system

Abstract

Provide a system. 【Solution means】 Means for obtaining health-related data from a user, Means for transferring the obtained data to an information processing device, Means for analyzing the transferred data and generating a health management plan for the user, Means for visually and aurally providing the generated health management plan to the user, Means for receiving the achievements and feedback from the user and utilizing them for the creation of the next health management plan, Means for recording dietary information using a mobile terminal, Means for proposing exercise time and dietary advice based on the data transmitted from the mobile terminal, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional health management system, there is often a problem that a uniform plan for general users is provided, and it is difficult to provide specific guidance corresponding to individual health conditions and lifestyles. For this reason, there is a problem that it is difficult for users to perform effective health management suitable for themselves.

Means for Solving the Problems

[0005] This invention acquires health-related data from users and transfers it to a server to perform a detailed analysis of each user's individual health status. Based on this analysis, it has a means of generating and providing customized plans for diet, exercise, and sleep. Furthermore, it receives feedback from users and incorporates it into the creation of future plans, thereby supporting continuous health improvement. This overcomes the problems of conventional methods and enables health management optimized for each user.

[0006] A "user" is an individual who uses the system to receive their own health management plan.

[0007] "Health-related data" refers to information about a user's physical condition, such as diet, exercise, sleep, heart rate, and steps taken.

[0008] A "server" refers to a computer system used to receive and analyze health-related data.

[0009] "Transfer" refers to the act of securely sending data from one device to another.

[0010] "Analysis" refers to the process of examining data in detail and evaluating the user's health status.

[0011] A "health management plan" is a compilation of specific instructions and advice provided to users to maintain and improve their health.

[0012] "Feedback" refers to information about responses and results that users send to the system.

[0013] "Customization" refers to the act of adjusting and personalizing a plan to suit the individual needs and health conditions of the user. [Brief explanation of the drawing]

[0014] [Figure 1]It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the labeled 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.

[0018] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the labeled 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.

[0020] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention relates to an AI system that provides a health management plan tailored to the individual needs of a user using health-related data. This system is implemented through data exchange between a client terminal, a server, and the user.

[0036] The system's first step is to acquire health-related data from the user. The user inputs information such as their diet, exercise time, and sleep patterns into a client terminal. Meanwhile, the terminal can also acquire data such as heart rate, steps taken, and calories burned from connected wearable devices. This data is then transferred to the server as information reflecting the user's health status.

[0037] The server uses the received data to perform a detailed assessment of the user's health status. Based on this assessment, a customized health management plan is generated for the user. This plan includes appropriate dietary advice, a recommended exercise schedule, and suggestions for improving the amount and quality of sleep needed. The generated plan is sent to the user's device and displayed in an easy-to-understand visual format.

[0038] After the user completes the plan, the device sends the user's results and feedback back to the server. Based on this feedback, the server makes adjustments when creating the next plan, continuously providing personalized health management support tailored to each user.

[0039] As a concrete example, suppose a user has a goal of losing weight. In this case, the server might suggest a plan that combines calorie restriction in diet with high-intensity exercise. It might also include advice on improving sleep quality. In this way, the system can provide a plan based on individual data to effectively support the user in achieving their goals.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user inputs health-related data such as diet, exercise time, and sleep patterns into the device. The device also acquires data from wearable devices.

[0043] Step 2:

[0044] The device transfers the collected health-related data to the server using a security protocol. The data is encrypted at this stage to protect the information during transmission.

[0045] Step 3:

[0046] The server saves the received data to a database. This database is used to store the user's health history.

[0047] Step 4:

[0048] The server uses stored data to run AI algorithms and assess the user's current health status. This includes trend analysis compared to historical data.

[0049] Step 5:

[0050] The server generates a customized health management plan based on the evaluation results. The plan includes specific dietary advice, exercise programs, and sleep improvement strategies.

[0051] Step 6:

[0052] The server sends the generated plan to the device. The device notifies the user of the plan and sets up alerts and reminders to encourage implementation.

[0053] Step 7:

[0054] The user actually implements the plan and inputs the results and feedback into the device. The device then resends the user's feedback to the server.

[0055] Step 8:

[0056] The server analyzes user feedback and makes adjustments as needed to reflect it in future plan creation. This makes it possible to continuously provide plans optimized for individual users.

[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 the field of health management, there is a need to provide appropriate and effective health management plans based on individual user health data. However, conventional systems have limited data collection and analysis capabilities, making it difficult to provide specific plans tailored to individual needs. This hinders effective health management.

[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 acquiring health-related information from a user, means for transferring the acquired information to an information provider, and means for analyzing the transferred information and generating a health management plan for the user. This makes it possible to provide a highly accurate health management plan based on individual user data.

[0062] A "user" is an entity that utilizes the system, provides health-related information, and receives an individualized health management plan.

[0063] "Health-related information" includes information such as the user's diet, physical activity, sleep patterns, and data obtained from wearable devices.

[0064] An "information provision device" is a computing device that analyzes health-related information obtained from users and generates a health management plan based on that information.

[0065] A "health management plan" is a plan that includes personalized advice on diet, physical activity, and rest, created based on the user's individual health condition.

[0066] A "wearable device" is a device that, when worn by a user, can acquire health-related data in real time.

[0067] An "automated learning model" is an algorithm that continuously learns from past data and newly acquired feedback to optimize health management plans.

[0068] "Visually easy to understand" means presenting a health management plan to users using visual elements such as diagrams and graphs to make its contents easy to comprehend.

[0069] This invention relates to a system in which an information provider and a terminal work together to support a user's health management. This system collects information about the user's health and provides an individualized health management plan based on that information. Specifically, it is configured as follows:

[0070] Users input health-related information such as their diet, exercise time, and sleep patterns using the device. The device also connects with wearable devices to acquire real-time data such as heart rate, steps taken, and calories burned. This information is transmitted from the device to the information provider via a secure communication protocol.

[0071] The information provider uses an automated learning model to analyze the acquired information. This model assesses the user's health status and generates a health management plan tailored to their individual needs. The generated plan includes personalized advice on diet, physical activity, and rest. For example, a prompt such as, "Please suggest a diet and exercise plan for a woman in her 30s who wants to lose 3 kg," might be input into the AI ​​model.

[0072] The terminal displays the health management plan sent from the information provider in a visually easy-to-understand manner for the user. This allows the user to put the plan into action.

[0073] After a user completes their health management plan, the device sends the results and feedback back to the information provider. Based on this feedback, the information provider learns and adjusts to provide an even more appropriate plan when generating the next one. This allows the system to provide continuous and effective support to the user.

[0074] By using the system of the present invention, it becomes possible to implement personalized health management, which can greatly contribute to maintaining and improving health.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] Users input health-related information into the device. Specifically, they record their diet, exercise time, and sleep patterns through a dedicated application. The device automatically retrieves heart rate, steps taken, and calories burned from wearable devices. This information is used as input data and is temporarily stored in a database.

[0078] Step 2:

[0079] The terminal transmits the collected health information to the information provider. Secure protocols (e.g., HTTPS) are used to ensure data transfer while protecting user privacy. The input data is converted to an analysis format on the server side.

[0080] Step 3:

[0081] The server analyzes the received data. An automated learning model is used to assess the user's health status. Temporal trend analysis is also performed using historical data. This generates output data that evaluates the user's health status and trends.

[0082] Step 4:

[0083] The server generates a health management plan based on the analysis results. Using a generation AI model, it designs a personalized plan for diet, exercise, and sleep that is suitable for the user. The user inputs specific information (e.g., "I want to lose weight") as a prompt to the AI, and the generated plan is output.

[0084] Step 5:

[0085] The server sends the generated health management plan to the terminal. The terminal displays this plan to the user in a visually easy-to-understand format. Graphs and icons are used to ensure effective communication.

[0086] Step 6:

[0087] The user implements a health management plan and records the results and feedback on their device. The device then sends this information back to the server. The feedback content is collected as input data.

[0088] Step 7:

[0089] The server adjusts the next health management plan based on the feedback. It then uses the automated learning model again to process the data and improve the plan. This results in output data that provides a next plan better suited to the user's individual needs.

[0090] (Application Example 1)

[0091] 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."

[0092] In modern society, the importance of individual health management is increasing, but providing personalized and appropriate health management plans requires efficiently collecting and analyzing large amounts of health-related data and presenting it to users in an easy-to-understand manner. Furthermore, continuous improvement of plans based on user feedback is also important, but there is a challenge in efficiently implementing this.

[0093] 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.

[0094] In this invention, the server includes means for acquiring health-related data from a user, means for transferring the acquired data to an information processing device, and means for analyzing the transferred data and generating a health management plan for the user. This enables the provision of a personalized health management plan to the user and allows for the improvement of the plan through real-time feedback.

[0095] "Health-related data" is a general term for information that indicates a user's health status, such as their diet, exercise level, sleep duration, heart rate, and steps taken.

[0096] An "information processing device" refers to a server or computer used to analyze data acquired from users and generate health management plans.

[0097] A "health management plan" is a plan that includes advice on diet, exercise, and sleep, tailored to the user's individual goals and needs, based on analyzed health-related data.

[0098] "Means of providing information visually and audibly" refers to interfaces and devices that convey the generated health management plan to the user in an easily viewable or easily audible format.

[0099] A "mobile device" refers to a portable device such as a smartphone or tablet that a user uses to record meal information or receive health management plans.

[0100] "Wearable devices" refer to wearable devices that users attach to their bodies to collect data such as heart rate and steps taken.

[0101] To implement this invention, the user begins by inputting daily health-related data using a mobile device. The device automatically acquires data such as heart rate and steps from a wearable device and transmits it to an information processing unit. This information processing unit is a server, and upon receiving the data, it evaluates the user's health status and generates a customized health management plan based on a generated AI model. This plan includes meal suggestions tailored to the user's goals, recommended exercise menus, and optimal sleep patterns.

[0102] The server provides the generated health management plan to the user visually and audibly via a mobile device. For example, dietary advice may be displayed on the smartphone screen, or exercise instructions may be given via voice guidance. The user can also input feedback on the completed plan from their device, which is then sent back to the server. The server analyzes this feedback and incorporates it into future health management plans.

[0103] For example, if a user sets a goal of losing weight, the server will propose a plan that includes low-calorie meal suggestions and aerobic exercise schedules, and the user will act accordingly. An example of a prompt message would be: "Username: ABC, Goal: Weight loss, Current weight: 85kg, Desired weight: 75kg. Please create an optimal 2-week meal and exercise plan for ABC." The generating AI model would then operate in this format.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] Users input health-related data using their mobile devices. The device receives information about meals and exercise as text and images, and automatically acquires data such as heart rate and steps from wearable devices. This input data is pre-processed within the device and converted into a unified format. The output is health-related data converted into a unified format.

[0107] Step 2:

[0108] The terminal transfers pre-processed data to the server, which acts as an information processing unit. The transfer protocol uses HTTP or HTTPS, and the data is posted to the API endpoint. The input is health-related data from the terminal, and the output is data that has been confirmed to have been received by the server.

[0109] Step 3:

[0110] The server retrieves the received data and stores it in the database. During this process, data validation and management are performed to check data integrity. The input is data transferred from the terminal, and the output is the storage of the data in a consistent database.

[0111] Step 4:

[0112] The server invokes a generative AI model based on data stored in the database to generate a personalized health management plan for each user. The generative AI model uses machine learning algorithms to calculate the optimal plan based on the user's past data and goals. The input is the user's health-related data stored in the database, and the output is the generated health management plan.

[0113] Step 5:

[0114] The generated health management plan is transferred from the server to the mobile device and presented to the user visually and audibly on the device. For example, dietary advice may be displayed on the smartphone screen. The input is the generated health management plan, and the output is its display on the device's user interface.

[0115] Step 6:

[0116] Users perform their daily activities based on the proposed health management plan and input the results and feedback into the device. This feedback includes information on the exercise and diet they actually performed. The input is user feedback data, and the output is feedback that will be used to create the next plan.

[0117] Step 7:

[0118] The server receives feedback from the user and updates the data to reflect that feedback when creating the next health management plan. This ensures that the plan continuously improves in the direction the user desires. The input is feedback data, and the output is the updated health management plan.

[0119] 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.

[0120] This invention relates to an AI system that generates and provides a health management plan that comprehensively considers a user's health-related data and emotions. The system includes a client terminal, a server, and an emotion engine, all of which work together in coordination.

[0121] First, the user inputs daily health-related data into the device. This includes diet, exercise history, sleep records, and self-reported emotional state. Furthermore, the device acquires objective data from wearable devices, such as heart rate and skin electrical activity, and uses this data to estimate the user's emotional state.

[0122] The device transfers all collected data to the server. The server analyzes this data and evaluates the correlation between the user's health status and emotional state. In the analysis, an emotion engine plays a central role, using algorithms to rationally determine the impact of the user's emotions on health management.

[0123] The server generates a customized health management plan based on the evaluation results. This plan adjusts advice on diet, exercise, and sleep based on the user's current health status and emotions. For example, if the emotion engine assesses that the user is in a high-stress emotional state, it may suggest a diet and exercise routine focused on stress management.

[0124] The generated plan is sent to the device and presented to the user in a clear and actionable format. The user acts according to the plan and records the results and feedback on the device. This feedback is sent to the server to verify the user's emotions and the effectiveness of health management, and is used to adjust the next plan.

[0125] For example, if a user is experiencing stress at work, the system uses an emotion engine to recognize their stress level. The server then designs a plan incorporating relaxation effects such as smoothies and light exercise, and proposes it to the user via the device. As the user implements this plan and inputs its effects and feedback into the device, the system can continuously refine and personalize its approach.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The user inputs health-related data such as diet, exercise time, sleep habits, and emotional state into the device. This input also includes selecting options for emotional state. In addition, the device acquires data such as heart rate and skin electrical activity from connected wearable devices.

[0129] Step 2:

[0130] The device transfers all acquired data to the server. During this process, the data is encrypted to protect user privacy.

[0131] Step 3:

[0132] The server saves the received data to a database. This allows for centralized management of the user's health history and emotional state.

[0133] Step 4:

[0134] The server uses data retrieved from the database to execute an AI algorithm. Here, the emotion engine is utilized to evaluate the user's emotional state and analyze its correlation with their health status.

[0135] Step 5:

[0136] The server generates a customized health management plan based on the analysis results. This plan includes advice on diet, exercise, and sleep tailored to your emotional state. For example, if the emotional engine detects a high level of stress, it will suggest exercises and nutrients that promote relaxation.

[0137] Step 6:

[0138] The server sends the generated customized plan to the device. The device notifies the user of the plan and sets up alerts and reminders to support its implementation.

[0139] Step 7:

[0140] The user executes the presented plan and inputs the results and changes in their emotions into the device. This process is carried out on a daily basis.

[0141] Step 8:

[0142] The device sends user feedback to the server. The server analyzes this feedback to verify the user's emotions and the effectiveness of the health management plan, and uses this information to create the next plan.

[0143] (Example 2)

[0144] 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".

[0145] Providing comprehensive and effective health management plans that take into account individual health and emotional states is a challenging task. In particular, it is essential to appropriately manage the impact of users' daily emotional fluctuations on their health and to provide effective advice based on that information.

[0146] 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.

[0147] In this invention, the server includes means for analyzing data on the user's health and emotional state, means for generating a health maintenance plan using an emotional analysis engine, and means for adjusting the next plan based on user feedback. This makes it possible to provide a customized health management plan that comprehensively considers the individual's emotional and physical condition.

[0148] A "user" refers to an individual who uses the system to provide information about their health and emotional state and receive a health maintenance plan.

[0149] "Health status" refers to information related to the user's physical health level, including factors such as nutrition, physical activity, and rest.

[0150] "Emotional state" refers to information that indicates the user's feelings and psychological state, and is evaluated based on self-reported data and physiological data.

[0151] "Data" refers to information about health and emotional status, including numerical data obtained from users and physiological data obtained from wearable devices, etc.

[0152] A "server" refers to a central information processing device that analyzes data acquired from users and generates and provides health maintenance plans.

[0153] The term "emotional analysis engine" refers to a component within a system that has a specialized algorithm for analyzing the user's emotional state and reflecting it in a health maintenance plan.

[0154] A "health maintenance plan" refers to a plan that includes customized advice on nutrition, physical activity, and rest based on the user's individual health and emotional state.

[0155] "Feedback" refers to the act of users providing information about the results of their health maintenance plan and related information.

[0156] This invention is a system that comprehensively analyzes a user's physical and emotional state and provides a personalized health maintenance plan. By combining a terminal, a server, and an emotional analysis engine, this system enables personalized advice tailored to each user.

[0157] The device receives daily health data from the user. This includes information on nutrition intake, physical activity, and rest entered by the user, as well as self-reported information about their emotional state. Furthermore, physiological indicators such as heart rate and skin electrical activity are acquired via the wearable device. This data serves as material for objectively estimating the user's emotional state.

[0158] All collected data is transferred to a server, which then performs data analysis based on it. A central component of this analysis is the emotional analysis engine. This engine uses AI algorithms to analyze the correlation between the user's physical and emotional state, helping to create a rational and effective personalized health maintenance plan.

[0159] The generated health maintenance plan is sent from the server to the terminal. This plan takes into account the user's current health and emotional state and includes tailored advice on nutrition, physical activity, and rest. For example, if the emotional analysis engine assesses the user's stress level as high, advice may be provided that includes a meal plan emphasizing relaxation and light exercise. The user adjusts their daily actions based on this plan and records the results and their impressions as feedback on the terminal.

[0160] As a concrete example, consider a situation where a user is experiencing stress at work. Using an emotion analysis engine, the server analyzes the user's stress level and creates a health maintenance plan that includes suggestions for relaxing foods and drinks, as well as light exercise. This plan is then presented to the user via their device, and the user incorporates the advice into their daily life.

[0161] Possible inputs to the generating AI model include prompts such as, "Create a stress-reducing health maintenance plan based on the user's emotional state and heart rate data." This allows the AI ​​model to assist in outputting a plan optimized for each individual user's condition.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] Users input data about their daily health status into the device. This input data includes diet, exercise history, sleep records, and self-reported emotional states. In addition, the device acquires physiological data such as heart rate and skin electrical activity from wearable devices. The inputs in this process are numerical data and physiological indicators provided by the user. As output, a dataset of the user's overall health and emotional state is constructed.

[0165] Step 2:

[0166] The terminal transfers the constructed dataset to the server. The server saves the received data to storage and prepares it for analysis. In this step, the input is the dataset sent from the terminal, and the output is the data structure ready for analysis.

[0167] Step 3:

[0168] The server begins data analysis using an emotion analysis engine. The input here is stored user health-related data and emotional data. The server applies an AI algorithm to evaluate the correlation between health status and emotional status and designs a specific health maintenance plan. The output is a health maintenance plan optimized for the user's individual condition.

[0169] Step 4:

[0170] The server sends the generated health maintenance plan to the terminal. The terminal notifies the user of the action plan through the user interface. The input here is the individually customized health maintenance plan, and the output is the specific plan information that the user receives.

[0171] Step 5:

[0172] The user adjusts their daily life based on the health maintenance plan received via the device. The user inputs the results of implementing the plan and their impressions as feedback into the device. The input in this step is the result of the user implementing the plan, and the output is the information recorded as feedback.

[0173] Step 6:

[0174] The device sends user feedback to the server. The server uses this feedback for analysis and to improve the next health maintenance plan. The input in this step is user feedback data, and the output is insights needed to adjust the plan in the future.

[0175] (Application Example 2)

[0176] 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 device 14 will be referred to as the "terminal."

[0177] In modern living environments, effectively managing health problems and emotional stress is essential. However, many people find it difficult to find appropriate solutions tailored to their individual health and emotional states. Furthermore, for the elderly and those receiving care, the lack of adequate external support exacerbates feelings of isolation, further impacting their health and well-being. Solving these problems is crucial.

[0178] 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.

[0179] In this invention, the server includes means for acquiring lifestyle-related data from the user, means for transferring the acquired data to an information processing device, and means for estimating the user's emotional state using an emotion estimation engine and reflecting the emotional state in the health management plan. This makes it possible to provide an individually optimized health management plan that takes into account both the user's lifestyle and emotions, and ultimately to provide a connection plan that reduces feelings of isolation among the elderly and those receiving care.

[0180] "Users" refer to individuals who use the system to provide lifestyle-related data and receive personalized health management plans.

[0181] "Lifestyle-related data" refers to data related to the user's daily life, including information on diet, exercise, sleep, and data acquired by wearable information collection devices.

[0182] An "information processing device" refers to a computer system that plays a central role in analyzing acquired lifestyle-related data and generating individually optimized health management plans.

[0183] An "emotion inference engine" refers to a software component that has the function of estimating a user's emotional state based on biometric information acquired by a wearable information collection device and the user's self-reported information.

[0184] A "health management plan" refers to a plan that includes tailored instructions regarding nutrition, physical activity, and rest, generated considering the user's lifestyle data and emotional state.

[0185] A "connection plan to reduce feelings of isolation" refers to a plan that includes suggested actions and activities to help users feel socially connected, and is designed to promote contact with family and society.

[0186] The system that realizes this invention mainly consists of the user's smartphone, a wearable data collection device, and a server in the cloud. The smartphone plays the role of collecting lifestyle-related data that the user inputs in their daily life. This includes information on diet, exercise, and sleep, as well as data such as heart rate and physical activity level obtained from a wearable data collection device (e.g., a fitness tracker).

[0187] The server operates in the cloud, receiving this lifestyle-related data and estimating the user's emotional state using an emotion inference engine. This emotion inference utilizes machine learning algorithms (e.g., TENSORFLOW® and PyTorch). The server analyzes the received data and systematically generates a personalized health management plan for the user. This plan includes instructions regarding nutrition, physical activity, and rest, and is adjusted based on the user's emotional state.

[0188] The generated health management plan is sent to the user's smartphone and presented in a clear and actionable format. Users can input feedback on the proposed plan via their smartphone, and this information is returned to the server, contributing to system improvement and further personalization.

[0189] For example, if an elderly user is experiencing feelings of isolation, the system, based on the emotion prediction engine's judgment, can suggest activities to promote social connection and alleviate this isolation. For instance, it could incorporate regular communication time into their health management plan.

[0190] An example of a prompt message might be, "Elderly person T feels isolated in their daily life. Please generate an optimal care plan for T using T's daily health data and emotional state." This allows the generating AI model to create a specific plan tailored to the user's needs.

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The device collects lifestyle-related data from the user. Input includes information about diet, exercise, and sleep entered by the user on their smartphone, as well as heart rate and physical activity data measured by wearable data collection devices. This data is temporarily stored within the device and prepared for transfer to the server in the next step.

[0194] Step 2:

[0195] The device transfers collected lifestyle-related data to the server. The device sends data from the user's smartphone to the server in the cloud using a secure communication protocol. As output, the data arrives at the server in a structured data format.

[0196] Step 3:

[0197] The server analyzes the received data and evaluates the user's health and emotional state. It acquires transmitted lifestyle-related data as input and uses data analysis techniques (e.g., machine learning algorithms) with a generative AI model to determine the user's health and emotional state. The analysis results are generated as output and used in the next step.

[0198] Step 4:

[0199] The server uses an emotion inference engine to estimate the user's emotional state. Using the analysis data obtained in the previous step as input, the emotion inference engine applies a machine learning model to estimate the user's emotional level. The output provides information about the user's emotional state.

[0200] Step 5:

[0201] The server generates an individually optimized health management plan based on the user's condition. The server uses the results of analysis and emotion inference as input to create a plan that includes instructions regarding nutrition, physical activity, and rest. This plan is adjusted to the user's health and emotional state. The output is a health management plan that includes specific action guidelines.

[0202] Step 6:

[0203] The server sends the generated health management plan to the terminal. The server delivers the plan to the user's smartphone and presents it in an actionable format. The health management plan is displayed on the smartphone as output.

[0204] Step 7:

[0205] Users input feedback on their health management plan via a terminal. The terminal registers user actions, their results, and improvement requests as input data. This information is used for subsequent analysis.

[0206] Step 8:

[0207] The server receives feedback and uses it to generate the next plan. It uses user feedback data as input to adjust future health management plans. The server uses a generation AI model to incorporate the feedback and create an improved plan. The output provides insights reflected in the next plan.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] This invention relates to an AI system that provides a health management plan tailored to the individual needs of a user using health-related data. This system is implemented through data exchange between a client terminal, a server, and the user.

[0225] The system's first step is to acquire health-related data from the user. The user inputs information such as their diet, exercise time, and sleep patterns into a client terminal. Meanwhile, the terminal can also acquire data such as heart rate, steps taken, and calories burned from connected wearable devices. This data is then transferred to the server as information reflecting the user's health status.

[0226] The server uses the received data to perform a detailed assessment of the user's health status. Based on this assessment, a customized health management plan is generated for the user. This plan includes appropriate dietary advice, a recommended exercise schedule, and suggestions for improving the amount and quality of sleep needed. The generated plan is sent to the user's device and displayed in an easy-to-understand visual format.

[0227] After the user completes the plan, the device sends the user's results and feedback back to the server. Based on this feedback, the server makes adjustments when creating the next plan, continuously providing personalized health management support tailored to each user.

[0228] As a concrete example, suppose a user has a goal of losing weight. In this case, the server might suggest a plan that combines calorie restriction in diet with high-intensity exercise. It might also include advice on improving sleep quality. In this way, the system can provide a plan based on individual data to effectively support the user in achieving their goals.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The user inputs health-related data such as diet, exercise time, and sleep patterns into the device. The device also acquires data from wearable devices.

[0232] Step 2:

[0233] The device transfers the collected health-related data to the server using a security protocol. The data is encrypted at this stage to protect the information during transmission.

[0234] Step 3:

[0235] The server saves the received data to a database. This database is used to store the user's health history.

[0236] Step 4:

[0237] The server uses stored data to run AI algorithms and assess the user's current health status. This includes trend analysis compared to historical data.

[0238] Step 5:

[0239] The server generates a customized health management plan based on the evaluation results. The plan includes specific dietary advice, exercise programs, and sleep improvement strategies.

[0240] Step 6:

[0241] The server sends the generated plan to the device. The device notifies the user of the plan and sets up alerts and reminders to encourage implementation.

[0242] Step 7:

[0243] The user actually implements the plan and inputs the results and feedback into the device. The device then resends the user's feedback to the server.

[0244] Step 8:

[0245] The server analyzes user feedback and makes adjustments as needed to reflect it in future plan creation. This makes it possible to continuously provide plans optimized for individual users.

[0246] (Example 1)

[0247] 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."

[0248] In the field of health management, there is a need to provide appropriate and effective health management plans based on individual user health data. However, conventional systems have limited data collection and analysis capabilities, making it difficult to provide specific plans tailored to individual needs. This hinders effective health management.

[0249] 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.

[0250] In this invention, the server includes means for acquiring health-related information from a user, means for transferring the acquired information to an information provider, and means for analyzing the transferred information and generating a health management plan for the user. This makes it possible to provide a highly accurate health management plan based on individual user data.

[0251] A "user" is an entity that utilizes the system, provides health-related information, and receives an individualized health management plan.

[0252] "Health-related information" includes information such as the user's diet, physical activity, sleep patterns, and data obtained from wearable devices.

[0253] An "information provision device" is a computing device that analyzes health-related information obtained from users and generates a health management plan based on that information.

[0254] A "health management plan" is a plan that includes personalized advice on diet, physical activity, and rest, created based on the user's individual health condition.

[0255] A "wearable device" is a device that, when worn by a user, can acquire health-related data in real time.

[0256] An "automated learning model" is an algorithm that continuously learns from past data and newly acquired feedback to optimize health management plans.

[0257] "Visually easy to understand" means presenting a health management plan to users using visual elements such as diagrams and graphs to make its contents easy to comprehend.

[0258] This invention relates to a system in which an information provider and a terminal work together to support a user's health management. This system collects information about the user's health and provides an individualized health management plan based on that information. Specifically, it is configured as follows:

[0259] Users input health-related information such as their diet, exercise time, and sleep patterns using the device. The device also connects with wearable devices to acquire real-time data such as heart rate, steps taken, and calories burned. This information is transmitted from the device to the information provider via a secure communication protocol.

[0260] The information provider uses an automated learning model to analyze the acquired information. This model assesses the user's health status and generates a health management plan tailored to their individual needs. The generated plan includes personalized advice on diet, physical activity, and rest. For example, a prompt such as, "Please suggest a diet and exercise plan for a woman in her 30s who wants to lose 3 kg," might be input into the AI ​​model.

[0261] The terminal displays the health management plan sent from the information provider in a visually easy-to-understand manner for the user. This allows the user to put the plan into action.

[0262] After a user completes their health management plan, the device sends the results and feedback back to the information provider. Based on this feedback, the information provider learns and adjusts to provide an even more appropriate plan when generating the next one. This allows the system to provide continuous and effective support to the user.

[0263] By using the system of the present invention, it becomes possible to implement personalized health management, which can greatly contribute to maintaining and improving health.

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] Users input health-related information into the device. Specifically, they record their diet, exercise time, and sleep patterns through a dedicated application. The device automatically retrieves heart rate, steps taken, and calories burned from wearable devices. This information is used as input data and is temporarily stored in a database.

[0267] Step 2:

[0268] The terminal transmits the collected health information to the information provider. Secure protocols (e.g., HTTPS) are used to ensure data transfer while protecting user privacy. The input data is converted to an analysis format on the server side.

[0269] Step 3:

[0270] The server analyzes the received data. An automated learning model is used to assess the user's health status. Temporal trend analysis is also performed using historical data. This generates output data that evaluates the user's health status and trends.

[0271] Step 4:

[0272] The server generates a health management plan based on the analysis results. Using a generation AI model, it designs a personalized plan for diet, exercise, and sleep that is suitable for the user. The user inputs specific information (e.g., "I want to lose weight") as a prompt to the AI, and the generated plan is output.

[0273] Step 5:

[0274] The server sends the generated health management plan to the terminal. The terminal displays this plan to the user in a visually easy-to-understand format. Graphs and icons are used to ensure effective communication.

[0275] Step 6:

[0276] The user implements a health management plan and records the results and feedback on their device. The device then sends this information back to the server. The feedback content is collected as input data.

[0277] Step 7:

[0278] The server adjusts the next health management plan based on the feedback. It then uses the automated learning model again to process the data and improve the plan. This results in output data that provides a next plan better suited to the user's individual needs.

[0279] (Application Example 1)

[0280] 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."

[0281] In modern society, the importance of individual health management is increasing, but providing personalized and appropriate health management plans requires efficiently collecting and analyzing large amounts of health-related data and presenting it to users in an easy-to-understand manner. Furthermore, continuous improvement of plans based on user feedback is also important, but there is a challenge in efficiently implementing this.

[0282] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following respective means.

[0283] In this invention, the server includes means for acquiring health-related data from a user, means for transferring the acquired data to an information processing apparatus, and means for analyzing the transferred data and generating a health management plan for the user. Thereby, an individualized health management plan can be provided to the user, and the plan can be improved by real-time feedback.

[0284] "Health-related data" is a general term for information indicating the health status of a user, such as the user's diet content, amount of exercise, sleep time, heart rate, and number of steps.

[0285] "Information processing apparatus" refers to a server or computer used for analyzing data acquired from a user and generating a health management plan.

[0286] "Health management plan" is a plan including advice on diet, exercise, and sleep customized according to the individual goals and needs of the user based on the analyzed health-related data.

[0287] "Means for visually and aurally providing" refers to an interface or device for conveying the generated health management plan to the user in an easy-to-see or easy-to-hear form.

[0288] "Portable terminal" is a portable device such as a smartphone or tablet used by a user to record diet information or receive a health management plan.

[0289] "Wearable device" refers to a wearable device worn on the body by a user to collect data such as heart rate and number of steps.

[0290] To implement this invention, the user begins by inputting daily health-related data using a mobile device. The device automatically acquires data such as heart rate and steps from a wearable device and transmits it to an information processing unit. This information processing unit is a server, and upon receiving the data, it evaluates the user's health status and generates a customized health management plan based on a generated AI model. This plan includes meal suggestions tailored to the user's goals, recommended exercise menus, and optimal sleep patterns.

[0291] The server provides the generated health management plan to the user visually and audibly via a mobile device. For example, dietary advice may be displayed on the smartphone screen, or exercise instructions may be given via voice guidance. The user can also input feedback on the completed plan from their device, which is then sent back to the server. The server analyzes this feedback and incorporates it into future health management plans.

[0292] For example, if a user sets a goal of losing weight, the server will propose a plan that includes low-calorie meal suggestions and aerobic exercise schedules, and the user will act accordingly. An example of a prompt message would be: "Username: ABC, Goal: Weight loss, Current weight: 85kg, Desired weight: 75kg. Please create an optimal 2-week meal and exercise plan for ABC." The generating AI model would then operate in this format.

[0293] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0294] Step 1:

[0295] Users input health-related data using their mobile devices. The device receives information about meals and exercise as text and images, and automatically acquires data such as heart rate and steps from wearable devices. This input data is pre-processed within the device and converted into a unified format. The output is health-related data converted into a unified format.

[0296] Step 2:

[0297] The terminal transfers pre-processed data to the server, which acts as an information processing unit. The transfer protocol uses HTTP or HTTPS, and the data is posted to the API endpoint. The input is health-related data from the terminal, and the output is data that has been confirmed to have been received by the server.

[0298] Step 3:

[0299] The server retrieves the received data and stores it in the database. During this process, data validation and management are performed to check data integrity. The input is data transferred from the terminal, and the output is the storage of the data in a consistent database.

[0300] Step 4:

[0301] The server invokes a generative AI model based on data stored in the database to generate a personalized health management plan for each user. The generative AI model uses machine learning algorithms to calculate the optimal plan based on the user's past data and goals. The input is the user's health-related data stored in the database, and the output is the generated health management plan.

[0302] Step 5:

[0303] The generated health management plan is transferred from the server to the mobile device and presented to the user visually and audibly on the device. For example, dietary advice may be displayed on the smartphone screen. The input is the generated health management plan, and the output is its display on the device's user interface.

[0304] Step 6:

[0305] The user conducts daily activities based on the proposed health management plan and inputs the results and feedback into the terminal. This feedback includes information on the actual exercise and diet. The input is the feedback data from the user, and the output is the feedback to be utilized for creating the next plan.

[0306] Step 7:

[0307] The server receives the feedback from the user and updates the data to reflect the feedback when creating the next health management plan. This enables the plan to be continuously improved in the direction desired by the user. The input is the feedback data, and the output is the updated health management plan.

[0308] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0309] The present invention relates to an AI system that generates and provides a health management plan comprehensively considering the user's health-related data and emotions. This system includes a client terminal, a server, and an emotion engine, each of which functions in cooperation.

[0310] First, the user inputs daily health-related data into the terminal. This includes, in addition to dietary content, exercise achievements, and sleep records, self-reporting of emotional states. Furthermore, the terminal acquires objective data such as heart rate and skin electrical activity from wearable devices and uses these to estimate the user's emotional state.

[0311] The terminal transfers all the collected data to the server. The server analyzes this data and evaluates the correlation between the user's health state and emotional state. In the analysis, the emotion engine plays a central role, and an algorithm for reasonably determining the impact of the user's emotions on health management is utilized.

[0312] The server generates a customized health management plan based on the evaluation results. This plan adjusts advice on diet, exercise, and sleep based on the user's current health status and emotions. For example, if the emotion engine assesses that the user is in a high-stress emotional state, it may suggest a diet and exercise routine focused on stress management.

[0313] The generated plan is sent to the device and presented to the user in a clear and actionable format. The user acts according to the plan and records the results and feedback on the device. This feedback is sent to the server to verify the user's emotions and the effectiveness of health management, and is used to adjust the next plan.

[0314] For example, if a user is experiencing stress at work, the system uses an emotion engine to recognize their stress level. The server then designs a plan incorporating relaxation effects such as smoothies and light exercise, and proposes it to the user via the device. As the user implements this plan and inputs its effects and feedback into the device, the system can continuously refine and personalize its approach.

[0315] The following describes the processing flow.

[0316] Step 1:

[0317] The user inputs health-related data such as diet, exercise time, sleep habits, and emotional state into the device. This input also includes selecting options for emotional state. In addition, the device acquires data such as heart rate and skin electrical activity from connected wearable devices.

[0318] Step 2:

[0319] The device transfers all acquired data to the server. During this process, the data is encrypted to protect user privacy.

[0320] Step 3:

[0321] The server saves the received data to a database. This allows for centralized management of the user's health history and emotional state.

[0322] Step 4:

[0323] The server uses data retrieved from the database to execute an AI algorithm. Here, the emotion engine is utilized to evaluate the user's emotional state and analyze its correlation with their health status.

[0324] Step 5:

[0325] The server generates a customized health management plan based on the analysis results. This plan includes advice on diet, exercise, and sleep tailored to your emotional state. For example, if the emotional engine detects a high level of stress, it will suggest exercises and nutrients that promote relaxation.

[0326] Step 6:

[0327] The server sends the generated customized plan to the device. The device notifies the user of the plan and sets up alerts and reminders to support its implementation.

[0328] Step 7:

[0329] The user executes the presented plan and inputs the results and changes in their emotions into the device. This process is carried out on a daily basis.

[0330] Step 8:

[0331] The device sends user feedback to the server. The server analyzes this feedback to verify the user's emotions and the effectiveness of the health management plan, and uses this information to create the next plan.

[0332] (Example 2)

[0333] 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".

[0334] Providing comprehensive and effective health management plans that take into account individual health and emotional states is a challenging task. In particular, it is essential to appropriately manage the impact of users' daily emotional fluctuations on their health and to provide effective advice based on that information.

[0335] 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.

[0336] In this invention, the server includes means for analyzing data on the user's health and emotional state, means for generating a health maintenance plan using an emotional analysis engine, and means for adjusting the next plan based on user feedback. This makes it possible to provide a customized health management plan that comprehensively considers the individual's emotional and physical condition.

[0337] A "user" refers to an individual who uses the system to provide information about their health and emotional state and receive a health maintenance plan.

[0338] "Health status" refers to information related to the user's physical health level, including factors such as nutrition, physical activity, and rest.

[0339] "Emotional state" refers to information that indicates the user's feelings and psychological state, and is evaluated based on self-reported data and physiological data.

[0340] "Data" refers to information about health and emotional status, including numerical data obtained from users and physiological data obtained from wearable devices, etc.

[0341] A "server" refers to a central information processing device that analyzes data acquired from users and generates and provides health maintenance plans.

[0342] The term "emotional analysis engine" refers to a component within a system that has a specialized algorithm for analyzing the user's emotional state and reflecting it in a health maintenance plan.

[0343] A "health maintenance plan" refers to a plan that includes customized advice on nutrition, physical activity, and rest based on the user's individual health and emotional state.

[0344] "Feedback" refers to the act of users providing information about the results of their health maintenance plan and related information.

[0345] This invention is a system that comprehensively analyzes a user's physical and emotional state and provides a personalized health maintenance plan. By combining a terminal, a server, and an emotional analysis engine, this system enables personalized advice tailored to each user.

[0346] The device receives daily health data from the user. This includes information on nutrition intake, physical activity, and rest entered by the user, as well as self-reported information about their emotional state. Furthermore, physiological indicators such as heart rate and skin electrical activity are acquired via the wearable device. This data serves as material for objectively estimating the user's emotional state.

[0347] All collected data is transferred to a server, which then performs data analysis based on it. A central component of this analysis is the emotional analysis engine. This engine uses AI algorithms to analyze the correlation between the user's physical and emotional state, helping to create a rational and effective personalized health maintenance plan.

[0348] The generated health maintenance plan is sent from the server to the terminal. This plan takes into account the user's current health and emotional state and includes tailored advice on nutrition, physical activity, and rest. For example, if the emotional analysis engine assesses the user's stress level as high, advice may be provided that includes a meal plan emphasizing relaxation and light exercise. The user adjusts their daily actions based on this plan and records the results and their impressions as feedback on the terminal.

[0349] As a concrete example, consider a situation where a user is experiencing stress at work. Using an emotion analysis engine, the server analyzes the user's stress level and creates a health maintenance plan that includes suggestions for relaxing foods and drinks, as well as light exercise. This plan is then presented to the user via their device, and the user incorporates the advice into their daily life.

[0350] Possible inputs to the generating AI model include prompts such as, "Create a stress-reducing health maintenance plan based on the user's emotional state and heart rate data." This allows the AI ​​model to assist in outputting a plan optimized for each individual user's condition.

[0351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0352] Step 1:

[0353] Users input data about their daily health status into the device. This input data includes diet, exercise history, sleep records, and self-reported emotional states. In addition, the device acquires physiological data such as heart rate and skin electrical activity from wearable devices. The inputs in this process are numerical data and physiological indicators provided by the user. As output, a dataset of the user's overall health and emotional state is constructed.

[0354] Step 2:

[0355] The terminal transfers the constructed dataset to the server. The server saves the received data to storage and prepares it for analysis. In this step, the input is the dataset sent from the terminal, and the output is the data structure ready for analysis.

[0356] Step 3:

[0357] The server begins data analysis using an emotion analysis engine. The input here is stored user health-related data and emotional data. The server applies an AI algorithm to evaluate the correlation between health status and emotional status and designs a specific health maintenance plan. The output is a health maintenance plan optimized for the user's individual condition.

[0358] Step 4:

[0359] The server sends the generated health maintenance plan to the terminal. The terminal notifies the user of the action plan through the user interface. The input here is the individually customized health maintenance plan, and the output is the specific plan information that the user receives.

[0360] Step 5:

[0361] The user adjusts their daily life based on the health maintenance plan received via the device. The user inputs the results of implementing the plan and their impressions as feedback into the device. The input in this step is the result of the user implementing the plan, and the output is the information recorded as feedback.

[0362] Step 6:

[0363] The device sends user feedback to the server. The server uses this feedback for analysis and to improve the next health maintenance plan. The input in this step is user feedback data, and the output is insights needed to adjust the plan in the future.

[0364] (Application Example 2)

[0365] 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."

[0366] In modern living environments, effectively managing health problems and emotional stress is essential. However, many people find it difficult to find appropriate solutions tailored to their individual health and emotional states. Furthermore, for the elderly and those receiving care, the lack of adequate external support exacerbates feelings of isolation, further impacting their health and well-being. Solving these problems is crucial.

[0367] 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.

[0368] In this invention, the server includes means for acquiring lifestyle-related data from the user, means for transferring the acquired data to an information processing device, and means for estimating the user's emotional state using an emotion estimation engine and reflecting the emotional state in the health management plan. This makes it possible to provide an individually optimized health management plan that takes into account both the user's lifestyle and emotions, and ultimately to provide a connection plan that reduces feelings of isolation among the elderly and those receiving care.

[0369] "Users" refer to individuals who use the system to provide lifestyle-related data and receive personalized health management plans.

[0370] "Lifestyle-related data" refers to data related to the user's daily life, including information on diet, exercise, sleep, and data acquired through wearable information collection devices.

[0371] An "information processing device" refers to a computer system that plays a central role in analyzing acquired lifestyle-related data and generating individually optimized health management plans.

[0372] An "emotion inference engine" refers to a software component that has the function of estimating a user's emotional state based on biometric information acquired by a wearable information collection device and the user's self-reported information.

[0373] A "health management plan" refers to a plan that includes tailored instructions regarding nutrition, physical activity, and rest, generated considering the user's lifestyle data and emotional state.

[0374] A "connection plan to reduce feelings of isolation" refers to a plan that includes suggested actions and activities to help users feel socially connected, and is designed to promote contact with family and society.

[0375] The system that realizes this invention mainly consists of the user's smartphone, a wearable data collection device, and a server in the cloud. The smartphone plays the role of collecting lifestyle-related data that the user inputs in their daily life. This includes information on diet, exercise, and sleep, as well as data such as heart rate and physical activity level obtained from a wearable data collection device (e.g., a fitness tracker).

[0376] The server operates in the cloud, receiving this lifestyle-related data and estimating the user's emotional state using an emotion inference engine. This emotion inference utilizes machine learning algorithms (e.g., TensorFlow or PyTorch). The server analyzes the received data and systematically generates a personalized health management plan for the user. This plan includes instructions regarding nutrition, physical activity, and rest, and is adjusted based on the user's emotional state.

[0377] The generated health management plan is sent to the user's smartphone and presented in a clear and actionable format. Users can input feedback on the proposed plan via their smartphone, and this information is returned to the server, contributing to system improvement and further personalization.

[0378] For example, if an elderly user is experiencing feelings of isolation, the system, based on the emotion prediction engine's judgment, can suggest activities to promote social connection and alleviate this isolation. For instance, it could incorporate regular communication time into their health management plan.

[0379] An example of a prompt message might be, "Elderly person T feels isolated in their daily life. Please generate an optimal care plan for T using T's daily health data and emotional state." This allows the generating AI model to create a specific plan tailored to the user's needs.

[0380] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0381] Step 1:

[0382] The device collects lifestyle-related data from the user. Input includes information about diet, exercise, and sleep entered by the user on their smartphone, as well as heart rate and physical activity data measured by wearable data collection devices. This data is temporarily stored within the device and prepared for transfer to the server in the next step.

[0383] Step 2:

[0384] The device transfers collected lifestyle-related data to the server. The device sends data from the user's smartphone to the cloud server using a secure communication protocol. The data arrives at the server as output in a structured data format.

[0385] Step 3:

[0386] The server analyzes the received data and evaluates the user's health and emotional state. It acquires transmitted lifestyle-related data as input and uses data analysis techniques (e.g., machine learning algorithms) with a generative AI model to determine the user's health and emotional state. The analysis results are generated as output and used in the next step.

[0387] Step 4:

[0388] The server uses an emotion inference engine to estimate the user's emotional state. Using the analysis data obtained in the previous step as input, the emotion inference engine applies a machine learning model to estimate the user's emotional level. The output provides information about the user's emotional state.

[0389] Step 5:

[0390] The server generates an individually optimized health management plan based on the user's condition. The server uses the results of analysis and emotion inference as input to create a plan that includes instructions regarding nutrition, physical activity, and rest. This plan is adjusted to the user's health and emotional state. The output is a health management plan that includes specific action guidelines.

[0391] Step 6:

[0392] The server sends the generated health management plan to the terminal. The server delivers the plan to the user's smartphone and presents it in an actionable format. The health management plan is displayed on the smartphone as output.

[0393] Step 7:

[0394] Users input feedback on their health management plan via a terminal. The terminal registers user actions, their results, and improvement requests as input data. This information is used for subsequent analysis.

[0395] Step 8:

[0396] The server receives feedback and uses it to generate the next plan. It uses user feedback data as input to adjust future health management plans. The server uses a generation AI model to incorporate the feedback and create an improved plan. The output provides insights reflected in the next plan.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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".

[0413] This invention relates to an AI system that provides a health management plan tailored to the individual needs of a user using health-related data. This system is implemented through data exchange between a client terminal, a server, and the user.

[0414] The system's first step is to acquire health-related data from the user. The user inputs information such as their diet, exercise time, and sleep patterns into a client terminal. Meanwhile, the terminal can also acquire data such as heart rate, steps taken, and calories burned from connected wearable devices. This data is then transferred to the server as information reflecting the user's health status.

[0415] The server uses the received data to perform a detailed assessment of the user's health status. Based on this assessment, a customized health management plan is generated for the user. This plan includes appropriate dietary advice, a recommended exercise schedule, and suggestions for improving the amount and quality of sleep needed. The generated plan is sent to the user's device and displayed in an easy-to-understand visual format.

[0416] After the user completes the plan, the device sends the user's results and feedback back to the server. Based on this feedback, the server makes adjustments when creating the next plan, continuously providing personalized health management support tailored to each user.

[0417] As a concrete example, suppose a user has a goal of losing weight. In this case, the server might suggest a plan that combines calorie restriction in diet with high-intensity exercise. It might also include advice on improving sleep quality. In this way, the system can provide a plan based on individual data to effectively support the user in achieving their goals.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] The user inputs health-related data such as diet, exercise time, and sleep patterns into the device. The device also acquires data from wearable devices.

[0421] Step 2:

[0422] The device transfers the collected health-related data to the server using a security protocol. The data is encrypted at this stage to protect the information during transmission.

[0423] Step 3:

[0424] The server saves the received data to a database. This database is used to store the user's health history.

[0425] Step 4:

[0426] The server uses stored data to run AI algorithms and assess the user's current health status. This includes trend analysis compared to historical data.

[0427] Step 5:

[0428] The server generates a customized health management plan based on the evaluation results. The plan includes specific dietary advice, exercise programs, and sleep improvement strategies.

[0429] Step 6:

[0430] The server sends the generated plan to the device. The device notifies the user of the plan and sets up alerts and reminders to encourage implementation.

[0431] Step 7:

[0432] The user actually implements the plan and inputs the results and feedback into the device. The device then resends the user's feedback to the server.

[0433] Step 8:

[0434] The server analyzes user feedback and makes adjustments as needed to reflect it in future plan creation. This makes it possible to continuously provide plans optimized for individual users.

[0435] (Example 1)

[0436] 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."

[0437] In the field of health management, there is a need to provide appropriate and effective health management plans based on individual user health data. However, conventional systems have limited data collection and analysis capabilities, making it difficult to provide specific plans tailored to individual needs. This hinders effective health management.

[0438] 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.

[0439] In this invention, the server includes means for acquiring health-related information from a user, means for transferring the acquired information to an information provider, and means for analyzing the transferred information and generating a health management plan for the user. This makes it possible to provide a highly accurate health management plan based on individual user data.

[0440] A "user" is an entity that utilizes the system, provides health-related information, and receives an individualized health management plan.

[0441] "Health-related information" includes information such as the user's diet, physical activity, sleep patterns, and data obtained from wearable devices.

[0442] An "information provision device" is a computing device that analyzes health-related information obtained from users and generates a health management plan based on that information.

[0443] A "health management plan" is a plan that includes personalized advice on diet, physical activity, and rest, created based on the user's individual health condition.

[0444] A "wearable device" is a device that, when worn by a user, can acquire health-related data in real time.

[0445] An "automated learning model" is an algorithm that continuously learns from past data and newly acquired feedback to optimize health management plans.

[0446] "Visually easy to understand" means presenting a health management plan to users using visual elements such as diagrams and graphs to make its contents easy to comprehend.

[0447] This invention relates to a system in which an information provider and a terminal work together to support a user's health management. This system collects information about the user's health and provides an individualized health management plan based on that information. Specifically, it is configured as follows:

[0448] Users input health-related information such as their diet, exercise time, and sleep patterns using the device. The device also connects with wearable devices to acquire real-time data such as heart rate, steps taken, and calories burned. This information is transmitted from the device to the information provider via a secure communication protocol.

[0449] The information provider uses an automated learning model to analyze the acquired information. This model assesses the user's health status and generates a health management plan tailored to their individual needs. The generated plan includes personalized advice on diet, physical activity, and rest. For example, a prompt such as, "Please suggest a diet and exercise plan for a woman in her 30s who wants to lose 3 kg," might be input into the AI ​​model.

[0450] The terminal displays the health management plan sent from the information provider in a visually easy-to-understand manner for the user. This allows the user to put the plan into action.

[0451] After a user completes their health management plan, the device sends the results and feedback back to the information provider. Based on this feedback, the information provider learns and adjusts to provide an even more appropriate plan when generating the next one. This allows the system to provide continuous and effective support to the user.

[0452] By using the system of the present invention, it becomes possible to implement personalized health management, which can greatly contribute to maintaining and improving health.

[0453] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0454] Step 1:

[0455] Users input health-related information into the device. Specifically, they record their diet, exercise time, and sleep patterns through a dedicated application. The device automatically retrieves heart rate, steps taken, and calories burned from wearable devices. This information is used as input data and is temporarily stored in a database.

[0456] Step 2:

[0457] The terminal transmits the collected health information to the information provider. Secure protocols (e.g., HTTPS) are used to ensure data transfer while protecting user privacy. The input data is converted to an analysis format on the server side.

[0458] Step 3:

[0459] The server analyzes the received data. An automated learning model is used to assess the user's health status. Temporal trend analysis is also performed using historical data. This generates output data that evaluates the user's health status and trends.

[0460] Step 4:

[0461] The server generates a health management plan based on the analysis results. Using a generation AI model, it designs a personalized plan for diet, exercise, and sleep that is suitable for the user. The user inputs specific information (e.g., "I want to lose weight") as a prompt to the AI, and the generated plan is output.

[0462] Step 5:

[0463] The server sends the generated health management plan to the terminal. The terminal displays this plan to the user in a visually easy-to-understand format. Graphs and icons are used to ensure effective communication.

[0464] Step 6:

[0465] The user implements a health management plan and records the results and feedback on their device. The device then sends this information back to the server. The feedback content is collected as input data.

[0466] Step 7:

[0467] The server adjusts the next health management plan based on the feedback. It then uses the automated learning model again to process the data and improve the plan. This results in output data that provides a next plan better suited to the user's individual needs.

[0468] (Application Example 1)

[0469] 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."

[0470] In modern society, the importance of individual health management is increasing, but providing personalized and appropriate health management plans requires efficiently collecting and analyzing large amounts of health-related data and presenting it to users in an easy-to-understand manner. Furthermore, continuous improvement of plans based on user feedback is also important, but there is a challenge in efficiently implementing this.

[0471] 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.

[0472] In this invention, the server includes means for acquiring health-related data from a user, means for transferring the acquired data to an information processing device, and means for analyzing the transferred data and generating a health management plan for the user. This enables the provision of a personalized health management plan to the user and allows for the improvement of the plan through real-time feedback.

[0473] "Health-related data" is a general term for information that indicates a user's health status, such as their diet, exercise level, sleep duration, heart rate, and steps taken.

[0474] An "information processing device" refers to a server or computer used to analyze data acquired from users and generate health management plans.

[0475] A "health management plan" is a plan that includes advice on diet, exercise, and sleep, tailored to the user's individual goals and needs, based on analyzed health-related data.

[0476] "Means of providing information visually and audibly" refers to interfaces and devices that convey the generated health management plan to the user in an easily viewable or easily audible format.

[0477] A "mobile device" refers to a portable device such as a smartphone or tablet that a user uses to record meal information or receive health management plans.

[0478] "Wearable devices" refer to wearable devices that users attach to their bodies to collect data such as heart rate and steps taken.

[0479] To implement this invention, the user begins by inputting daily health-related data using a mobile device. The device automatically acquires data such as heart rate and steps from a wearable device and transmits it to an information processing unit. This information processing unit is a server, and upon receiving the data, it evaluates the user's health status and generates a customized health management plan based on a generated AI model. This plan includes meal suggestions tailored to the user's goals, recommended exercise menus, and optimal sleep patterns.

[0480] The server provides the generated health management plan to the user visually and audibly via a mobile device. For example, dietary advice may be displayed on the smartphone screen, or exercise instructions may be given via voice guidance. The user can also input feedback on the completed plan from their device, which is then sent back to the server. The server analyzes this feedback and incorporates it into future health management plans.

[0481] For example, if a user sets a goal of losing weight, the server will propose a plan that includes low-calorie meal suggestions and aerobic exercise schedules, and the user will act accordingly. An example of a prompt message would be: "Username: ABC, Goal: Weight loss, Current weight: 85kg, Desired weight: 75kg. Please create an optimal 2-week meal and exercise plan for ABC." The generating AI model would then operate in this format.

[0482] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0483] Step 1:

[0484] Users input health-related data using their mobile devices. The device receives information about meals and exercise as text and images, and automatically acquires data such as heart rate and steps from wearable devices. This input data is pre-processed within the device and converted into a unified format. The output is health-related data converted into a unified format.

[0485] Step 2:

[0486] The terminal transfers pre-processed data to the server, which acts as an information processing unit. The transfer protocol uses HTTP or HTTPS, and the data is posted to the API endpoint. The input is health-related data from the terminal, and the output is data that has been confirmed to have been received by the server.

[0487] Step 3:

[0488] The server retrieves the received data and stores it in the database. During this process, data validation and management are performed to check data integrity. The input is data transferred from the terminal, and the output is the storage of the data in a consistent database.

[0489] Step 4:

[0490] The server invokes a generative AI model based on data stored in the database to generate a personalized health management plan for each user. The generative AI model uses machine learning algorithms to calculate the optimal plan based on the user's past data and goals. The input is the user's health-related data stored in the database, and the output is the generated health management plan.

[0491] Step 5:

[0492] The generated health management plan is transferred from the server to the mobile device and presented to the user visually and audibly on the device. For example, dietary advice may be displayed on the smartphone screen. The input is the generated health management plan, and the output is its display on the device's user interface.

[0493] Step 6:

[0494] Users perform their daily activities based on the proposed health management plan and input the results and feedback into the device. This feedback includes information on the exercise and diet they actually performed. The input is user feedback data, and the output is feedback that will be used to create the next plan.

[0495] Step 7:

[0496] The server receives feedback from the user and updates the data to reflect that feedback when creating the next health management plan. This ensures that the plan continuously improves in the direction the user desires. The input is feedback data, and the output is the updated health management plan.

[0497] 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.

[0498] This invention relates to an AI system that generates and provides a health management plan that comprehensively considers a user's health-related data and emotions. The system includes a client terminal, a server, and an emotion engine, all of which work together in coordination.

[0499] First, the user inputs daily health-related data into the device. This includes diet, exercise history, sleep records, and self-reported emotional state. Furthermore, the device acquires objective data from wearable devices, such as heart rate and skin electrical activity, and uses this data to estimate the user's emotional state.

[0500] The device transfers all collected data to the server. The server analyzes this data and evaluates the correlation between the user's health status and emotional state. In the analysis, an emotion engine plays a central role, using algorithms to rationally determine the impact of the user's emotions on health management.

[0501] The server generates a customized health management plan based on the evaluation results. This plan adjusts advice on diet, exercise, and sleep based on the user's current health status and emotions. For example, if the emotion engine assesses that the user is in a high-stress emotional state, it may suggest a diet and exercise routine focused on stress management.

[0502] The generated plan is sent to the device and presented to the user in a clear and actionable format. The user acts according to the plan and records the results and feedback on the device. This feedback is sent to the server to verify the user's emotions and the effectiveness of health management, and is used to adjust the next plan.

[0503] For example, if a user is experiencing stress at work, the system uses an emotion engine to recognize their stress level. The server then designs a plan incorporating relaxation effects such as smoothies and light exercise, and proposes it to the user via the device. As the user implements this plan and inputs its effects and feedback into the device, the system can continuously refine and personalize its approach.

[0504] The following describes the processing flow.

[0505] Step 1:

[0506] The user inputs health-related data such as diet, exercise time, sleep habits, and emotional state into the device. This input also includes selecting options for emotional state. In addition, the device acquires data such as heart rate and skin electrical activity from connected wearable devices.

[0507] Step 2:

[0508] The device transfers all acquired data to the server. During this process, the data is encrypted to protect user privacy.

[0509] Step 3:

[0510] The server saves the received data to a database. This allows for centralized management of the user's health history and emotional state.

[0511] Step 4:

[0512] The server uses data retrieved from the database to execute an AI algorithm. Here, the emotion engine is utilized to evaluate the user's emotional state and analyze its correlation with their health status.

[0513] Step 5:

[0514] The server generates a customized health management plan based on the analysis results. This plan includes advice on diet, exercise, and sleep tailored to your emotional state. For example, if the emotional engine detects a high level of stress, it will suggest exercises and nutrients that promote relaxation.

[0515] Step 6:

[0516] The server sends the generated customized plan to the device. The device notifies the user of the plan and sets up alerts and reminders to support its implementation.

[0517] Step 7:

[0518] The user executes the presented plan and inputs the results and changes in their emotions into the device. This process is carried out on a daily basis.

[0519] Step 8:

[0520] The device sends user feedback to the server. The server analyzes this feedback to verify the user's emotions and the effectiveness of the health management plan, and uses this information to create the next plan.

[0521] (Example 2)

[0522] 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."

[0523] Providing comprehensive and effective health management plans that take into account individual health and emotional states is a challenging task. In particular, it is essential to appropriately manage the impact of users' daily emotional fluctuations on their health and to provide effective advice based on that information.

[0524] 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.

[0525] In this invention, the server includes means for analyzing data on the user's health and emotional state, means for generating a health maintenance plan using an emotional analysis engine, and means for adjusting the next plan based on user feedback. This makes it possible to provide a customized health management plan that comprehensively considers the individual's emotional and physical condition.

[0526] A "user" refers to an individual who uses the system to provide information about their health and emotional state and receive a health maintenance plan.

[0527] "Health status" refers to information related to the user's physical health level, including factors such as nutrition, physical activity, and rest.

[0528] "Emotional state" refers to information that indicates the user's feelings and psychological state, and is evaluated based on self-reported data and physiological data.

[0529] "Data" refers to information about health and emotional status, including numerical data obtained from users and physiological data obtained from wearable devices, etc.

[0530] A "server" refers to a central information processing device that analyzes data acquired from users and generates and provides health maintenance plans.

[0531] The term "emotional analysis engine" refers to a component within a system that has a specialized algorithm for analyzing the user's emotional state and reflecting it in a health maintenance plan.

[0532] A "health maintenance plan" refers to a plan that includes customized advice on nutrition, physical activity, and rest based on the user's individual health and emotional state.

[0533] "Feedback" refers to the act of users providing information about the results of their health maintenance plan and related information.

[0534] This invention is a system that comprehensively analyzes a user's physical and emotional state and provides a personalized health maintenance plan. By combining a terminal, a server, and an emotional analysis engine, this system enables personalized advice tailored to each user.

[0535] The device receives daily health data from the user. This includes information on nutrition intake, physical activity, and rest entered by the user, as well as self-reported information about their emotional state. Furthermore, physiological indicators such as heart rate and skin electrical activity are acquired via the wearable device. This data serves as material for objectively estimating the user's emotional state.

[0536] All collected data is transferred to a server, which then performs data analysis based on it. A central component of this analysis is the emotional analysis engine. This engine uses AI algorithms to analyze the correlation between the user's physical and emotional state, helping to create a rational and effective personalized health maintenance plan.

[0537] The generated health maintenance plan is sent from the server to the terminal. This plan takes into account the user's current health and emotional state and includes tailored advice on nutrition, physical activity, and rest. For example, if the emotional analysis engine assesses the user's stress level as high, advice may be provided that includes a meal plan emphasizing relaxation and light exercise. The user adjusts their daily actions based on this plan and records the results and their impressions as feedback on the terminal.

[0538] As a concrete example, consider a situation where a user is experiencing stress at work. Using an emotion analysis engine, the server analyzes the user's stress level and creates a health maintenance plan that includes suggestions for relaxing foods and drinks, as well as light exercise. This plan is then presented to the user via their device, and the user incorporates the advice into their daily life.

[0539] Possible inputs to the generating AI model include prompts such as, "Create a stress-reducing health maintenance plan based on the user's emotional state and heart rate data." This allows the AI ​​model to assist in outputting a plan optimized for each individual user's condition.

[0540] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0541] Step 1:

[0542] Users input data about their daily health status into the device. This input data includes diet, exercise history, sleep records, and self-reported emotional states. In addition, the device acquires physiological data such as heart rate and skin electrical activity from wearable devices. The inputs in this process are numerical data and physiological indicators provided by the user. As output, a dataset of the user's overall health and emotional state is constructed.

[0543] Step 2:

[0544] The terminal transfers the constructed dataset to the server. The server saves the received data to storage and prepares it for analysis. In this step, the input is the dataset sent from the terminal, and the output is the data structure ready for analysis.

[0545] Step 3:

[0546] The server begins data analysis using an emotion analysis engine. The input here is stored user health-related data and emotional data. The server applies an AI algorithm to evaluate the correlation between health status and emotional status and designs a specific health maintenance plan. The output is a health maintenance plan optimized for the user's individual condition.

[0547] Step 4:

[0548] The server sends the generated health maintenance plan to the terminal. The terminal notifies the user of the action plan through the user interface. The input here is the individually customized health maintenance plan, and the output is the specific plan information that the user receives.

[0549] Step 5:

[0550] The user adjusts their daily life based on the health maintenance plan received via the device. The user inputs the results of implementing the plan and their impressions as feedback into the device. The input in this step is the result of the user implementing the plan, and the output is the information recorded as feedback.

[0551] Step 6:

[0552] The device sends user feedback to the server. The server uses this feedback for analysis and to improve the next health maintenance plan. The input in this step is user feedback data, and the output is insights needed to adjust the plan in the future.

[0553] (Application Example 2)

[0554] 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."

[0555] In modern living environments, effectively managing health problems and emotional stress is essential. However, many people find it difficult to find appropriate solutions tailored to their individual health and emotional states. Furthermore, for the elderly and those receiving care, the lack of adequate external support exacerbates feelings of isolation, further impacting their health and well-being. Solving these problems is crucial.

[0556] 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.

[0557] In this invention, the server includes means for acquiring lifestyle-related data from the user, means for transferring the acquired data to an information processing device, and means for estimating the user's emotional state using an emotion estimation engine and reflecting the emotional state in the health management plan. This makes it possible to provide an individually optimized health management plan that takes into account both the user's lifestyle and emotions, and ultimately to provide a connection plan that reduces feelings of isolation among the elderly and those receiving care.

[0558] "Users" refer to individuals who use the system to provide lifestyle-related data and receive personalized health management plans.

[0559] "Lifestyle-related data" refers to data related to the user's daily life, including information on diet, exercise, sleep, and data acquired through wearable information collection devices.

[0560] An "information processing device" refers to a computer system that plays a central role in analyzing acquired lifestyle-related data and generating individually optimized health management plans.

[0561] An "emotion inference engine" refers to a software component that has the function of estimating a user's emotional state based on biometric information acquired by a wearable information collection device and the user's self-reported information.

[0562] A "health management plan" refers to a plan that includes tailored instructions regarding nutrition, physical activity, and rest, generated considering the user's lifestyle data and emotional state.

[0563] A "connection plan to reduce feelings of isolation" refers to a plan that includes suggested actions and activities to help users feel socially connected, and is designed to promote contact with family and society.

[0564] The system that realizes this invention mainly consists of the user's smartphone, a wearable data collection device, and a server in the cloud. The smartphone plays the role of collecting lifestyle-related data that the user inputs in their daily life. This includes information on diet, exercise, and sleep, as well as data such as heart rate and physical activity level obtained from a wearable data collection device (e.g., a fitness tracker).

[0565] The server operates in the cloud, receiving this lifestyle-related data and estimating the user's emotional state using an emotion inference engine. This emotion inference utilizes machine learning algorithms (e.g., TensorFlow or PyTorch). The server analyzes the received data and systematically generates a personalized health management plan for the user. This plan includes instructions regarding nutrition, physical activity, and rest, and is adjusted based on the user's emotional state.

[0566] The generated health management plan is sent to the user's smartphone and presented in a clear and actionable format. Users can input feedback on the proposed plan via their smartphone, and this information is returned to the server, contributing to system improvement and further personalization.

[0567] For example, if an elderly user is experiencing feelings of isolation, the system, based on the emotion prediction engine's judgment, can suggest activities to promote social connection and alleviate this isolation. For instance, it could incorporate regular communication time into their health management plan.

[0568] An example of a prompt message might be, "Elderly person T feels isolated in their daily life. Please generate an optimal care plan for T using T's daily health data and emotional state." This allows the generating AI model to create a specific plan tailored to the user's needs.

[0569] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0570] Step 1:

[0571] The device collects lifestyle-related data from the user. Input includes information about diet, exercise, and sleep entered by the user on their smartphone, as well as heart rate and physical activity data measured by wearable data collection devices. This data is temporarily stored within the device and prepared for transfer to the server in the next step.

[0572] Step 2:

[0573] The device transfers collected lifestyle-related data to the server. The device sends data from the user's smartphone to the cloud server using a secure communication protocol. The data arrives at the server as output in a structured data format.

[0574] Step 3:

[0575] The server analyzes the received data and evaluates the user's health and emotional state. It acquires transmitted lifestyle-related data as input and uses data analysis techniques (e.g., machine learning algorithms) with a generative AI model to determine the user's health and emotional state. The analysis results are generated as output and used in the next step.

[0576] Step 4:

[0577] The server uses an emotion inference engine to estimate the user's emotional state. Using the analysis data obtained in the previous step as input, the emotion inference engine applies a machine learning model to estimate the user's emotional level. The output provides information about the user's emotional state.

[0578] Step 5:

[0579] The server generates an individually optimized health management plan based on the user's condition. The server uses the results of analysis and emotion inference as input to create a plan that includes instructions regarding nutrition, physical activity, and rest. This plan is adjusted to the user's health and emotional state. The output is a health management plan that includes specific action guidelines.

[0580] Step 6:

[0581] The server sends the generated health management plan to the terminal. The server delivers the plan to the user's smartphone and presents it in an actionable format. The health management plan is displayed on the smartphone as output.

[0582] Step 7:

[0583] Users input feedback on their health management plan via a terminal. The terminal registers user actions, their results, and improvement requests as input data. This information is used for subsequent analysis.

[0584] Step 8:

[0585] The server receives feedback and uses it to generate the next plan. It uses user feedback data as input to adjust future health management plans. The server uses a generation AI model to incorporate the feedback and create an improved plan. The output provides insights reflected in the next plan.

[0586] 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.

[0587] 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.

[0588] 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.

[0589] [Fourth Embodiment]

[0590] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0591] 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.

[0592] 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).

[0593] 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.

[0594] 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.

[0595] 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).

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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".

[0603] This invention relates to an AI system that provides a health management plan tailored to the individual needs of a user using health-related data. This system is implemented through data exchange between a client terminal, a server, and the user.

[0604] The system's first step is to acquire health-related data from the user. The user inputs information such as their diet, exercise time, and sleep patterns into a client terminal. Meanwhile, the terminal can also acquire data such as heart rate, steps taken, and calories burned from connected wearable devices. This data is then transferred to the server as information reflecting the user's health status.

[0605] The server uses the received data to perform a detailed assessment of the user's health status. Based on this assessment, a customized health management plan is generated for the user. This plan includes appropriate dietary advice, a recommended exercise schedule, and suggestions for improving the amount and quality of sleep needed. The generated plan is sent to the user's device and displayed in an easy-to-understand visual format.

[0606] After the user completes the plan, the device sends the user's results and feedback back to the server. Based on this feedback, the server makes adjustments when creating the next plan, continuously providing personalized health management support tailored to each user.

[0607] As a concrete example, suppose a user has a goal of losing weight. In this case, the server might suggest a plan that combines calorie restriction in diet with high-intensity exercise. It might also include advice on improving sleep quality. In this way, the system can provide a plan based on individual data to effectively support the user in achieving their goals.

[0608] The following describes the processing flow.

[0609] Step 1:

[0610] The user inputs health-related data such as diet, exercise time, and sleep patterns into the device. The device also acquires data from wearable devices.

[0611] Step 2:

[0612] The device transfers the collected health-related data to the server using a security protocol. The data is encrypted at this stage to protect the information during transmission.

[0613] Step 3:

[0614] The server saves the received data to a database. This database is used to store the user's health history.

[0615] Step 4:

[0616] The server uses stored data to run AI algorithms and assess the user's current health status. This includes trend analysis compared to historical data.

[0617] Step 5:

[0618] The server generates a customized health management plan based on the evaluation results. The plan includes specific dietary advice, exercise programs, and sleep improvement strategies.

[0619] Step 6:

[0620] The server sends the generated plan to the device. The device notifies the user of the plan and sets up alerts and reminders to encourage implementation.

[0621] Step 7:

[0622] The user actually implements the plan and inputs the results and feedback into the device. The device then resends the user's feedback to the server.

[0623] Step 8:

[0624] The server analyzes user feedback and makes adjustments as needed to reflect it in future plan creation. This makes it possible to continuously provide plans optimized for individual users.

[0625] (Example 1)

[0626] 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".

[0627] In the field of health management, there is a need to provide appropriate and effective health management plans based on individual user health data. However, conventional systems have limited data collection and analysis capabilities, making it difficult to provide specific plans tailored to individual needs. This hinders effective health management.

[0628] 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.

[0629] In this invention, the server includes means for acquiring health-related information from a user, means for transferring the acquired information to an information provider, and means for analyzing the transferred information and generating a health management plan for the user. This makes it possible to provide a highly accurate health management plan based on individual user data.

[0630] A "user" is an entity that utilizes the system, provides health-related information, and receives an individualized health management plan.

[0631] "Health-related information" includes information such as the user's diet, physical activity, sleep patterns, and data obtained from wearable devices.

[0632] An "information provision device" is a computing device that analyzes health-related information obtained from users and generates a health management plan based on that information.

[0633] A "health management plan" is a plan that includes personalized advice on diet, physical activity, and rest, created based on the user's individual health condition.

[0634] A "wearable device" is a device that, when worn by a user, can acquire health-related data in real time.

[0635] An "automated learning model" is an algorithm that continuously learns from past data and newly acquired feedback to optimize health management plans.

[0636] "Visually easy to understand" means presenting a health management plan to users using visual elements such as diagrams and graphs to make its contents easy to comprehend.

[0637] This invention relates to a system in which an information provider and a terminal work together to support a user's health management. This system collects information about the user's health and provides an individualized health management plan based on that information. Specifically, it is configured as follows:

[0638] Users input health-related information such as their diet, exercise time, and sleep patterns using the device. The device also connects with wearable devices to acquire real-time data such as heart rate, steps taken, and calories burned. This information is transmitted from the device to the information provider via a secure communication protocol.

[0639] The information provider uses an automated learning model to analyze the acquired information. This model assesses the user's health status and generates a health management plan tailored to their individual needs. The generated plan includes personalized advice on diet, physical activity, and rest. For example, a prompt such as, "Please suggest a diet and exercise plan for a woman in her 30s who wants to lose 3 kg," might be input into the AI ​​model.

[0640] The terminal displays the health management plan sent from the information provider in a visually easy-to-understand manner for the user. This allows the user to put the plan into action.

[0641] After a user completes their health management plan, the device sends the results and feedback back to the information provider. Based on this feedback, the information provider learns and adjusts to provide an even more appropriate plan when generating the next one. This allows the system to provide continuous and effective support to the user.

[0642] By using the system of the present invention, it becomes possible to implement personalized health management, which can greatly contribute to maintaining and improving health.

[0643] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0644] Step 1:

[0645] Users input health-related information into the device. Specifically, they record their diet, exercise time, and sleep patterns through a dedicated application. The device automatically retrieves heart rate, steps taken, and calories burned from wearable devices. This information is used as input data and is temporarily stored in a database.

[0646] Step 2:

[0647] The terminal transmits the collected health information to the information provider. Secure protocols (e.g., HTTPS) are used to ensure data transfer while protecting user privacy. The input data is converted to an analysis format on the server side.

[0648] Step 3:

[0649] The server analyzes the received data. An automated learning model is used to assess the user's health status. Temporal trend analysis is also performed using historical data. This generates output data that evaluates the user's health status and trends.

[0650] Step 4:

[0651] The server generates a health management plan based on the analysis results. Using a generation AI model, it designs a personalized plan for diet, exercise, and sleep that is suitable for the user. The user inputs specific information (e.g., "I want to lose weight") as a prompt to the AI, and the generated plan is output.

[0652] Step 5:

[0653] The server sends the generated health management plan to the terminal. The terminal displays this plan to the user in a visually easy-to-understand format. Graphs and icons are used to ensure effective communication.

[0654] Step 6:

[0655] The user implements a health management plan and records the results and feedback on their device. The device then sends this information back to the server. The feedback content is collected as input data.

[0656] Step 7:

[0657] The server adjusts the next health management plan based on the feedback. It then uses the automated learning model again to process the data and improve the plan. This results in output data that provides a next plan better suited to the user's individual needs.

[0658] (Application Example 1)

[0659] 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".

[0660] In modern society, the importance of individual health management is increasing, but providing personalized and appropriate health management plans requires efficiently collecting and analyzing large amounts of health-related data and presenting it to users in an easy-to-understand manner. Furthermore, continuous improvement of plans based on user feedback is also important, but there is a challenge in efficiently implementing this.

[0661] 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.

[0662] In this invention, the server includes means for acquiring health-related data from a user, means for transferring the acquired data to an information processing device, and means for analyzing the transferred data and generating a health management plan for the user. This enables the provision of a personalized health management plan to the user and allows for the improvement of the plan through real-time feedback.

[0663] "Health-related data" is a general term for information that indicates a user's health status, such as their diet, exercise level, sleep duration, heart rate, and steps taken.

[0664] An "information processing device" refers to a server or computer used to analyze data acquired from users and generate health management plans.

[0665] A "health management plan" is a plan that includes advice on diet, exercise, and sleep, tailored to the user's individual goals and needs, based on analyzed health-related data.

[0666] "Means of providing information visually and audibly" refers to interfaces and devices that convey the generated health management plan to the user in an easily viewable or easily audible format.

[0667] A "mobile device" refers to a portable device such as a smartphone or tablet that a user uses to record meal information or receive health management plans.

[0668] "Wearable devices" refer to wearable devices that users attach to their bodies to collect data such as heart rate and steps taken.

[0669] To implement this invention, the user begins by inputting daily health-related data using a mobile device. The device automatically acquires data such as heart rate and steps from a wearable device and transmits it to an information processing unit. This information processing unit is a server, and upon receiving the data, it evaluates the user's health status and generates a customized health management plan based on a generated AI model. This plan includes meal suggestions tailored to the user's goals, recommended exercise menus, and optimal sleep patterns.

[0670] The server provides the generated health management plan to the user visually and audibly via a mobile device. For example, dietary advice may be displayed on the smartphone screen, or exercise instructions may be given via voice guidance. The user can also input feedback on the completed plan from their device, which is then sent back to the server. The server analyzes this feedback and incorporates it into future health management plans.

[0671] For example, if a user sets a goal of losing weight, the server will propose a plan that includes low-calorie meal suggestions and aerobic exercise schedules, and the user will act accordingly. An example of a prompt message would be: "Username: ABC, Goal: Weight loss, Current weight: 85kg, Desired weight: 75kg. Please create an optimal 2-week meal and exercise plan for ABC." The generating AI model would then operate in this format.

[0672] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0673] Step 1:

[0674] Users input health-related data using their mobile devices. The device receives information about meals and exercise as text and images, and automatically acquires data such as heart rate and steps from wearable devices. This input data is pre-processed within the device and converted into a unified format. The output is health-related data converted into a unified format.

[0675] Step 2:

[0676] The terminal transfers pre-processed data to the server, which acts as an information processing unit. The transfer protocol uses HTTP or HTTPS, and the data is posted to the API endpoint. The input is health-related data from the terminal, and the output is data that has been confirmed to have been received by the server.

[0677] Step 3:

[0678] The server retrieves the received data and stores it in the database. During this process, data validation and management are performed to check data integrity. The input is data transferred from the terminal, and the output is the storage of the data in a consistent database.

[0679] Step 4:

[0680] The server invokes a generative AI model based on data stored in the database to generate a personalized health management plan for each user. The generative AI model uses machine learning algorithms to calculate the optimal plan based on the user's past data and goals. The input is the user's health-related data stored in the database, and the output is the generated health management plan.

[0681] Step 5:

[0682] The generated health management plan is transferred from the server to the mobile device and presented to the user visually and audibly on the device. For example, dietary advice may be displayed on the smartphone screen. The input is the generated health management plan, and the output is its display on the device's user interface.

[0683] Step 6:

[0684] Users perform their daily activities based on the proposed health management plan and input the results and feedback into the device. This feedback includes information on the exercise and diet they actually performed. The input is user feedback data, and the output is feedback that will be used to create the next plan.

[0685] Step 7:

[0686] The server receives feedback from the user and updates the data to reflect that feedback when creating the next health management plan. This ensures that the plan continuously improves in the direction the user desires. The input is feedback data, and the output is the updated health management plan.

[0687] 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.

[0688] This invention relates to an AI system that generates and provides a health management plan that comprehensively considers a user's health-related data and emotions. The system includes a client terminal, a server, and an emotion engine, all of which work together in coordination.

[0689] First, the user inputs daily health-related data into the device. This includes diet, exercise history, sleep records, and self-reported emotional state. Furthermore, the device acquires objective data from wearable devices, such as heart rate and skin electrical activity, and uses this data to estimate the user's emotional state.

[0690] The device transfers all collected data to the server. The server analyzes this data and evaluates the correlation between the user's health status and emotional state. In the analysis, an emotion engine plays a central role, using algorithms to rationally determine the impact of the user's emotions on health management.

[0691] The server generates a customized health management plan based on the evaluation results. This plan adjusts advice on diet, exercise, and sleep based on the user's current health status and emotions. For example, if the emotion engine assesses that the user is in a high-stress emotional state, it may suggest a diet and exercise routine focused on stress management.

[0692] The generated plan is sent to the device and presented to the user in a clear and actionable format. The user acts according to the plan and records the results and feedback on the device. This feedback is sent to the server to verify the user's emotions and the effectiveness of health management, and is used to adjust the next plan.

[0693] For example, if a user is experiencing stress at work, the system uses an emotion engine to recognize their stress level. The server then designs a plan incorporating relaxation effects such as smoothies and light exercise, and proposes it to the user via the device. As the user implements this plan and inputs its effects and feedback into the device, the system can continuously refine and personalize its approach.

[0694] The following describes the processing flow.

[0695] Step 1:

[0696] The user inputs health-related data such as diet, exercise time, sleep habits, and emotional state into the device. This input also includes selecting options for emotional state. In addition, the device acquires data such as heart rate and skin electrical activity from connected wearable devices.

[0697] Step 2:

[0698] The device transfers all acquired data to the server. During this process, the data is encrypted to protect user privacy.

[0699] Step 3:

[0700] The server saves the received data to a database. This allows for centralized management of the user's health history and emotional state.

[0701] Step 4:

[0702] The server uses data retrieved from the database to execute an AI algorithm. Here, the emotion engine is utilized to evaluate the user's emotional state and analyze its correlation with their health status.

[0703] Step 5:

[0704] The server generates a customized health management plan based on the analysis results. This plan includes advice on diet, exercise, and sleep tailored to your emotional state. For example, if the emotional engine detects a high level of stress, it will suggest exercises and nutrients that promote relaxation.

[0705] Step 6:

[0706] The server sends the generated customized plan to the device. The device notifies the user of the plan and sets up alerts and reminders to support its implementation.

[0707] Step 7:

[0708] The user executes the presented plan and inputs the results and changes in their emotions into the device. This process is carried out on a daily basis.

[0709] Step 8:

[0710] The device sends user feedback to the server. The server analyzes this feedback to verify the user's emotions and the effectiveness of the health management plan, and uses this information to create the next plan.

[0711] (Example 2)

[0712] 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".

[0713] Providing comprehensive and effective health management plans that take into account individual health and emotional states is a challenging task. In particular, it is essential to appropriately manage the impact of users' daily emotional fluctuations on their health and to provide effective advice based on that information.

[0714] 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.

[0715] In this invention, the server includes means for analyzing data on the user's health and emotional state, means for generating a health maintenance plan using an emotional analysis engine, and means for adjusting the next plan based on user feedback. This makes it possible to provide a customized health management plan that comprehensively considers the individual's emotional and physical condition.

[0716] A "user" refers to an individual who uses the system to provide information about their health and emotional state and receive a health maintenance plan.

[0717] "Health status" refers to information related to the user's physical health level, including factors such as nutrition, physical activity, and rest.

[0718] "Emotional state" refers to information that indicates the user's feelings and psychological state, and is evaluated based on self-reported data and physiological data.

[0719] "Data" refers to information about health and emotional status, including numerical data obtained from users and physiological data obtained from wearable devices, etc.

[0720] A "server" refers to a central information processing device that analyzes data acquired from users and generates and provides health maintenance plans.

[0721] The term "emotional analysis engine" refers to a component within a system that has a specialized algorithm for analyzing the user's emotional state and reflecting it in a health maintenance plan.

[0722] A "health maintenance plan" refers to a plan that includes customized advice on nutrition, physical activity, and rest based on the user's individual health and emotional state.

[0723] "Feedback" refers to the act of users providing information about the results of their health maintenance plan and related information.

[0724] This invention is a system that comprehensively analyzes a user's physical and emotional state and provides a personalized health maintenance plan. By combining a terminal, a server, and an emotional analysis engine, this system enables personalized advice tailored to each user.

[0725] The device receives daily health data from the user. This includes information on nutrition intake, physical activity, and rest entered by the user, as well as self-reported information about their emotional state. Furthermore, physiological indicators such as heart rate and skin electrical activity are acquired via the wearable device. This data serves as material for objectively estimating the user's emotional state.

[0726] All collected data is transferred to a server, which then performs data analysis based on it. A central component of this analysis is the emotional analysis engine. This engine uses AI algorithms to analyze the correlation between the user's physical and emotional state, helping to create a rational and effective personalized health maintenance plan.

[0727] The generated health maintenance plan is sent from the server to the terminal. This plan takes into account the user's current health and emotional state and includes tailored advice on nutrition, physical activity, and rest. For example, if the emotional analysis engine assesses the user's stress level as high, advice may be provided that includes a meal plan emphasizing relaxation and light exercise. The user adjusts their daily actions based on this plan and records the results and their impressions as feedback on the terminal.

[0728] As a concrete example, consider a situation where a user is experiencing stress at work. Using an emotion analysis engine, the server analyzes the user's stress level and creates a health maintenance plan that includes suggestions for relaxing foods and drinks, as well as light exercise. This plan is then presented to the user via their device, and the user incorporates the advice into their daily life.

[0729] Possible inputs to the generating AI model include prompts such as, "Create a stress-reducing health maintenance plan based on the user's emotional state and heart rate data." This allows the AI ​​model to assist in outputting a plan optimized for each individual user's condition.

[0730] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0731] Step 1:

[0732] Users input data about their daily health status into the device. This input data includes diet, exercise history, sleep records, and self-reported emotional states. In addition, the device acquires physiological data such as heart rate and skin electrical activity from wearable devices. The inputs in this process are numerical data and physiological indicators provided by the user. As output, a dataset of the user's overall health and emotional state is constructed.

[0733] Step 2:

[0734] The terminal transfers the constructed dataset to the server. The server saves the received data to storage and prepares it for analysis. In this step, the input is the dataset sent from the terminal, and the output is the data structure ready for analysis.

[0735] Step 3:

[0736] The server begins data analysis using an emotion analysis engine. The input here is stored user health-related data and emotional data. The server applies an AI algorithm to evaluate the correlation between health status and emotional status and designs a specific health maintenance plan. The output is a health maintenance plan optimized for the user's individual condition.

[0737] Step 4:

[0738] The server sends the generated health maintenance plan to the terminal. The terminal notifies the user of the action plan through the user interface. The input here is the individually customized health maintenance plan, and the output is the specific plan information that the user receives.

[0739] Step 5:

[0740] The user adjusts their daily life based on the health maintenance plan received via the device. The user inputs the results of implementing the plan and their impressions as feedback into the device. The input in this step is the result of the user implementing the plan, and the output is the information recorded as feedback.

[0741] Step 6:

[0742] The device sends user feedback to the server. The server uses this feedback for analysis and to improve the next health maintenance plan. The input in this step is user feedback data, and the output is insights needed to adjust the plan in the future.

[0743] (Application Example 2)

[0744] 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".

[0745] In modern living environments, effectively managing health problems and emotional stress is essential. However, many people find it difficult to find appropriate solutions tailored to their individual health and emotional states. Furthermore, for the elderly and those receiving care, the lack of adequate external support exacerbates feelings of isolation, further impacting their health and well-being. Solving these problems is crucial.

[0746] 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.

[0747] In this invention, the server includes means for acquiring lifestyle-related data from the user, means for transferring the acquired data to an information processing device, and means for estimating the user's emotional state using an emotion estimation engine and reflecting the emotional state in the health management plan. This makes it possible to provide an individually optimized health management plan that takes into account both the user's lifestyle and emotions, and ultimately to provide a connection plan that reduces feelings of isolation among the elderly and those receiving care.

[0748] "Users" refer to individuals who use the system to provide lifestyle-related data and receive personalized health management plans.

[0749] "Lifestyle-related data" refers to data related to the user's daily life, including information on diet, exercise, sleep, and data acquired through wearable information collection devices.

[0750] An "information processing device" refers to a computer system that plays a central role in analyzing acquired lifestyle-related data and generating individually optimized health management plans.

[0751] An "emotion inference engine" refers to a software component that has the function of estimating a user's emotional state based on biometric information acquired by a wearable information collection device and the user's self-reported information.

[0752] A "health management plan" refers to a plan that includes tailored instructions regarding nutrition, physical activity, and rest, generated considering the user's lifestyle data and emotional state.

[0753] A "connection plan to reduce feelings of isolation" refers to a plan that includes suggested actions and activities to help users feel socially connected, and is designed to promote contact with family and society.

[0754] The system that realizes this invention mainly consists of the user's smartphone, a wearable data collection device, and a server in the cloud. The smartphone plays the role of collecting lifestyle-related data that the user inputs in their daily life. This includes information on diet, exercise, and sleep, as well as data such as heart rate and physical activity level obtained from a wearable data collection device (e.g., a fitness tracker).

[0755] The server operates in the cloud, receiving this lifestyle-related data and estimating the user's emotional state using an emotion inference engine. This emotion inference utilizes machine learning algorithms (e.g., TensorFlow or PyTorch). The server analyzes the received data and systematically generates a personalized health management plan for the user. This plan includes instructions regarding nutrition, physical activity, and rest, and is adjusted based on the user's emotional state.

[0756] The generated health management plan is sent to the user's smartphone and presented in a clear and actionable format. Users can input feedback on the proposed plan via their smartphone, and this information is returned to the server, contributing to system improvement and further personalization.

[0757] For example, if an elderly user is experiencing feelings of isolation, the system, based on the emotion prediction engine's judgment, can suggest activities to promote social connection and alleviate this isolation. For instance, it could incorporate regular communication time into their health management plan.

[0758] An example of a prompt message might be, "Elderly person T feels isolated in their daily life. Please generate an optimal care plan for T using T's daily health data and emotional state." This allows the generating AI model to create a specific plan tailored to the user's needs.

[0759] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0760] Step 1:

[0761] The device collects lifestyle-related data from the user. Input includes information about diet, exercise, and sleep entered by the user on their smartphone, as well as heart rate and physical activity data measured by wearable data collection devices. This data is temporarily stored within the device and prepared for transfer to the server in the next step.

[0762] Step 2:

[0763] The device transfers collected lifestyle-related data to the server. The device sends data from the user's smartphone to the cloud server using a secure communication protocol. The data arrives at the server as output in a structured data format.

[0764] Step 3:

[0765] The server analyzes the received data and evaluates the user's health and emotional state. It acquires transmitted lifestyle-related data as input and uses data analysis techniques (e.g., machine learning algorithms) with a generative AI model to determine the user's health and emotional state. The analysis results are generated as output and used in the next step.

[0766] Step 4:

[0767] The server uses an emotion inference engine to estimate the user's emotional state. Using the analysis data obtained in the previous step as input, the emotion inference engine applies a machine learning model to estimate the user's emotional level. The output provides information about the user's emotional state.

[0768] Step 5:

[0769] The server generates an individually optimized health management plan based on the user's condition. The server uses the results of analysis and emotion inference as input to create a plan that includes instructions regarding nutrition, physical activity, and rest. This plan is adjusted to the user's health and emotional state. The output is a health management plan that includes specific action guidelines.

[0770] Step 6:

[0771] The server sends the generated health management plan to the terminal. The server delivers the plan to the user's smartphone and presents it in an actionable format. The health management plan is displayed on the smartphone as output.

[0772] Step 7:

[0773] Users input feedback on their health management plan via a terminal. The terminal registers user actions, their results, and improvement requests as input data. This information is used for subsequent analysis.

[0774] Step 8:

[0775] The server receives feedback and uses it to generate the next plan. It uses user feedback data as input to adjust future health management plans. The server uses a generation AI model to incorporate the feedback and create an improved plan. The output provides insights reflected in the next plan.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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."

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] The following is further disclosed regarding the embodiments described above.

[0798] (Claim 1)

[0799] Means of obtaining health-related data from users,

[0800] A means of transferring the acquired data to the server,

[0801] A means of analyzing the transferred data and generating a health management plan for the user,

[0802] A means of providing the generated plan to the user,

[0803] A means of receiving user feedback and using it to create the next plan,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, wherein the health management plan provided to the user includes customized advice regarding diet, exercise, and sleep.

[0807] (Claim 3)

[0808] The system according to claim 1, wherein the health-related data obtained from the user includes data from a wearable device.

[0809] "Example 1"

[0810] (Claim 1)

[0811] Means of obtaining health information from users,

[0812] Means for transferring acquired information to an information providing device,

[0813] A means for analyzing the transferred information and generating a health management plan for the user,

[0814] A means of providing the generated plan to the user and displaying it in a visually easy-to-understand manner,

[0815] A means of receiving results and feedback from users and using them to create the next plan,

[0816] A means of continuously adjusting the plan using automated learning models,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, wherein the health management plan provided to the user includes personalized advice regarding diet, physical activity, and rest.

[0820] (Claim 3)

[0821] The system according to claim 1, wherein the health information obtained from the user includes information from wearable devices.

[0822] "Application Example 1"

[0823] (Claim 1)

[0824] Means of obtaining health-related data from users,

[0825] A means for transferring the acquired data to an information processing device,

[0826] A means for analyzing the transferred data and generating a health management plan for the user,

[0827] Means for providing the generated health management plan to the user visually and audibly,

[0828] A means of receiving results and feedback from users and using them to create the next health management plan,

[0829] A means of recording meal information using a mobile device,

[0830] A method for suggesting exercise time and dietary advice based on data transmitted from a mobile device,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, wherein the health management plan provided to the user includes personalized advice regarding diet, exercise, and sleep.

[0834] (Claim 3)

[0835] The system according to claim 1, wherein the health-related data obtained from the user includes data from wearable devices.

[0836] "Example 2 of combining an emotion engine"

[0837] (Claim 1)

[0838] A means of obtaining data from users regarding their health and emotional state,

[0839] Means for transmitting acquired data to an information processing device,

[0840] A means of analyzing the correlation between a user's health maintenance and emotional state using an emotion analysis engine,

[0841] A means for generating a customized health maintenance plan based on analysis results,

[0842] A means of presenting the generated plan to the user,

[0843] A means of receiving user feedback and using it to create future plans,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, wherein the health maintenance plan provided to the user includes personalized advice regarding nutrition, physical activity, and rest.

[0847] (Claim 3)

[0848] The system according to claim 1, wherein the health-related data obtained from the user includes numerical values ​​from a wearable terminal.

[0849] "Application example 2 when combining with an emotional engine"

[0850] (Claim 1)

[0851] Means of obtaining lifestyle-related data from users,

[0852] A means for transferring the acquired data to an information processing device,

[0853] A means for analyzing the transferred data and generating an individually optimized health management plan for the user,

[0854] Means for providing the generated plan to the user,

[0855] A means of receiving feedback from users and using it to create the next plan,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, wherein the health management plan provided to the user includes tailored instructions regarding nutrition, physical activity, and rest.

[0859] (Claim 3)

[0860] The system according to claim 1, wherein the lifestyle-related data obtained from the user includes data from a wearable information collection device.

[0861] (Claim 4)

[0862] A means of estimating a user's emotional state using an emotion inference engine and reflecting that emotional state in a health management plan,

[0863] The system according to claim 1, including the following:

[0864] (Claim 5)

[0865] The system according to claim 4, which generates and provides a connection plan to reduce the user's sense of isolation when used in a caregiving environment. [Explanation of symbols]

[0866] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of obtaining health-related data from users, A means for transferring the acquired data to an information processing device, A means for analyzing the transferred data and generating a health management plan for the user, Means for providing the generated health management plan to the user visually and audibly, A means of receiving results and feedback from users and using them to create the next health management plan, A means of recording meal information using a mobile device, A method for suggesting exercise time and dietary advice based on data transmitted from a mobile device, A system that includes this.

2. The system according to claim 1, wherein the health management plan provided to the user includes personalized advice regarding diet, exercise, and sleep.

3. The system according to claim 1, wherein the health-related data obtained from the user includes data from wearable devices.

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