is easy for the user to understand.

[0414] Step 7:

[0415] The user acts according to the provided plan and inputs the results of their actions and changes in their emotions as feedback into the terminal. The input is user feedback data, and the output is that same feedback data. This data is sent to the server as feedback so that it can be used to generate the next program.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] [Third Embodiment]

[0420] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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".

[0432] Embodiments of the present invention are systems for providing specific health management programs to individual users, and are carried out by the following process.

[0433] First, the device collects health-related data from the user through their smartphone or wearable device. This includes, for example, steps taken, heart rate, sleep duration, and location information. The device then transmits this data to a server using a secure communication protocol.

[0434] Next, the server integrates the received data for each user to generate a comprehensive dataset. This makes it possible to get a chronological overview of the user's health status. After data cleansing and preprocessing, the generated dataset is analyzed by machine learning algorithms.

[0435] The server analyzes and evaluates the user's current health status. Based on this, it generates a health management program tailored to each user's needs. The generated program includes a wide range of content, such as exercise recommendations, dietary advice, and relaxation methods. This program is written in natural language in a format that the user can immediately execute, and notifications are sent to the device.

[0436] Furthermore, users can provide feedback when they perform the provided program. This feedback, such as the frequency of activity and perceived effectiveness, is sent to the server via the device.

[0437] Ultimately, the server receives feedback and incorporates that information into the next health management program generated. This cycle enables the service to be continuously optimized for the user. This embodiment allows users to manage their own health more effectively and improve their quality of life.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] The device collects health-related data via the user's smartphone or wearable device. This data includes steps taken, heart rate, location information, and sleep patterns. The collected data is temporarily stored on the device.

[0441] Step 2:

[0442] The device prepares the collected data for transmission to the server. The data is encrypted to protect privacy and sent to the server using a secure communication protocol.

[0443] Step 3:

[0444] The server integrates the received health data for analysis. It combines data from multiple data sources into time-series datasets for each user. This process includes noise reduction and outlier removal.

[0445] Step 4:

[0446] The server analyzes the integrated dataset using machine learning techniques. The goal of the analysis is to assess the user's health status in real time and identify important patterns and trends.

[0447] Step 5:

[0448] Based on the analysis results, the server generates a health management program optimized for the user's individual needs. This program may include, for example, exercise recommendations, dietary advice, and mental health care suggestions.

[0449] Step 6:

[0450] The server converts the generated health management program into natural language and notifies the user's device in an easy-to-understand format. This allows users to smoothly implement specific health improvement activities in their daily lives.

[0451] Step 7:

[0452] The user completes the provided health management program and enters feedback, including results and impressions, into the device. The device then sends this feedback back to the server.

[0453] Step 8:

[0454] The server adjusts its analysis algorithm based on the feedback it receives, improving the accuracy of the next health management program it generates. This, in turn, improves the quality of ongoing service provided to the user.

[0455] (Example 1)

[0456] 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."

[0457] In modern society, health management is increasingly important, but finding effective, personalized health management methods is difficult. Traditional methods fail to provide accurate suggestions based on the user's health condition, making efficient health maintenance challenging. Furthermore, there is a need to utilize collected data to continuously provide users with optimal advice.

[0458] 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.

[0459] In this invention, the server includes means for analyzing the user's behavior patterns, means for evaluating the user's data in real time, and means for creating a health management plan using machine learning technology. This enables personalized health management tailored to each user.

[0460] "User" refers to an individual who uses this system for health management.

[0461] "Device" refers to a device used to collect health-related information and transmit it to a server.

[0462] "Information" refers to all data related to the user's health status, specifically including steps taken, heart rate, sleep duration, and location information.

[0463] "Analysis methods" refer to the methods and techniques used to process collected information and to evaluate the health status and analyze behavioral patterns of users.

[0464] A "health management plan" refers to a series of plans that include various activities and behavioral guidelines proposed to improve the health status of users.

[0465] "Information display means" refers to an interface for displaying health management plans and notifications to users.

[0466] "Evaluation" refers to the assessments and opinions that users provide regarding the implementation of their health management plan.

[0467] "Transmission means" refers to the technologies and protocols used to securely transmit information from a device to a server.

[0468] A "generative AI model" refers to an algorithm or model that uses machine learning to predict a user's health status and propose an optimal health management plan.

[0469] One embodiment of the present invention is a system that provides personalized health management. This system aims to support the user's health maintenance by collecting and analyzing the user's health-related information and generating an optimal health management plan.

[0470] The terminal collects data related to the user's health using devices such as smartphones and wearable devices. This data includes steps taken, heart rate, sleep duration, and location information. The terminal securely transmits this data to a central server using Bluetooth or Wi-Fi.

[0471] The server uses machine learning libraries such as Scikit-learn and TensorFlow to integrate and analyze received data for each target. Specifically, it performs data cleansing and preprocessing to create a model for predicting the user's health status. Using the generated model, it builds a health management plan optimized for each user. This plan includes exercise recommendations, suggestions for moderate eating, and guidance on relaxation techniques.

[0472] Users receive a health management plan provided through their device and actually perform activities based on that plan. Users can send feedback on their impressions and areas for improvement, which can then be incorporated into the next plan. This feedback is processed again on the server and contributes to the optimization of the entire system.

[0473] As a concrete example, the prompt message to the generating AI model might be set as follows: "Based on User A's health data for this week, please generate a recommended exercise plan for next week." Using such prompts enables detailed health support that matches the user's needs.

[0474] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0475] Step 1:

[0476] The device collects health data using the user's smartphone or wearable device. This data includes steps taken, heart rate, sleep duration, and location information. The device formats this data and transmits it to a server via Bluetooth or Wi-Fi. The output is encrypted data packets using a secure protocol.

[0477] Step 2:

[0478] The server integrates the data received from the terminals. The input consists of various data formats from multiple users. The server cleanses the data, including imputing missing values ​​and removing outliers. The formatted data is output as a consistent dataset for each user.

[0479] Step 3:

[0480] The server inputs the dataset into a machine learning algorithm and performs the analysis. Specifically, it uses Scikit-learn and TensorFlow to build time series analysis and classification models. The output of this step is a prediction of each user's health status.

[0481] Step 4:

[0482] The server generates a personalized health management plan based on the analysis results. The input is the prediction results obtained through the analysis, which are used to create a plan that includes specific exercise suggestions and dietary recommendations, utilizing a powerful AI model. The output is the health management plan in content format, which is then provided to the user.

[0483] Step 5:

[0484] The terminal notifies the user of the health management plan sent from the server. The input here is plan data from the server, and the terminal visually displays the information through an application. The output is a list of tasks and guidelines that the user can execute.

[0485] Step 6:

[0486] Users perform activities based on the notified health management plan and provide feedback. Input consists of the user's thoughts and evaluations of the completed plan and its results. This information is sent to the server via the device and becomes feedback data that is incorporated into the next plan.

[0487] Step 7:

[0488] The server receives user feedback and incorporates it into generating the next health management plan. The input is user feedback data, which is analyzed and used to identify areas for improvement, which are then fed back into the model. The output is a more optimized health management plan for the next session.

[0489] (Application Example 1)

[0490] 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."

[0491] In modern society, it is becoming increasingly important for individual users to manage their own health. However, conventional health management systems often offer uniform suggestions and fail to adequately address individual needs. Furthermore, there are limited ways to provide continuous health support within the home, and coordination with family members can be difficult.

[0492] 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.

[0493] In this invention, the server includes means for collecting diverse health-related data from a device that acquires user data; means for generating and integrating sets of past and present user data; means for applying a data analysis method for real-time evaluation of progress; means for generating individually optimized health management plans based on the analysis results; means for providing an interface for presenting the proposed plan to the user; means for collecting responses from the user and utilizing them for generating the next plan; and means for a home robot to support the user's life and provide health data via voice. This enables optimal health management for each individual user and allows for continuous health support within the home.

[0494] A "device for acquiring user data" refers to a terminal, such as a smartphone or wearable device, that collects health-related information from users.

[0495] "Diverse health-related data" refers to a collection of information necessary to evaluate a user's health status, such as steps taken, heart rate, sleep duration, and location information.

[0496] "Means for generating and integrating data sets" refers to the process of organizing multiple types of collected user data chronologically and combining them into a single, comprehensive dataset.

[0497] "Data analysis methods for immediate progress evaluation" refers to technologies that process collected data in real time and instantly analyze the user's health status.

[0498] A "personally optimized health management plan" is a routine and advice designed to maintain and improve the health of each user, based on their individual health status and lifestyle.

[0499] "Means of providing an interface" refers to a point of contact with the user to effectively notify them of the generated health management plan and encourage them to implement it.

[0500] "Means for collecting responses and using them to generate the next plan" refers to a system that obtains user feedback and uses it to inform the next health management plan.

[0501] A "household robot" is an autonomous device installed in a home that provides various forms of support to its inhabitants.

[0502] "A means of providing health data via voice" refers to a voice output function that can convey analysis results and advice to users in voice.

[0503] This invention is built around a home robot as a system to support health management within the home. The server collects and manages various health-related data from devices that acquire user data. This generates and integrates a set of data for evaluating the user's health status, such as the user's heart rate, steps taken, and sleep duration.

[0504] The server processes this data in real time and applies machine learning algorithms using software such as Python and TensorFlow. This allows for immediate evaluation of the user's progress. Based on the analysis results, it also generates a health management plan optimized for each user and forms suggestions for maintaining and improving health that meet the user's needs.

[0505] Through its interface, the home robot notifies the user of its generated health management plan via voice. This robot uses voice recognition technology similar to Google Assistant, enabling it to receive user instructions and feedback.

[0506] Users can provide feedback to the robot, sharing their opinions and feelings, and this response is sent to the server. The server uses this feedback to inform the next health management plan, enabling more effective health management by continuously optimizing the service for the user.

[0507] For example, if a user uses a caregiving robot on a daily basis, in the morning the robot will notify the user based on their sleep data and activity data from the previous day, saying something like, "Your condition this morning is good. We recommend a 30-minute walk today." If the user then tells the robot that they would like to exercise a little more, the server can incorporate that information into the next suggestion.

[0508] An example of a prompt to a generative AI model is, "Based on recent data, suggest the best health management program for this user." This prompt prompts the server to use machine learning to generate a personalized health program.

[0509] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0510] Step 1:

[0511] The device collects health-related data through the user's smartphone or wearable device. Inputs include steps taken, heart rate, sleep duration, and location information, which the device sends to a server in batches. Outputs are formalized data packets.

[0512] Step 2:

[0513] The server analyzes the received data and generates and integrates data sets for each user. The input is data packets sent from the terminal. The server cleanses the data, filters out abnormal values, and then arranges the data in chronological order to obtain an integrated dataset as output.

[0514] Step 3:

[0515] The server uses machine learning algorithms to analyze health status based on collected data. The input is an integrated dataset, and model analysis is performed using TensorFlow. The output is an indicator showing the user's health status and its fluctuations.

[0516] Step 4:

[0517] The server generates an individually optimized health management program based on the analysis results. The input is an indicator of health status, and the output is a specific health program tailored to the user's needs, including exercise, diet, and relaxation. The server constructs this program using a generative AI model through natural language processing.

[0518] Step 5:

[0519] The server notifies the user of the generated health management program via an interface. The input is the health management program, and the output is a voice or text notification to the user. The user is notified by a home robot reading the program aloud.

[0520] Step 6:

[0521] After completing the proposed health management program, the user provides feedback to the home robot. The input is the user's execution status and impressions, and the output is feedback data sent to the server via the robot.

[0522] Step 7:

[0523] The server analyzes user feedback and incorporates it into subsequent health management programs. The input is feedback data, and the output is an optimized health management program. This allows for continuous optimization for each user, enabling more effective health management.

[0524] 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.

[0525] Embodiments of the present invention are systems that provide a comprehensive health management program that takes into account not only the user's physical condition but also their emotional state.

[0526] First, the device collects health-related data such as steps taken, heart rate, and location information from the user's smartphone or wearable device. In addition, an emotion engine acquires the user's voice and facial expression data and estimates their emotions in real time. For example, by analyzing the tone of the user's voice when they speak and changes in facial expressions detected by the camera, the emotion engine estimates the user's stress level and emotional state.

[0527] Next, the server integrates health-related data and emotional data obtained from the user. Based on this integrated data, a machine learning algorithm is used to evaluate the user's current health and emotional state and generate a personalized health management program. In this process, the program content is dynamically adjusted; for example, if the system determines that the user is experiencing stress, it will proactively suggest relaxing activities and advice to alleviate stress.

[0528] The generated health management program is translated into natural language and notified to the user via their device. This notification is presented in a way that users can easily implement in their daily lives, and includes, for example, relaxation advice. This approach allows users to receive support not only for their physical health but also for their emotional well-being.

[0529] Furthermore, users provide feedback on the results of the health programs they have completed and changes in their emotions, which is used to generate future programs. The server analyzes this feedback to optimize the overall system algorithm. This loop improves the accuracy and relevance of suggestions to users over time.

[0530] Thus, by using the system of the present invention, users can comprehensively manage both their physical and emotional states and improve their quality of life.

[0531] The following describes the processing flow.

[0532] Step 1:

[0533] The device collects user health-related and emotional data via smartphones and wearable devices. Health-related data includes steps taken, heart rate, and location information, while emotional data includes voice tone and facial expressions. The device utilizes voice recognition and a camera to transmit the user's voice and facial expressions to the emotion engine.

[0534] Step 2:

[0535] The device encrypts the collected health and emotional data and transmits it to the server via a secure network. This data is tagged with a user ID and timestamp to facilitate data organization during subsequent integration processing.

[0536] Step 3:

[0537] The server integrates the received data and creates detailed datasets for each user. It then checks the consistency of health and emotional data included in the integrated dataset, preparing it for accurate data analysis.

[0538] Step 4:

[0539] The server applies machine learning algorithms to the integrated dataset to assess the user's health and emotional state in real time. This analysis places particular emphasis on the user's stress levels and well-being, identifying areas for improvement.

[0540] Step 5:

[0541] Based on the analysis results, the server generates a health management program optimized for the user. If the user's emotional state is unstable, it suggests activities such as relaxation and stress reduction; if their health is good, it provides lifestyle advice to maintain their current state.

[0542] Step 6:

[0543] The server translates the generated program into natural language and notifies the user's device in a format that is intuitively understandable. The notification includes specific action suggestions, designed to be easily incorporated into the user's daily routine.

[0544] Step 7:

[0545] Users perform the suggested program and provide feedback on the results, their experiences, and any changes in their physical condition. This feedback, including the effectiveness of the activity and their wishes for the next time, is submitted to the server via their device.

[0546] Step 8:

[0547] The server updates its analysis algorithm based on user feedback, improving the accuracy of subsequent health management programs. This iterative process ensures that users continue to receive the most optimal service over time.

[0548] (Example 2)

[0549] 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."

[0550] In modern society, it is difficult for individuals to effectively manage their physical and emotional states simultaneously. Traditional methods often analyze health and emotional data separately, lacking integrated support. Furthermore, there is a need for efficient systems to generate personalized health management programs.

[0551] 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.

[0552] In this invention, the server includes means for integrating health-related data with voice and facial expression data to generate a data profile, means for analyzing the data using a generative AI model to generate a personalized health management program, and means for incorporating a learning algorithm to collect user feedback and utilize it for system optimization. This makes it possible to comprehensively manage both an individual's health and emotions and improve their quality of life.

[0553] A "user device" is a portable computing device used by an individual, including smartphones and wearable technology. This makes it possible to acquire the user's physiological and emotional data.

[0554] "Physiological data" refers to information that indicates an individual's physical condition, and includes things like step count, heart rate, and location information.

[0555] "Emotional data" refers to information used to evaluate an individual's mental state, and is data related to emotions estimated based on changes in voice and facial expressions.

[0556] A "generative AI model" is an algorithm that performs machine learning based on a large amount of data, and its role is to analyze the user's data profile and generate a personalized health management program.

[0557] A "data profile" is a general term for information that integrates health data and emotional data about a user, and is used to evaluate the individual user's state.

[0558] A "health management program" is a set of activities and advice suggested based on the user's health and emotional state, and is characterized by its individualized nature.

[0559] "Natural language processing technology" is a technology that enables computers to understand and generate natural human language, and it is used when communicating health management programs to users.

[0560] A "learning algorithm" is a computational method that learns from feedback and aims to improve the performance of a system, and is incorporated with the purpose of improving the accuracy of program proposals.

[0561] Embodiments of the present invention are advanced health management systems that integrate and manage a user's physical and emotional state. This system operates using a user device, a server, and a generative AI model.

[0562] First, the terminal uses a smartphone or wearable device as the user device to collect physiological and emotional data from the user. Physiological data includes steps taken and heart rate, which are acquired by built-in sensors. Emotional data is acquired via the device's camera and microphone and estimated based on changes in voice tone and facial expressions.

[0563] Next, the server receives this data and integrates the health and emotional data together. This integrated data profile is used to comprehensively assess the user's health and emotional state. A generative AI model running on the server analyzes the data profile using machine learning algorithms and generates a health management program optimized for each user.

[0564] The generated program is communicated to the user via the terminal in natural language. This communication may include suggestions for specific breathing techniques or exercises for relaxation. This allows the user to receive practical advice for improving their health.

[0565] Furthermore, after users complete the provided health management program, they provide feedback on the results and changes in their emotions. The server analyzes this feedback and optimizes the algorithm of the generated AI model. This feedback loop improves the accuracy and relevance of the proposed program.

[0566] As a concrete example, here are some prompt statements that can be input to a generative AI model:

[0567] "Suggest ways for users to relax."

[0568] "Estimate stress levels from heart rate and facial expression data."

[0569] In this way, this system comprehensively addresses both the user's health and emotions, providing concrete support to improve their quality of life.

[0570] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0571] Step 1:

[0572] The terminal collects physiological and emotional data from the user's device. Specifically, it uses built-in sensors to acquire physiological data such as steps and heart rate, and cameras and microphones to acquire voice and facial expression data. Inputs include numerical data and voice / image data obtained from each sensor. This data is then converted into a digital format and prepared for transmission to the server.

[0573] Step 2:

[0574] The server receives physiological and emotional data transmitted from the terminal and integrates them to generate a data profile. The input data consists of numerical data as physiological data and audio / image data as emotional data. By analyzing this data, the server understands the user's current health and mental state and integrates it as a data profile. This data profile forms the basis for subsequent processing steps.

[0575] Step 3:

[0576] The server uses a generative AI model to analyze data profiles and generate personalized health management programs. The input to this process is an integrated data profile, and the output is a user-specific health management program. The generative AI model compares past data with the user's current state and provides specific suggestions for health improvement. This determines the optimal advice and exercise program tailored to the user's needs.

[0577] Step 4:

[0578] The device translates the health management program sent from the server into natural language and notifies the user. The input is the generated health management program, and the output is a notification in natural language format that is easy for the user to understand. The device provides the notification visually and aurally, or audibly, to help the user take immediate action.

[0579] Step 5:

[0580] Users complete the provided health management program and provide feedback on the results via a terminal. The input consists of changes in their health status and emotions after completing the program, which are returned to the feedback system as qualitative or quantitative data. This feedback is used to improve the accuracy of future program generation.

[0581] Step 6:

[0582] The server analyzes user feedback and modifies and optimizes the generated AI model. The input is user-provided feedback information, and the output is the improved program generation algorithm. This optimization improves the system's suggestion accuracy over time, enabling it to provide health management programs that are more tailored to the user.

[0583] (Application Example 2)

[0584] 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."

[0585] In modern times, health management and emotional management are closely related, but conventional systems have been unable to comprehensively evaluate them and provide individually optimized programs. Furthermore, there is a demand for real-time monitoring of health and emotional states and the provision of personalized management plans, but the technology to adequately address this has not yet been established.

[0586] 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.

[0587] In this invention, the server includes means for collecting health-related information from a data acquisition device, means for generating and integrating a set of past and present information of the user, and means for performing voice analysis and facial expression analysis to estimate the emotional state. This makes it possible to comprehensively evaluate the health and emotional state of each individual user and provide them with an individually optimized management plan in real time.

[0588] A "data acquisition device" is a device that collects health-related information from users, and includes wearable devices and mobile terminals.

[0589] An "information set" is a collection of data that integrates a user's past and present health and emotional data.

[0590] "Voice analysis" is a technology that estimates a user's emotional state by analyzing the tone and manner of speaking of their voice.

[0591] "Facial expression analysis" is a technology that recognizes and analyzes a user's facial expressions to estimate their emotional state.

[0592] A "server" is a computing device responsible for data collection and analysis, processing large amounts of data and generating individually optimized management plans.

[0593] A "management plan" is a plan that includes individually optimized health and emotional management suggestions based on the user's health and emotional state.

[0594] To implement this invention, it is necessary to collect health-related data and emotional data using a wearable device worn by the user or a mobile device used (e.g., a smartphone). The collected data, including heart rate, steps taken, voice, and facial expressions, is transmitted to a server to evaluate the user's health and emotional state.

[0595] The server receives this data and estimates the emotional state using voice and facial expression analysis. Furthermore, it integrates past and present health data and uses machine learning algorithms to evaluate the user's health and emotional state. Based on this evaluation, the server creates an individually optimized health and emotional management plan. This allows the server to provide users with real-time suggestions for optimal health and emotional management.

[0596] For example, if a user exhibits a high stress level and a higher-than-normal heart rate, the server generates a program recommending relaxation exercises and notifies the user via the terminal. This system can support both the user's physical and emotional state, thereby improving the quality of care.

[0597] For example, if the system detects that resident A in a nursing care facility is experiencing stress, it will suggest activities to promote relaxation.

[0598] Examples of prompts to input into a generative AI model are as follows:

[0599] "We are developing an application that monitors the health and emotional state of residents in a nursing home and proposes the optimal care program. Please tell us how to acquire data from wearable devices and how to use machine learning to generate the program."

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] The device acquires health data (heart rate, steps, etc.), voice data, and facial expression data from wearable devices and mobile terminals. This data is collected through the device's sensors, microphone, and camera. Health data and emotional data are inputs, and these data are aggregated as outputs.

[0603] Step 2:

[0604] The terminal sends the collected data to the server. A secure communication protocol is used for transmission. In this step, the terminal receives the aggregated data as input and sends that data to the server as output. The data is encrypted and transmitted to the server over the internet.

[0605] Step 3:

[0606] The server analyzes the received data, performing speech analysis on the audio data and facial expression analysis on the facial expression data. The input is the audio and facial expression data sent to the server, and the output is the estimated emotional state. A speech analysis engine and a facial recognition algorithm are used here.

[0607] Step 4:

[0608] The server integrates analyzed health data and estimated emotional states, and uses machine learning algorithms to evaluate the user's health and emotional state. The input is the integrated health and emotional data, and the output is the evaluated user state. A machine learning model based on historical datasets is used to evaluate the data.

[0609] Step 5:

[0610] The server generates individually optimized health and emotional management plans based on the assessment. The input is the user's health and emotional assessment results, and the output is the optimized management plan. This plan includes specific action plans tailored to the user's condition.

[0611] Step 6:

[0612] The generated management plan is notified to the user via the terminal. The input is the generated management plan, and the output is the notification message to the user. It is displayed in a format that is easy for the user to understand.

[0613] Step 7:

[0614] The user acts according to the provided plan and inputs the results of their actions and changes in their emotions as feedback into the terminal. The input is user feedback data, and the output is that same feedback data. This data is sent to the server as feedback so that it can be used to generate the next program.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] [Fourth Embodiment]

[0619] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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".

[0632] Embodiments of the present invention are systems for providing specific health management programs to individual users, and are carried out by the following process.

[0633] First, the device collects health-related data from the user through their smartphone or wearable device. This includes, for example, steps taken, heart rate, sleep duration, and location information. The device then transmits this data to a server using a secure communication protocol.

[0634] Next, the server integrates the received data for each user to generate a comprehensive dataset. This makes it possible to get a chronological overview of the user's health status. After data cleansing and preprocessing, the generated dataset is analyzed by machine learning algorithms.

[0635] The server analyzes and evaluates the user's current health status. Based on this, it generates a health management program tailored to each user's needs. The generated program includes a wide range of content, such as exercise recommendations, dietary advice, and relaxation methods. This program is written in natural language in a format that the user can immediately execute, and notifications are sent to the device.

[0636] Furthermore, users can provide feedback when they perform the provided program. This feedback, such as the frequency of activity and perceived effectiveness, is sent to the server via the device.

[0637] Ultimately, the server receives feedback and incorporates that information into the next health management program generated. This cycle enables the service to be continuously optimized for the user. This embodiment allows users to manage their own health more effectively and improve their quality of life.

[0638] The following describes the processing flow.

[0639] Step 1:

[0640] The device collects health-related data via the user's smartphone or wearable device. This data includes steps taken, heart rate, location information, and sleep patterns. The collected data is temporarily stored on the device.

[0641] Step 2:

[0642] The device prepares the collected data for transmission to the server. The data is encrypted to protect privacy and sent to the server using a secure communication protocol.

[0643] Step 3:

[0644] The server integrates the received health data for analysis. It combines data from multiple data sources into time-series datasets for each user. This process includes noise reduction and outlier removal.

[0645] Step 4:

[0646] The server analyzes the integrated dataset using machine learning techniques. The goal of the analysis is to assess the user's health status in real time and identify important patterns and trends.

[0647] Step 5:

[0648] Based on the analysis results, the server generates a health management program optimized for the user's individual needs. This program may include, for example, exercise recommendations, dietary advice, and mental health care suggestions.

[0649] Step 6:

[0650] The server converts the generated health management program into natural language and notifies the user's device in an easy-to-understand format. This allows users to smoothly implement specific health improvement activities in their daily lives.

[0651] Step 7:

[0652] The user completes the provided health management program and enters feedback, including results and impressions, into the device. The device then sends this feedback back to the server.

[0653] Step 8:

[0654] The server adjusts its analysis algorithm based on the feedback it receives, improving the accuracy of the next health management program it generates. This, in turn, improves the quality of ongoing service provided to the user.

[0655] (Example 1)

[0656] 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".

[0657] In modern society, health management is increasingly important, but finding effective, personalized health management methods is difficult. Traditional methods fail to provide accurate suggestions based on the user's health condition, making efficient health maintenance challenging. Furthermore, there is a need to utilize collected data to continuously provide users with optimal advice.

[0658] 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.

[0659] In this invention, the server includes means for analyzing the user's behavior patterns, means for evaluating the user's data in real time, and means for creating a health management plan using machine learning technology. This enables personalized health management tailored to each user.

[0660] "User" refers to an individual who uses this system for health management.

[0661] "Device" refers to a device used to collect health-related information and transmit it to a server.

[0662] "Information" refers to all data related to the user's health status, specifically including steps taken, heart rate, sleep duration, and location information.

[0663] "Analysis methods" refer to the methods and techniques used to process collected information and to evaluate the health status and analyze behavioral patterns of users.

[0664] A "health management plan" refers to a series of plans that include various activities and behavioral guidelines proposed to improve the health status of users.

[0665] "Information display means" refers to an interface for displaying health management plans and notifications to users.

[0666] "Evaluation" refers to the assessments and opinions that users provide regarding the implementation of their health management plan.

[0667] "Transmission means" refers to the technologies and protocols used to securely transmit information from a device to a server.

[0668] A "generative AI model" refers to an algorithm or model that uses machine learning to predict a user's health status and propose an optimal health management plan.

[0669] One embodiment of the present invention is a system that provides personalized health management. This system aims to support the user's health maintenance by collecting and analyzing the user's health-related information and generating an optimal health management plan.

[0670] The terminal collects data related to the user's health using devices such as smartphones and wearable devices. This data includes steps taken, heart rate, sleep duration, and location information. The terminal securely transmits this data to a central server using Bluetooth or Wi-Fi.

[0671] The server uses machine learning libraries such as Scikit-learn and TensorFlow to integrate and analyze received data for each target. Specifically, it performs data cleansing and preprocessing to create a model for predicting the user's health status. Using the generated model, it builds a health management plan optimized for each user. This plan includes exercise recommendations, suggestions for moderate eating, and guidance on relaxation techniques.

[0672] Users receive a health management plan provided through their device and actually perform activities based on that plan. Users can send feedback on their impressions and areas for improvement, which can then be incorporated into the next plan. This feedback is processed again on the server and contributes to the optimization of the entire system.

[0673] As a concrete example, the prompt message to the generating AI model might be set as follows: "Based on User A's health data for this week, please generate a recommended exercise plan for next week." Using such prompts enables detailed health support that matches the user's needs.

[0674] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0675] Step 1:

[0676] The device collects health data using the user's smartphone or wearable device. This data includes steps taken, heart rate, sleep duration, and location information. The device formats this data and transmits it to a server via Bluetooth or Wi-Fi. The output is encrypted data packets using a secure protocol.

[0677] Step 2:

[0678] The server integrates the data received from the terminals. The input consists of various data formats from multiple users. The server cleanses the data, including imputing missing values ​​and removing outliers. The formatted data is output as a consistent dataset for each user.

[0679] Step 3:

[0680] The server inputs the dataset into a machine learning algorithm and performs the analysis. Specifically, it uses Scikit-learn and TensorFlow to build time series analysis and classification models. The output of this step is a prediction of each user's health status.

[0681] Step 4:

[0682] The server generates a personalized health management plan based on the analysis results. The input is the prediction results obtained through the analysis, which are used to create a plan that includes specific exercise suggestions and dietary recommendations, utilizing a powerful AI model. The output is the health management plan in content format, which is then provided to the user.

[0683] Step 5:

[0684] The terminal notifies the user of the health management plan sent from the server. The input here is plan data from the server, and the terminal visually displays the information through an application. The output is a list of tasks and guidelines that the user can execute.

[0685] Step 6:

[0686] Users perform activities based on the notified health management plan and provide feedback. Input consists of the user's thoughts and evaluations of the completed plan and its results. This information is sent to the server via the device and becomes feedback data that is incorporated into the next plan.

[0687] Step 7:

[0688] The server receives user feedback and incorporates it into generating the next health management plan. The input is user feedback data, which is analyzed and used to identify areas for improvement, which are then fed back into the model. The output is a more optimized health management plan for the next session.

[0689] (Application Example 1)

[0690] 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".

[0691] In modern society, it is becoming increasingly important for individual users to manage their own health. However, conventional health management systems often offer uniform suggestions and fail to adequately address individual needs. Furthermore, there are limited ways to provide continuous health support within the home, and coordination with family members can be difficult.

[0692] 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.

[0693] In this invention, the server includes means for collecting diverse health-related data from a device that acquires user data; means for generating and integrating sets of past and present user data; means for applying a data analysis method for real-time evaluation of progress; means for generating individually optimized health management plans based on the analysis results; means for providing an interface for presenting the proposed plan to the user; means for collecting responses from the user and utilizing them for generating the next plan; and means for a home robot to support the user's life and provide health data via voice. This enables optimal health management for each individual user and allows for continuous health support within the home.

[0694] A "device for acquiring user data" refers to a terminal, such as a smartphone or wearable device, that collects health-related information from users.

[0695] "Diverse health-related data" refers to a collection of information necessary to evaluate a user's health status, such as steps taken, heart rate, sleep duration, and location information.

[0696] "Means for generating and integrating data sets" refers to the process of organizing multiple types of collected user data chronologically and combining them into a single, comprehensive dataset.

[0697] "Data analysis methods for immediate progress evaluation" refers to technologies that process collected data in real time and instantly analyze the user's health status.

[0698] A "personally optimized health management plan" is a routine and advice designed to maintain and improve the health of each user, based on their individual health status and lifestyle.

[0699] "Means of providing an interface" refers to a point of contact with the user to effectively notify them of the generated health management plan and encourage them to implement it.

[0700] "Means for collecting responses and using them to generate the next plan" refers to a system that obtains user feedback and uses it to inform the next health management plan.

[0701] A "household robot" is an autonomous device installed in a home that provides various forms of support to its inhabitants.

[0702] "A means of providing health data via voice" refers to a voice output function that can convey analysis results and advice to users in voice.

[0703] This invention is built around a home robot as a system to support health management within the home. The server collects and manages various health-related data from devices that acquire user data. This generates and integrates a set of data for evaluating the user's health status, such as the user's heart rate, steps taken, and sleep duration.

[0704] The server processes this data in real time and applies machine learning algorithms using software such as Python and TensorFlow. This allows for immediate evaluation of the user's progress. Based on the analysis results, it also generates a health management plan optimized for each user and forms suggestions for maintaining and improving health that meet the user's needs.

[0705] Through its interface, the home robot notifies the user of its generated health management plan via voice. This robot uses voice recognition technology similar to Google Assistant, enabling it to receive user instructions and feedback.

[0706] Users can provide feedback to the robot, sharing their opinions and feelings, and this response is sent to the server. The server uses this feedback to inform the next health management plan, enabling more effective health management by continuously optimizing the service for the user.

[0707] For example, if a user uses a caregiving robot on a daily basis, in the morning the robot will notify the user based on their sleep data and activity data from the previous day, saying something like, "Your condition this morning is good. We recommend a 30-minute walk today." If the user then tells the robot that they would like to exercise a little more, the server can incorporate that information into the next suggestion.

[0708] An example of a prompt to a generative AI model is, "Based on recent data, suggest the best health management program for this user." This prompt prompts the server to use machine learning to generate a personalized health program.

[0709] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0710] Step 1:

[0711] The device collects health-related data through the user's smartphone or wearable device. Inputs include steps taken, heart rate, sleep duration, and location information, which the device sends to a server in batches. Outputs are formalized data packets.

[0712] Step 2:

[0713] The server analyzes the received data and generates and integrates data sets for each user. The input is data packets sent from the terminal. The server cleanses the data, filters out abnormal values, and then arranges the data in chronological order to obtain an integrated dataset as output.

[0714] Step 3:

[0715] The server uses machine learning algorithms to analyze health status based on collected data. The input is an integrated dataset, and model analysis is performed using TensorFlow. The output is an indicator showing the user's health status and its fluctuations.

[0716] Step 4:

[0717] The server generates an individually optimized health management program based on the analysis results. The input is an indicator of health status, and the output is a specific health program tailored to the user's needs, including exercise, diet, and relaxation. The server constructs this program using a generative AI model through natural language processing.

[0718] Step 5:

[0719] The server notifies the user of the generated health management program via an interface. The input is the health management program, and the output is a voice or text notification to the user. The user is notified by a home robot reading the program aloud.

[0720] Step 6:

[0721] After completing the proposed health management program, the user provides feedback to the home robot. The input is the user's execution status and impressions, and the output is feedback data sent to the server via the robot.

[0722] Step 7:

[0723] The server analyzes user feedback and incorporates it into subsequent health management programs. The input is feedback data, and the output is an optimized health management program. This allows for continuous optimization for each user, enabling more effective health management.

[0724] 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.

[0725] Embodiments of the present invention are systems that provide a comprehensive health management program that takes into account not only the user's physical condition but also their emotional state.

[0726] First, the device collects health-related data such as steps taken, heart rate, and location information from the user's smartphone or wearable device. In addition, an emotion engine acquires the user's voice and facial expression data and estimates their emotions in real time. For example, by analyzing the tone of the user's voice when they speak and changes in facial expressions detected by the camera, the emotion engine estimates the user's stress level and emotional state.

[0727] Next, the server integrates health-related data and emotional data obtained from the user. Based on this integrated data, a machine learning algorithm is used to evaluate the user's current health and emotional state and generate a personalized health management program. In this process, the program content is dynamically adjusted; for example, if the system determines that the user is experiencing stress, it will proactively suggest relaxing activities and advice to alleviate stress.

[0728] The generated health management program is translated into natural language and notified to the user via their device. This notification is presented in a way that users can easily implement in their daily lives, and includes, for example, relaxation advice. This approach allows users to receive support not only for their physical health but also for their emotional well-being.

[0729] Furthermore, users provide feedback on the results of the health programs they have completed and changes in their emotions, which is used to generate future programs. The server analyzes this feedback to optimize the overall system algorithm. This loop improves the accuracy and relevance of suggestions to users over time.

[0730] Thus, by using the system of the present invention, users can comprehensively manage both their physical and emotional states and improve their quality of life.

[0731] The following describes the processing flow.

[0732] Step 1:

[0733] The device collects user health-related and emotional data via smartphones and wearable devices. Health-related data includes steps taken, heart rate, and location information, while emotional data includes voice tone and facial expressions. The device utilizes voice recognition and a camera to transmit the user's voice and facial expressions to the emotion engine.

[0734] Step 2:

[0735] The device encrypts the collected health and emotional data and transmits it to the server via a secure network. This data is tagged with a user ID and timestamp to facilitate data organization during subsequent integration processing.

[0736] Step 3:

[0737] The server integrates the received data and creates detailed datasets for each user. It then checks the consistency of health and emotional data included in the integrated dataset, preparing it for accurate data analysis.

[0738] Step 4:

[0739] The server applies machine learning algorithms to the integrated dataset to assess the user's health and emotional state in real time. This analysis places particular emphasis on the user's stress levels and well-being, identifying areas for improvement.

[0740] Step 5:

[0741] Based on the analysis results, the server generates a health management program optimized for the user. If the user's emotional state is unstable, it suggests activities such as relaxation and stress reduction; if their health is good, it provides lifestyle advice to maintain their current state.

[0742] Step 6:

[0743] The server translates the generated program into natural language and notifies the user's device in a format that is intuitively understandable. The notification includes specific action suggestions, designed to be easily incorporated into the user's daily routine.

[0744] Step 7:

[0745] Users perform the suggested program and provide feedback on the results, their experiences, and any changes in their physical condition. This feedback, including the effectiveness of the activity and their wishes for the next time, is submitted to the server via their device.

[0746] Step 8:

[0747] The server updates its analysis algorithm based on user feedback, improving the accuracy of subsequent health management programs. This iterative process ensures that users continue to receive the most optimal service over time.

[0748] (Example 2)

[0749] 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".

[0750] In modern society, it is difficult for individuals to effectively manage their physical and emotional states simultaneously. Traditional methods often analyze health and emotional data separately, lacking integrated support. Furthermore, there is a need for efficient systems to generate personalized health management programs.

[0751] 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.

[0752] In this invention, the server includes means for integrating health-related data with voice and facial expression data to generate a data profile, means for analyzing the data using a generative AI model to generate a personalized health management program, and means for incorporating a learning algorithm to collect user feedback and utilize it for system optimization. This makes it possible to comprehensively manage both an individual's health and emotions and improve their quality of life.

[0753] A "user device" is a portable computing device used by an individual, including smartphones and wearable technology. This makes it possible to acquire the user's physiological and emotional data.

[0754] "Physiological data" refers to information that indicates an individual's physical condition, and includes things like step count, heart rate, and location information.

[0755] "Emotional data" refers to information used to evaluate an individual's mental state, and is data related to emotions estimated based on changes in voice and facial expressions.

[0756] A "generative AI model" is an algorithm that performs machine learning based on a large amount of data, and its role is to analyze the user's data profile and generate a personalized health management program.

[0757] A "data profile" is a general term for information that integrates health data and emotional data about a user, and is used to evaluate the individual user's state.

[0758] A "health management program" is a set of activities and advice suggested based on the user's health and emotional state, and is characterized by its individualized nature.

[0759] "Natural language processing technology" is a technology that enables computers to understand and generate natural human language, and it is used when communicating health management programs to users.

[0760] A "learning algorithm" is a computational method that learns from feedback and aims to improve the performance of a system, and is incorporated with the purpose of improving the accuracy of program proposals.

[0761] Embodiments of the present invention are advanced health management systems that integrate and manage a user's physical and emotional state. This system operates using a user device, a server, and a generative AI model.

[0762] First, the terminal uses a smartphone or wearable device as the user device to collect physiological and emotional data from the user. Physiological data includes steps taken and heart rate, which are acquired by built-in sensors. Emotional data is acquired via the device's camera and microphone and estimated based on changes in voice tone and facial expressions.

[0763] Next, the server receives this data and integrates the health and emotional data together. This integrated data profile is used to comprehensively assess the user's health and emotional state. A generative AI model running on the server analyzes the data profile using machine learning algorithms and generates a health management program optimized for each user.

[0764] The generated program is communicated to the user via the terminal in natural language. This communication may include suggestions for specific breathing techniques or exercises for relaxation. This allows the user to receive practical advice for improving their health.

[0765] Furthermore, after users complete the provided health management program, they provide feedback on the results and changes in their emotions. The server analyzes this feedback and optimizes the algorithm of the generated AI model. This feedback loop improves the accuracy and relevance of the proposed program.

[0766] As a concrete example, here are some prompt statements that can be input to a generative AI model:

[0767] "Suggest ways for users to relax."

[0768] "Estimate stress levels from heart rate and facial expression data."

[0769] In this way, this system comprehensively addresses both the user's health and emotions, providing concrete support to improve their quality of life.

[0770] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0771] Step 1:

[0772] The terminal collects physiological and emotional data from the user's device. Specifically, it uses built-in sensors to acquire physiological data such as steps and heart rate, and cameras and microphones to acquire voice and facial expression data. Inputs include numerical data and voice / image data obtained from each sensor. This data is then converted into a digital format and prepared for transmission to the server.

[0773] Step 2:

[0774] The server receives physiological and emotional data transmitted from the terminal and integrates them to generate a data profile. The input data consists of numerical data as physiological data and audio / image data as emotional data. By analyzing this data, the server understands the user's current health and mental state and integrates it as a data profile. This data profile forms the basis for subsequent processing steps.

[0775] Step 3:

[0776] The server uses a generative AI model to analyze data profiles and generate personalized health management programs. The input to this process is an integrated data profile, and the output is a user-specific health management program. The generative AI model compares past data with the user's current state and provides specific suggestions for health improvement. This determines the optimal advice and exercise program tailored to the user's needs.

[0777] Step 4:

[0778] The device translates the health management program sent from the server into natural language and notifies the user. The input is the generated health management program, and the output is a notification in natural language format that is easy for the user to understand. The device provides the notification visually and aurally, or audibly, to help the user take immediate action.

[0779] Step 5:

[0780] Users complete the provided health management program and provide feedback on the results via a terminal. The input consists of changes in their health status and emotions after completing the program, which are returned to the feedback system as qualitative or quantitative data. This feedback is used to improve the accuracy of future program generation.

[0781] Step 6:

[0782] The server analyzes user feedback and modifies and optimizes the generated AI model. The input is user-provided feedback information, and the output is the improved program generation algorithm. This optimization improves the system's suggestion accuracy over time, enabling it to provide health management programs that are more tailored to the user.

[0783] (Application Example 2)

[0784] 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".

[0785] In modern times, health management and emotional management are closely related, but conventional systems have been unable to comprehensively evaluate them and provide individually optimized programs. Furthermore, there is a demand for real-time monitoring of health and emotional states and the provision of personalized management plans, but the technology to adequately address this has not yet been established.

[0786] 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.

[0787] In this invention, the server includes means for collecting health-related information from a data acquisition device, means for generating and integrating a set of past and present information of the user, and means for performing voice analysis and facial expression analysis to estimate the emotional state. This makes it possible to comprehensively evaluate the health and emotional state of each individual user and provide them with an individually optimized management plan in real time.

[0788] A "data acquisition device" is a device that collects health-related information from users, and includes wearable devices and mobile terminals.

[0789] An "information set" is a collection of data that integrates a user's past and present health and emotional data.

[0790] "Voice analysis" is a technology that estimates a user's emotional state by analyzing the tone and manner of speaking of their voice.

[0791] "Facial expression analysis" is a technology that recognizes and analyzes a user's facial expressions to estimate their emotional state.

[0792] A "server" is a computing device responsible for data collection and analysis, processing large amounts of data and generating individually optimized management plans.

[0793] A "management plan" is a plan that includes individually optimized health and emotional management suggestions based on the user's health and emotional state.

[0794] To implement this invention, it is necessary to collect health-related data and emotional data using a wearable device worn by the user or a mobile device used (e.g., a smartphone). The collected data, including heart rate, steps taken, voice, and facial expressions, is transmitted to a server to evaluate the user's health and emotional state.

[0795] The server receives this data and estimates the emotional state using voice and facial expression analysis. Furthermore, it integrates past and present health data and uses machine learning algorithms to evaluate the user's health and emotional state. Based on this evaluation, the server creates an individually optimized health and emotional management plan. This allows the server to provide users with real-time suggestions for optimal health and emotional management.

[0796] For example, if a user exhibits a high stress level and a higher-than-normal heart rate, the server generates a program recommending relaxation exercises and notifies the user via the terminal. This system can support both the user's physical and emotional state, thereby improving the quality of care.

[0797] For example, if the system detects that resident A in a nursing care facility is experiencing stress, it will suggest activities to promote relaxation.

[0798] Examples of prompts to input into a generative AI model are as follows:

[0799] "We are developing an application that monitors the health and emotional state of residents in a nursing home and proposes the optimal care program. Please tell us how to acquire data from wearable devices and how to use machine learning to generate the program."

[0800] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0801] Step 1:

[0802] The device acquires health data (heart rate, steps, etc.), voice data, and facial expression data from wearable devices and mobile terminals. This data is collected through the device's sensors, microphone, and camera. Health data and emotional data are inputs, and these data are aggregated as outputs.

[0803] Step 2:

[0804] The terminal sends the collected data to the server. A secure communication protocol is used for transmission. In this step, the terminal receives the aggregated data as input and sends that data to the server as output. The data is encrypted and transmitted to the server over the internet.

[0805] Step 3:

[0806] The server analyzes the received data, performing speech analysis on the audio data and facial expression analysis on the facial expression data. The input is the audio and facial expression data sent to the server, and the output is the estimated emotional state. A speech analysis engine and a facial recognition algorithm are used here.

[0807] Step 4:

[0808] The server integrates analyzed health data and estimated emotional states, and uses machine learning algorithms to evaluate the user's health and emotional state. The input is the integrated health and emotional data, and the output is the evaluated user state. A machine learning model based on historical datasets is used to evaluate the data.

[0809] Step 5:

[0810] The server generates individually optimized health and emotional management plans based on the assessment. The input is the user's health and emotional assessment results, and the output is the optimized management plan. This plan includes specific action plans tailored to the user's condition.

[0811] Step 6:

[0812] The generated management plan is notified to the user via the terminal. The input is the generated management plan, and the output is the notification message to the user. It is displayed in a format that is easy for the user to understand.

[0813] Step 7:

[0814] The user acts according to the provided plan and inputs the results of their actions and changes in their emotions as feedback into the terminal. The input is user feedback data, and the output is that same feedback data. This data is sent to the server as feedback so that it can be used to generate the next program.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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."

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] The following is further disclosed regarding the embodiments described above.

[0837] (Claim 1)

[0838] A means of collecting multiple types of health-related data from a device that acquires user data,

[0839] Means for generating and integrating the user's past and present datasets,

[0840] A means of applying a data analysis algorithm for real-time evaluation of progress,

[0841] A means for generating individually optimized health management programs based on analysis results,

[0842] A means of providing an interface to notify the user of the proposed program,

[0843] A system that includes means for collecting user feedback and utilizing it in subsequent program generation.

[0844] (Claim 2)

[0845] The system according to claim 1, comprising secure communication technology for transmitting health-related data from a user device to a server.

[0846] (Claim 3)

[0847] The system according to claim 1, wherein a data analysis algorithm uses machine learning techniques to estimate the user's health status.

[0848] "Example 1"

[0849] (Claim 1)

[0850] A means of collecting various types of health-related information from a device that acquires user information,

[0851] Means for generating and integrating a user's past and present information set,

[0852] A means of applying analytical methods for real-time evaluation of user information,

[0853] A means of creating an individually optimized health management plan based on the analysis results,

[0854] Information display means for notifying users of the proposed plan,

[0855] A means of collecting user feedback and incorporating it into the next plan generation,

[0856] A system that includes means for analyzing user behavior patterns based on machine learning technology.

[0857] (Claim 2)

[0858] The system according to claim 1, comprising secure communication means for transmitting health-related information from a user device to a central device.

[0859] (Claim 3)

[0860] The system according to claim 1, comprising an analytical method for predicting the user's health status using a generative AI model.

[0861] "Application Example 1"

[0862] (Claim 1)

[0863] A means of collecting diverse health-related data from a device that acquires user data,

[0864] Means for generating and integrating the user's past and present data sets,

[0865] A means of applying a data analysis method for immediate evaluation of progress,

[0866] A means for generating individually optimized health management plans based on analysis results,

[0867] A means of providing an interface for presenting a proposed plan to the user,

[0868] A means of collecting user responses and using them to generate the next plan,

[0869] A system that includes means for home robots to support users' daily lives and provide health data via voice.

[0870] (Claim 2)

[0871] The system according to claim 1, comprising secure communication technology for transmitting health-related data from a user device to a processing device.

[0872] (Claim 3)

[0873] The system according to claim 1, wherein the data analysis method uses learning technology to evaluate the user's health status.

[0874] "Example 2 of combining an emotion engine"

[0875] (Claim 1)

[0876] A means for collecting multiple types of physiological data and emotional data, including voice and facial expression data, from a user device.

[0877] A means of integrating health-related data and emotional data to generate a data profile,

[0878] A means of analyzing data profiles using a generative AI model and generating personalized health management programs,

[0879] A means of communicating a health management program generated using natural language processing technology to the user,

[0880] A means of collecting and analyzing user feedback and using it to optimize the system,

[0881] A system that includes means for incorporating a learning algorithm to improve the accuracy of program suggestions based on user feedback.

[0882] (Claim 2)

[0883] The system according to claim 1, which transmits data from a user device to a server using secure communication technology.

[0884] (Claim 3)

[0885] The system according to claim 1, which continuously evaluates the user's health and emotional state using machine learning technology.

[0886] "Application example 2 when combining with an emotional engine"

[0887] (Claim 1)

[0888] A means for collecting health-related information from a data acquisition device,

[0889] Means for generating and integrating a user's past and present information set,

[0890] A means for performing voice analysis and facial expression analysis to estimate emotional state,

[0891] A means of applying data analysis techniques to perform real-time evaluation of progress,

[0892] A means for generating individually optimized health and emotional management plans based on analysis results,

[0893] A means of providing a medium for notifying the user of the proposed plan,

[0894] A system that includes means for collecting user feedback and utilizing it in future plan generation.

[0895] (Claim 2)

[0896] The system according to claim 1, which transmits health and emotion-related information from a communication device to an integrator using secure communication means.

[0897] (Claim 3)

[0898] The system according to claim 1, wherein data analysis technology uses a learning model to estimate the user's health and emotional state. [Explanation of symbols]

[0899] 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 means of collecting diverse health-related data from a device that acquires user data, A means for generating and integrating the user's past and present data sets, A means of applying a data analysis method for immediate evaluation of progress, A means for generating individually optimized health management plans based on analysis results, A means of providing an interface for presenting a proposed plan to the user, A means of collecting user responses and using them to generate the next plan, A system that includes means for a home robot to support the user's daily life and provide health data via voice.

2. The system according to claim 1, comprising secure communication technology for transmitting health-related data from a user device to a processing device.

3. The system according to claim 1, wherein the data analysis method uses learning technology to evaluate the user's health status.

Images