system

Abstract

Provide a system. 【Solution means】 Means for collecting health-related information from an information communication network, Means for collecting the living activity data of residents through a device, Means for integrating the health-related information and the living activity data and analyzing them using a generation model, Means for generating a health improvement plan suitable for residents based on the analysis results, Means for notifying the residents of the health improvement plan, Means for collecting the reactions of residents and adjusting the generation model, Means for monitoring the health status in a care environment and optimizing individual care plans, 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 a chatbot 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] With the progress of an aging society, many people need appropriate guidance to maintain a healthy lifestyle. However, general health information often does not match individual lifestyles and health conditions, and there may be attempts to improve life in incorrect ways. As a result, there is a problem that continuous health maintenance becomes difficult and efficient improvement cannot be obtained.

Means for Solving the Problems

[0005] This invention comprises means for collecting health-related information from the internet and means for collecting user daily life data through a device. This enables the integration of general health information and the user's personal data, and analysis using a generative AI model. Based on the analysis results, a health improvement program tailored to the user is automatically generated and notified to the user. Furthermore, by collecting user feedback and reflecting it in the generative AI model to improve the program's accuracy, it becomes possible to provide individually optimized health improvement support.

[0006] "Health-related information" refers to general knowledge about health and medicine, research data, expert opinions, guidelines, etc., provided on the internet.

[0007] "Daily life data" refers to digital information generated by users in their daily lives, and includes behavioral history, exercise data, location information, and social media posts obtained through smartphones and wearable devices.

[0008] A "generative AI model" refers to an artificial intelligence framework that uses machine learning algorithms to analyze data and perform predictions and classifications according to a specific purpose.

[0009] A "health improvement program" refers to a set of guidelines that encompass exercise guidance, meal plans, stress management methods, and other elements designed based on each user's individual health condition and lifestyle.

[0010] "Feedback" refers to the results and impressions that users provide after running a program, and this information is used by the system to improve the program. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes 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.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention is a system designed to improve user health and support longevity, and is primarily based on programs executed by servers and terminals.

[0033] First, personal lifestyle data is collected using smartphones and wearable devices (hereinafter referred to as "devices") that users use on a daily basis. For example, if a user records their daily step count through a smartphone app or measures their heart rate during exercise with a heart rate monitor, that data is stored on the device.

[0034] Next, the device periodically sends this data to the server. Content posted by users on social media and health-related topics in emails are also extracted based on specific keywords and analyzed.

[0035] The server acquires a wide range of health information from the internet. This includes information from government agencies, medical institutions, scientific papers, and news sites. This information, along with the user's personal data, is aggregated and analyzed by a generative AI model on the server.

[0036] This generative AI model employs machine learning algorithms to design appropriate health improvement programs based on the user's health status and behavioral patterns. For example, if the server determines that a user has been inactive recently, it can suggest a specific exercise plan such as "walk for 30 minutes every day." It can also detect nutritional imbalances from the user's eating history and provide dietary guidelines for improvement.

[0037] The generated health improvement program is notified to the user via the device. The user adjusts their daily life according to this program and provides feedback on the results and their impressions to the device. For example, they can report that they lost weight after following the suggested exercise plan for a week.

[0038] The server receives this feedback, readjusts the AI ​​model, and improves the accuracy of subsequent programs. Through this process, it is possible to continuously provide health improvement support that is best suited to each user. Users can enjoy new value in maintaining and improving their health in a way that suits their individual lifestyle.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device collects data from the user's daily life. This includes recording data such as steps, heart rate, and location information using smartphones and wearable devices. Additional health-related information is collected by extracting specific keywords from social media posts and emails.

[0042] Step 2:

[0043] The device periodically sends the collected data to the server. When transmitting data, it encrypts the data as needed to protect privacy.

[0044] Step 3:

[0045] The server collects general health-related information from the internet. This includes the latest research papers, health guidelines, and expert opinions. This information is also integrated into a dataset used for analysis.

[0046] Step 4:

[0047] The server aggregates personal data and general health information received from terminals and performs analysis using a generative AI model. The analysis applies machine learning algorithms to evaluate the user's health status and lifestyle patterns.

[0048] Step 5:

[0049] The server generates a health improvement program tailored to the user based on the analysis results. This program includes, for example, exercise guidelines, dietary improvement strategies, and stress management methods.

[0050] Step 6:

[0051] The device notifies the user of a health improvement program generated on the server. This notification is delivered via push notifications or in-app alerts. The user is shown program details and instructions for implementation.

[0052] Step 7:

[0053] Users incorporate the suggested health improvement program into their daily routines. They perform actions based on the program and receive feedback on the results and changes in their physical condition via their device. For example, they might record changes in weight after exercise or details of their meals.

[0054] Step 8:

[0055] The device sends user feedback to the server. This feedback is required to include both the user's subjective opinions and objective results.

[0056] Step 9:

[0057] The server readjusts the generated AI model based on the feedback received and uses it for the next analysis. This completes the cycle of improving the program's accuracy and providing more personalized health improvement suggestions.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] Current health management systems are insufficient to quickly and efficiently integrate users' lifestyle data and related information, and to appropriately generate personalized health improvement plans. Furthermore, the lack of real-time feedback capabilities that leverage user responses makes it difficult to provide support optimized for individual health conditions.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for collecting health-related information from a wide-area information network, means for collecting user daily life information through a mobile information terminal, and means for analyzing the user's transmitted information. This makes it possible to integrate the user's lifestyle data with a wide range of health information and automatically generate an individually optimized health improvement plan using a generative AI model.

[0063] "Health-related information" refers to a wide variety of information related to physical condition and medical management, obtained from a broad information network.

[0064] "Daily life information" refers to data that reflects a user's daily activities and physiological state, and is collected from mobile devices.

[0065] "Personal information terminals" refer to information processing devices that users use on a daily basis, including smartphones and wearable devices.

[0066] A "generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to analyze data and generate results tailored to a specific purpose.

[0067] A "health improvement plan" refers to a personalized set of action plans and habits designed by a generative AI model to improve the user's health.

[0068] "Response information" refers to feedback and comments from users regarding their health improvement plans, and the data used to adjust the model.

[0069] This invention realizes a system that optimizes an individual's health status by collecting daily lifestyle information using a mobile information terminal. The system is mainly executed using a server and terminals.

[0070] 1. The role of the terminal

[0071] Users collect daily life information using mobile devices such as smartphones and wearable devices. This includes steps taken, heart rate, and sleep patterns. The devices are configured to collect this data in real time and transfer it to a server at regular intervals.

[0072] 2. Server Role

[0073] The server has the capability to collect health-related information from a wide-area information network. This includes medical research, government health guidelines, and news articles. The server integrates this collected information and analyzes the user's health status using a generative AI model.

[0074] 3. Use of Generative AI Models

[0075] The generative AI model uses machine learning algorithms to analyze data and generate an optimal health improvement plan for the user. This plan includes exercise plans, dietary guidance, and methods for managing mental stress.

[0076] 4. Notification and feedback on the health improvement plan

[0077] The device notifies the user of the generated health improvement plan, and the user adjusts their lifestyle according to the plan. The user reports their response information via the device, such as changes in weight or improvements in stress levels based on the plan. The server receives this feedback and continuously updates the generating AI model.

[0078] As a concrete example, the server collects new health information from a wide-area information network several times a day and integrates and analyzes it with the user's activity data. For instance, by inputting a prompt such as, "I'm a woman in my 30s, and I aim to exercise three times a week and eat a balanced diet, but I've recently been feeling more tired. What improvements can I make to maintain a healthy lifestyle?" into the AI ​​model, it can provide specific health improvement suggestions. This system allows users to always receive the latest and most personalized health support.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] Data collection

[0082] Device: Users collect daily life data using smartphones or wearable devices. Specifically, applications run to measure steps, heart rate, sleep patterns, etc. This data is stored in the device's storage.

[0083] Input: User's physical activity and physiological data

[0084] Output: User's daily life information data

[0085] Step 2:

[0086] Data transmission

[0087] Terminal: Sends daily life information to the server at regular intervals. This is done by the terminal's application running in the background, scheduled to transfer data, for example, at midnight every night.

[0088] Input: Daily life information stored on the device

[0089] Output: User's daily life information sent to the server

[0090] Step 3:

[0091] Collection and integration of health information

[0092] Server: Collects health-related information from a wide-area information network. This involves using API-based data collection functions to gather information from government health data, the latest medical papers, news, and other sources. This information is then integrated with the received information about daily life.

[0093] Input: Health information from the internet, user's daily life information

[0094] Output: Integrated health information

[0095] Step 4:

[0096] Data analysis and program generation

[0097] Server: The integrated data is fed into a generating AI model for analysis. The AI ​​model uses machine learning algorithms to assess the user's current health status and generate a personalized health improvement plan.

[0098] Input: Integrated health information

[0099] Output: Health Improvement Plan

[0100] Step 5:

[0101] Program Notices and Implementation

[0102] Device: Notifies the user of the generated health improvement plan. The user receives notifications from the app and follows the suggested exercise and meal plans. This notification feature makes it easy for the user to see the next steps toward their health.

[0103] Input: Health Improvement Plan

[0104] Output: Health improvement plan notified to the user

[0105] Step 6:

[0106] Gathering feedback and updating the model

[0107] User: Provides feedback on the results and impressions of the health improvement plan implemented to the device. For example, records the results of exercising for a week according to the plan, changes in weight, etc.

[0108] Server: We receive this feedback information, readjust the AI ​​model, and use it to inform the next health improvement plan.

[0109] Input: User feedback information

[0110] Output: Updated generative AI model

[0111] (Application Example 1)

[0112] 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."

[0113] In elderly care, there is a need to continuously and efficiently manage each individual's health condition. If health risks can be quickly detected in daily life and care plans can be flexibly adjusted, the quality of care will improve and contribute to maintaining a comfortable life for the user. However, current methods have problems such as inefficient data collection and analysis, making it difficult to provide individualized care.

[0114] 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.

[0115] In this invention, the server includes means for collecting health-related information from an information and communication network, means for collecting residents' daily activity data via a device, means for integrating the health-related information and the daily activity data and analyzing them using a generative model, and means for monitoring health status in a care environment and optimizing individual care plans. This makes it possible to monitor changes in the health status of elderly people in real time and provide optimal health improvement plans.

[0116] "Health-related information" refers to information including statistical data, guidelines, or the latest research findings necessary to assess and improve the health status of residents.

[0117] "Information and communication network" refers to network infrastructure that enables the exchange of digital data, including the internet and dedicated lines.

[0118] "Resident" refers to an individual who receives services in a care environment.

[0119] "Lifestyle activity data" refers to detailed behavioral data such as the number of steps taken, heart rate, and dietary content collected during residents' daily lives.

[0120] "Device" refers to hardware equipped with data collection and communication functions, such as smartphones and wearable devices.

[0121] A "generative model" refers to a program that includes a learning algorithm to analyze the health status of residents and propose an optimal health improvement plan.

[0122] "Analysis" refers to the process of evaluating the health status of residents based on collected data and identifying problems and areas for improvement.

[0123] "Care environment" refers to the physical and institutional framework for providing residents with health maintenance and life support.

[0124] A "health improvement plan" refers to a program that includes specific action guidelines aimed at maintaining and improving one's health.

[0125] The system that realizes this invention first acquires residents' lifestyle activity data using smartphones or wearable devices (hereinafter referred to as terminals). These devices are equipped with functions for measuring steps and monitoring heart rate, and transmit data to a server via Bluetooth or Wi-Fi. Examples of hardware that can be used include general smartphones and wearable devices with heart rate measurement functions.

[0126] The server integrates health information collected via the information and communication network with lifestyle activity data transmitted from terminals. Generative models running on cloud platforms (e.g., Google Cloud, AWS) are used for data processing and analysis. These generative AI models utilize machine learning algorithms to assess the health status of each resident and create appropriate health improvement plans. The created plans, including specific action guidelines, are communicated to the residents.

[0127] For example, if a resident's physical activity level is low, this model suggests a simple and actionable exercise plan, such as "run five laps around the living room at 3 PM." Through this implementation, the quality of care can be improved by monitoring the resident's health status in real time and quickly providing optimal health improvement measures.

[0128] An example of a prompt is: "Assess the resident's recent health status based on their data and create an exercise plan. If they appear to be inactive, what would you suggest?" This prompting process enables the generative model to provide appropriate health guidance.

[0129] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0130] Step 1:

[0131] The device collects data on residents' daily lives and transmits it to a server. Specifically, steps taken, heart rate, and other data are measured by the device and sent to the server via Bluetooth or Wi-Fi. The input is sensor data from the device, and the output is raw activity data transmitted to the server.

[0132] Step 2:

[0133] The server retrieves health-related information collected through information and communication networks. This includes obtaining the latest health news and research data from databases of medical institutions and government agencies. The input is health-related information obtained online, and the output is integrable health information data.

[0134] Step 3:

[0135] The server integrates collected daily life data and health information and performs analysis using a generative AI model. By inputting the integrated data into the generative model, it evaluates the health status of residents and analyzes short-term and long-term health trends. The output is the analysis results regarding the health status of residents.

[0136] Step 4:

[0137] The server generates a health improvement plan for each resident based on the analysis results. The generated plan includes specific action guidelines such as exercise suggestions and dietary recommendations. The input is the analysis results, and the output is the health improvement plan.

[0138] Step 5:

[0139] The server notifies the terminal of the generated health improvement plan. The resident reviews this notification and follows the proposed health improvement measures. The input is the health improvement plan, and the output is the plan notification received by the resident.

[0140] Step 6:

[0141] The user inputs the results and feedback of the proposed health improvement plan into the terminal. Input is the feedback data entered by the user, and output is the feedback data sent to the server.

[0142] Step 7:

[0143] The server updates the generative AI model based on feedback data to improve the accuracy of the next analysis. The input is the feedback data, and the output is the adjusted generative AI model.

[0144] 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.

[0145] This invention is a health improvement system that incorporates an emotion engine. It is primarily based on programs executed by a server and terminals, and provides a health program that takes the user's emotional state into consideration.

[0146] First, data from the user's daily life is constantly collected through smartphones and wearable devices (hereinafter referred to as "devices") that the user uses on a daily basis. In addition to physical data such as steps taken and heart rate, the devices also collect emotional data by automatically extracting keywords related to emotions from social media posts and emails.

[0147] The data collected by the device is periodically sent to the server. The server receives this data and integrates it into a dataset along with general health-related information found on the web. In addition, the server uses an emotion engine to analyze the user's emotions and understand changes in their emotions in their daily life.

[0148] The analysis utilizes a generative AI model based on machine learning algorithms, and the server designs an optimal health improvement program for the user, taking into account newly collected emotional data. For example, if the server detects that the user is experiencing stress, it can suggest exercises or meals that promote relaxation.

[0149] The health improvement program generated in this way is notified to the user via the device. The user improves their daily life according to the suggested program and provides feedback on the results and changes in their emotions to the device. Information such as whether the user is satisfied or dissatisfied with a particular program is interpreted from the emotional data.

[0150] The server readjusts the generated AI model based on user feedback and uses that information for the next analysis. This process improves the program's accuracy and allows it to provide health improvement support that is more tailored to each user's individual condition. As a result, users can enjoy new value in maintaining and improving their health in a way that suits their individual emotions.

[0151] The following describes the processing flow.

[0152] Step 1:

[0153] The device collects the user's physical and emotional data. Specifically, it records data such as steps taken, heart rate, and location information through sensors, and extracts emotional keywords from social media posts and emails.

[0154] Step 2:

[0155] The device transmits the collected data to the server at regular intervals. This data includes information about the user's current health and emotional state.

[0156] Step 3:

[0157] The server also collects health-related information from the internet and integrates it with user data. The data is then organized into sets for analysis.

[0158] Step 4:

[0159] The server uses an emotion engine to analyze the user's emotional data. Through analysis of social media posts and email messages, it identifies the user's mood and stress level.

[0160] Step 5:

[0161] The server inputs integrated data, including emotional states, into a generating AI model, which then analyzes it using machine learning algorithms. This generates an optimized improvement program tailored to the user's health and emotional state.

[0162] Step 6:

[0163] The server sends the generated health improvement program to the terminal and notifies the user. The program may include, for example, exercises aimed at stress reduction and dietary guidance to promote relaxation.

[0164] Step 7:

[0165] Users attempt to improve their lifestyle based on the notified program. They record what they did, the results, and any subjective changes in their emotions on their device.

[0166] Step 8:

[0167] The device sends user feedback to the server. This feedback includes program performance and emotional satisfaction.

[0168] Step 9:

[0169] The server updates the generated AI model based on feedback, taking into account new data and changes in user sentiment to improve analysis accuracy for the next cycle.

[0170] (Example 2)

[0171] 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".

[0172] In modern society, there is a growing need to provide health improvement plans tailored to individual users and optimize their physical and mental health. However, conventional systems have struggled to provide health improvement plans that fully consider the emotional state of users. In particular, in areas requiring individual attention, such as stress management, dietary guidance, and exercise plans, there is a need to provide suggestions based on the user's specific condition, rather than generalized information.

[0173] 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.

[0174] In this invention, the server includes means for collecting health-related information from an information network, means for collecting the user's daily life data through a terminal, and means for integrating the health-related information and the daily life data and analyzing the emotional state using a generative AI model. This makes it possible to effectively design and provide a specific and personalized health improvement plan that is suitable for the user.

[0175] "Health-related information" refers to data and indicators necessary to assess or improve the physical and psychological health status of users.

[0176] An "information network" refers to information sources and databases that are accessible via a digital network.

[0177] "Users" refers to individuals who aim to improve their health by using this system.

[0178] "Daily life data" refers to various data from a user's daily life, including their activities, lifestyle habits, and emotional state.

[0179] "Terminal" refers to electronic devices that users carry with them on a daily basis, such as smartphones and wearable devices.

[0180] A "generative AI model" refers to a system that uses machine learning algorithms to learn patterns from data and generate suggestions tailored to the user.

[0181] "Emotional state" refers to information that indicates the type, intensity, and changes in the emotions a user experiences.

[0182] A "health improvement plan" refers to proposals and activities designed to maintain or improve the health of users.

[0183] "Response" refers to the evaluations and feedback that users give regarding their health improvement plan.

[0184] "Adjustment" refers to the optimization process that improves the overall system performance and the accuracy of proposals based on previous feedback.

[0185] This invention is a system based on servers and terminals that provides personalized health improvement plans for individual users. The main components of this system are data collection, data integration and analysis, generation of health improvement plans, and processing of user notifications and feedback.

[0186] First, the device is responsible for collecting data on the user's daily life. Specifically, it uses smartphones and wearable devices to monitor the user's physical data such as steps taken and heart rate, and extracts keywords related to emotions from social media posts and email content. The device also securely transmits this data to a server on a regular basis.

[0187] The server integrates received data with health-related information available on the internet to form a dataset. Using a generative AI model, the server analyzes this data and evaluates the user's emotional state. The generative AI model incorporates machine learning algorithms, enabling efficient data analysis. This analysis then generates a personalized health improvement plan for the user. This plan includes stress management, exercise plans, and dietary guidance.

[0188] After the health improvement plan is completed, the server notifies the user via the terminal. This notification includes practical information that can be used in daily life, such as specific exercise suggestions and meal recipes. For example, if data reveals that the user tends to feel stressed in the afternoon, the prompt "The user has been feeling stressed recently. Please suggest effective ways to improve their health." is entered into the model, and a suggestion recommending relaxing yoga is generated.

[0189] Finally, the user acts on the proposed health improvement plan and inputs the results as feedback into the device. The server uses this feedback to readjust the generated AI model so that it can provide a more accurate plan in the future.

[0190] In this way, the entire system works together to provide personalized health improvement support to users. Because users can improve their daily lives based on individually customized information, they can enjoy a healthier and more balanced life.

[0191] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0192] Step 1:

[0193] The device collects data on the user's daily life. Specifically, it uses smartphones and wearable devices to collect physical data (such as steps and heart rate) and keywords related to emotions (extracted from social media posts and emails). The input is various data sensed by the user's device, and the output is daily life data stored in the device's internal database. At this stage, the data is not processed and is stored as raw data.

[0194] Step 2:

[0195] The device periodically sends collected daily life data to the server. A secure transmission protocol is used to protect data privacy. The input is the data collected in step 1, and the output is accurate and secure data transmission to the server. After transmission, a transmission log is saved to check for transmission errors.

[0196] Step 3:

[0197] The server receives user data sent from the terminal. It then integrates this data with general health information obtained from the internet to form a dataset. The input is user data and externally collected health information, and the output is the integrated dataset. This dataset is used in subsequent analysis processes.

[0198] Step 4:

[0199] The server analyzes the integrated dataset using a generative AI model. It utilizes an emotion engine to identify the user's emotional state and understand its changes. The input is the dataset formed in step 3, and the output is the analysis results regarding the user's emotional state and its trends. Specifically, it prompts the model and performs analysis based on individual data points.

[0200] Step 5:

[0201] The server generates an optimal health improvement plan based on the user's emotion analysis results. The suggestions from the generated AI model include stress management, nutritional guidance, and exercise plans. The input is the analysis results from step 4, and the output is a personalized health improvement plan for the user. This plan is provided as feedback to the user via the terminal.

[0202] Step 6:

[0203] The device notifies the user of the generated health improvement plan. The notification includes specific exercise methods and meal recipes. The input is the health improvement plan sent from the server, and the output is specific improvement suggestions for the user. The device utilizes push notification functionality to provide an interface that allows the user to start implementing the plan immediately.

[0204] Step 7:

[0205] The user acts according to the proposed health improvement plan and provides feedback on the results to the device. The input is the user's direct actions and their results, and the output is the feedback data entered into the device. This data will be used for subsequent analysis and model adjustments.

[0206] Step 8:

[0207] The server readjusts the generated AI model based on feedback collected from the user. This improves the accuracy of the plan provided to the user in the next analysis. The input is the feedback data obtained in step 7, and the output is the adjusted state of the model and the improved analysis results. Regular model adjustments aim to improve the user experience.

[0208] (Application Example 2)

[0209] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0210] In modern society, individual health management is highly valued, but conventional health improvement systems do not adequately consider the emotional state of the user, making it difficult to provide optimal health support tailored to individual needs. Furthermore, there is a lack of support from autonomous machines that physically assist in health improvement in the user's daily life, which hinders the implementation of daily health improvements.

[0211] 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.

[0212] In this invention, the server includes means for acquiring health-related information from a network, means for acquiring the user's daily life data through an information processing device, and means for integrating the health-related information and daily life data and analyzing them using a generative intelligence model. This makes it possible to analyze the user's emotional state, provide health support in accordance with emotional fluctuations in daily life, and further physically support health improvement through an autonomous machine.

[0213] "Health-related information" refers to general data and knowledge about a user's health status and how to improve it, and is obtained through the network.

[0214] "Daily life data" refers to data that includes the user's daily activities and biometric information, and is acquired through an information processing device.

[0215] An "information processing device" is a device used to acquire and process data, and is a device that a user carries with them or installs in their home.

[0216] A "generative intelligence model" is an algorithmic model that uses machine learning and artificial intelligence technologies to analyze data and derive results that are suitable for the user.

[0217] An "autonomous machine" is a mechanical device that automatically performs programmed actions to support the improvement of the user's health.

[0218] "Emotional state" refers to the user's psychological or emotional condition, which is analyzed using a generative intelligence model.

[0219] A "health improvement plan" is a set of guidelines and action plans aimed at improving or maintaining the user's health.

[0220] Modes for carrying out the invention

[0221] The system implementing this invention consists of numerous autonomously operating elements. The server first acquires health-related information via a network and forms foundational data for optimization based on that information. This information includes the latest medical guidelines and data from health-related databases. The server further acquires the user's daily life data through an information processing device. This data includes not only physiological data such as movement and heart rate, but also text data from social media used to assess emotional state.

[0222] The software installed on the device integrates this data and performs complex analyses using a generative intelligence model. The generative intelligence model is developed using Python and processes data in real time using machine learning frameworks such as TENSORFLOW® or PyTorch. The data is pre-formatted and processed into a form suitable for analysis via data processing frameworks such as Pandas and NumPy. During the analysis, emotional states and behavioral patterns are modeled in detail, and based on the results, an optimal health improvement plan is generated for each individual user.

[0223] Users are notified of this health improvement plan through their device. The plan may include specific advice on physical activity and diet, as well as relaxation techniques based on their emotional state. For example, if the system determines that stress levels are high due to prolonged desk work, it may suggest performing relaxation exercises. User feedback is collected through prompts. These prompts ask about the user's satisfaction with the improvements suggested by the system and their desire for further assistance, and may be presented in the form of "Are you satisfied with the current relaxation techniques?"

[0224] Through this structure, the system continuously provides personalized health support to each individual user. This process is physically assisted by an autonomous machine that flexibly responds to the user's emotional fluctuations. This autonomous machine can be integrated into daily life through a series of actions, efficiently providing concrete means to support health.

[0225] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0226] Step 1:

[0227] The server retrieves health-related information from the network. This information is collected by obtaining data from online medical databases and the latest health guidelines via APIs, and stored as a base dataset. The input is health-related database information, and the output is an integrated health information dataset.

[0228] Step 2:

[0229] The device acquires the user's daily life data through an information processing device. This data includes activity information from smartphone sensors and physiological information from wearable devices. The input is raw data from sensors, and the output is formatted daily life data. The data is pre-processed using Pandas.

[0230] Step 3:

[0231] The device extracts emotional states from social media and text messages. This emotional data extraction uses a natural language processing library to analyze keywords and phrases related to emotions. The input is text data, and the output is data representing emotional states.

[0232] Step 4:

[0233] The server integrates the health information dataset, daily life data, and emotional data acquired in the previous step, and begins analysis using a generative AI model. The generative AI model analyzes the data using TensorFlow or PyTorch to evaluate the user's health and emotional state. Thus, the input is the integrated dataset, and the output is the analyzed health and emotional state.

[0234] Step 5:

[0235] The server uses a generative intelligence model to generate an optimal health improvement plan for the user. The generated plan includes exercise guidance, dietary advice, and stress management strategies. At this stage, the input is the analyzed health and emotional state, and the output is the specific health improvement plan.

[0236] Step 6:

[0237] The device notifies the user of the generated health improvement plan. The notification is provided via voice and visual alerts, offering instructions tailored to the user's daily activities. Specific actions include verbally communicating the plan's contents. In this context, the input is the health improvement plan, and the output is the notification to the user.

[0238] Step 7:

[0239] User feedback is sent to the server via the terminal. This feedback is presented as prompts, inquiring about the user's actual behavior and satisfaction level. The generative intelligence model is updated based on this feedback. The input is user feedback, and the output is new data for model tuning.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] [Second Embodiment]

[0244] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0245] 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.

[0246] 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).

[0247] 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.

[0248] 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.

[0249] 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).

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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.

[0255] 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".

[0256] This invention is a system designed to improve user health and support longevity, and is primarily based on programs executed by servers and terminals.

[0257] First, personal lifestyle data is collected using smartphones and wearable devices (hereinafter referred to as "devices") that users use on a daily basis. For example, if a user records their daily step count through a smartphone app or measures their heart rate during exercise with a heart rate monitor, that data is stored on the device.

[0258] Next, the device periodically sends this data to the server. Content posted by users on social media and health-related topics in emails are also extracted based on specific keywords and analyzed.

[0259] The server acquires a wide range of health information from the internet. This includes information from government agencies, medical institutions, scientific papers, and news sites. This information, along with the user's personal data, is aggregated and analyzed by a generative AI model on the server.

[0260] This generative AI model employs machine learning algorithms to design appropriate health improvement programs based on the user's health status and behavioral patterns. For example, if the server determines that a user has been inactive recently, it can suggest a specific exercise plan such as "walk for 30 minutes every day." It can also detect nutritional imbalances from the user's eating history and provide dietary guidelines for improvement.

[0261] The generated health improvement program is notified to the user via the device. The user adjusts their daily life according to this program and provides feedback on the results and their impressions to the device. For example, they can report that they lost weight after following the suggested exercise plan for a week.

[0262] The server receives this feedback, readjusts the AI ​​model, and improves the accuracy of subsequent programs. Through this process, it is possible to continuously provide health improvement support that is best suited to each user. Users can enjoy new value in maintaining and improving their health in a way that suits their individual lifestyle.

[0263] The following describes the processing flow.

[0264] Step 1:

[0265] The device collects data from the user's daily life. This includes recording data such as steps, heart rate, and location information using smartphones and wearable devices. Additional health-related information is collected by extracting specific keywords from social media posts and emails.

[0266] Step 2:

[0267] The device periodically sends the collected data to the server. When transmitting data, it encrypts the data as needed to protect privacy.

[0268] Step 3:

[0269] The server collects general health-related information from the internet. This includes the latest research papers, health guidelines, and expert opinions. This information is also integrated into a dataset used for analysis.

[0270] Step 4:

[0271] The server aggregates personal data and general health information received from terminals and performs analysis using a generative AI model. The analysis applies machine learning algorithms to evaluate the user's health status and lifestyle patterns.

[0272] Step 5:

[0273] The server generates a health improvement program tailored to the user based on the analysis results. This program includes, for example, exercise guidelines, dietary improvement strategies, and stress management methods.

[0274] Step 6:

[0275] The device notifies the user of a health improvement program generated on the server. This notification is delivered via push notifications or in-app alerts. The user is shown program details and instructions for implementation.

[0276] Step 7:

[0277] The user incorporates the proposed health improvement program into daily life. The user performs actions based on the program and feeds back the results and changes in physical condition to the terminal. For example, the user records the change in weight after exercise and the content of the diet.

[0278] Step 8:

[0279] The terminal sends the feedback from the user to the server. The feedback is required to include both the user's subjective opinions and objective results.

[0280] Step 9:

[0281] Based on the received feedback, the server readjusts the generated AI model for use in the next analysis. This completes the cycle of improving the accuracy of the program and providing more personalized health improvement proposals.

[0282] (Example 1)

[0283] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0284] The current health management system is not sufficient for quickly and efficiently integrating the user's life data and related information and appropriately generating a health improvement plan for each user. In addition, due to the lack of a real-time feedback function that utilizes the user's response, it is difficult to provide support optimized for individual health conditions.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0286] In this invention, the server includes means for collecting health-related information from a wide-area information network, means for collecting the user's daily life information through a portable information terminal, and means for analyzing the user's information transmission content. As a result, it becomes possible to integrate the user's life data and extensive health information and automatically generate an individually optimized health improvement plan by utilizing a generated AI model.

[0287] "Health-related information" refers to various types of information related to the physical condition and medical management obtained from a wide-area information network.

[0288] "Daily life information" refers to data reflecting the user's daily activities and physiological states, and refers to the information collected from a portable information terminal.

[0289] "Portable information terminal" refers to an information processing device that the user uses daily, including devices such as smartphones and wearable devices.

[0290] "Generated AI model" refers to an artificial intelligence model that analyzes data using machine learning algorithms and generates results according to specific purposes.

[0291] "Health improvement plan" refers to an individualized action guideline or habit plan designed by a generated AI model to improve the user's health condition.

[0292] "Reaction information" refers to feedback or impressions on the health improvement plan provided by the user, and refers to the data used for adjusting the model.

[0293] This invention realizes a system for optimizing an individual's health condition by the user collecting daily life information using a portable information terminal. The system is mainly executed using a server and a terminal.

[0294] 1. Role of the Terminal

[0295] Users collect daily life information using mobile devices such as smartphones and wearable devices. This includes steps taken, heart rate, and sleep patterns. The devices are configured to collect this data in real time and transfer it to a server at regular intervals.

[0296] 2. Server Role

[0297] The server has the capability to collect health-related information from a wide-area information network. This includes medical research, government health guidelines, and news articles. The server integrates this collected information and analyzes the user's health status using a generative AI model.

[0298] 3. Use of Generative AI Models

[0299] The generative AI model uses machine learning algorithms to analyze data and generate an optimal health improvement plan for the user. This plan includes exercise plans, dietary guidance, and methods for managing mental stress.

[0300] 4. Notification and feedback on the health improvement plan

[0301] The device notifies the user of the generated health improvement plan, and the user adjusts their lifestyle according to the plan. The user reports their response information via the device, such as changes in weight or improvements in stress levels based on the plan. The server receives this feedback and continuously updates the generating AI model.

[0302] As a specific example, the server collects new health information from a wide - area information network several times a day, integrates it with the user's activity data, and performs analysis. For example, by inputting a prompt sentence such as "I am a woman in my 30s, aiming for three - times - a - week exercise and a balanced diet, but I recently feel easily fatigued. What improvements should I make to maintain a healthy life?" into the generation AI model, specific health improvement suggestions can be provided. With this system, users can always receive the latest and individualized health support.

[0303] The flow of the specific process in Example 1 will be described using FIG. 11.

[0304] Step 1:

[0305] Data collection

[0306] Terminal: The user uses a smartphone or a wearable device to collect daily life data. Specifically, applications that measure steps, heart rate, sleep patterns, etc. are running. These data are stored in the storage within the terminal.

[0307] Input: The user's physical activity and physiological data

[0308] Output: The user's daily life information data

[0309] Step 2:

[0310] Data transmission

[0311] Terminal: Transmits the daily life information to the server at regular time intervals. This is scheduled such that the terminal application operates in the background and performs data transfer, for example, at 0:00 every night.

[0312] Input: The daily life information stored in the terminal

[0313] Output: The user's daily life information transmitted to the server

[0314] Step 3:

[0315] Collection and integration of health information

[0316] Server: Collects health-related information from a wide-area information network. This involves using API-based data collection functions to gather information from government health data, the latest medical papers, news, and other sources. This information is then integrated with the received information about daily life.

[0317] Input: Health information from the internet, user's daily life information

[0318] Output: Integrated health information

[0319] Step 4:

[0320] Data analysis and program generation

[0321] Server: The integrated data is fed into a generating AI model for analysis. The AI ​​model uses machine learning algorithms to assess the user's current health status and generate a personalized health improvement plan.

[0322] Input: Integrated health information

[0323] Output: Health Improvement Plan

[0324] Step 5:

[0325] Program Notices and Implementation

[0326] Device: Notifies the user of the generated health improvement plan. The user receives notifications from the app and follows the suggested exercise and meal plans. This notification feature makes it easy for the user to see the next steps toward their health.

[0327] Input: Health Improvement Plan

[0328] Output: Health improvement plan notified to the user

[0329] Step 6:

[0330] Gathering feedback and updating the model

[0331] User: Provides feedback on the results and impressions of the health improvement plan implemented to the device. For example, records the results of exercising for a week according to the plan, changes in weight, etc.

[0332] Server: We receive this feedback information, readjust the AI ​​model, and use it to inform the next health improvement plan.

[0333] Input: User feedback information

[0334] Output: Updated generative AI model

[0335] (Application Example 1)

[0336] 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."

[0337] In elderly care, there is a need to continuously and efficiently manage each individual's health condition. If health risks can be quickly detected in daily life and care plans can be flexibly adjusted, the quality of care will improve and contribute to maintaining a comfortable life for the user. However, current methods have problems such as inefficient data collection and analysis, making it difficult to provide individualized care.

[0338] 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.

[0339] In this invention, the server includes means for collecting health-related information from an information and communication network, means for collecting residents' daily activity data via a device, means for integrating the health-related information and the daily activity data and analyzing them using a generative model, and means for monitoring health status in a care environment and optimizing individual care plans. This makes it possible to monitor changes in the health status of elderly people in real time and provide optimal health improvement plans.

[0340] "Health-related information" refers to information including statistical data, guidelines, or the latest research findings necessary to assess and improve the health status of residents.

[0341] "Information and communication network" refers to network infrastructure that enables the exchange of digital data, including the internet and dedicated lines.

[0342] "Resident" refers to an individual who receives services in a care environment.

[0343] "Lifestyle activity data" refers to detailed behavioral data such as the number of steps taken, heart rate, and dietary content collected during residents' daily lives.

[0344] "Device" refers to hardware equipped with data collection and communication functions, such as smartphones and wearable devices.

[0345] A "generative model" refers to a program that includes a learning algorithm to analyze the health status of residents and propose an optimal health improvement plan.

[0346] "Analysis" refers to the process of evaluating the health status of residents based on collected data and identifying problems and areas for improvement.

[0347] "Care environment" refers to the physical and institutional framework for providing residents with health maintenance and life support.

[0348] A "health improvement plan" refers to a program that includes specific action guidelines aimed at maintaining and improving one's health.

[0349] The system that realizes this invention first acquires residents' lifestyle activity data using smartphones or wearable devices (hereinafter referred to as terminals). These devices are equipped with functions for measuring steps and monitoring heart rate, and transmit data to a server via Bluetooth or Wi-Fi. Examples of hardware that can be used include general smartphones and wearable devices with heart rate measurement functions.

[0350] The server integrates health information collected via the information and communication network with lifestyle activity data transmitted from terminals. A generative model running on a cloud platform (e.g., Google Cloud, AWS) is used for data processing and analysis. This generative AI model utilizes machine learning algorithms to assess the health status of each resident and create an appropriate health improvement plan. The created plan, including specific action guidelines, is communicated to the resident.

[0351] For example, if a resident's physical activity level is low, this model suggests a simple and actionable exercise plan, such as "run five laps around the living room at 3 PM." Through this implementation, the quality of care can be improved by monitoring the resident's health status in real time and quickly providing optimal health improvement measures.

[0352] An example of a prompt is: "Assess the resident's recent health status based on their data and create an exercise plan. If they appear to be inactive, what would you suggest?" This prompting process enables the generative model to provide appropriate health guidance.

[0353] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0354] Step 1:

[0355] The device collects data on residents' daily lives and transmits it to a server. Specifically, steps taken, heart rate, and other data are measured by the device and sent to the server via Bluetooth or Wi-Fi. The input is sensor data from the device, and the output is raw activity data transmitted to the server.

[0356] Step 2:

[0357] The server retrieves health-related information collected through information and communication networks. This includes obtaining the latest health news and research data from databases of medical institutions and government agencies. The input is health-related information obtained online, and the output is integrable health information data.

[0358] Step 3:

[0359] The server integrates collected daily life data and health information and performs analysis using a generative AI model. By inputting the integrated data into the generative model, it evaluates the health status of residents and analyzes short-term and long-term health trends. The output is the analysis results regarding the health status of residents.

[0360] Step 4:

[0361] The server generates a health improvement plan for each resident based on the analysis results. The generated plan includes specific action guidelines such as exercise suggestions and dietary recommendations. The input is the analysis results, and the output is the health improvement plan.

[0362] Step 5:

[0363] The server notifies the terminal of the generated health improvement plan. The resident reviews this notification and follows the proposed health improvement measures. The input is the health improvement plan, and the output is the plan notification received by the resident.

[0364] Step 6:

[0365] The user inputs the results and feedback of the proposed health improvement plan into the terminal. Input is the feedback data entered by the user, and output is the feedback data sent to the server.

[0366] Step 7:

[0367] The server updates the generative AI model based on feedback data to improve the accuracy of the next analysis. The input is the feedback data, and the output is the adjusted generative AI model.

[0368] 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.

[0369] This invention is a health improvement system that incorporates an emotion engine. It is primarily based on programs executed by a server and terminals, and provides a health program that takes the user's emotional state into consideration.

[0370] First, data from the user's daily life is constantly collected through smartphones and wearable devices (hereinafter referred to as "devices") that the user uses on a daily basis. In addition to physical data such as steps taken and heart rate, the devices also collect emotional data by automatically extracting keywords related to emotions from social media posts and emails.

[0371] The data collected by the device is periodically sent to the server. The server receives this data and integrates it into a dataset along with general health-related information found on the web. In addition, the server uses an emotion engine to analyze the user's emotions and understand changes in their emotions in their daily life.

[0372] The analysis utilizes a generative AI model based on machine learning algorithms, and the server designs an optimal health improvement program for the user, taking into account newly collected emotional data. For example, if the server detects that the user is experiencing stress, it can suggest exercises or meals that promote relaxation.

[0373] The health improvement program generated in this way is notified to the user via the device. The user improves their daily life according to the suggested program and provides feedback on the results and changes in their emotions to the device. Information such as whether the user is satisfied or dissatisfied with a particular program is interpreted from the emotional data.

[0374] The server readjusts the generated AI model based on user feedback and uses that information for the next analysis. This process improves the program's accuracy and allows it to provide health improvement support that is more tailored to each user's individual condition. As a result, users can enjoy new value in maintaining and improving their health in a way that suits their individual emotions.

[0375] The following describes the processing flow.

[0376] Step 1:

[0377] The device collects the user's physical and emotional data. Specifically, it records data such as steps taken, heart rate, and location information through sensors, and extracts emotional keywords from social media posts and emails.

[0378] Step 2:

[0379] The device transmits the collected data to the server at regular intervals. This data includes information about the user's current health and emotional state.

[0380] Step 3:

[0381] The server also collects health-related information from the internet and integrates it with user data. The data is then organized into sets for analysis.

[0382] Step 4:

[0383] The server uses an emotion engine to analyze the user's emotional data. Through analysis of social media posts and email messages, it identifies the user's mood and stress level.

[0384] Step 5:

[0385] The server inputs integrated data, including emotional states, into a generating AI model, which then analyzes it using machine learning algorithms. This generates an optimized improvement program tailored to the user's health and emotional state.

[0386] Step 6:

[0387] The server sends the generated health improvement program to the terminal and notifies the user. The program may include, for example, exercises aimed at stress reduction and dietary guidance to promote relaxation.

[0388] Step 7:

[0389] Users attempt to improve their lifestyle based on the notified program. They record what they did, the results, and any subjective changes in their emotions on their device.

[0390] Step 8:

[0391] The device sends user feedback to the server. This feedback includes program performance and emotional satisfaction.

[0392] Step 9:

[0393] The server updates the generated AI model based on feedback, taking into account new data and changes in user sentiment to improve analysis accuracy for the next cycle.

[0394] (Example 2)

[0395] 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".

[0396] In modern society, there is a growing need to provide health improvement plans tailored to individual users and optimize their physical and mental health. However, conventional systems have struggled to provide health improvement plans that fully consider the emotional state of users. In particular, in areas requiring individual attention, such as stress management, dietary guidance, and exercise plans, there is a need to provide suggestions based on the user's specific condition, rather than generalized information.

[0397] 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.

[0398] In this invention, the server includes means for collecting health-related information from an information network, means for collecting the user's daily life data through a terminal, and means for integrating the health-related information and the daily life data and analyzing the emotional state using a generative AI model. This makes it possible to effectively design and provide a specific and personalized health improvement plan that is suitable for the user.

[0399] "Health-related information" refers to data and indicators necessary to assess or improve the physical and psychological health status of users.

[0400] An "information network" refers to information sources and databases that are accessible via a digital network.

[0401] "Users" refers to individuals who aim to improve their health by using this system.

[0402] "Daily life data" refers to various data from a user's daily life, including their activities, lifestyle habits, and emotional state.

[0403] "Terminal" refers to electronic devices that users carry with them on a daily basis, such as smartphones and wearable devices.

[0404] A "generative AI model" refers to a system that uses machine learning algorithms to learn patterns from data and generate suggestions tailored to the user.

[0405] "Emotional state" refers to information that indicates the type, intensity, and changes in the emotions a user experiences.

[0406] A "health improvement plan" refers to proposals and activities designed to maintain or improve the health of users.

[0407] "Response" refers to the evaluations and feedback that users give regarding their health improvement plan.

[0408] "Adjustment" refers to the optimization process that improves the overall system performance and the accuracy of proposals based on previous feedback.

[0409] This invention is a system based on servers and terminals that provides personalized health improvement plans for individual users. The main components of this system are data collection, data integration and analysis, generation of health improvement plans, and processing of user notifications and feedback.

[0410] First, the device is responsible for collecting data on the user's daily life. Specifically, it uses smartphones and wearable devices to monitor the user's physical data such as steps taken and heart rate, and extracts keywords related to emotions from social media posts and email content. The device also securely transmits this data to a server on a regular basis.

[0411] The server integrates received data with health-related information available on the internet to form a dataset. Using a generative AI model, the server analyzes this data and evaluates the user's emotional state. The generative AI model incorporates machine learning algorithms, enabling efficient data analysis. This analysis then generates a personalized health improvement plan for the user. This plan includes stress management, exercise plans, and dietary guidance.

[0412] After the health improvement plan is completed, the server notifies the user via the terminal. This notification includes practical information that can be used in daily life, such as specific exercise suggestions and meal recipes. For example, if data reveals that the user tends to feel stressed in the afternoon, the prompt "The user has been feeling stressed recently. Please suggest effective ways to improve their health." is entered into the model, and a suggestion recommending relaxing yoga is generated.

[0413] Finally, the user acts on the proposed health improvement plan and inputs the results as feedback into the device. The server uses this feedback to readjust the generated AI model so that it can provide a more accurate plan in the future.

[0414] In this way, the entire system works together to provide personalized health improvement support to users. Because users can improve their daily lives based on individually customized information, they can enjoy a healthier and more balanced life.

[0415] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0416] Step 1:

[0417] The device collects data on the user's daily life. Specifically, it uses smartphones and wearable devices to collect physical data (such as steps and heart rate) and keywords related to emotions (extracted from social media posts and emails). The input is various data sensed by the user's device, and the output is daily life data stored in the device's internal database. At this stage, the data is not processed and is stored as raw data.

[0418] Step 2:

[0419] The device periodically sends collected daily life data to the server. A secure transmission protocol is used to protect data privacy. The input is the data collected in step 1, and the output is accurate and secure data transmission to the server. After transmission, a transmission log is saved to check for transmission errors.

[0420] Step 3:

[0421] The server receives user data sent from the terminal. It then integrates this data with general health information obtained from the internet to form a dataset. The input is user data and externally collected health information, and the output is the integrated dataset. This dataset is used in subsequent analysis processes.

[0422] Step 4:

[0423] The server analyzes the integrated dataset using a generative AI model. It utilizes an emotion engine to identify the user's emotional state and understand its changes. The input is the dataset formed in step 3, and the output is the analysis results regarding the user's emotional state and its trends. Specifically, it prompts the model and performs analysis based on individual data points.

[0424] Step 5:

[0425] The server generates an optimal health improvement plan based on the user's emotion analysis results. The suggestions from the generated AI model include stress management, nutritional guidance, and exercise plans. The input is the analysis results from step 4, and the output is a personalized health improvement plan for the user. This plan is provided as feedback to the user via the terminal.

[0426] Step 6:

[0427] The device notifies the user of the generated health improvement plan. The notification includes specific exercise methods and meal recipes. The input is the health improvement plan sent from the server, and the output is specific improvement suggestions for the user. The device utilizes push notification functionality to provide an interface that allows the user to start implementing the plan immediately.

[0428] Step 7:

[0429] The user acts according to the proposed health improvement plan and provides feedback on the results to the device. The input is the user's direct actions and their results, and the output is the feedback data entered into the device. This data will be used for subsequent analysis and model adjustments.

[0430] Step 8:

[0431] The server readjusts the generated AI model based on feedback collected from the user. This improves the accuracy of the plan provided to the user in the next analysis. The input is the feedback data obtained in step 7, and the output is the adjusted state of the model and the improved analysis results. Regular model adjustments aim to improve the user experience.

[0432] (Application Example 2)

[0433] 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 as the "terminal".

[0434] In modern society, individual health management is highly valued, but conventional health improvement systems do not adequately consider the emotional state of the user, making it difficult to provide optimal health support tailored to individual needs. Furthermore, there is a lack of support from autonomous machines that physically assist in health improvement in the user's daily life, which hinders the implementation of daily health improvements.

[0435] 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.

[0436] In this invention, the server includes means for acquiring health-related information from a network, means for acquiring the user's daily life data through an information processing device, and means for integrating the health-related information and daily life data and analyzing them using a generative intelligence model. This makes it possible to analyze the user's emotional state, provide health support in accordance with emotional fluctuations in daily life, and further physically support health improvement through an autonomous machine.

[0437] "Health-related information" refers to general data and knowledge about a user's health status and how to improve it, and is obtained through the network.

[0438] "Daily life data" refers to data that includes the user's daily activities and biometric information, and is acquired through an information processing device.

[0439] An "information processing device" is a device used to acquire and process data, and is a device that a user carries with them or installs in their home.

[0440] A "generative intelligence model" is an algorithmic model that uses machine learning and artificial intelligence technologies to analyze data and derive results that are suitable for the user.

[0441] An "autonomous machine" is a mechanical device that automatically performs programmed actions to support the improvement of the user's health.

[0442] "Emotional state" refers to the user's psychological or emotional condition, which is analyzed using a generative intelligence model.

[0443] A "health improvement plan" is a set of guidelines and action plans aimed at improving or maintaining the user's health.

[0444] Modes for carrying out the invention

[0445] The system implementing this invention consists of numerous autonomously operating elements. The server first acquires health-related information via a network and forms foundational data for optimization based on that information. This information includes the latest medical guidelines and data from health-related databases. The server further acquires the user's daily life data through an information processing device. This data includes not only physiological data such as movement and heart rate, but also text data from social media used to assess emotional state.

[0446] The software installed on the device integrates this data and performs complex analyses using a generative intelligence model. The generative intelligence model is developed using Python and utilizes machine learning frameworks such as TensorFlow or PyTorch to process data in real time. The data is pre-formatted and processed into a suitable form for analysis via data processing frameworks such as Pandas and NumPy. During the analysis, emotional states and behavioral patterns are modeled in detail, and based on the results, an optimal health improvement plan is generated for each individual user.

[0447] Users are notified of this health improvement plan through their device. The plan may include specific advice on physical activity and diet, as well as relaxation techniques based on their emotional state. For example, if the system determines that stress levels are high due to prolonged desk work, it may suggest performing relaxation exercises. User feedback is collected through prompts. These prompts ask about the user's satisfaction with the improvements suggested by the system and their desire for further assistance, and may be presented in the form of "Are you satisfied with the current relaxation techniques?"

[0448] Through this structure, the system continuously provides personalized health support to each individual user. This process is physically assisted by an autonomous machine that flexibly responds to the user's emotional fluctuations. This autonomous machine can be integrated into daily life through a series of actions, efficiently providing concrete means to support health.

[0449] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0450] Step 1:

[0451] The server retrieves health-related information from the network. This information is collected by obtaining data from online medical databases and the latest health guidelines via APIs, and stored as a base dataset. The input is health-related database information, and the output is an integrated health information dataset.

[0452] Step 2:

[0453] The device acquires the user's daily life data through an information processing device. This data includes activity information from smartphone sensors and physiological information from wearable devices. The input is raw data from sensors, and the output is formatted daily life data. The data is pre-processed using Pandas.

[0454] Step 3:

[0455] The device extracts emotional states from social media and text messages. This emotional data extraction uses a natural language processing library to analyze keywords and phrases related to emotions. The input is text data, and the output is data representing emotional states.

[0456] Step 4:

[0457] The server integrates the health information dataset, daily life data, and emotional data acquired in the previous step, and begins analysis using a generative AI model. The generative AI model analyzes the data using TensorFlow or PyTorch to evaluate the user's health and emotional state. Thus, the input is the integrated dataset, and the output is the analyzed health and emotional state.

[0458] Step 5:

[0459] The server uses a generative intelligence model to generate an optimal health improvement plan for the user. The generated plan includes exercise guidance, dietary advice, and stress management strategies. At this stage, the input is the analyzed health and emotional state, and the output is the specific health improvement plan.

[0460] Step 6:

[0461] The device notifies the user of the generated health improvement plan. The notification is provided via voice and visual alerts, offering instructions tailored to the user's daily activities. Specific actions include verbally communicating the plan's contents. In this context, the input is the health improvement plan, and the output is the notification to the user.

[0462] Step 7:

[0463] User feedback is sent to the server via the terminal. This feedback is presented as prompts, inquiring about the user's actual behavior and satisfaction level. The generative intelligence model is updated based on this feedback. The input is user feedback, and the output is new data for model tuning.

[0464] 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.

[0465] 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.

[0466] 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.

[0467] [Third Embodiment]

[0468] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0469] 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.

[0470] 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).

[0471] 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.

[0472] 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.

[0473] 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).

[0474] 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.

[0475] 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.

[0476] 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.

[0477] 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.

[0478] 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.

[0479] 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".

[0480] This invention is a system designed to improve user health and support longevity, and is primarily based on programs executed by servers and terminals.

[0481] First, personal lifestyle data is collected using smartphones and wearable devices (hereinafter referred to as "devices") that users use on a daily basis. For example, if a user records their daily step count through a smartphone app or measures their heart rate during exercise with a heart rate monitor, that data is stored on the device.

[0482] Next, the device periodically sends this data to the server. Content posted by users on social media and health-related topics in emails are also extracted based on specific keywords and analyzed.

[0483] The server acquires a wide range of health information from the internet. This includes information from government agencies, medical institutions, scientific papers, and news sites. This information, along with the user's personal data, is aggregated and analyzed by a generative AI model on the server.

[0484] This generative AI model employs machine learning algorithms to design appropriate health improvement programs based on the user's health status and behavioral patterns. For example, if the server determines that a user has been inactive recently, it can suggest a specific exercise plan such as "walk for 30 minutes every day." It can also detect nutritional imbalances from the user's eating history and provide dietary guidelines for improvement.

[0485] The generated health improvement program is notified to the user via the device. The user adjusts their daily life according to this program and provides feedback on the results and their impressions to the device. For example, they can report that they lost weight after following the suggested exercise plan for a week.

[0486] The server receives this feedback, readjusts the AI ​​model, and improves the accuracy of subsequent programs. Through this process, it is possible to continuously provide health improvement support that is best suited to each user. Users can enjoy new value in maintaining and improving their health in a way that suits their individual lifestyle.

[0487] The following describes the processing flow.

[0488] Step 1:

[0489] The device collects data from the user's daily life. This includes recording data such as steps, heart rate, and location information using smartphones and wearable devices. Additional health-related information is collected by extracting specific keywords from social media posts and emails.

[0490] Step 2:

[0491] The device periodically sends the collected data to the server. When transmitting data, it encrypts the data as needed to protect privacy.

[0492] Step 3:

[0493] The server collects general health-related information from the internet. This includes the latest research papers, health guidelines, and expert opinions. This information is also integrated into a dataset used for analysis.

[0494] Step 4:

[0495] The server aggregates personal data and general health information received from terminals and performs analysis using a generative AI model. The analysis applies machine learning algorithms to evaluate the user's health status and lifestyle patterns.

[0496] Step 5:

[0497] The server generates a health improvement program tailored to the user based on the analysis results. This program includes, for example, exercise guidelines, dietary improvement strategies, and stress management methods.

[0498] Step 6:

[0499] The device notifies the user of a health improvement program generated on the server. This notification is delivered via push notifications or in-app alerts. The user is shown program details and instructions for implementation.

[0500] Step 7:

[0501] Users incorporate the suggested health improvement program into their daily routines. They perform actions based on the program and receive feedback on the results and changes in their physical condition via their device. For example, they might record changes in weight after exercise or details of their meals.

[0502] Step 8:

[0503] The device sends user feedback to the server. This feedback is required to include both the user's subjective opinions and objective results.

[0504] Step 9:

[0505] The server readjusts the generated AI model based on the feedback received and uses it for the next analysis. This completes the cycle of improving the program's accuracy and providing more personalized health improvement suggestions.

[0506] (Example 1)

[0507] 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."

[0508] Current health management systems are insufficient to quickly and efficiently integrate users' lifestyle data and related information, and to appropriately generate personalized health improvement plans. Furthermore, the lack of real-time feedback capabilities that leverage user responses makes it difficult to provide support optimized for individual health conditions.

[0509] 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.

[0510] In this invention, the server includes means for collecting health-related information from a wide-area information network, means for collecting user daily life information through a mobile information terminal, and means for analyzing the user's transmitted information. This makes it possible to integrate the user's lifestyle data with a wide range of health information and automatically generate an individually optimized health improvement plan using a generative AI model.

[0511] "Health-related information" refers to a wide variety of information related to physical condition and medical management, obtained from a broad information network.

[0512] "Daily life information" refers to data that reflects a user's daily activities and physiological state, and is collected from mobile devices.

[0513] "Personal information terminals" refer to information processing devices that users use on a daily basis, including smartphones and wearable devices.

[0514] A "generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to analyze data and generate results tailored to a specific purpose.

[0515] A "health improvement plan" refers to a personalized set of action plans and habits designed by a generative AI model to improve the user's health.

[0516] "Response information" refers to feedback and comments from users regarding their health improvement plans, and the data used to adjust the model.

[0517] This invention realizes a system that optimizes an individual's health status by collecting daily lifestyle information using a mobile information terminal. The system is mainly executed using a server and terminals.

[0518] 1. The role of the terminal

[0519] Users collect daily life information using mobile devices such as smartphones and wearable devices. This includes steps taken, heart rate, and sleep patterns. The devices are configured to collect this data in real time and transfer it to a server at regular intervals.

[0520] 2. Server Role

[0521] The server has the capability to collect health-related information from a wide-area information network. This includes medical research, government health guidelines, and news articles. The server integrates this collected information and analyzes the user's health status using a generative AI model.

[0522] 3. Use of Generative AI Models

[0523] The generative AI model uses machine learning algorithms to analyze data and generate an optimal health improvement plan for the user. This plan includes exercise plans, dietary guidance, and methods for managing mental stress.

[0524] 4. Notification and feedback on the health improvement plan

[0525] The device notifies the user of the generated health improvement plan, and the user adjusts their lifestyle according to the plan. The user reports their response information via the device, such as changes in weight or improvements in stress levels based on the plan. The server receives this feedback and continuously updates the generating AI model.

[0526] As a concrete example, the server collects new health information from a wide-area information network several times a day and integrates and analyzes it with the user's activity data. For instance, by inputting a prompt such as, "I'm a woman in my 30s, and I aim to exercise three times a week and eat a balanced diet, but I've recently been feeling more tired. What improvements can I make to maintain a healthy lifestyle?" into the AI ​​model, it can provide specific health improvement suggestions. This system allows users to always receive the latest and most personalized health support.

[0527] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0528] Step 1:

[0529] Data collection

[0530] Device: Users collect daily life data using smartphones or wearable devices. Specifically, applications run to measure steps, heart rate, sleep patterns, etc. This data is stored in the device's storage.

[0531] Input: User's physical activity and physiological data

[0532] Output: User's daily life information data

[0533] Step 2:

[0534] Data transmission

[0535] Terminal: Sends daily life information to the server at regular intervals. This is done by the terminal's application running in the background, scheduled to transfer data, for example, at midnight every night.

[0536] Input: Daily life information stored on the device

[0537] Output: User's daily life information sent to the server

[0538] Step 3:

[0539] Collection and integration of health information

[0540] Server: Collects health-related information from a wide-area information network. This involves using API-based data collection functions to gather information from government health data, the latest medical papers, news, and other sources. This information is then integrated with the received information about daily life.

[0541] Input: Health information from the internet, user's daily life information

[0542] Output: Integrated health information

[0543] Step 4:

[0544] Data analysis and program generation

[0545] Server: The integrated data is fed into a generating AI model for analysis. The AI ​​model uses machine learning algorithms to assess the user's current health status and generate a personalized health improvement plan.

[0546] Input: Integrated health information

[0547] Output: Health Improvement Plan

[0548] Step 5:

[0549] Program Notices and Implementation

[0550] Device: Notifies the user of the generated health improvement plan. The user receives notifications from the app and follows the suggested exercise and meal plans. This notification feature makes it easy for the user to see the next steps toward their health.

[0551] Input: Health Improvement Plan

[0552] Output: Health improvement plan notified to the user

[0553] Step 6:

[0554] Gathering feedback and updating the model

[0555] User: Provides feedback on the results and impressions of the health improvement plan implemented to the device. For example, records the results of exercising for a week according to the plan, changes in weight, etc.

[0556] Server: We receive this feedback information, readjust the AI ​​model, and use it to inform the next health improvement plan.

[0557] Input: User feedback information

[0558] Output: Updated generative AI model

[0559] (Application Example 1)

[0560] 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."

[0561] In elderly care, there is a need to continuously and efficiently manage each individual's health condition. If health risks can be quickly detected in daily life and care plans can be flexibly adjusted, the quality of care will improve and contribute to maintaining a comfortable life for the user. However, current methods have problems such as inefficient data collection and analysis, making it difficult to provide individualized care.

[0562] 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.

[0563] In this invention, the server includes means for collecting health-related information from an information and communication network, means for collecting residents' daily activity data via a device, means for integrating the health-related information and the daily activity data and analyzing them using a generative model, and means for monitoring health status in a care environment and optimizing individual care plans. This makes it possible to monitor changes in the health status of elderly people in real time and provide optimal health improvement plans.

[0564] "Health-related information" refers to information including statistical data, guidelines, or the latest research findings necessary to assess and improve the health status of residents.

[0565] "Information and communication network" refers to network infrastructure that enables the exchange of digital data, including the internet and dedicated lines.

[0566] "Resident" refers to an individual who receives services in a care environment.

[0567] "Lifestyle activity data" refers to detailed behavioral data such as the number of steps taken, heart rate, and dietary content collected during residents' daily lives.

[0568] "Device" refers to hardware equipped with data collection and communication functions, such as smartphones and wearable devices.

[0569] A "generative model" refers to a program that includes a learning algorithm to analyze the health status of residents and propose an optimal health improvement plan.

[0570] "Analysis" refers to the process of evaluating the health status of residents based on collected data and identifying problems and areas for improvement.

[0571] "Care environment" refers to the physical and institutional framework for providing residents with health maintenance and life support.

[0572] A "health improvement plan" refers to a program that includes specific action guidelines aimed at maintaining and improving one's health.

[0573] The system that realizes this invention first acquires residents' lifestyle activity data using smartphones or wearable devices (hereinafter referred to as terminals). These devices are equipped with functions for measuring steps and monitoring heart rate, and transmit data to a server via Bluetooth or Wi-Fi. Examples of hardware that can be used include general smartphones and wearable devices with heart rate measurement functions.

[0574] The server integrates health information collected via the information and communication network with lifestyle activity data transmitted from terminals. A generative model running on a cloud platform (e.g., Google Cloud, AWS) is used for data processing and analysis. This generative AI model utilizes machine learning algorithms to assess the health status of each resident and create an appropriate health improvement plan. The created plan, including specific action guidelines, is communicated to the resident.

[0575] For example, if a resident's physical activity level is low, this model suggests a simple and actionable exercise plan, such as "run five laps around the living room at 3 PM." Through this implementation, the quality of care can be improved by monitoring the resident's health status in real time and quickly providing optimal health improvement measures.

[0576] An example of a prompt is: "Assess the resident's recent health status based on their data and create an exercise plan. If they appear to be inactive, what would you suggest?" This prompting process enables the generative model to provide appropriate health guidance.

[0577] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0578] Step 1:

[0579] The device collects data on residents' daily lives and transmits it to a server. Specifically, steps taken, heart rate, and other data are measured by the device and sent to the server via Bluetooth or Wi-Fi. The input is sensor data from the device, and the output is raw activity data transmitted to the server.

[0580] Step 2:

[0581] The server retrieves health-related information collected through information and communication networks. This includes obtaining the latest health news and research data from databases of medical institutions and government agencies. The input is health-related information obtained online, and the output is integrable health information data.

[0582] Step 3:

[0583] The server integrates collected daily life data and health information and performs analysis using a generative AI model. By inputting the integrated data into the generative model, it evaluates the health status of residents and analyzes short-term and long-term health trends. The output is the analysis results regarding the health status of residents.

[0584] Step 4:

[0585] The server generates a health improvement plan for each resident based on the analysis results. The generated plan includes specific action guidelines such as exercise suggestions and dietary recommendations. The input is the analysis results, and the output is the health improvement plan.

[0586] Step 5:

[0587] The server notifies the terminal of the generated health improvement plan. The resident reviews this notification and follows the proposed health improvement measures. The input is the health improvement plan, and the output is the plan notification received by the resident.

[0588] Step 6:

[0589] The user inputs the results and feedback of the proposed health improvement plan into the terminal. Input is the feedback data entered by the user, and output is the feedback data sent to the server.

[0590] Step 7:

[0591] The server updates the generative AI model based on feedback data to improve the accuracy of the next analysis. The input is the feedback data, and the output is the adjusted generative AI model.

[0592] 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.

[0593] This invention is a health improvement system that incorporates an emotion engine. It is primarily based on programs executed by a server and terminals, and provides a health program that takes the user's emotional state into consideration.

[0594] First, data from the user's daily life is constantly collected through smartphones and wearable devices (hereinafter referred to as "devices") that the user uses on a daily basis. In addition to physical data such as steps taken and heart rate, the devices also collect emotional data by automatically extracting keywords related to emotions from social media posts and emails.

[0595] The data collected by the device is periodically sent to the server. The server receives this data and integrates it into a dataset along with general health-related information found on the web. In addition, the server uses an emotion engine to analyze the user's emotions and understand changes in their emotions in their daily life.

[0596] The analysis utilizes a generative AI model based on machine learning algorithms, and the server designs an optimal health improvement program for the user, taking into account newly collected emotional data. For example, if the server detects that the user is experiencing stress, it can suggest exercises or meals that promote relaxation.

[0597] The health improvement program generated in this way is notified to the user via the device. The user improves their daily life according to the suggested program and provides feedback on the results and changes in their emotions to the device. Information such as whether the user is satisfied or dissatisfied with a particular program is interpreted from the emotional data.

[0598] The server readjusts the generated AI model based on user feedback and uses that information for the next analysis. This process improves the program's accuracy and allows it to provide health improvement support that is more tailored to each user's individual condition. As a result, users can enjoy new value in maintaining and improving their health in a way that suits their individual emotions.

[0599] The following describes the processing flow.

[0600] Step 1:

[0601] The device collects the user's physical and emotional data. Specifically, it records data such as steps taken, heart rate, and location information through sensors, and extracts emotional keywords from social media posts and emails.

[0602] Step 2:

[0603] The device transmits the collected data to the server at regular intervals. This data includes information about the user's current health and emotional state.

[0604] Step 3:

[0605] The server also collects health-related information from the internet and integrates it with user data. The data is then organized into sets for analysis.

[0606] Step 4:

[0607] The server uses an emotion engine to analyze the user's emotional data. Through analysis of social media posts and email messages, it identifies the user's mood and stress level.

[0608] Step 5:

[0609] The server inputs integrated data, including emotional states, into a generating AI model, which then analyzes it using machine learning algorithms. This generates an optimized improvement program tailored to the user's health and emotional state.

[0610] Step 6:

[0611] The server sends the generated health improvement program to the terminal and notifies the user. The program may include, for example, exercises aimed at stress reduction and dietary guidance to promote relaxation.

[0612] Step 7:

[0613] Users attempt to improve their lifestyle based on the notified program. They record what they did, the results, and any subjective changes in their emotions on their device.

[0614] Step 8:

[0615] The device sends user feedback to the server. This feedback includes program performance and emotional satisfaction.

[0616] Step 9:

[0617] The server updates the generated AI model based on feedback, taking into account new data and changes in user sentiment to improve analysis accuracy for the next cycle.

[0618] (Example 2)

[0619] 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."

[0620] In modern society, there is a growing need to provide health improvement plans tailored to individual users and optimize their physical and mental health. However, conventional systems have struggled to provide health improvement plans that fully consider the emotional state of users. In particular, in areas requiring individual attention, such as stress management, dietary guidance, and exercise plans, there is a need to provide suggestions based on the user's specific condition, rather than generalized information.

[0621] 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.

[0622] In this invention, the server includes means for collecting health-related information from an information network, means for collecting the user's daily life data through a terminal, and means for integrating the health-related information and the daily life data and analyzing the emotional state using a generative AI model. This makes it possible to effectively design and provide a specific and personalized health improvement plan that is suitable for the user.

[0623] "Health-related information" refers to data and indicators necessary to assess or improve the physical and psychological health status of users.

[0624] An "information network" refers to information sources and databases that are accessible via a digital network.

[0625] "Users" refers to individuals who aim to improve their health by using this system.

[0626] "Daily life data" refers to various data from a user's daily life, including their activities, lifestyle habits, and emotional state.

[0627] "Terminal" refers to electronic devices that users carry with them on a daily basis, such as smartphones and wearable devices.

[0628] A "generative AI model" refers to a system that uses machine learning algorithms to learn patterns from data and generate suggestions tailored to the user.

[0629] "Emotional state" refers to information that indicates the type, intensity, and changes in the emotions a user experiences.

[0630] A "health improvement plan" refers to proposals and activities designed to maintain or improve the health of users.

[0631] "Response" refers to the evaluations and feedback that users give regarding their health improvement plan.

[0632] "Adjustment" refers to the optimization process that improves the overall system performance and the accuracy of proposals based on previous feedback.

[0633] This invention is a system based on servers and terminals that provides personalized health improvement plans for individual users. The main components of this system are data collection, data integration and analysis, generation of health improvement plans, and processing of user notifications and feedback.

[0634] First, the device is responsible for collecting data on the user's daily life. Specifically, it uses smartphones and wearable devices to monitor the user's physical data such as steps taken and heart rate, and extracts keywords related to emotions from social media posts and email content. The device also securely transmits this data to a server on a regular basis.

[0635] The server integrates received data with health-related information available on the internet to form a dataset. Using a generative AI model, the server analyzes this data and evaluates the user's emotional state. The generative AI model incorporates machine learning algorithms, enabling efficient data analysis. This analysis then generates a personalized health improvement plan for the user. This plan includes stress management, exercise plans, and dietary guidance.

[0636] After the health improvement plan is completed, the server notifies the user via the terminal. This notification includes practical information that can be used in daily life, such as specific exercise suggestions and meal recipes. For example, if data reveals that the user tends to feel stressed in the afternoon, the prompt "The user has been feeling stressed recently. Please suggest effective ways to improve their health." is entered into the model, and a suggestion recommending relaxing yoga is generated.

[0637] Finally, the user acts on the proposed health improvement plan and inputs the results as feedback into the device. The server uses this feedback to readjust the generated AI model so that it can provide a more accurate plan in the future.

[0638] In this way, the entire system works together to provide personalized health improvement support to users. Because users can improve their daily lives based on individually customized information, they can enjoy a healthier and more balanced life.

[0639] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0640] Step 1:

[0641] The device collects data on the user's daily life. Specifically, it uses smartphones and wearable devices to collect physical data (such as steps and heart rate) and keywords related to emotions (extracted from social media posts and emails). The input is various data sensed by the user's device, and the output is daily life data stored in the device's internal database. At this stage, the data is not processed and is stored as raw data.

[0642] Step 2:

[0643] The device periodically sends collected daily life data to the server. A secure transmission protocol is used to protect data privacy. The input is the data collected in step 1, and the output is accurate and secure data transmission to the server. After transmission, a transmission log is saved to check for transmission errors.

[0644] Step 3:

[0645] The server receives user data sent from the terminal. It then integrates this data with general health information obtained from the internet to form a dataset. The input is user data and externally collected health information, and the output is the integrated dataset. This dataset is used in subsequent analysis processes.

[0646] Step 4:

[0647] The server analyzes the integrated dataset using a generative AI model. It utilizes an emotion engine to identify the user's emotional state and understand its changes. The input is the dataset formed in step 3, and the output is the analysis results regarding the user's emotional state and its trends. Specifically, it prompts the model and performs analysis based on individual data points.

[0648] Step 5:

[0649] The server generates an optimal health improvement plan based on the user's emotion analysis results. The suggestions from the generated AI model include stress management, nutritional guidance, and exercise plans. The input is the analysis results from step 4, and the output is a personalized health improvement plan for the user. This plan is provided as feedback to the user via the terminal.

[0650] Step 6:

[0651] The device notifies the user of the generated health improvement plan. The notification includes specific exercise methods and meal recipes. The input is the health improvement plan sent from the server, and the output is specific improvement suggestions for the user. The device utilizes push notification functionality to provide an interface that allows the user to start implementing the plan immediately.

[0652] Step 7:

[0653] The user acts according to the proposed health improvement plan and provides feedback on the results to the device. The input is the user's direct actions and their results, and the output is the feedback data entered into the device. This data will be used for subsequent analysis and model adjustments.

[0654] Step 8:

[0655] The server readjusts the generated AI model based on feedback collected from the user. This improves the accuracy of the plan provided to the user in the next analysis. The input is the feedback data obtained in step 7, and the output is the adjusted state of the model and the improved analysis results. Regular model adjustments aim to improve the user experience.

[0656] (Application Example 2)

[0657] 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."

[0658] In modern society, individual health management is highly valued, but conventional health improvement systems do not adequately consider the emotional state of the user, making it difficult to provide optimal health support tailored to individual needs. Furthermore, there is a lack of support from autonomous machines that physically assist in health improvement in the user's daily life, which hinders the implementation of daily health improvements.

[0659] 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.

[0660] In this invention, the server includes means for acquiring health-related information from a network, means for acquiring the user's daily life data through an information processing device, and means for integrating the health-related information and daily life data and analyzing them using a generative intelligence model. This makes it possible to analyze the user's emotional state, provide health support in accordance with emotional fluctuations in daily life, and further physically support health improvement through an autonomous machine.

[0661] "Health-related information" refers to general data and knowledge about a user's health status and how to improve it, and is obtained through the network.

[0662] "Daily life data" refers to data that includes the user's daily activities and biometric information, and is acquired through an information processing device.

[0663] An "information processing device" is a device used to acquire and process data, and is a device that a user carries with them or installs in their home.

[0664] A "generative intelligence model" is an algorithmic model that uses machine learning and artificial intelligence technologies to analyze data and derive results that are suitable for the user.

[0665] An "autonomous machine" is a mechanical device that automatically performs programmed actions to support the improvement of the user's health.

[0666] "Emotional state" refers to the user's psychological or emotional condition, which is analyzed using a generative intelligence model.

[0667] A "health improvement plan" is a set of guidelines and action plans aimed at improving or maintaining the user's health.

[0668] Modes for carrying out the invention

[0669] The system implementing this invention consists of numerous autonomously operating elements. The server first acquires health-related information via a network and forms foundational data for optimization based on that information. This information includes the latest medical guidelines and data from health-related databases. The server further acquires the user's daily life data through an information processing device. This data includes not only physiological data such as movement and heart rate, but also text data from social media used to assess emotional state.

[0670] The software installed on the device integrates this data and performs complex analyses using a generative intelligence model. The generative intelligence model is developed using Python and utilizes machine learning frameworks such as TensorFlow or PyTorch to process data in real time. The data is pre-formatted and processed into a suitable form for analysis via data processing frameworks such as Pandas and NumPy. During the analysis, emotional states and behavioral patterns are modeled in detail, and based on the results, an optimal health improvement plan is generated for each individual user.

[0671] Users are notified of this health improvement plan through their device. The plan may include specific advice on physical activity and diet, as well as relaxation techniques based on their emotional state. For example, if the system determines that stress levels are high due to prolonged desk work, it may suggest performing relaxation exercises. User feedback is collected through prompts. These prompts ask about the user's satisfaction with the improvements suggested by the system and their desire for further assistance, and may be presented in the form of "Are you satisfied with the current relaxation techniques?"

[0672] Through this structure, the system continuously provides personalized health support to each individual user. This process is physically assisted by an autonomous machine that flexibly responds to the user's emotional fluctuations. This autonomous machine can be integrated into daily life through a series of actions, efficiently providing concrete means to support health.

[0673] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0674] Step 1:

[0675] The server retrieves health-related information from the network. This information is collected by obtaining data from online medical databases and the latest health guidelines via APIs, and stored as a base dataset. The input is health-related database information, and the output is an integrated health information dataset.

[0676] Step 2:

[0677] The device acquires the user's daily life data through an information processing device. This data includes activity information from smartphone sensors and physiological information from wearable devices. The input is raw data from sensors, and the output is formatted daily life data. The data is pre-processed using Pandas.

[0678] Step 3:

[0679] The device extracts emotional states from social media and text messages. This emotional data extraction uses a natural language processing library to analyze keywords and phrases related to emotions. The input is text data, and the output is data representing emotional states.

[0680] Step 4:

[0681] The server integrates the health information dataset, daily life data, and emotional data acquired in the previous step, and begins analysis using a generative AI model. The generative AI model analyzes the data using TensorFlow or PyTorch to evaluate the user's health and emotional state. Thus, the input is the integrated dataset, and the output is the analyzed health and emotional state.

[0682] Step 5:

[0683] The server uses a generative intelligence model to generate an optimal health improvement plan for the user. The generated plan includes exercise guidance, dietary advice, and stress management strategies. At this stage, the input is the analyzed health and emotional state, and the output is the specific health improvement plan.

[0684] Step 6:

[0685] The device notifies the user of the generated health improvement plan. The notification is provided via voice and visual alerts, offering instructions tailored to the user's daily activities. Specific actions include verbally communicating the plan's contents. In this context, the input is the health improvement plan, and the output is the notification to the user.

[0686] Step 7:

[0687] User feedback is sent to the server via the terminal. This feedback is presented as prompts, inquiring about the user's actual behavior and satisfaction level. The generative intelligence model is updated based on this feedback. The input is user feedback, and the output is new data for model tuning.

[0688] 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.

[0689] 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.

[0690] 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.

[0691] [Fourth Embodiment]

[0692] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0693] 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.

[0694] 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).

[0695] 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.

[0696] 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.

[0697] 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).

[0698] 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.

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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".

[0705] This invention is a system designed to improve user health and support longevity, and is primarily based on programs executed by servers and terminals.

[0706] First, personal lifestyle data is collected using smartphones and wearable devices (hereinafter referred to as "devices") that users use on a daily basis. For example, if a user records their daily step count through a smartphone app or measures their heart rate during exercise with a heart rate monitor, that data is stored on the device.

[0707] Next, the device periodically sends this data to the server. Content posted by users on social media and health-related topics in emails are also extracted based on specific keywords and analyzed.

[0708] The server acquires a wide range of health information from the internet. This includes information from government agencies, medical institutions, scientific papers, and news sites. This information, along with the user's personal data, is aggregated and analyzed by a generative AI model on the server.

[0709] This generative AI model employs machine learning algorithms to design appropriate health improvement programs based on the user's health status and behavioral patterns. For example, if the server determines that a user has been inactive recently, it can suggest a specific exercise plan such as "walk for 30 minutes every day." It can also detect nutritional imbalances from the user's eating history and provide dietary guidelines for improvement.

[0710] The generated health improvement program is notified to the user via the device. The user adjusts their daily life according to this program and provides feedback on the results and their impressions to the device. For example, they can report that they lost weight after following the suggested exercise plan for a week.

[0711] The server receives this feedback, readjusts the AI ​​model, and improves the accuracy of subsequent programs. Through this process, it is possible to continuously provide health improvement support that is best suited to each user. Users can enjoy new value in maintaining and improving their health in a way that suits their individual lifestyle.

[0712] The following describes the processing flow.

[0713] Step 1:

[0714] The device collects data from the user's daily life. This includes recording data such as steps, heart rate, and location information using smartphones and wearable devices. Additional health-related information is collected by extracting specific keywords from social media posts and emails.

[0715] Step 2:

[0716] The device periodically sends the collected data to the server. When transmitting data, it encrypts the data as needed to protect privacy.

[0717] Step 3:

[0718] The server collects general health-related information from the internet. This includes the latest research papers, health guidelines, and expert opinions. This information is also integrated into a dataset used for analysis.

[0719] Step 4:

[0720] The server aggregates personal data and general health information received from terminals and performs analysis using a generative AI model. The analysis applies machine learning algorithms to evaluate the user's health status and lifestyle patterns.

[0721] Step 5:

[0722] The server generates a health improvement program tailored to the user based on the analysis results. This program includes, for example, exercise guidelines, dietary improvement strategies, and stress management methods.

[0723] Step 6:

[0724] The device notifies the user of a health improvement program generated on the server. This notification is delivered via push notifications or in-app alerts. The user is shown program details and instructions for implementation.

[0725] Step 7:

[0726] Users incorporate the suggested health improvement program into their daily routines. They perform actions based on the program and receive feedback on the results and changes in their physical condition via their device. For example, they might record changes in weight after exercise or details of their meals.

[0727] Step 8:

[0728] The device sends user feedback to the server. This feedback is required to include both the user's subjective opinions and objective results.

[0729] Step 9:

[0730] The server readjusts the generated AI model based on the feedback received and uses it for the next analysis. This completes the cycle of improving the program's accuracy and providing more personalized health improvement suggestions.

[0731] (Example 1)

[0732] 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".

[0733] Current health management systems are insufficient to quickly and efficiently integrate users' lifestyle data and related information, and to appropriately generate personalized health improvement plans. Furthermore, the lack of real-time feedback capabilities that leverage user responses makes it difficult to provide support optimized for individual health conditions.

[0734] 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.

[0735] In this invention, the server includes means for collecting health-related information from a wide-area information network, means for collecting user daily life information through a mobile information terminal, and means for analyzing the user's transmitted information. This makes it possible to integrate the user's lifestyle data with a wide range of health information and automatically generate an individually optimized health improvement plan using a generative AI model.

[0736] "Health-related information" refers to a wide variety of information related to physical condition and medical management, obtained from a broad information network.

[0737] "Daily life information" refers to data that reflects a user's daily activities and physiological state, and is collected from mobile devices.

[0738] "Personal information terminals" refer to information processing devices that users use on a daily basis, including smartphones and wearable devices.

[0739] A "generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to analyze data and generate results tailored to a specific purpose.

[0740] A "health improvement plan" refers to a personalized set of action plans and habits designed by a generative AI model to improve the user's health.

[0741] "Response information" refers to feedback and comments from users regarding their health improvement plans, and the data used to adjust the model.

[0742] This invention realizes a system that optimizes an individual's health status by collecting daily lifestyle information using a mobile information terminal. The system is mainly executed using a server and terminals.

[0743] 1. The role of the terminal

[0744] Users collect daily life information using mobile devices such as smartphones and wearable devices. This includes steps taken, heart rate, and sleep patterns. The devices are configured to collect this data in real time and transfer it to a server at regular intervals.

[0745] 2. Server Role

[0746] The server has the capability to collect health-related information from a wide-area information network. This includes medical research, government health guidelines, and news articles. The server integrates this collected information and analyzes the user's health status using a generative AI model.

[0747] 3. Use of Generative AI Models

[0748] The generative AI model uses machine learning algorithms to analyze data and generate an optimal health improvement plan for the user. This plan includes exercise plans, dietary guidance, and methods for managing mental stress.

[0749] 4. Notification and feedback on the health improvement plan

[0750] The device notifies the user of the generated health improvement plan, and the user adjusts their lifestyle according to the plan. The user reports their response information via the device, such as changes in weight or improvements in stress levels based on the plan. The server receives this feedback and continuously updates the generating AI model.

[0751] As a concrete example, the server collects new health information from a wide-area information network several times a day and integrates and analyzes it with the user's activity data. For instance, by inputting a prompt such as, "I'm a woman in my 30s, and I aim to exercise three times a week and eat a balanced diet, but I've recently been feeling more tired. What improvements can I make to maintain a healthy lifestyle?" into the AI ​​model, it can provide specific health improvement suggestions. This system allows users to always receive the latest and most personalized health support.

[0752] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0753] Step 1:

[0754] Data collection

[0755] Device: Users collect daily life data using smartphones or wearable devices. Specifically, applications run to measure steps, heart rate, sleep patterns, etc. This data is stored in the device's storage.

[0756] Input: User's physical activity and physiological data

[0757] Output: User's daily life information data

[0758] Step 2:

[0759] Data transmission

[0760] Terminal: Sends daily life information to the server at regular intervals. This is done by the terminal's application running in the background, scheduled to transfer data, for example, at midnight every night.

[0761] Input: Daily life information stored on the device

[0762] Output: User's daily life information sent to the server

[0763] Step 3:

[0764] Collection and integration of health information

[0765] Server: Collects health-related information from a wide-area information network. This involves using API-based data collection functions to gather information from government health data, the latest medical papers, news, and other sources. This information is then integrated with the received information about daily life.

[0766] Input: Health information from the internet, user's daily life information

[0767] Output: Integrated health information

[0768] Step 4:

[0769] Data analysis and program generation

[0770] Server: The integrated data is fed into a generating AI model for analysis. The AI ​​model uses machine learning algorithms to assess the user's current health status and generate a personalized health improvement plan.

[0771] Input: Integrated health information

[0772] Output: Health Improvement Plan

[0773] Step 5:

[0774] Program Notices and Implementation

[0775] Device: Notifies the user of the generated health improvement plan. The user receives notifications from the app and follows the suggested exercise and meal plans. This notification feature makes it easy for the user to see the next steps toward their health.

[0776] Input: Health Improvement Plan

[0777] Output: Health improvement plan notified to the user

[0778] Step 6:

[0779] Gathering feedback and updating the model

[0780] User: Provides feedback on the results and impressions of the health improvement plan implemented to the device. For example, records the results of exercising for a week according to the plan, changes in weight, etc.

[0781] Server: We receive this feedback information, readjust the AI ​​model, and use it to inform the next health improvement plan.

[0782] Input: User feedback information

[0783] Output: Updated generative AI model

[0784] (Application Example 1)

[0785] 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".

[0786] In elderly care, there is a need to continuously and efficiently manage each individual's health condition. If health risks can be quickly detected in daily life and care plans can be flexibly adjusted, the quality of care will improve and contribute to maintaining a comfortable life for the user. However, current methods have problems such as inefficient data collection and analysis, making it difficult to provide individualized care.

[0787] 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.

[0788] In this invention, the server includes means for collecting health-related information from an information and communication network, means for collecting residents' daily activity data via a device, means for integrating the health-related information and the daily activity data and analyzing them using a generative model, and means for monitoring health status in a care environment and optimizing individual care plans. This makes it possible to monitor changes in the health status of elderly people in real time and provide optimal health improvement plans.

[0789] "Health-related information" refers to information including statistical data, guidelines, or the latest research findings necessary to assess and improve the health status of residents.

[0790] "Information and communication network" refers to network infrastructure that enables the exchange of digital data, including the internet and dedicated lines.

[0791] "Resident" refers to an individual who receives services in a care environment.

[0792] "Lifestyle activity data" refers to detailed behavioral data such as the number of steps taken, heart rate, and dietary content collected during residents' daily lives.

[0793] "Device" refers to hardware equipped with data collection and communication functions, such as smartphones and wearable devices.

[0794] A "generative model" refers to a program that includes a learning algorithm to analyze the health status of residents and propose an optimal health improvement plan.

[0795] "Analysis" refers to the process of evaluating the health status of residents based on collected data and identifying problems and areas for improvement.

[0796] "Care environment" refers to the physical and institutional framework for providing residents with health maintenance and life support.

[0797] A "health improvement plan" refers to a program that includes specific action guidelines aimed at maintaining and improving one's health.

[0798] The system that realizes this invention first acquires residents' lifestyle activity data using smartphones or wearable devices (hereinafter referred to as terminals). These devices are equipped with functions for measuring steps and monitoring heart rate, and transmit data to a server via Bluetooth or Wi-Fi. Examples of hardware that can be used include general smartphones and wearable devices with heart rate measurement functions.

[0799] The server integrates health information collected via the information and communication network with lifestyle activity data transmitted from terminals. A generative model running on a cloud platform (e.g., Google Cloud, AWS) is used for data processing and analysis. This generative AI model utilizes machine learning algorithms to assess the health status of each resident and create an appropriate health improvement plan. The created plan, including specific action guidelines, is communicated to the resident.

[0800] For example, if a resident's physical activity level is low, this model suggests a simple and actionable exercise plan, such as "run five laps around the living room at 3 PM." Through this implementation, the quality of care can be improved by monitoring the resident's health status in real time and quickly providing optimal health improvement measures.

[0801] An example of a prompt is: "Assess the resident's recent health status based on their data and create an exercise plan. If they appear to be inactive, what would you suggest?" This prompting process enables the generative model to provide appropriate health guidance.

[0802] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0803] Step 1:

[0804] The device collects data on residents' daily lives and transmits it to a server. Specifically, steps taken, heart rate, and other data are measured by the device and sent to the server via Bluetooth or Wi-Fi. The input is sensor data from the device, and the output is raw activity data transmitted to the server.

[0805] Step 2:

[0806] The server retrieves health-related information collected through information and communication networks. This includes obtaining the latest health news and research data from databases of medical institutions and government agencies. The input is health-related information obtained online, and the output is integrable health information data.

[0807] Step 3:

[0808] The server integrates collected daily life data and health information and performs analysis using a generative AI model. By inputting the integrated data into the generative model, it evaluates the health status of residents and analyzes short-term and long-term health trends. The output is the analysis results regarding the health status of residents.

[0809] Step 4:

[0810] The server generates a health improvement plan for each resident based on the analysis results. The generated plan includes specific action guidelines such as exercise suggestions and dietary recommendations. The input is the analysis results, and the output is the health improvement plan.

[0811] Step 5:

[0812] The server notifies the terminal of the generated health improvement plan. The resident reviews this notification and follows the proposed health improvement measures. The input is the health improvement plan, and the output is the plan notification received by the resident.

[0813] Step 6:

[0814] The user inputs the results and feedback of the proposed health improvement plan into the terminal. Input is the feedback data entered by the user, and output is the feedback data sent to the server.

[0815] Step 7:

[0816] The server updates the generative AI model based on feedback data to improve the accuracy of the next analysis. The input is the feedback data, and the output is the adjusted generative AI model.

[0817] 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.

[0818] This invention is a health improvement system that incorporates an emotion engine. It is primarily based on programs executed by a server and terminals, and provides a health program that takes the user's emotional state into consideration.

[0819] First, data from the user's daily life is constantly collected through smartphones and wearable devices (hereinafter referred to as "devices") that the user uses on a daily basis. In addition to physical data such as steps taken and heart rate, the devices also collect emotional data by automatically extracting keywords related to emotions from social media posts and emails.

[0820] The data collected by the device is periodically sent to the server. The server receives this data and integrates it into a dataset along with general health-related information found on the web. In addition, the server uses an emotion engine to analyze the user's emotions and understand changes in their emotions in their daily life.

[0821] The analysis utilizes a generative AI model based on machine learning algorithms, and the server designs an optimal health improvement program for the user, taking into account newly collected emotional data. For example, if the server detects that the user is experiencing stress, it can suggest exercises or meals that promote relaxation.

[0822] The health improvement program generated in this way is notified to the user via the device. The user improves their daily life according to the suggested program and provides feedback on the results and changes in their emotions to the device. Information such as whether the user is satisfied or dissatisfied with a particular program is interpreted from the emotional data.

[0823] The server readjusts the generated AI model based on user feedback and uses that information for the next analysis. This process improves the program's accuracy and allows it to provide health improvement support that is more tailored to each user's individual condition. As a result, users can enjoy new value in maintaining and improving their health in a way that suits their individual emotions.

[0824] The following describes the processing flow.

[0825] Step 1:

[0826] The device collects the user's physical and emotional data. Specifically, it records data such as steps taken, heart rate, and location information through sensors, and extracts emotional keywords from social media posts and emails.

[0827] Step 2:

[0828] The device transmits the collected data to the server at regular intervals. This data includes information about the user's current health and emotional state.

[0829] Step 3:

[0830] The server also collects health-related information from the internet and integrates it with user data. The data is then organized into sets for analysis.

[0831] Step 4:

[0832] The server uses an emotion engine to analyze the user's emotional data. Through analysis of social media posts and email messages, it identifies the user's mood and stress level.

[0833] Step 5:

[0834] The server inputs integrated data, including emotional states, into a generating AI model, which then analyzes it using machine learning algorithms. This generates an optimized improvement program tailored to the user's health and emotional state.

[0835] Step 6:

[0836] The server sends the generated health improvement program to the terminal and notifies the user. The program may include, for example, exercises aimed at stress reduction and dietary guidance to promote relaxation.

[0837] Step 7:

[0838] Users attempt to improve their lifestyle based on the notified program. They record what they did, the results, and any subjective changes in their emotions on their device.

[0839] Step 8:

[0840] The device sends user feedback to the server. This feedback includes program performance and emotional satisfaction.

[0841] Step 9:

[0842] The server updates the generated AI model based on feedback, taking into account new data and changes in user sentiment to improve analysis accuracy for the next cycle.

[0843] (Example 2)

[0844] 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".

[0845] In modern society, there is a growing need to provide health improvement plans tailored to individual users and optimize their physical and mental health. However, conventional systems have struggled to provide health improvement plans that fully consider the emotional state of users. In particular, in areas requiring individual attention, such as stress management, dietary guidance, and exercise plans, there is a need to provide suggestions based on the user's specific condition, rather than generalized information.

[0846] 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.

[0847] In this invention, the server includes means for collecting health-related information from an information network, means for collecting the user's daily life data through a terminal, and means for integrating the health-related information and the daily life data and analyzing the emotional state using a generative AI model. This makes it possible to effectively design and provide a specific and personalized health improvement plan that is suitable for the user.

[0848] "Health-related information" refers to data and indicators necessary to assess or improve the physical and psychological health status of users.

[0849] An "information network" refers to information sources and databases that are accessible via a digital network.

[0850] "Users" refers to individuals who aim to improve their health by using this system.

[0851] "Daily life data" refers to various data from a user's daily life, including their activities, lifestyle habits, and emotional state.

[0852] "Terminal" refers to electronic devices that users carry with them on a daily basis, such as smartphones and wearable devices.

[0853] A "generative AI model" refers to a system that uses machine learning algorithms to learn patterns from data and generate suggestions tailored to the user.

[0854] "Emotional state" refers to information that indicates the type, intensity, and changes in the emotions a user experiences.

[0855] A "health improvement plan" refers to proposals and activities designed to maintain or improve the health of users.

[0856] "Response" refers to the evaluations and feedback that users give regarding their health improvement plan.

[0857] "Adjustment" refers to the optimization process that improves the overall system performance and the accuracy of proposals based on previous feedback.

[0858] This invention is a system based on servers and terminals that provides personalized health improvement plans for individual users. The main components of this system are data collection, data integration and analysis, generation of health improvement plans, and processing of user notifications and feedback.

[0859] First, the device is responsible for collecting data on the user's daily life. Specifically, it uses smartphones and wearable devices to monitor the user's physical data such as steps taken and heart rate, and extracts keywords related to emotions from social media posts and email content. The device also securely transmits this data to a server on a regular basis.

[0860] The server integrates received data with health-related information available on the internet to form a dataset. Using a generative AI model, the server analyzes this data and evaluates the user's emotional state. The generative AI model incorporates machine learning algorithms, enabling efficient data analysis. This analysis then generates a personalized health improvement plan for the user. This plan includes stress management, exercise plans, and dietary guidance.

[0861] After the health improvement plan is completed, the server notifies the user via the terminal. This notification includes practical information that can be used in daily life, such as specific exercise suggestions and meal recipes. For example, if data reveals that the user tends to feel stressed in the afternoon, the prompt "The user has been feeling stressed recently. Please suggest effective ways to improve their health." is entered into the model, and a suggestion recommending relaxing yoga is generated.

[0862] Finally, the user acts on the proposed health improvement plan and inputs the results as feedback into the device. The server uses this feedback to readjust the generated AI model so that it can provide a more accurate plan in the future.

[0863] In this way, the entire system works together to provide personalized health improvement support to users. Because users can improve their daily lives based on individually customized information, they can enjoy a healthier and more balanced life.

[0864] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0865] Step 1:

[0866] The device collects data on the user's daily life. Specifically, it uses smartphones and wearable devices to collect physical data (such as steps and heart rate) and keywords related to emotions (extracted from social media posts and emails). The input is various data sensed by the user's device, and the output is daily life data stored in the device's internal database. At this stage, the data is not processed and is stored as raw data.

[0867] Step 2:

[0868] The device periodically sends collected daily life data to the server. A secure transmission protocol is used to protect data privacy. The input is the data collected in step 1, and the output is accurate and secure data transmission to the server. After transmission, a transmission log is saved to check for transmission errors.

[0869] Step 3:

[0870] The server receives user data sent from the terminal. It then integrates this data with general health information obtained from the internet to form a dataset. The input is user data and externally collected health information, and the output is the integrated dataset. This dataset is used in subsequent analysis processes.

[0871] Step 4:

[0872] The server analyzes the integrated dataset using a generative AI model. It utilizes an emotion engine to identify the user's emotional state and understand its changes. The input is the dataset formed in step 3, and the output is the analysis results regarding the user's emotional state and its trends. Specifically, it prompts the model and performs analysis based on individual data points.

[0873] Step 5:

[0874] The server generates an optimal health improvement plan based on the user's emotion analysis results. The suggestions from the generated AI model include stress management, nutritional guidance, and exercise plans. The input is the analysis results from step 4, and the output is a personalized health improvement plan for the user. This plan is provided as feedback to the user via the terminal.

[0875] Step 6:

[0876] The device notifies the user of the generated health improvement plan. The notification includes specific exercise methods and meal recipes. The input is the health improvement plan sent from the server, and the output is specific improvement suggestions for the user. The device utilizes push notification functionality to provide an interface that allows the user to start implementing the plan immediately.

[0877] Step 7:

[0878] The user acts according to the proposed health improvement plan and provides feedback on the results to the device. The input is the user's direct actions and their results, and the output is the feedback data entered into the device. This data will be used for subsequent analysis and model adjustments.

[0879] Step 8:

[0880] The server readjusts the generated AI model based on feedback collected from the user. This improves the accuracy of the plan provided to the user in the next analysis. The input is the feedback data obtained in step 7, and the output is the adjusted state of the model and the improved analysis results. Regular model adjustments aim to improve the user experience.

[0881] (Application Example 2)

[0882] 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".

[0883] In modern society, individual health management is highly valued, but conventional health improvement systems do not adequately consider the emotional state of the user, making it difficult to provide optimal health support tailored to individual needs. Furthermore, there is a lack of support from autonomous machines that physically assist in health improvement in the user's daily life, which hinders the implementation of daily health improvements.

[0884] 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.

[0885] In this invention, the server includes means for acquiring health-related information from a network, means for acquiring the user's daily life data through an information processing device, and means for integrating the health-related information and daily life data and analyzing them using a generative intelligence model. This makes it possible to analyze the user's emotional state, provide health support in accordance with emotional fluctuations in daily life, and further physically support health improvement through an autonomous machine.

[0886] "Health-related information" refers to general data and knowledge about a user's health status and how to improve it, and is obtained through the network.

[0887] "Daily life data" refers to data that includes the user's daily activities and biometric information, and is acquired through an information processing device.

[0888] An "information processing device" is a device used to acquire and process data, and is a device that a user carries with them or installs in their home.

[0889] A "generative intelligence model" is an algorithmic model that uses machine learning and artificial intelligence technologies to analyze data and derive results that are suitable for the user.

[0890] An "autonomous machine" is a mechanical device that automatically performs programmed actions to support the improvement of the user's health.

[0891] "Emotional state" refers to the user's psychological or emotional condition, which is analyzed using a generative intelligence model.

[0892] A "health improvement plan" is a set of guidelines and action plans aimed at improving or maintaining the user's health.

[0893] Modes for carrying out the invention

[0894] The system implementing this invention consists of numerous autonomously operating elements. The server first acquires health-related information via a network and forms foundational data for optimization based on that information. This information includes the latest medical guidelines and data from health-related databases. The server further acquires the user's daily life data through an information processing device. This data includes not only physiological data such as movement and heart rate, but also text data from social media used to assess emotional state.

[0895] The software installed on the device integrates this data and performs complex analyses using a generative intelligence model. The generative intelligence model is developed using Python and utilizes machine learning frameworks such as TensorFlow or PyTorch to process data in real time. The data is pre-formatted and processed into a suitable form for analysis via data processing frameworks such as Pandas and NumPy. During the analysis, emotional states and behavioral patterns are modeled in detail, and based on the results, an optimal health improvement plan is generated for each individual user.

[0896] Users are notified of this health improvement plan through their device. The plan may include specific advice on physical activity and diet, as well as relaxation techniques based on their emotional state. For example, if the system determines that stress levels are high due to prolonged desk work, it may suggest performing relaxation exercises. User feedback is collected through prompts. These prompts ask about the user's satisfaction with the improvements suggested by the system and their desire for further assistance, and may be presented in the form of "Are you satisfied with the current relaxation techniques?"

[0897] Through this structure, the system continuously provides personalized health support to each individual user. This process is physically assisted by an autonomous machine that flexibly responds to the user's emotional fluctuations. This autonomous machine can be integrated into daily life through a series of actions, efficiently providing concrete means to support health.

[0898] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0899] Step 1:

[0900] The server retrieves health-related information from the network. This information is collected by obtaining data from online medical databases and the latest health guidelines via APIs, and stored as a base dataset. The input is health-related database information, and the output is an integrated health information dataset.

[0901] Step 2:

[0902] The device acquires the user's daily life data through an information processing device. This data includes activity information from smartphone sensors and physiological information from wearable devices. The input is raw data from sensors, and the output is formatted daily life data. The data is pre-processed using Pandas.

[0903] Step 3:

[0904] The device extracts emotional states from social media and text messages. This emotional data extraction uses a natural language processing library to analyze keywords and phrases related to emotions. The input is text data, and the output is data representing emotional states.

[0905] Step 4:

[0906] The server integrates the health information dataset, daily life data, and emotional data acquired in the previous step, and begins analysis using a generative AI model. The generative AI model analyzes the data using TensorFlow or PyTorch to evaluate the user's health and emotional state. Thus, the input is the integrated dataset, and the output is the analyzed health and emotional state.

[0907] Step 5:

[0908] The server uses a generative intelligence model to generate an optimal health improvement plan for the user. The generated plan includes exercise guidance, dietary advice, and stress management strategies. At this stage, the input is the analyzed health and emotional state, and the output is the specific health improvement plan.

[0909] Step 6:

[0910] The device notifies the user of the generated health improvement plan. The notification is provided via voice and visual alerts, offering instructions tailored to the user's daily activities. Specific actions include verbally communicating the plan's contents. In this context, the input is the health improvement plan, and the output is the notification to the user.

[0911] Step 7:

[0912] User feedback is sent to the server via the terminal. This feedback is presented as prompts, inquiring about the user's actual behavior and satisfaction level. The generative intelligence model is updated based on this feedback. The input is user feedback, and the output is new data for model tuning.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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.

[0917] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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."

[0922] 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.

[0923] 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.

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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.

[0931] 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.

[0932] 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.

[0933] 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.

[0934] The following is further disclosed regarding the embodiments described above.

[0935] (Claim 1)

[0936] Methods for gathering health-related information from the internet,

[0937] A means of collecting users' daily life data through devices,

[0938] A means for integrating the aforementioned health information and the aforementioned daily life data and analyzing them using a generative AI model,

[0939] A means for generating a health improvement program suitable for the user based on the analysis results,

[0940] A means for notifying the user of the aforementioned health improvement program,

[0941] A means for collecting user feedback and updating the generated AI model,

[0942] A system that includes this.

[0943] (Claim 2)

[0944] The system according to claim 1, wherein the generated AI model uses a machine learning algorithm.

[0945] (Claim 3)

[0946] The system according to claim 1, wherein the health improvement program includes an exercise plan, dietary guidance, and stress management methods.

[0947] "Example 1"

[0948] (Claim 1)

[0949] Means of collecting health-related information from a wide-area information network,

[0950] A means of collecting users' daily life information through mobile devices,

[0951] A means for integrating the aforementioned health information and the aforementioned daily life information and analyzing it using a generative AI model,

[0952] A means for generating a health improvement plan suitable for the user based on the analysis results,

[0953] A means for notifying the user of the aforementioned health improvement plan,

[0954] A means for collecting user reaction information and updating the generated AI model,

[0955] A means of analyzing the information transmitted by users,

[0956] A system that includes this.

[0957] (Claim 2)

[0958] The system according to claim 1, wherein the generated AI model uses machine learning computational methods.

[0959] (Claim 3)

[0960] The system according to claim 1, wherein the health improvement plan includes an exercise plan, dietary guidance, and a method for managing mental stress.

[0961] "Application Example 1"

[0962] (Claim 1)

[0963] Means of collecting health-related information from information and communication networks,

[0964] A means of collecting residents' lifestyle activity data via a device,

[0965] A means for integrating the aforementioned health information and the aforementioned lifestyle activity data and analyzing them using a generative model,

[0966] A means for generating a health improvement plan suitable for residents based on the analysis results,

[0967] Means for notifying residents of the aforementioned health improvement plan,

[0968] A means for collecting resident responses and adjusting the generative model,

[0969] A means of monitoring health status in a care environment and optimizing individual care plans,

[0970] A system that includes this.

[0971] (Claim 2)

[0972] The system according to claim 1, wherein the generative model uses a learning algorithm.

[0973] (Claim 3)

[0974] The system according to claim 1, wherein the health improvement plan includes an exercise plan, dietary guidance, and methods for managing mental stress.

[0975] "Example 2 of combining an emotion engine"

[0976] (Claim 1)

[0977] Means of collecting health-related information from information networks,

[0978] A means of collecting users' daily life data through a terminal,

[0979] A means for integrating the aforementioned health information and the aforementioned daily life data, and analyzing emotional states using a generative AI model,

[0980] A means of designing a health improvement plan suitable for the user based on the analysis results,

[0981] A means of communicating the aforementioned health improvement plan to the user,

[0982] A means for collecting user feedback and adjusting the aforementioned AI model,

[0983] A system that includes this.

[0984] (Claim 2)

[0985] The system according to claim 1, wherein the generated AI model uses a machine learning technique.

[0986] (Claim 3)

[0987] The system according to claim 1, wherein the health improvement plan includes an exercise plan, dietary guidance, and methods for managing physical and mental health.

[0988] "Application example 2 when combining with an emotional engine"

[0989] (Claim 1)

[0990] Means of obtaining health-related information from a network,

[0991] A means of acquiring user daily life data through an information processing device,

[0992] A means for integrating the aforementioned health information and the aforementioned daily life data and analyzing them using a generative intelligence model,

[0993] A means for generating a health improvement plan suitable for the user based on the analysis results,

[0994] A means of presenting the aforementioned health improvement plan to the user,

[0995] A means for collecting user responses and adjusting the generative intelligence model,

[0996] A means of analyzing the user's emotional state and providing health support tailored to emotional fluctuations in daily life,

[0997] The means of physically assisting the user in improving their health through an autonomous machine that operates the aforementioned health support,

[0998] A system that includes this.

[0999] (Claim 2)

[1000] The system according to claim 1, wherein the generative intelligence model uses a learning algorithm.

[1001] (Claim 3)

[1002] The system according to claim 1, wherein the health improvement plan includes a physical activity plan, dietary advice, and mental health management methods. [Explanation of Symbols]

[1003] 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 collecting health-related information from information and communication networks, A means of collecting residents' lifestyle activity data via a device, A means for integrating the aforementioned health information and the aforementioned lifestyle activity data and analyzing them using a generative model, A means for generating a health improvement plan suitable for residents based on the analysis results, Means for notifying residents of the aforementioned health improvement plan, A means for collecting resident responses and adjusting the generative model, A means of monitoring health status in a care environment and optimizing individual care plans, A system that includes this.

2. The system according to claim 1, wherein the generative model uses a learning algorithm.

3. The system according to claim 1, wherein the health improvement plan includes an exercise plan, dietary guidance, and a method for managing mental stress.

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