system

Abstract

We provide the system. [Solution] Means of obtaining long-term health information, A means of analyzing acquired health information and deriving health prediction results, A means of generating personalized preventive measures based on health prediction results, A means of notifying the terminal of the generated preventive measures, A system that includes this.

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] In long-term health management, it is difficult to accurately predict future health risks based on different living habits and health conditions for each user and to provide specific and effective preventive measures. In the conventional technology, it is very difficult to provide a health management approach optimized for each individual user, and there is a problem that appropriate interventions for maintaining individual health have not been carried out.

Means for Solving the Problems

[0005] This invention provides a system that acquires long-term health information, analyzes that information, and predicts future health risks. Based on the analyzed data, it generates personalized preventive measures for each user and provides them to the user via a notification terminal. This system acquires feedback from users and updates the preventive measures as needed, thereby enabling continuous health support. The aim is to enable health management tailored to each individual user and reduce future health risks.

[0006] "Long-term health information" refers to personal health-related data that is collected continuously over a certain period of time.

[0007] "Means of acquisition" refers to methods or devices for collecting and retaining information.

[0008] "Means of analyzing health information" refers to methods or techniques for analyzing collected health information and deriving patterns or trends.

[0009] "Health prediction results" refer to predicted results regarding future health status based on analyzed health information.

[0010] "Personalized preventive measures" refer to methods or plans for mitigating health risks that are customized based on a specific individual's health data.

[0011] "Means of generation" refers to methods or techniques for producing specific information or plans.

[0012] "Means of notifying a terminal" refers to a method or device for transmitting information to a user via an electronic device.

[0013] "External devices" refer to independent devices outside the system that are used to acquire health information.

[0014] "User feedback" refers to the reactions and opinions that users provide after using a product or service.

[0015] The "updating means" refers to a method or technology for revising existing information or plans based on new data or situations.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a 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.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] The present invention provides a method for effectively collecting and analyzing long-term health information and providing users with appropriate health predictions and preventive measures. Embodiments thereof are described below.

[0038] Users collect daily activity data (e.g., heart rate, steps taken, calories burned, etc.) using smartphones and wearable devices. These devices transmit the collected health information to a server. In this process, the devices use encryption technology to protect the security and privacy of the data.

[0039] The server analyzes the received health information and uses statistical models to assess the user's current health status. This analysis includes techniques that use machine learning algorithms to predict future health risks from past health data. Based on the analysis results, the server generates personalized health predictions.

[0040] Furthermore, the server utilizes AI to automatically create specific preventative measures based on the user's lifestyle and health risks. These preventative measures may include, for example, "aim for 7,500 steps a day" or "reduce salt intake," and an action plan tailored to the user's past lifestyle is proposed.

[0041] The device receives notifications from the server and presents them to the user in an easy-to-understand format. For example, it may visually display information within a smartphone application or set reminders to prompt action.

[0042] Users can incorporate the suggested preventative measures into their daily lives and provide feedback on their implementation and effectiveness. The device collects this feedback and sends it to the server, which can then update the preventative measures based on the newly acquired data.

[0043] Through this iterative process, the system continuously supports the user's health and helps maintain a better state of health. Furthermore, it can be applied to health programs and medical institutions, and is expected to have widespread use.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users use smartphones or wearable devices to measure everyday health information such as heart rate, steps taken, and calories burned. The device periodically records this data.

[0047] Step 2:

[0048] The device encrypts the recorded health information at regular intervals and sends it to the server. At this stage, security measures are in place to prevent data leakage.

[0049] Step 3:

[0050] The server stores the received health information in a database and performs analysis using a statistical model. This model learns from past data patterns and identifies individual user health risk factors.

[0051] Step 4:

[0052] The server predicts future health risks based on the analysis results. Machine learning algorithms are applied to the predictions, for example, to assess the risk of diabetes and cardiovascular disease.

[0053] Step 5:

[0054] The server uses generative AI technology to create personalized preventative measures based on predicted health risks. This includes daily exercise plans and suggestions for dietary improvements.

[0055] Step 6:

[0056] The device receives preventative measures sent from the server and notifies the user. The notification is delivered through an application on the smartphone and may include visual or audio alerts.

[0057] Step 7:

[0058] Users incorporate the suggested preventative measures into their daily lives and input the results as feedback into their device. This feedback includes the amount of exercise performed and any changes made to their diet.

[0059] Step 8:

[0060] The device sends user feedback to the server. The server re-evaluates the effectiveness of preventative measures based on the newly collected data and updates them as needed.

[0061] Step 9:

[0062] By repeatedly performing this feedback loop, the system provides continuous health support tailored to the user's lifestyle, thereby reducing health risks.

[0063] (Example 1)

[0064] 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."

[0065] In modern society, there is a need to effectively manage users' health over the long term and provide personalized preventative measures. However, there are challenges in providing precise health predictions and flexible preventative measures tailored to individual users. Furthermore, there is a lack of mechanisms to continuously improve preventative measures by incorporating feedback while ensuring data security and privacy.

[0066] 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.

[0067] In this invention, the server includes a mechanism for acquiring health information over a long period, a mechanism for analyzing the acquired health information and deriving health prediction results, and a mechanism for creating preventive measures using a generated AI model. This enables the provision of personalized preventive measures and a function to continuously improve those preventive measures.

[0068] "Long-term health information" refers to data about a user's health status that is continuously collected and recorded over several weeks to several years.

[0069] A "mechanism for acquiring health information" refers to a device or system that monitors a user's activities and physical condition and collects data.

[0070] A "mechanism for analysis and deriving health prediction results" refers to a device or algorithm that analyzes collected health data and uses the results to estimate future health conditions.

[0071] A "generative AI model" refers to an artificial intelligence algorithm or system that performs predictions and generation based on large amounts of data.

[0072] A "mechanism for generating personalized preventive measures" refers to a device or program that creates specific health improvement plans based on each user's characteristics and circumstances.

[0073] A "device notification mechanism" refers to a means of communication or an interface for delivering information and instructions generated from a server to a user's device.

[0074] A "mechanism using encryption technology" refers to a device or method that applies technology to transform information in order to protect data from unauthorized access.

[0075] A "machine learning algorithm" refers to a computational method or model that analyzes data, learns patterns, and predicts future outcomes.

[0076] A "mechanism for obtaining feedback and updating preventative measures" refers to a device or system that collects user responses and results and uses them to revise preventative measures.

[0077] This invention is a system that collects and analyzes users' health information over a long period of time and provides personalized health predictions and preventive measures. Users collect their own activity information using smartphones and wearable devices. These devices record health-related data such as heart rate, steps taken, and calories burned, and utilize encryption technology, including AES encryption, when transmitting data.

[0078] The server receives data sent from the terminal and analyzes it using statistical analysis and machine learning algorithms (e.g., random forests and neural networks). This allows the server to assess the user's health status and predict future health risks.

[0079] The server also uses a generative AI model (e.g., the GPT model) to automatically generate personalized preventative measures based on health predictions. These generated preventative measures are notified to the user's smartphone or other devices. The user practices these preventative measures in their daily life and provides feedback on their implementation. The device collects the feedback and sends it to the server, which uses this to continuously improve the preventative measures.

[0080] As a concrete example, consider a case where a user records their daily step count. Based on this data, the server generates a preventative measure such as: "Aim for more than 7,500 steps per day this week." In addition, an example of a prompt to the generating AI model would be: "Generate appropriate health predictions and preventative measures based on the user's recent health data. Example: If the user needs stress management, what activities should be recommended?"

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] Users collect data on their daily activities using smartphones and wearable devices. Specifically, users wear the devices and record things like steps and heart rate in real time. The input is the user's physical actions related to their activities, and the output is health information converted into digital format.

[0084] Step 2:

[0085] The device encrypts the collected health data using AES encryption before sending it to the server. The input is the collected raw health data, and the output is the encrypted data. Specifically, the device securely sends the encrypted data to the server via a security protocol (e.g., HTTPS).

[0086] Step 3:

[0087] The server receives the incoming health data and performs data analysis. First, the server decodes the data and then performs statistical analysis on it. The input is encrypted health data, and the output is data in an analyzable format. The server then applies machine learning algorithms (e.g., random forest) to predict future health risks based on the user's past data. Specifically, the server also considers feedback to update the model.

[0088] Step 4:

[0089] The server uses a generated AI model based on the analysis results to produce personalized health predictions and preventive measures. The prompt is "Generate appropriate health predictions and preventive measures based on the user's recent health data." The input is the analyzed health data, and the generated preventive measures are obtained as output.

[0090] Step 5:

[0091] The server notifies the terminal of the generated health predictions and preventive measures. The terminal receives this and presents it to the user in an easy-to-understand format. The input is the generated preventive measures, and the output is specific advice provided to the user. Specifically, the terminal can display the preventive measures within the application and set reminders.

[0092] Step 6:

[0093] Users adjust their daily lives based on the generated preventative measures and provide feedback on their implementation status to their device. Specifically, users input the results and their impressions of their actions through the application. The input is user feedback, and the output is data used in the next improvement cycle.

[0094] Step 7:

[0095] The device collects user feedback and sends it to the server. The server updates preventative measures and health prediction models based on this feedback. The input is user feedback data, and the output is improved preventative measures and updated models. Specifically, the server modifies data-driven recommendations and uses them in subsequent notifications.

[0096] (Application Example 1)

[0097] 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."

[0098] Among the elderly and individuals requiring health management, a decline in sustained motivation for daily health maintenance and a lack of concrete action plans for appropriate health management are common. Therefore, there is a need to present optimal health improvement measures tailored to individual lifestyles and integrate them into daily life in a sustainable manner. Furthermore, flexible system updates that take feedback into account are necessary.

[0099] 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.

[0100] In this invention, the server includes means for acquiring biometric information over a long period of time, means for analyzing the acquired biometric information and deriving health prediction results, means for generating personalized lifestyle improvement measures based on the health prediction results, means for automatically creating the lifestyle improvement measures using a generating AI, and means for setting reminders based on biometric data and behavioral goals and notifying the user. This enables the provision of health maintenance measures tailored to the user, ensuring continuity of health management in daily life and improving behavior through effective feedback.

[0101] "Biometric information" refers to data such as heart rate, steps taken, and calories burned that indicate the user's health status.

[0102] "Health prediction results" are estimates of future health status based on collected biometric information.

[0103] "Lifestyle improvement measures" refer to specific action plans and advice tailored to the user's current health condition.

[0104] An "information processing device" is an electronic device used to collect biometric information and notify users of the generated improvement measures.

[0105] "Generative AI" is artificial intelligence that uses machine learning technology to automatically generate optimal lifestyle improvement strategies from data.

[0106] A "reminder" is a function that notifies users at appropriate times to help them achieve their behavioral goals.

[0107] "Feedback" refers to responses from users regarding the effectiveness and perceived impact of lifestyle improvements they have implemented.

[0108] This invention is a system for continuously managing and improving the health of users. The central components of the system are a portable information device (e.g., a smartphone or smartwatch) as a terminal, a server for analysis, and a generative AI.

[0109] The server first receives biometric information transmitted from the device. This information includes heart rate, steps taken, and calories burned. The device collects data through platforms such as Apple HealthKit and Google Fit®, and secure data transfer is ensured using the TLS protocol. The server uses the received biometric information to perform data analysis using machine learning algorithms with Python and the scikit-learn library. This analysis generates a prediction of the user's health status.

[0110] Next, the generative AI model operates based on the prediction results. This AI model automatically creates personalized lifestyle improvement plans using OpenAI® GPT models, etc. The generative AI generates various action suggestions in response to prompts. For example, by providing a prompt such as, "Analyze the user's health trends based on their heart rate changes and walking records over the past 30 days, and suggest the best health maintenance measures for the coming week. The user is a man in his 70s and prefers advice on improving physical fitness," it can output specific health maintenance measures.

[0111] The generated lifestyle improvement suggestions are sent to the user's device, and the user is notified so that they can incorporate them into their health maintenance activities. Furthermore, the device has a reminder function that notifies the user of their set behavioral goals as needed. In this way, the system supports the user's health management in their daily life and continuously updates the improvement suggestions through feedback.

[0112] Users implement suggested improvements in their daily lives and provide feedback on their effectiveness via their device. This feedback is then sent back to the server and used to generate future lifestyle improvement suggestions, allowing for dynamic improvement of the user's health maintenance program. Through this process, the system provides customized services tailored to each user.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The device collects biometric information from the user. Specifically, it uses smartwatches and smartphones to record data such as heart rate, steps taken, and calories burned, and aggregates this data through Apple HealthKit and Google Fit®. The input is biometric information from the user's daily activities, and the output is formatted data for transmission to the server.

[0116] Step 2:

[0117] The device securely transmits the collected biometric information to the server using the TLS protocol. This ensures data privacy and security. The input is the biometric information collected by the device, and the output is encrypted data received by the server.

[0118] Step 3:

[0119] The server decodes the received biometric information, runs a machine learning model using the scikit-learn library in Python, and performs health predictions. The input is encrypted biometric information, and the output is a prediction showing the user's future health risks.

[0120] Step 4:

[0121] The server automatically generates personalized lifestyle improvement plans using a generative AI model based on health risk prediction results. It instructs the generative AI via prompt messages to create appropriate advice. Inputs are health prediction results and prompt messages for the generative AI, while output is specific lifestyle improvement plans tailored to each user.

[0122] Step 5:

[0123] The generated lifestyle improvement suggestions are sent to the terminal and notified to the user's application. The terminal presents the information to the user visually or in notification format, prompting action. The input is the lifestyle improvement suggestions from the server, and the output is the user's confirmation of the notification and implementation of the action.

[0124] Step 6:

[0125] Users incorporate the suggested lifestyle improvements into their daily lives and input feedback on their implementation status and effects via a terminal. The input is user feedback data, and the output is updated user data including the feedback content.

[0126] Step 7:

[0127] The terminal sends user feedback to the server, which receives this feedback and updates its lifestyle improvement strategies. This allows the system to continuously generate new improvement strategies. The input is user data including feedback, and the output is a database of updated lifestyle improvement strategies.

[0128] 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.

[0129] The system of the present invention aims to provide more accurate and effective health management by considering both health information and user emotions. An embodiment thereof is shown below.

[0130] Users collect daily health information using smartphones and wearable devices. These devices measure data such as heart rate, steps taken, and calorie consumption, and periodically transmit it to a server using encryption technology. In addition, the devices utilize cameras and voice input functions to recognize the user's emotions, obtaining emotional data from facial expressions and tone of voice.

[0131] The server analyzes emotional data along with health information and stores it in an integrated database. Probabilistic models and machine learning algorithms are used in the analysis to evaluate the user's current health and emotional state. Based on these analysis results, the server predicts the user's future health risks and emotional trends.

[0132] The server utilizes an emotion engine to generate optimal preventative measures based on the user's emotional state. For example, if a high-stress emotional state is detected, suggestions for relaxation exercises or meditation will be made. Conversely, if positive emotions are detected, further exercise or activity to maintain that state will be recommended.

[0133] The device notifies the user of the generated preventative measures. The notification is presented clearly to the user through visual alerts and voice guidance.

[0134] Users incorporate the proposed health management plan into their daily lives and input feedback on their implementation and reactions into the device. This includes their satisfaction with the proposal and how easy it was to follow. The device sends this feedback to a server, which uses it for further data analysis and updating of preventative measures.

[0135] By repeating this data-driven feedback loop, the system continuously provides health support optimized for the individual needs of each user. Considering emotional states adds innovative value to conventional health management systems.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] Users routinely collect health information such as heart rate, steps taken, and calories burned using smartphones and wearable devices. These devices also acquire emotional data through facial expression photos and voice input from the user.

[0139] Step 2:

[0140] The device encrypts the collected health and emotional data at regular intervals and securely transmits it to the server. The transmission frequency is adjusted according to the usage scenario.

[0141] Step 3:

[0142] The server stores the received data in an integrated database and performs analysis using statistical models and machine learning algorithms. This analysis includes evaluating the user's health status as well as analyzing emotional patterns.

[0143] Step 4:

[0144] Based on the analysis results, the server predicts the user's future health risks and emotional tendencies. For example, it may determine that the user has high stress levels and is experiencing a prolonged lack of exercise.

[0145] Step 5:

[0146] The server uses an emotion engine to generate preventative measures tailored to the user's emotional state. These include guidance on deep breathing exercises to reduce stress and suggestions for enjoyable exercises.

[0147] Step 6:

[0148] The device notifies the user of the generated preventative measures. Notification methods are diverse, including smartphone pop-up messages and voice alerts, prioritizing user convenience.

[0149] Step 7:

[0150] Users implement recommended preventative measures and provide feedback on their device regarding the results and their reactions to the suggestions. This feedback includes information such as the difficulty of implementation and its impact on their emotions.

[0151] Step 8:

[0152] The device collects user feedback and sends it to the server. The server then performs further analysis based on this feedback and updates preventative measures as needed.

[0153] Step 9:

[0154] By iterating through this process, the system improves individual user health management and helps maintain better health. By providing a detailed assessment of how emotional states affect health management, it offers more appropriate advice.

[0155] (Example 2)

[0156] 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".

[0157] Traditional health management systems are limited to analysis based solely on health information, making it difficult to provide personalized recommendations that take into account the user's emotional state. Furthermore, they lack sufficient functionality to update preventative measures based on user feedback, highlighting the need for more accurate, long-term health and emotional management.

[0158] 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.

[0159] In this invention, the server includes means for acquiring health information and emotional information, means for analyzing the acquired health information and emotional information to evaluate the health and emotional state, and means for generating personalized preventive measures based on the health and emotional state and notifying the user. This enables comprehensive health management that also takes into account the user's emotional state.

[0160] "Health information" refers to data that indicates the user's physical condition, including heart rate, steps taken, and calorie consumption.

[0161] "Emotional information" refers to data that indicates the user's emotional state, and is obtained from things like tone of voice and facial expressions.

[0162] "Analysis" is the process of processing acquired data to evaluate health and emotional states and derive patterns and trends.

[0163] "Preventive measures" refer to specific suggestions for improving health tailored to each individual user, based on their health and emotional state.

[0164] "Feedback" refers to information about users' experiences and opinions, which the system uses to update preventative measures.

[0165] "External devices" refer to devices used to acquire health information and emotional information, and include wearable devices and smartphones.

[0166] An "emotion engine" is a component within a system that analyzes a user's emotional state and generates appropriate preventative measures based on the results.

[0167] This invention is a system for providing more personalized health management by utilizing the user's health and emotional information. First, the user uses external devices such as wearable devices and smartphones to collect health and emotional information through their daily activities. Specifically, data such as heart rate, steps taken, and calorie consumption are recorded using sensors, and emotional information is obtained from facial expressions and tone of voice. OpenCV and speech recognition technology are utilized for this purpose.

[0168] The device periodically encrypts the acquired data and sends it to the server using Bluetooth or Wi-Fi. AES technology is used for this encryption. The server analyzes the received data using analysis platforms such as Python's scikit-learn and TENSORFLOW®. During this analysis process, the system assesses the user's health and emotional state and predicts user tendencies and risks.

[0169] Furthermore, the server uses an emotion engine to generate preventative measures based on the analysis results. The emotion analysis used here employs NLP techniques that leverage generative AI models. For example, if high stress levels are detected, specific measures such as "try meditating for 5 minutes a day" may be suggested.

[0170] The device notifies the user of the generated preventative measures. This utilizes smartphone push notifications and voice guidance. The user implements the suggested preventative measures and inputs feedback into the device, thereby feeding back their health management to the system. The server collects this feedback information and uses it to improve the preventative measures.

[0171] For example, if the user's average stress level is high based on data from the past week, relaxation activities will be recommended. Also, if many positive emotions are detected, suggestions for increasing activity will be made. An example of a prompt for the generating AI model would be: "Based on my health and emotional data, please provide appropriate health management advice for me this week. Please include relaxation measures tailored to my stress level."

[0172] In this way, the system can provide comprehensive health and emotional management optimized for each user.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] Users collect daily health and emotional information using wearable devices and smartphones. Inputs include physical information such as heart rate, steps taken, and calorie consumption, as well as emotional information extracted from facial expressions and tone of voice. Outputs are these data, which are used for analysis in the next step. Specifically, this involves wearable devices recording this data in real time using sensors, and smartphones capturing emotional information through cameras and microphones.

[0176] Step 2:

[0177] The device periodically encrypts the collected health and emotional information and sends it to the server. The input is the raw data obtained, which is securely encrypted using AES technology. The output is encrypted data that is sent to the server. The specific actions involved include the transfer of data to the server via Bluetooth or Wi-Fi.

[0178] Step 3:

[0179] The server analyzes the received data. It receives encrypted health and emotional information as input and first decrypts it. Next, it analyzes the data using machine learning algorithms to evaluate the health and emotional state. The output is the analysis result, which includes the user's health tendencies and stress level. The data analysis process uses scikit-learn and TensorFlow to demonstrate its operation.

[0180] Step 4:

[0181] The server generates personalized preventative measures based on the analysis results. The input is the evaluation of health and emotional states, and based on this, an emotional engine using NLP technology devises appropriate preventative measures. The output is specific advice and action plans for the user. The generation of preventative measures using a generative AI model is the concrete operation.

[0182] Step 5:

[0183] The device notifies the user of the generated preventative measures. The input consists of advice and action plans generated on the server, which are communicated to the user via smartphone push notifications and voice guidance. The output consists of specific notifications received by the user, which include visual and auditory actions.

[0184] Step 6:

[0185] The user implements the suggested preventative measures and inputs feedback into the device. The input includes comments on satisfaction with and the effectiveness of the implemented preventative measures. The output is this feedback data, which is sent to the server and used for subsequent analysis. The user's action of inputting feedback into the device is the concrete operation.

[0186] Step 7:

[0187] The server improves preventative measures based on user feedback. The input is user feedback information, which is analyzed and used as new data to inform the next preventative measures. The output is the improved preventative measures, which are then used for subsequent notifications. The concrete operation involves data analysis based on feedback and updating preventative measures.

[0188] (Application Example 2)

[0189] 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".

[0190] In modern society, personal health management should ideally focus not only on physical aspects but also on emotional ones. However, conventional health management systems primarily collect and analyze only physical data, making it difficult to achieve multifaceted health management that takes into account an individual's emotional state. This poses a challenge in providing more accurate and effective individual health management, particularly in caregiving settings.

[0191] 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.

[0192] In this invention, the server includes means for acquiring biometric information over a long period of time, means for analyzing the acquired biometric and emotional information to predict health and mental states, and means for generating personalized lifestyle improvement measures and mental health maintenance measures based on these prediction results and notifying the information processing device accordingly. This enables the integrated evaluation of individual biometric and emotional states, allowing for individualized responses in care settings. Furthermore, by continuously updating improvement measures based on user feedback, it becomes possible to provide continuous health support optimized for the user.

[0193] "Biometric information" refers to data that indicates an individual's physical condition, and includes heart rate, steps taken, and calorie consumption.

[0194] "Emotional information" refers to data used to evaluate an individual's emotional state, and is acquired using methods such as facial expressions and tone of voice.

[0195] "Health status" refers to the results of an assessment of an individual's physical condition, derived from the analysis of various biological information.

[0196] "Mental state" refers to an individual's emotional and psychological condition and is evaluated by analyzing emotional information.

[0197] "Lifestyle improvement measures" refer to guidelines for actions and activities proposed to improve individual vitality based on acquired biometric information and predicted health status.

[0198] "Mental health maintenance measures" are guidelines for actions and activities proposed to maintain an individual's mental state, and are generated based on emotional information and assessments of their mental state.

[0199] To implement this invention, it is essential to use terminals such as smartphones and wearable devices. These terminals function as hardware for collecting biometric and emotional information. Specifically, they incorporate sensors to collect biometric information such as heart rate, steps taken, and calorie consumption, and use cameras and microphones to acquire emotional information such as facial expressions and tone of voice.

[0200] The terminal temporarily stores the collected data and sends it to the server using encryption technology. On the server, software for analyzing the received data, specifically an analysis system equipped with machine learning algorithms, runs. This analysis system, using platforms such as TensorFlow, can evaluate health status from biometric information and mental state from emotional information.

[0201] Based on these evaluation results, the server generates lifestyle improvement and mental health maintenance strategies tailored to each individual user. The generated suggestions are communicated to the user via an information processing device, and the user is encouraged to adjust their daily actions accordingly. User feedback is entered via a terminal and sent to the server. Based on this feedback, the analysis system further optimizes the generated suggestions.

[0202] As a concrete example, data such as heart rate, steps taken, and emotions are automatically collected as the user goes about their daily life. If the system determines that the user is experiencing stress, it will notify the user's smartphone with a suggestion such as, "Let's try some yoga today to help you relax." An example of a prompt message would be, "Analyze the user's heart rate and emotion data and suggest exercises aimed at reducing stress."

[0203] In this way, the entire system can continuously collect and analyze data, incorporate feedback, and provide users with the most optimal health support.

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The device acquires biometric information such as the user's heart rate, steps taken, and calorie consumption using sensors. At the same time, it also collects emotional information from facial expressions and voice tone using a camera and microphone. The input data consists of biometric and emotional information, which is temporarily stored within the device to be used as output.

[0207] Step 2:

[0208] The device periodically transmits stored biometric and emotional information to the server using encryption technology. The input is the data acquired in step 1, which is output to the server as encrypted data based on the transmission protocol.

[0209] Step 3:

[0210] The server decodes the received biometric and emotional information and stores it in a database. During this process, machine learning algorithms are used to analyze the data. The input is encrypted data, and the processing results in an evaluation of health and mental state.

[0211] Step 4:

[0212] The server uses a generative AI model to generate personalized lifestyle improvement plans and mental health maintenance plans based on the assessment results of physical and mental health status. The input is the analysis results from step 3, and the output is the specific suggestions that are generated.

[0213] Step 5:

[0214] The server notifies the information processing device of the generated lifestyle improvement measures and mental health maintenance measures. The input is the formulated proposals, and the output is the notification delivered to the user via the terminal.

[0215] Step 6:

[0216] The user receives a notification via their device and enters feedback into the device regarding the implementation of lifestyle improvement measures. This input is user feedback information, which is then sent to the server as input data for the next analysis.

[0217] Step 7:

[0218] The server receives feedback from users and updates the analysis system. The input is feedback information, and the output is data that will be used to generate the next lifestyle improvement plan, taking into account the results of measuring the effectiveness of the suggestions.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] The present invention provides a method for effectively collecting and analyzing long-term health information and providing users with appropriate health predictions and preventive measures. Embodiments thereof are described below.

[0236] Users collect daily activity data (e.g., heart rate, steps taken, calories burned, etc.) using smartphones and wearable devices. These devices transmit the collected health information to a server. In this process, the devices use encryption technology to protect the security and privacy of the data.

[0237] The server analyzes the received health information and uses statistical models to assess the user's current health status. This analysis includes techniques that use machine learning algorithms to predict future health risks from past health data. Based on the analysis results, the server generates personalized health predictions.

[0238] Furthermore, the server utilizes AI to automatically create specific preventative measures based on the user's lifestyle and health risks. These preventative measures may include, for example, "aim for 7,500 steps a day" or "reduce salt intake," and an action plan tailored to the user's past lifestyle is proposed.

[0239] The device receives notifications from the server and presents them to the user in an easy-to-understand format. For example, it may visually display information within a smartphone application or set reminders to prompt action.

[0240] Users can incorporate the suggested preventative measures into their daily lives and provide feedback on their implementation and effectiveness. The device collects this feedback and sends it to the server, which can then update the preventative measures based on the newly acquired data.

[0241] Through this iterative process, the system continuously supports the user's health and helps maintain a better state of health. Furthermore, it can be applied to health programs and medical institutions, and is expected to have widespread use.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] Users use smartphones or wearable devices to measure everyday health information such as heart rate, steps taken, and calories burned. The device periodically records this data.

[0245] Step 2:

[0246] The device encrypts the recorded health information at regular intervals and sends it to the server. At this stage, security measures are in place to prevent data leakage.

[0247] Step 3:

[0248] The server stores the received health information in a database and performs analysis using a statistical model. This model learns from past data patterns and identifies individual user health risk factors.

[0249] Step 4:

[0250] The server predicts future health risks based on the analysis results. Machine learning algorithms are applied to the predictions, for example, to assess the risk of diabetes and cardiovascular disease.

[0251] Step 5:

[0252] The server uses generative AI technology to create personalized preventative measures based on predicted health risks. This includes daily exercise plans and suggestions for dietary improvements.

[0253] Step 6:

[0254] The device receives preventative measures sent from the server and notifies the user. The notification is delivered through an application on the smartphone and may include visual or audio alerts.

[0255] Step 7:

[0256] Users incorporate the suggested preventative measures into their daily lives and input the results as feedback into their device. This feedback includes the amount of exercise performed and any changes made to their diet.

[0257] Step 8:

[0258] The device sends user feedback to the server. The server re-evaluates the effectiveness of preventative measures based on the newly collected data and updates them as needed.

[0259] Step 9:

[0260] By repeatedly performing this feedback loop, the system provides continuous health support tailored to the user's lifestyle, thereby reducing health risks.

[0261] (Example 1)

[0262] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0263] In modern society, there is a need to effectively manage users' health over the long term and provide personalized preventative measures. However, there are challenges in providing precise health predictions and flexible preventative measures tailored to individual users. Furthermore, there is a lack of mechanisms to continuously improve preventative measures by incorporating feedback while ensuring data security and privacy.

[0264] 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.

[0265] In this invention, the server includes a mechanism for acquiring health information over a long period, a mechanism for analyzing the acquired health information and deriving health prediction results, and a mechanism for creating preventive measures using a generated AI model. This enables the provision of personalized preventive measures and a function to continuously improve those preventive measures.

[0266] "Long-term health information" refers to data about a user's health status that is continuously collected and recorded over several weeks to several years.

[0267] A "mechanism for acquiring health information" refers to a device or system that monitors a user's activities and physical condition and collects data.

[0268] A "mechanism for analysis and deriving health prediction results" refers to a device or algorithm that analyzes collected health data and uses the results to estimate future health conditions.

[0269] A "generative AI model" refers to an artificial intelligence algorithm or system that performs predictions and generation based on large amounts of data.

[0270] A "mechanism for generating personalized preventive measures" refers to a device or program that creates specific health improvement plans based on each user's characteristics and circumstances.

[0271] A "device notification mechanism" refers to a means of communication or an interface for delivering information and instructions generated from a server to a user's device.

[0272] A "mechanism using encryption technology" refers to a device or method that applies technology to transform information in order to protect data from unauthorized access.

[0273] A "machine learning algorithm" refers to a computational method or model that analyzes data, learns patterns, and predicts future outcomes.

[0274] A "mechanism for obtaining feedback and updating preventative measures" refers to a device or system that collects user responses and results and uses them to revise preventative measures.

[0275] This invention is a system that collects and analyzes users' health information over a long period of time and provides personalized health predictions and preventive measures. Users collect their own activity information using smartphones and wearable devices. These devices record health-related data such as heart rate, steps taken, and calories burned, and utilize encryption technology, including AES encryption, when transmitting data.

[0276] The server receives data sent from the terminal and analyzes it using statistical analysis and machine learning algorithms (e.g., random forests and neural networks). This allows the server to assess the user's health status and predict future health risks.

[0277] The server also uses a generative AI model (e.g., the GPT model) to automatically generate personalized preventative measures based on health predictions. These generated preventative measures are notified to the user's smartphone or other devices. The user practices these preventative measures in their daily life and provides feedback on their implementation. The device collects the feedback and sends it to the server, which uses this to continuously improve the preventative measures.

[0278] As a concrete example, consider a case where a user records their daily step count. Based on this data, the server generates a preventative measure such as: "Aim for more than 7,500 steps per day this week." In addition, an example of a prompt to the generating AI model would be: "Generate appropriate health predictions and preventative measures based on the user's recent health data. Example: If the user needs stress management, what activities should be recommended?"

[0279] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0280] Step 1:

[0281] Users collect data on their daily activities using smartphones and wearable devices. Specifically, users wear the devices and record things like steps and heart rate in real time. The input is the user's physical actions related to their activities, and the output is health information converted into digital format.

[0282] Step 2:

[0283] Before sending the collected health data to the server, the terminal encrypts the data using AES encryption. The input is the collected raw health data, and the output is the encrypted data. Specifically, the terminal securely sends the encrypted data to the server through a security protocol (e.g., HTTPS).

[0284] Step 3:

[0285] The server receives the received health data and performs data analysis. First, the server decodes the data and performs statistical analysis on it. The input is the encrypted health data, and the output is data in an analyzable format. The server further applies a machine learning algorithm (e.g., random forest) to predict the future health risk based on the user's past data. Specifically, the server also considers feedback to update the model.

[0286] Step 4:

[0287] The server uses the generated AI model based on the analysis results to generate individualized health predictions and preventive measures. As the prompt text, "Please generate appropriate health predictions and preventive measures based on the user's recent health data." is used. The input is the analyzed health data, and the generated preventive measures are obtained as the output.

[0288] Step 5:

[0289] The server notifies the terminal of the generated health predictions and preventive measures. The terminal receives this and presents it to the user in an easy-to-understand format. The input is the generated preventive measures, and the specific advice provided to the user is obtained as the output. Specifically, the terminal can display the preventive measures within the application and set reminders.

[0290] Step 6:

[0291] Users adjust their daily lives based on the generated preventative measures and provide feedback on their implementation status to their device. Specifically, users input the results and their impressions of their actions through the application. The input is user feedback, and the output is data used in the next improvement cycle.

[0292] Step 7:

[0293] The device collects user feedback and sends it to the server. The server updates preventative measures and health prediction models based on this feedback. The input is user feedback data, and the output is improved preventative measures and updated models. Specifically, the server modifies data-driven recommendations and uses them in subsequent notifications.

[0294] (Application Example 1)

[0295] 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."

[0296] Among the elderly and individuals requiring health management, a decline in sustained motivation for daily health maintenance and a lack of concrete action plans for appropriate health management are common. Therefore, there is a need to present optimal health improvement measures tailored to individual lifestyles and integrate them into daily life in a sustainable manner. Furthermore, flexible system updates that take feedback into account are necessary.

[0297] 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.

[0298] In this invention, the server includes means for acquiring biometric information over a long period of time, means for analyzing the acquired biometric information and deriving health prediction results, means for generating personalized lifestyle improvement measures based on the health prediction results, means for automatically creating the lifestyle improvement measures using a generating AI, and means for setting reminders based on biometric data and behavioral goals and notifying the user. This enables the provision of health maintenance measures tailored to the user, ensuring continuity of health management in daily life and improving behavior through effective feedback.

[0299] "Biometric information" refers to data such as heart rate, steps taken, and calories burned that indicate the user's health status.

[0300] "Health prediction results" are estimates of future health status based on collected biometric information.

[0301] "Lifestyle improvement measures" refer to specific action plans and advice tailored to the user's current health condition.

[0302] An "information processing device" is an electronic device used to collect biometric information and notify users of the generated improvement measures.

[0303] "Generative AI" is artificial intelligence that uses machine learning technology to automatically generate optimal lifestyle improvement strategies from data.

[0304] A "reminder" is a function that notifies users at appropriate times to help them achieve their behavioral goals.

[0305] "Feedback" refers to responses from users regarding the effectiveness and perceived impact of lifestyle improvements they have implemented.

[0306] This invention is a system for continuously managing and improving the health of users. The central components of the system are a portable information device (e.g., a smartphone or smartwatch) as a terminal, a server for analysis, and a generative AI.

[0307] The server first receives the biometric information sent from the terminal. This information includes heart rate, number of steps, calories burned, etc. The terminal collects data through platforms such as Apple HealthKit and Google (registered trademark) Fit, and ensures the secure transfer of data by the TLS protocol. The server uses the received biometric information and employs the Python and scikit - learn libraries to perform data analysis using machine learning algorithms. Through this analysis, prediction results of the user's health status are generated.

[0308] Next, the generated AI model operates based on the prediction results. This AI model uses models such as the OpenAI GPT model to automatically create individualized life improvement measures. The generative AI takes prompts as input and generates various action proposals. For example, by giving a prompt like "Based on the user's heart rate changes and walking records in the past 30 days, analyze the trend of the health status and propose the optimal health maintenance measures for the next week. The user is a 70 - year - old male and prefers advice for physical strength improvement.", specific health maintenance measures can be output.

[0309] The generated life improvement measures are sent to the user's terminal and the user is notified so that they can incorporate them into their own health maintenance activities. Furthermore, the terminal has a reminder function and appropriately notifies the user of the set action goals. In this way, the system supports the user's health management in daily life and continuously updates the improvement measures through feedback.

[0310] The user executes the improvement measures proposed in daily life and collects feedback on their effects from the terminal. This feedback is sent back to the server again and utilized in the generation of the next life improvement measures, making it possible to dynamically improve the user's health maintenance program. Through such a process, the system realizes the provision of customized services tailored to the user.

[0311] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0312] Step 1:

[0313] The device collects biometric information from the user. Specifically, it uses smartwatches and smartphones to record data such as heart rate, steps taken, and calories burned, and aggregates this data through Apple HealthKit and Google Fit. The input is biometric information from the user's daily activities, and the output is formatted data for transmission to the server.

[0314] Step 2:

[0315] The device securely transmits the collected biometric information to the server using the TLS protocol. This ensures data privacy and security. The input is the biometric information collected by the device, and the output is encrypted data received by the server.

[0316] Step 3:

[0317] The server decodes the received biometric information, runs a machine learning model using the scikit-learn library in Python, and performs health predictions. The input is encrypted biometric information, and the output is a prediction showing the user's future health risks.

[0318] Step 4:

[0319] The server automatically generates personalized lifestyle improvement plans using a generative AI model based on health risk prediction results. It instructs the generative AI via prompt messages to create appropriate advice. Inputs are health prediction results and prompt messages for the generative AI, while output is specific lifestyle improvement plans tailored to each user.

[0320] Step 5:

[0321] The generated lifestyle improvement suggestions are sent to the terminal and notified to the user's application. The terminal presents the information to the user visually or in notification format, prompting action. The input is the lifestyle improvement suggestions from the server, and the output is the user's confirmation of the notification and implementation of the action.

[0322] Step 6:

[0323] Users incorporate the suggested lifestyle improvements into their daily lives and input feedback on their implementation status and effects via a terminal. The input is user feedback data, and the output is updated user data including the feedback content.

[0324] Step 7:

[0325] The terminal sends user feedback to the server, which receives this feedback and updates its lifestyle improvement strategies. This allows the system to continuously generate new improvement strategies. The input is user data including feedback, and the output is a database of updated lifestyle improvement strategies.

[0326] 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.

[0327] The system of the present invention aims to provide more accurate and effective health management by considering both health information and user emotions. An embodiment thereof is shown below.

[0328] Users collect daily health information using smartphones and wearable devices. These devices measure data such as heart rate, steps taken, and calorie consumption, and periodically transmit it to a server using encryption technology. In addition, the devices utilize cameras and voice input functions to recognize the user's emotions, obtaining emotional data from facial expressions and tone of voice.

[0329] The server analyzes emotional data along with health information and stores it in an integrated database. Probabilistic models and machine learning algorithms are used in the analysis to evaluate the user's current health and emotional state. Based on these analysis results, the server predicts the user's future health risks and emotional trends.

[0330] The server utilizes an emotion engine to generate optimal preventative measures based on the user's emotional state. For example, if a high-stress emotional state is detected, suggestions for relaxation exercises or meditation will be made. Conversely, if positive emotions are detected, further exercise or activity to maintain that state will be recommended.

[0331] The device notifies the user of the generated preventative measures. The notification is presented clearly to the user through visual alerts and voice guidance.

[0332] Users incorporate the proposed health management plan into their daily lives and input feedback on their implementation and reactions into the device. This includes their satisfaction with the proposal and how easy it was to follow. The device sends this feedback to a server, which uses it for further data analysis and updating of preventative measures.

[0333] By repeating this data-driven feedback loop, the system continuously provides health support optimized for the individual needs of each user. Considering emotional states adds innovative value to conventional health management systems.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] Users routinely collect health information such as heart rate, steps taken, and calories burned using smartphones and wearable devices. These devices also acquire emotional data through facial expression photos and voice input from the user.

[0337] Step 2:

[0338] The device encrypts the collected health and emotional data at regular intervals and securely transmits it to the server. The transmission frequency is adjusted according to the usage scenario.

[0339] Step 3:

[0340] The server stores the received data in an integrated database and performs analysis using statistical models and machine learning algorithms. This analysis includes evaluating the user's health status as well as analyzing emotional patterns.

[0341] Step 4:

[0342] Based on the analysis results, the server predicts the user's future health risks and emotional tendencies. For example, it may determine that the user has high stress levels and is experiencing a prolonged lack of exercise.

[0343] Step 5:

[0344] The server uses an emotion engine to generate preventative measures tailored to the user's emotional state. These include guidance on deep breathing exercises to reduce stress and suggestions for enjoyable exercises.

[0345] Step 6:

[0346] The device notifies the user of the generated preventative measures. Notification methods are diverse, including smartphone pop-up messages and voice alerts, prioritizing user convenience.

[0347] Step 7:

[0348] Users implement recommended preventative measures and provide feedback on their device regarding the results and their reactions to the suggestions. This feedback includes information such as the difficulty of implementation and its impact on their emotions.

[0349] Step 8:

[0350] The device collects user feedback and sends it to the server. The server then performs further analysis based on this feedback and updates preventative measures as needed.

[0351] Step 9:

[0352] By iterating through this process, the system improves individual user health management and helps maintain better health. By providing a detailed assessment of how emotional states affect health management, it offers more appropriate advice.

[0353] (Example 2)

[0354] 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".

[0355] Traditional health management systems are limited to analysis based solely on health information, making it difficult to provide personalized recommendations that take into account the user's emotional state. Furthermore, they lack sufficient functionality to update preventative measures based on user feedback, highlighting the need for more accurate, long-term health and emotional management.

[0356] 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.

[0357] In this invention, the server includes means for acquiring health information and emotional information, means for analyzing the acquired health information and emotional information to evaluate the health and emotional state, and means for generating personalized preventive measures based on the health and emotional state and notifying the user. This enables comprehensive health management that also takes into account the user's emotional state.

[0358] "Health information" refers to data that indicates the user's physical condition, including heart rate, steps taken, and calorie consumption.

[0359] "Emotional information" refers to data that indicates the user's emotional state, and is obtained from things like tone of voice and facial expressions.

[0360] "Analysis" is the process of processing acquired data to evaluate health and emotional states and derive patterns and trends.

[0361] "Preventive measures" refer to specific suggestions for improving health tailored to each individual user, based on their health and emotional state.

[0362] "Feedback" refers to information about users' experiences and opinions, which the system uses to update preventative measures.

[0363] "External devices" refer to devices used to acquire health information and emotional information, and include wearable devices and smartphones.

[0364] An "emotion engine" is a component within a system that analyzes a user's emotional state and generates appropriate preventative measures based on the results.

[0365] This invention is a system for providing more personalized health management by utilizing the user's health and emotional information. First, the user uses external devices such as wearable devices and smartphones to collect health and emotional information through their daily activities. Specifically, data such as heart rate, steps taken, and calorie consumption are recorded using sensors, and emotional information is obtained from facial expressions and tone of voice. OpenCV and speech recognition technology are utilized for this purpose.

[0366] The device periodically encrypts the acquired data and sends it to the server using Bluetooth or Wi-Fi. AES technology is used for this encryption. The server analyzes the received data using analysis platforms such as Python's scikit-learn and TensorFlow. During this analysis process, the system assesses the user's health and emotional state and predicts user tendencies and risks.

[0367] Furthermore, the server uses an emotion engine to generate preventative measures based on the analysis results. The emotion analysis used here employs NLP techniques that leverage generative AI models. For example, if high stress levels are detected, specific measures such as "try meditating for 5 minutes a day" may be suggested.

[0368] The device notifies the user of the generated preventative measures. This utilizes smartphone push notifications and voice guidance. The user implements the suggested preventative measures and inputs feedback into the device, thereby feeding back their health management to the system. The server collects this feedback information and uses it to improve the preventative measures.

[0369] For example, if the user's average stress level is high based on data from the past week, relaxation activities will be recommended. Also, if many positive emotions are detected, suggestions for increasing activity will be made. An example of a prompt for the generating AI model would be: "Based on my health and emotional data, please provide appropriate health management advice for me this week. Please include relaxation measures tailored to my stress level."

[0370] In this way, the system can provide comprehensive health and emotional management optimized for each user.

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

[0372] Step 1:

[0373] Users collect daily health and emotional information using wearable devices and smartphones. Inputs include physical information such as heart rate, steps taken, and calorie consumption, as well as emotional information extracted from facial expressions and tone of voice. Outputs are these data, which are used for analysis in the next step. Specifically, this involves wearable devices recording this data in real time using sensors, and smartphones capturing emotional information through cameras and microphones.

[0374] Step 2:

[0375] The device periodically encrypts the collected health and emotional information and sends it to the server. The input is the raw data obtained, which is securely encrypted using AES technology. The output is encrypted data that is sent to the server. The specific actions involved include the transfer of data to the server via Bluetooth or Wi-Fi.

[0376] Step 3:

[0377] The server analyzes the received data. It receives encrypted health and emotional information as input and first decrypts it. Next, it analyzes the data using machine learning algorithms to evaluate the health and emotional state. The output is the analysis result, which includes the user's health tendencies and stress level. The data analysis process uses scikit-learn and TensorFlow to demonstrate its operation.

[0378] Step 4:

[0379] The server generates personalized preventative measures based on the analysis results. The input is the evaluation of health and emotional states, and based on this, an emotional engine using NLP technology devises appropriate preventative measures. The output is specific advice and action plans for the user. The generation of preventative measures using a generative AI model is the concrete operation.

[0380] Step 5:

[0381] The device notifies the user of the generated preventative measures. The input consists of advice and action plans generated on the server, which are communicated to the user via smartphone push notifications and voice guidance. The output consists of specific notifications received by the user, which include visual and auditory actions.

[0382] Step 6:

[0383] The user implements the suggested preventative measures and inputs feedback into the device. The input includes comments on satisfaction with and the effectiveness of the implemented preventative measures. The output is this feedback data, which is sent to the server and used for subsequent analysis. The user's action of inputting feedback into the device is the concrete operation.

[0384] Step 7:

[0385] The server improves preventative measures based on user feedback. The input is user feedback information, which is analyzed and used as new data to inform the next preventative measures. The output is the improved preventative measures, which are then used for subsequent notifications. The concrete operation involves data analysis based on feedback and updating preventative measures.

[0386] (Application Example 2)

[0387] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0388] In modern society, personal health management should ideally focus not only on physical aspects but also on emotional ones. However, conventional health management systems primarily collect and analyze only physical data, making it difficult to achieve multifaceted health management that takes into account an individual's emotional state. This poses a challenge in providing more accurate and effective individual health management, particularly in caregiving settings.

[0389] 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.

[0390] In this invention, the server includes means for acquiring biometric information over a long period of time, means for analyzing the acquired biometric and emotional information to predict health and mental states, and means for generating personalized lifestyle improvement measures and mental health maintenance measures based on these prediction results and notifying the information processing device accordingly. This enables the integrated evaluation of individual biometric and emotional states, allowing for individualized responses in care settings. Furthermore, by continuously updating improvement measures based on user feedback, it becomes possible to provide continuous health support optimized for the user.

[0391] "Biometric information" refers to data that indicates an individual's physical condition, and includes heart rate, steps taken, and calorie consumption.

[0392] "Emotional information" refers to data used to evaluate an individual's emotional state, and is acquired using methods such as facial expressions and tone of voice.

[0393] "Health status" refers to the results of an assessment of an individual's physical condition, derived from the analysis of various biological information.

[0394] "Mental state" refers to an individual's emotional and psychological condition and is evaluated by analyzing emotional information.

[0395] "Lifestyle improvement measures" refer to guidelines for actions and activities proposed to improve individual vitality based on acquired biometric information and predicted health status.

[0396] "Mental health maintenance measures" are guidelines for actions and activities proposed to maintain an individual's mental state, and are generated based on emotional information and assessments of their mental state.

[0397] To implement this invention, it is essential to use terminals such as smartphones and wearable devices. These terminals function as hardware for collecting biometric and emotional information. Specifically, they incorporate sensors to collect biometric information such as heart rate, steps taken, and calorie consumption, and use cameras and microphones to acquire emotional information such as facial expressions and tone of voice.

[0398] The terminal temporarily stores the collected data and sends it to the server using encryption technology. On the server, software for analyzing the received data, specifically an analysis system equipped with machine learning algorithms, runs. This analysis system, using platforms such as TensorFlow, can evaluate health status from biometric information and mental state from emotional information.

[0399] Based on these evaluation results, the server generates lifestyle improvement and mental health maintenance strategies tailored to each individual user. The generated suggestions are communicated to the user via an information processing device, and the user is encouraged to adjust their daily actions accordingly. User feedback is entered via a terminal and sent to the server. Based on this feedback, the analysis system further optimizes the generated suggestions.

[0400] As a concrete example, data such as heart rate, steps taken, and emotions are automatically collected as the user goes about their daily life. If the system determines that the user is experiencing stress, it will notify the user's smartphone with a suggestion such as, "Let's try some yoga today to help you relax." An example of a prompt message would be, "Analyze the user's heart rate and emotion data and suggest exercises aimed at reducing stress."

[0401] In this way, the entire system can continuously collect and analyze data, incorporate feedback, and provide users with the most optimal health support.

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The device acquires biometric information such as the user's heart rate, steps taken, and calorie consumption using sensors. At the same time, it also collects emotional information from facial expressions and voice tone using a camera and microphone. The input data consists of biometric and emotional information, which is temporarily stored within the device to be used as output.

[0405] Step 2:

[0406] The device periodically transmits stored biometric and emotional information to the server using encryption technology. The input is the data acquired in step 1, which is output to the server as encrypted data based on the transmission protocol.

[0407] Step 3:

[0408] The server decodes the received biometric and emotional information and stores it in a database. During this process, machine learning algorithms are used to analyze the data. The input is encrypted data, and the processing results in an evaluation of health and mental state.

[0409] Step 4:

[0410] The server uses a generative AI model to generate personalized lifestyle improvement plans and mental health maintenance plans based on the assessment results of physical and mental health status. The input is the analysis results from step 3, and the output is the specific suggestions that are generated.

[0411] Step 5:

[0412] The server notifies the information processing device of the generated lifestyle improvement measures and mental health maintenance measures. The input is the formulated proposals, and the output is the notification delivered to the user via the terminal.

[0413] Step 6:

[0414] The user receives a notification via their device and enters feedback into the device regarding the implementation of lifestyle improvement measures. This input is user feedback information, which is then sent to the server as input data for the next analysis.

[0415] Step 7:

[0416] The server receives feedback from users and updates the analysis system. The input is feedback information, and the output is data that will be used to generate the next lifestyle improvement plan, taking into account the results of measuring the effectiveness of the suggestions.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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".

[0433] The present invention provides a method for effectively collecting and analyzing long-term health information and providing users with appropriate health predictions and preventive measures. Embodiments thereof are described below.

[0434] Users collect daily activity data (e.g., heart rate, steps taken, calories burned, etc.) using smartphones and wearable devices. These devices transmit the collected health information to a server. In this process, the devices use encryption technology to protect the security and privacy of the data.

[0435] The server analyzes the received health information and uses statistical models to assess the user's current health status. This analysis includes techniques that use machine learning algorithms to predict future health risks from past health data. Based on the analysis results, the server generates personalized health predictions.

[0436] Furthermore, the server utilizes AI to automatically create specific preventative measures based on the user's lifestyle and health risks. These preventative measures may include, for example, "aim for 7,500 steps a day" or "reduce salt intake," and an action plan tailored to the user's past lifestyle is proposed.

[0437] The device receives notifications from the server and presents them to the user in an easy-to-understand format. For example, it may visually display information within a smartphone application or set reminders to prompt action.

[0438] Users can incorporate the suggested preventative measures into their daily lives and provide feedback on their implementation and effectiveness. The device collects this feedback and sends it to the server, which can then update the preventative measures based on the newly acquired data.

[0439] Through this iterative process, the system continuously supports the user's health and helps maintain a better state of health. Furthermore, it can be applied to health programs and medical institutions, and is expected to have widespread use.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] Users use smartphones or wearable devices to measure everyday health information such as heart rate, steps taken, and calories burned. The device periodically records this data.

[0443] Step 2:

[0444] The device encrypts the recorded health information at regular intervals and sends it to the server. At this stage, security measures are in place to prevent data leakage.

[0445] Step 3:

[0446] The server stores the received health information in a database and performs analysis using a statistical model. This model learns from past data patterns and identifies individual user health risk factors.

[0447] Step 4:

[0448] The server predicts future health risks based on the analysis results. Machine learning algorithms are applied to the predictions, for example, to assess the risk of diabetes and cardiovascular disease.

[0449] Step 5:

[0450] The server uses generative AI technology to create personalized preventative measures based on predicted health risks. This includes daily exercise plans and suggestions for dietary improvements.

[0451] Step 6:

[0452] The device receives preventative measures sent from the server and notifies the user. The notification is delivered through an application on the smartphone and may include visual or audio alerts.

[0453] Step 7:

[0454] Users incorporate the suggested preventative measures into their daily lives and input the results as feedback into their device. This feedback includes the amount of exercise performed and any changes made to their diet.

[0455] Step 8:

[0456] The device sends user feedback to the server. The server re-evaluates the effectiveness of preventative measures based on the newly collected data and updates them as needed.

[0457] Step 9:

[0458] By repeatedly performing this feedback loop, the system provides continuous health support tailored to the user's lifestyle, thereby reducing health risks.

[0459] (Example 1)

[0460] 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."

[0461] In modern society, there is a need to effectively manage users' health over the long term and provide personalized preventative measures. However, there are challenges in providing precise health predictions and flexible preventative measures tailored to individual users. Furthermore, there is a lack of mechanisms to continuously improve preventative measures by incorporating feedback while ensuring data security and privacy.

[0462] 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.

[0463] In this invention, the server includes a mechanism for acquiring health information over a long period, a mechanism for analyzing the acquired health information and deriving health prediction results, and a mechanism for creating preventive measures using a generated AI model. This enables the provision of personalized preventive measures and a function to continuously improve those preventive measures.

[0464] "Long-term health information" refers to data about a user's health status that is continuously collected and recorded over several weeks to several years.

[0465] A "mechanism for acquiring health information" refers to a device or system that monitors a user's activities and physical condition and collects data.

[0466] A "mechanism for analysis and deriving health prediction results" refers to a device or algorithm that analyzes collected health data and uses the results to estimate future health conditions.

[0467] A "generative AI model" refers to an artificial intelligence algorithm or system that performs predictions and generation based on large amounts of data.

[0468] A "mechanism for generating personalized preventive measures" refers to a device or program that creates specific health improvement plans based on each user's characteristics and circumstances.

[0469] A "device notification mechanism" refers to a means of communication or an interface for delivering information and instructions generated from a server to a user's device.

[0470] A "mechanism using encryption technology" refers to a device or method that applies technology to transform information in order to protect data from unauthorized access.

[0471] A "machine learning algorithm" refers to a computational method or model that analyzes data, learns patterns, and predicts future outcomes.

[0472] A "mechanism for obtaining feedback and updating preventative measures" refers to a device or system that collects user responses and results and uses them to revise preventative measures.

[0473] This invention is a system that collects and analyzes users' health information over a long period of time and provides personalized health predictions and preventive measures. Users collect their own activity information using smartphones and wearable devices. These devices record health-related data such as heart rate, steps taken, and calories burned, and utilize encryption technology, including AES encryption, when transmitting data.

[0474] The server receives data sent from the terminal and analyzes it using statistical analysis and machine learning algorithms (e.g., random forests and neural networks). This allows the server to assess the user's health status and predict future health risks.

[0475] The server also uses a generative AI model (e.g., the GPT model) to automatically generate personalized preventative measures based on health predictions. These generated preventative measures are notified to the user's smartphone or other devices. The user practices these preventative measures in their daily life and provides feedback on their implementation. The device collects the feedback and sends it to the server, which uses this to continuously improve the preventative measures.

[0476] As a concrete example, consider a case where a user records their daily step count. Based on this data, the server generates a preventative measure such as: "Aim for more than 7,500 steps per day this week." In addition, an example of a prompt to the generating AI model would be: "Generate appropriate health predictions and preventative measures based on the user's recent health data. Example: If the user needs stress management, what activities should be recommended?"

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] Users collect data on their daily activities using smartphones and wearable devices. Specifically, users wear the devices and record things like steps and heart rate in real time. The input is the user's physical actions related to their activities, and the output is health information converted into digital format.

[0480] Step 2:

[0481] The device encrypts the collected health data using AES encryption before sending it to the server. The input is the collected raw health data, and the output is the encrypted data. Specifically, the device securely sends the encrypted data to the server via a security protocol (e.g., HTTPS).

[0482] Step 3:

[0483] The server receives the incoming health data and performs data analysis. First, the server decodes the data and then performs statistical analysis on it. The input is encrypted health data, and the output is data in an analyzable format. The server then applies machine learning algorithms (e.g., random forest) to predict future health risks based on the user's past data. Specifically, the server also considers feedback to update the model.

[0484] Step 4:

[0485] The server uses a generated AI model based on the analysis results to produce personalized health predictions and preventive measures. The prompt is "Generate appropriate health predictions and preventive measures based on the user's recent health data." The input is the analyzed health data, and the generated preventive measures are obtained as output.

[0486] Step 5:

[0487] The server notifies the terminal of the generated health predictions and preventive measures. The terminal receives this and presents it to the user in an easy-to-understand format. The input is the generated preventive measures, and the output is specific advice provided to the user. Specifically, the terminal can display the preventive measures within the application and set reminders.

[0488] Step 6:

[0489] Users adjust their daily lives based on the generated preventative measures and provide feedback on their implementation status to their device. Specifically, users input the results and their impressions of their actions through the application. The input is user feedback, and the output is data used in the next improvement cycle.

[0490] Step 7:

[0491] The device collects user feedback and sends it to the server. The server updates preventative measures and health prediction models based on this feedback. The input is user feedback data, and the output is improved preventative measures and updated models. Specifically, the server modifies data-driven recommendations and uses them in subsequent notifications.

[0492] (Application Example 1)

[0493] 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."

[0494] Among the elderly and individuals requiring health management, a decline in sustained motivation for daily health maintenance and a lack of concrete action plans for appropriate health management are common. Therefore, there is a need to present optimal health improvement measures tailored to individual lifestyles and integrate them into daily life in a sustainable manner. Furthermore, flexible system updates that take feedback into account are necessary.

[0495] 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.

[0496] In this invention, the server includes means for acquiring biometric information over a long period of time, means for analyzing the acquired biometric information and deriving health prediction results, means for generating personalized lifestyle improvement measures based on the health prediction results, means for automatically creating the lifestyle improvement measures using a generating AI, and means for setting reminders based on biometric data and behavioral goals and notifying the user. This enables the provision of health maintenance measures tailored to the user, ensuring continuity of health management in daily life and improving behavior through effective feedback.

[0497] "Biometric information" refers to data such as heart rate, steps taken, and calories burned that indicate the user's health status.

[0498] "Health prediction results" are estimates of future health status based on collected biometric information.

[0499] "Lifestyle improvement measures" refer to specific action plans and advice tailored to the user's current health condition.

[0500] An "information processing device" is an electronic device used to collect biometric information and notify users of the generated improvement measures.

[0501] "Generative AI" is artificial intelligence that uses machine learning technology to automatically generate optimal lifestyle improvement strategies from data.

[0502] A "reminder" is a function that notifies users at appropriate times to help them achieve their behavioral goals.

[0503] "Feedback" refers to responses from users regarding the effectiveness and perceived impact of lifestyle improvements they have implemented.

[0504] This invention is a system for continuously managing and improving the health of users. The central components of the system are a portable information device (e.g., a smartphone or smartwatch) as a terminal, a server for analysis, and a generative AI.

[0505] The server first receives biometric information transmitted from the device. This information includes heart rate, steps taken, and calories burned. The device collects data through platforms such as Apple HealthKit and Google Fit, and secure data transfer is ensured using the TLS protocol. The server uses the received biometric information to perform data analysis using machine learning algorithms with Python and the scikit-learn library. This analysis generates a prediction of the user's health status.

[0506] Next, the generative AI model operates based on the prediction results. This AI model automatically creates personalized lifestyle improvement plans using OpenAI GPT models, etc. The generative AI generates various action suggestions in response to prompts. For example, by giving a prompt such as, "Analyze the user's health trends based on their heart rate changes and walking records over the past 30 days, and suggest the best health maintenance measures for the coming week. The user is a man in his 70s and prefers advice on improving physical fitness," it can output specific health maintenance measures.

[0507] The generated lifestyle improvement suggestions are sent to the user's device, and the user is notified so that they can incorporate them into their health maintenance activities. Furthermore, the device has a reminder function that notifies the user of their set behavioral goals as needed. In this way, the system supports the user's health management in their daily life and continuously updates the improvement suggestions through feedback.

[0508] Users implement suggested improvements in their daily lives and provide feedback on their effectiveness via their device. This feedback is then sent back to the server and used to generate future lifestyle improvement suggestions, allowing for dynamic improvement of the user's health maintenance program. Through this process, the system provides customized services tailored to each user.

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

[0510] Step 1:

[0511] The device collects biometric information from the user. Specifically, it uses smartwatches and smartphones to record data such as heart rate, steps taken, and calories burned, and aggregates this data through Apple HealthKit and Google Fit. The input is biometric information from the user's daily activities, and the output is formatted data for transmission to the server.

[0512] Step 2:

[0513] The device securely transmits the collected biometric information to the server using the TLS protocol. This ensures data privacy and security. The input is the biometric information collected by the device, and the output is encrypted data received by the server.

[0514] Step 3:

[0515] The server decodes the received biometric information, runs a machine learning model using the scikit-learn library in Python, and performs health predictions. The input is encrypted biometric information, and the output is a prediction showing the user's future health risks.

[0516] Step 4:

[0517] The server automatically generates personalized lifestyle improvement plans using a generative AI model based on health risk prediction results. It instructs the generative AI via prompt messages to create appropriate advice. Inputs are health prediction results and prompt messages for the generative AI, while output is specific lifestyle improvement plans tailored to each user.

[0518] Step 5:

[0519] The generated lifestyle improvement suggestions are sent to the terminal and notified to the user's application. The terminal presents the information to the user visually or in notification format, prompting action. The input is the lifestyle improvement suggestions from the server, and the output is the user's confirmation of the notification and implementation of the action.

[0520] Step 6:

[0521] Users incorporate the suggested lifestyle improvements into their daily lives and input feedback on their implementation status and effects via a terminal. The input is user feedback data, and the output is updated user data including the feedback content.

[0522] Step 7:

[0523] The terminal sends user feedback to the server, which receives this feedback and updates its lifestyle improvement strategies. This allows the system to continuously generate new improvement strategies. The input is user data including feedback, and the output is a database of updated lifestyle improvement strategies.

[0524] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0525] The system of the present invention aims to provide more accurate and effective health management by considering both health information and user emotions. An embodiment thereof is shown below.

[0526] Users collect daily health information using smartphones and wearable devices. These devices measure data such as heart rate, steps taken, and calorie consumption, and periodically transmit it to a server using encryption technology. In addition, the devices utilize cameras and voice input functions to recognize the user's emotions, obtaining emotional data from facial expressions and tone of voice.

[0527] The server analyzes emotional data along with health information and stores it in an integrated database. Probabilistic models and machine learning algorithms are used in the analysis to evaluate the user's current health and emotional state. Based on these analysis results, the server predicts the user's future health risks and emotional trends.

[0528] The server utilizes an emotion engine to generate optimal preventative measures based on the user's emotional state. For example, if a high-stress emotional state is detected, suggestions for relaxation exercises or meditation will be made. Conversely, if positive emotions are detected, further exercise or activity to maintain that state will be recommended.

[0529] The device notifies the user of the generated preventative measures. The notification is presented clearly to the user through visual alerts and voice guidance.

[0530] Users incorporate the proposed health management plan into their daily lives and input feedback on their implementation and reactions into the device. This includes their satisfaction with the proposal and how easy it was to follow. The device sends this feedback to a server, which uses it for further data analysis and updating of preventative measures.

[0531] By repeating this data-driven feedback loop, the system continuously provides health support optimized for the individual needs of each user. Considering emotional states adds innovative value to conventional health management systems.

[0532] The following describes the processing flow.

[0533] Step 1:

[0534] Users routinely collect health information such as heart rate, steps taken, and calories burned using smartphones and wearable devices. These devices also acquire emotional data through facial expression photos and voice input from the user.

[0535] Step 2:

[0536] The device encrypts the collected health and emotional data at regular intervals and securely transmits it to the server. The transmission frequency is adjusted according to the usage scenario.

[0537] Step 3:

[0538] The server stores the received data in an integrated database and performs analysis using statistical models and machine learning algorithms. This analysis includes evaluating the user's health status as well as analyzing emotional patterns.

[0539] Step 4:

[0540] Based on the analysis results, the server predicts the user's future health risks and emotional tendencies. For example, it may determine that the user has high stress levels and is experiencing a prolonged lack of exercise.

[0541] Step 5:

[0542] The server uses an emotion engine to generate preventative measures tailored to the user's emotional state. These include guidance on deep breathing exercises to reduce stress and suggestions for enjoyable exercises.

[0543] Step 6:

[0544] The device notifies the user of the generated preventative measures. Notification methods are diverse, including smartphone pop-up messages and voice alerts, prioritizing user convenience.

[0545] Step 7:

[0546] Users implement recommended preventative measures and provide feedback on their device regarding the results and their reactions to the suggestions. This feedback includes information such as the difficulty of implementation and its impact on their emotions.

[0547] Step 8:

[0548] The device collects user feedback and sends it to the server. The server then performs further analysis based on this feedback and updates preventative measures as needed.

[0549] Step 9:

[0550] By iterating through this process, the system improves individual user health management and helps maintain better health. By providing a detailed assessment of how emotional states affect health management, it offers more appropriate advice.

[0551] (Example 2)

[0552] 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."

[0553] Traditional health management systems are limited to analysis based solely on health information, making it difficult to provide personalized recommendations that take into account the user's emotional state. Furthermore, they lack sufficient functionality to update preventative measures based on user feedback, highlighting the need for more accurate, long-term health and emotional management.

[0554] 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.

[0555] In this invention, the server includes means for acquiring health information and emotional information, means for analyzing the acquired health information and emotional information to evaluate the health and emotional state, and means for generating personalized preventive measures based on the health and emotional state and notifying the user. This enables comprehensive health management that also takes into account the user's emotional state.

[0556] "Health information" refers to data that indicates the user's physical condition, including heart rate, steps taken, and calorie consumption.

[0557] "Emotional information" refers to data that indicates the user's emotional state, and is obtained from things like tone of voice and facial expressions.

[0558] "Analysis" is the process of processing acquired data to evaluate health and emotional states and derive patterns and trends.

[0559] "Preventive measures" refer to specific suggestions for improving health tailored to each individual user, based on their health and emotional state.

[0560] "Feedback" refers to information about users' experiences and opinions, which the system uses to update preventative measures.

[0561] "External devices" refer to devices used to acquire health information and emotional information, and include wearable devices and smartphones.

[0562] An "emotion engine" is a component within a system that analyzes a user's emotional state and generates appropriate preventative measures based on the results.

[0563] This invention is a system for providing more personalized health management by utilizing the user's health and emotional information. First, the user uses external devices such as wearable devices and smartphones to collect health and emotional information through their daily activities. Specifically, data such as heart rate, steps taken, and calorie consumption are recorded using sensors, and emotional information is obtained from facial expressions and tone of voice. OpenCV and speech recognition technology are utilized for this purpose.

[0564] The device periodically encrypts the acquired data and sends it to the server using Bluetooth or Wi-Fi. AES technology is used for this encryption. The server analyzes the received data using analysis platforms such as Python's scikit-learn and TensorFlow. During this analysis process, the system assesses the user's health and emotional state and predicts user tendencies and risks.

[0565] Furthermore, the server uses an emotion engine to generate preventative measures based on the analysis results. The emotion analysis used here employs NLP techniques that leverage generative AI models. For example, if high stress levels are detected, specific measures such as "try meditating for 5 minutes a day" may be suggested.

[0566] The device notifies the user of the generated preventative measures. This utilizes smartphone push notifications and voice guidance. The user implements the suggested preventative measures and inputs feedback into the device, thereby feeding back their health management to the system. The server collects this feedback information and uses it to improve the preventative measures.

[0567] For example, if the user's average stress level is high based on data from the past week, relaxation activities will be recommended. Also, if many positive emotions are detected, suggestions for increasing activity will be made. An example of a prompt for the generating AI model would be: "Based on my health and emotional data, please provide appropriate health management advice for me this week. Please include relaxation measures tailored to my stress level."

[0568] In this way, the system can provide comprehensive health and emotional management optimized for each user.

[0569] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0570] Step 1:

[0571] Users collect daily health and emotional information using wearable devices and smartphones. Inputs include physical information such as heart rate, steps taken, and calorie consumption, as well as emotional information extracted from facial expressions and tone of voice. Outputs are these data, which are used for analysis in the next step. Specifically, this involves wearable devices recording this data in real time using sensors, and smartphones capturing emotional information through cameras and microphones.

[0572] Step 2:

[0573] The device periodically encrypts the collected health and emotional information and sends it to the server. The input is the raw data obtained, which is securely encrypted using AES technology. The output is encrypted data that is sent to the server. The specific actions involved include the transfer of data to the server via Bluetooth or Wi-Fi.

[0574] Step 3:

[0575] The server analyzes the received data. It receives encrypted health and emotional information as input and first decrypts it. Next, it analyzes the data using machine learning algorithms to evaluate the health and emotional state. The output is the analysis result, which includes the user's health tendencies and stress level. The data analysis process uses scikit-learn and TensorFlow to demonstrate its operation.

[0576] Step 4:

[0577] The server generates personalized preventative measures based on the analysis results. The input is the evaluation of health and emotional states, and based on this, an emotional engine using NLP technology devises appropriate preventative measures. The output is specific advice and action plans for the user. The generation of preventative measures using a generative AI model is the concrete operation.

[0578] Step 5:

[0579] The device notifies the user of the generated preventative measures. The input consists of advice and action plans generated on the server, which are communicated to the user via smartphone push notifications and voice guidance. The output consists of specific notifications received by the user, which include visual and auditory actions.

[0580] Step 6:

[0581] The user implements the suggested preventative measures and inputs feedback into the device. The input includes comments on satisfaction with and the effectiveness of the implemented preventative measures. The output is this feedback data, which is sent to the server and used for subsequent analysis. The user's action of inputting feedback into the device is the concrete operation.

[0582] Step 7:

[0583] The server improves preventative measures based on user feedback. The input is user feedback information, which is analyzed and used as new data to inform the next preventative measures. The output is the improved preventative measures, which are then used for subsequent notifications. The concrete operation involves data analysis based on feedback and updating preventative measures.

[0584] (Application Example 2)

[0585] 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."

[0586] In modern society, personal health management should ideally focus not only on physical aspects but also on emotional ones. However, conventional health management systems primarily collect and analyze only physical data, making it difficult to achieve multifaceted health management that takes into account an individual's emotional state. This poses a challenge in providing more accurate and effective individual health management, particularly in caregiving settings.

[0587] 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.

[0588] In this invention, the server includes means for acquiring biometric information over a long period of time, means for analyzing the acquired biometric and emotional information to predict health and mental states, and means for generating personalized lifestyle improvement measures and mental health maintenance measures based on these prediction results and notifying the information processing device accordingly. This enables the integrated evaluation of individual biometric and emotional states, allowing for individualized responses in care settings. Furthermore, by continuously updating improvement measures based on user feedback, it becomes possible to provide continuous health support optimized for the user.

[0589] "Biometric information" refers to data that indicates an individual's physical condition, and includes heart rate, steps taken, and calorie consumption.

[0590] "Emotional information" refers to data used to evaluate an individual's emotional state, and is acquired using methods such as facial expressions and tone of voice.

[0591] "Health status" refers to the results of an assessment of an individual's physical condition, derived from the analysis of various biological information.

[0592] "Mental state" refers to an individual's emotional and psychological condition and is evaluated by analyzing emotional information.

[0593] "Lifestyle improvement measures" refer to guidelines for actions and activities proposed to improve individual vitality based on acquired biometric information and predicted health status.

[0594] "Mental health maintenance measures" are guidelines for actions and activities proposed to maintain an individual's mental state, and are generated based on emotional information and assessments of their mental state.

[0595] To implement this invention, it is essential to use terminals such as smartphones and wearable devices. These terminals function as hardware for collecting biometric and emotional information. Specifically, they incorporate sensors to collect biometric information such as heart rate, steps taken, and calorie consumption, and use cameras and microphones to acquire emotional information such as facial expressions and tone of voice.

[0596] The terminal temporarily stores the collected data and sends it to the server using encryption technology. On the server, software for analyzing the received data, specifically an analysis system equipped with machine learning algorithms, runs. This analysis system, using platforms such as TensorFlow, can evaluate health status from biometric information and mental state from emotional information.

[0597] Based on these evaluation results, the server generates lifestyle improvement and mental health maintenance strategies tailored to each individual user. The generated suggestions are communicated to the user via an information processing device, and the user is encouraged to adjust their daily actions accordingly. User feedback is entered via a terminal and sent to the server. Based on this feedback, the analysis system further optimizes the generated suggestions.

[0598] As a concrete example, data such as heart rate, steps taken, and emotions are automatically collected as the user goes about their daily life. If the system determines that the user is experiencing stress, it will notify the user's smartphone with a suggestion such as, "Let's try some yoga today to help you relax." An example of a prompt message would be, "Analyze the user's heart rate and emotion data and suggest exercises aimed at reducing stress."

[0599] In this way, the entire system can continuously collect and analyze data, incorporate feedback, and provide users with the most optimal health support.

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

[0601] Step 1:

[0602] The device acquires biometric information such as the user's heart rate, steps taken, and calorie consumption using sensors. At the same time, it also collects emotional information from facial expressions and voice tone using a camera and microphone. The input data consists of biometric and emotional information, which is temporarily stored within the device to be used as output.

[0603] Step 2:

[0604] The device periodically transmits stored biometric and emotional information to the server using encryption technology. The input is the data acquired in step 1, which is output to the server as encrypted data based on the transmission protocol.

[0605] Step 3:

[0606] The server decodes the received biometric and emotional information and stores it in a database. During this process, machine learning algorithms are used to analyze the data. The input is encrypted data, and the processing results in an evaluation of health and mental state.

[0607] Step 4:

[0608] The server uses a generative AI model to generate personalized lifestyle improvement plans and mental health maintenance plans based on the assessment results of physical and mental health status. The input is the analysis results from step 3, and the output is the specific suggestions that are generated.

[0609] Step 5:

[0610] The server notifies the information processing device of the generated lifestyle improvement measures and mental health maintenance measures. The input is the formulated proposals, and the output is the notification delivered to the user via the terminal.

[0611] Step 6:

[0612] The user receives a notification via their device and enters feedback into the device regarding the implementation of lifestyle improvement measures. This input is user feedback information, which is then sent to the server as input data for the next analysis.

[0613] Step 7:

[0614] The server receives feedback from users and updates the analysis system. The input is feedback information, and the output is data that will be used to generate the next lifestyle improvement plan, taking into account the results of measuring the effectiveness of the suggestions.

[0615] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0616] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0617] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0618] [Fourth Embodiment]

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

[0620] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0621] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0622] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0623] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0624] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0625] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0626] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0627] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0628] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0629] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0630] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0631] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0632] The present invention provides a method for effectively collecting and analyzing long-term health information and providing users with appropriate health predictions and preventive measures. Embodiments thereof are described below.

[0633] Users collect daily activity data (e.g., heart rate, steps taken, calories burned, etc.) using smartphones and wearable devices. These devices transmit the collected health information to a server. In this process, the devices use encryption technology to protect the security and privacy of the data.

[0634] The server analyzes the received health information and uses statistical models to assess the user's current health status. This analysis includes techniques that use machine learning algorithms to predict future health risks from past health data. Based on the analysis results, the server generates personalized health predictions.

[0635] Furthermore, the server utilizes AI to automatically create specific preventative measures based on the user's lifestyle and health risks. These preventative measures may include, for example, "aim for 7,500 steps a day" or "reduce salt intake," and an action plan tailored to the user's past lifestyle is proposed.

[0636] The device receives notifications from the server and presents them to the user in an easy-to-understand format. For example, it may visually display information within a smartphone application or set reminders to prompt action.

[0637] Users can incorporate the suggested preventative measures into their daily lives and provide feedback on their implementation and effectiveness. The device collects this feedback and sends it to the server, which can then update the preventative measures based on the newly acquired data.

[0638] Through this iterative process, the system continuously supports the user's health and helps maintain a better state of health. Furthermore, it can be applied to health programs and medical institutions, and is expected to have widespread use.

[0639] The following describes the processing flow.

[0640] Step 1:

[0641] Users use smartphones or wearable devices to measure everyday health information such as heart rate, steps taken, and calories burned. The device periodically records this data.

[0642] Step 2:

[0643] The device encrypts the recorded health information at regular intervals and sends it to the server. At this stage, security measures are in place to prevent data leakage.

[0644] Step 3:

[0645] The server stores the received health information in a database and performs analysis using a statistical model. This model learns from past data patterns and identifies individual user health risk factors.

[0646] Step 4:

[0647] The server predicts future health risks based on the analysis results. Machine learning algorithms are applied to the predictions, for example, to assess the risk of diabetes and cardiovascular disease.

[0648] Step 5:

[0649] The server uses generative AI technology to create personalized preventative measures based on predicted health risks. This includes daily exercise plans and suggestions for dietary improvements.

[0650] Step 6:

[0651] The device receives preventative measures sent from the server and notifies the user. The notification is delivered through an application on the smartphone and may include visual or audio alerts.

[0652] Step 7:

[0653] Users incorporate the suggested preventative measures into their daily lives and input the results as feedback into their device. This feedback includes the amount of exercise performed and any changes made to their diet.

[0654] Step 8:

[0655] The device sends user feedback to the server. The server re-evaluates the effectiveness of preventative measures based on the newly collected data and updates them as needed.

[0656] Step 9:

[0657] By repeatedly performing this feedback loop, the system provides continuous health support tailored to the user's lifestyle, thereby reducing health risks.

[0658] (Example 1)

[0659] 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".

[0660] In modern society, there is a need to effectively manage users' health over the long term and provide personalized preventative measures. However, there are challenges in providing precise health predictions and flexible preventative measures tailored to individual users. Furthermore, there is a lack of mechanisms to continuously improve preventative measures by incorporating feedback while ensuring data security and privacy.

[0661] 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.

[0662] In this invention, the server includes a mechanism for acquiring health information over a long period, a mechanism for analyzing the acquired health information and deriving health prediction results, and a mechanism for creating preventive measures using a generated AI model. This enables the provision of personalized preventive measures and a function to continuously improve those preventive measures.

[0663] "Long-term health information" refers to data about a user's health status that is continuously collected and recorded over several weeks to several years.

[0664] A "mechanism for acquiring health information" refers to a device or system that monitors a user's activities and physical condition and collects data.

[0665] A "mechanism for analysis and deriving health prediction results" refers to a device or algorithm that analyzes collected health data and uses the results to estimate future health conditions.

[0666] A "generative AI model" refers to an artificial intelligence algorithm or system that performs predictions and generation based on large amounts of data.

[0667] A "mechanism for generating personalized preventive measures" refers to a device or program that creates specific health improvement plans based on each user's characteristics and circumstances.

[0668] A "device notification mechanism" refers to a means of communication or an interface for delivering information and instructions generated from a server to a user's device.

[0669] A "mechanism using encryption technology" refers to a device or method that applies technology to transform information in order to protect data from unauthorized access.

[0670] A "machine learning algorithm" refers to a computational method or model that analyzes data, learns patterns, and predicts future outcomes.

[0671] A "mechanism for obtaining feedback and updating preventative measures" refers to a device or system that collects user responses and results and uses them to revise preventative measures.

[0672] This invention is a system that collects and analyzes users' health information over a long period of time and provides personalized health predictions and preventive measures. Users collect their own activity information using smartphones and wearable devices. These devices record health-related data such as heart rate, steps taken, and calories burned, and utilize encryption technology, including AES encryption, when transmitting data.

[0673] The server receives data sent from the terminal and analyzes it using statistical analysis and machine learning algorithms (e.g., random forests and neural networks). This allows the server to assess the user's health status and predict future health risks.

[0674] The server also uses a generative AI model (e.g., the GPT model) to automatically generate personalized preventative measures based on health predictions. These generated preventative measures are notified to the user's smartphone or other devices. The user practices these preventative measures in their daily life and provides feedback on their implementation. The device collects the feedback and sends it to the server, which uses this to continuously improve the preventative measures.

[0675] As a concrete example, consider a case where a user records their daily step count. Based on this data, the server generates a preventative measure such as: "Aim for more than 7,500 steps per day this week." In addition, an example of a prompt to the generating AI model would be: "Generate appropriate health predictions and preventative measures based on the user's recent health data. Example: If the user needs stress management, what activities should be recommended?"

[0676] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0677] Step 1:

[0678] Users collect data on their daily activities using smartphones and wearable devices. Specifically, users wear the devices and record things like steps and heart rate in real time. The input is the user's physical actions related to their activities, and the output is health information converted into digital format.

[0679] Step 2:

[0680] The device encrypts the collected health data using AES encryption before sending it to the server. The input is the collected raw health data, and the output is the encrypted data. Specifically, the device securely sends the encrypted data to the server via a security protocol (e.g., HTTPS).

[0681] Step 3:

[0682] The server receives the incoming health data and performs data analysis. First, the server decodes the data and then performs statistical analysis on it. The input is encrypted health data, and the output is data in an analyzable format. The server then applies machine learning algorithms (e.g., random forest) to predict future health risks based on the user's past data. Specifically, the server also considers feedback to update the model.

[0683] Step 4:

[0684] The server uses a generated AI model based on the analysis results to produce personalized health predictions and preventive measures. The prompt is "Generate appropriate health predictions and preventive measures based on the user's recent health data." The input is the analyzed health data, and the generated preventive measures are obtained as output.

[0685] Step 5:

[0686] The server notifies the terminal of the generated health predictions and preventive measures. The terminal receives this and presents it to the user in an easy-to-understand format. The input is the generated preventive measures, and the output is specific advice provided to the user. Specifically, the terminal can display the preventive measures within the application and set reminders.

[0687] Step 6:

[0688] Users adjust their daily lives based on the generated preventative measures and provide feedback on their implementation status to their device. Specifically, users input the results and their impressions of their actions through the application. The input is user feedback, and the output is data used in the next improvement cycle.

[0689] Step 7:

[0690] The device collects user feedback and sends it to the server. The server updates preventative measures and health prediction models based on this feedback. The input is user feedback data, and the output is improved preventative measures and updated models. Specifically, the server modifies data-driven recommendations and uses them in subsequent notifications.

[0691] (Application Example 1)

[0692] 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".

[0693] Among the elderly and individuals requiring health management, a decline in sustained motivation for daily health maintenance and a lack of concrete action plans for appropriate health management are common. Therefore, there is a need to present optimal health improvement measures tailored to individual lifestyles and integrate them into daily life in a sustainable manner. Furthermore, flexible system updates that take feedback into account are necessary.

[0694] 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.

[0695] In this invention, the server includes means for acquiring biometric information over a long period of time, means for analyzing the acquired biometric information and deriving health prediction results, means for generating personalized lifestyle improvement measures based on the health prediction results, means for automatically creating the lifestyle improvement measures using a generating AI, and means for setting reminders based on biometric data and behavioral goals and notifying the user. This enables the provision of health maintenance measures tailored to the user, ensuring continuity of health management in daily life and improving behavior through effective feedback.

[0696] "Biometric information" refers to data such as heart rate, steps taken, and calories burned that indicate the user's health status.

[0697] "Health prediction results" are estimates of future health status based on collected biometric information.

[0698] "Lifestyle improvement measures" refer to specific action plans and advice tailored to the user's current health condition.

[0699] An "information processing device" is an electronic device used to collect biometric information and notify users of the generated improvement measures.

[0700] "Generative AI" is artificial intelligence that uses machine learning technology to automatically generate optimal lifestyle improvement strategies from data.

[0701] A "reminder" is a function that notifies users at appropriate times to help them achieve their behavioral goals.

[0702] "Feedback" refers to responses from users regarding the effectiveness and perceived impact of lifestyle improvements they have implemented.

[0703] This invention is a system for continuously managing and improving the health of users. The central components of the system are a portable information device (e.g., a smartphone or smartwatch) as a terminal, a server for analysis, and a generative AI.

[0704] The server first receives biometric information transmitted from the device. This information includes heart rate, steps taken, and calories burned. The device collects data through platforms such as Apple HealthKit and Google Fit, and secure data transfer is ensured using the TLS protocol. The server uses the received biometric information to perform data analysis using machine learning algorithms with Python and the scikit-learn library. This analysis generates a prediction of the user's health status.

[0705] Next, the generative AI model operates based on the prediction results. This AI model automatically creates personalized lifestyle improvement plans using OpenAI GPT models, etc. The generative AI generates various action suggestions in response to prompts. For example, by giving a prompt such as, "Analyze the user's health trends based on their heart rate changes and walking records over the past 30 days, and suggest the best health maintenance measures for the coming week. The user is a man in his 70s and prefers advice on improving physical fitness," it can output specific health maintenance measures.

[0706] The generated lifestyle improvement suggestions are sent to the user's device, and the user is notified so that they can incorporate them into their health maintenance activities. Furthermore, the device has a reminder function that notifies the user of their set behavioral goals as needed. In this way, the system supports the user's health management in their daily life and continuously updates the improvement suggestions through feedback.

[0707] Users implement suggested improvements in their daily lives and provide feedback on their effectiveness via their device. This feedback is then sent back to the server and used to generate future lifestyle improvement suggestions, allowing for dynamic improvement of the user's health maintenance program. Through this process, the system provides customized services tailored to each user.

[0708] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0709] Step 1:

[0710] The device collects biometric information from the user. Specifically, it uses smartwatches and smartphones to record data such as heart rate, steps taken, and calories burned, and aggregates this data through Apple HealthKit and Google Fit. The input is biometric information from the user's daily activities, and the output is formatted data for transmission to the server.

[0711] Step 2:

[0712] The device securely transmits the collected biometric information to the server using the TLS protocol. This ensures data privacy and security. The input is the biometric information collected by the device, and the output is encrypted data received by the server.

[0713] Step 3:

[0714] The server decodes the received biometric information, runs a machine learning model using the scikit-learn library in Python, and performs health predictions. The input is encrypted biometric information, and the output is a prediction showing the user's future health risks.

[0715] Step 4:

[0716] The server automatically generates personalized lifestyle improvement plans using a generative AI model based on health risk prediction results. It instructs the generative AI via prompt messages to create appropriate advice. Inputs are health prediction results and prompt messages for the generative AI, while output is specific lifestyle improvement plans tailored to each user.

[0717] Step 5:

[0718] The generated lifestyle improvement suggestions are sent to the terminal and notified to the user's application. The terminal presents the information to the user visually or in notification format, prompting action. The input is the lifestyle improvement suggestions from the server, and the output is the user's confirmation of the notification and implementation of the action.

[0719] Step 6:

[0720] Users incorporate the suggested lifestyle improvements into their daily lives and input feedback on their implementation status and effects via a terminal. The input is user feedback data, and the output is updated user data including the feedback content.

[0721] Step 7:

[0722] The terminal sends user feedback to the server, which receives this feedback and updates its lifestyle improvement strategies. This allows the system to continuously generate new improvement strategies. The input is user data including feedback, and the output is a database of updated lifestyle improvement strategies.

[0723] 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.

[0724] The system of the present invention aims to provide more accurate and effective health management by considering both health information and user emotions. An embodiment thereof is shown below.

[0725] Users collect daily health information using smartphones and wearable devices. These devices measure data such as heart rate, steps taken, and calorie consumption, and periodically transmit it to a server using encryption technology. In addition, the devices utilize cameras and voice input functions to recognize the user's emotions, obtaining emotional data from facial expressions and tone of voice.

[0726] The server analyzes emotional data along with health information and stores it in an integrated database. Probabilistic models and machine learning algorithms are used in the analysis to evaluate the user's current health and emotional state. Based on these analysis results, the server predicts the user's future health risks and emotional trends.

[0727] The server utilizes an emotion engine to generate optimal preventative measures based on the user's emotional state. For example, if a high-stress emotional state is detected, suggestions for relaxation exercises or meditation will be made. Conversely, if positive emotions are detected, further exercise or activity to maintain that state will be recommended.

[0728] The device notifies the user of the generated preventative measures. The notification is presented clearly to the user through visual alerts and voice guidance.

[0729] Users incorporate the proposed health management plan into their daily lives and input feedback on their implementation and reactions into the device. This includes their satisfaction with the proposal and how easy it was to follow. The device sends this feedback to a server, which uses it for further data analysis and updating of preventative measures.

[0730] By repeating this data-driven feedback loop, the system continuously provides health support optimized for the individual needs of each user. Considering emotional states adds innovative value to conventional health management systems.

[0731] The following describes the processing flow.

[0732] Step 1:

[0733] Users routinely collect health information such as heart rate, steps taken, and calories burned using smartphones and wearable devices. These devices also acquire emotional data through facial expression photos and voice input from the user.

[0734] Step 2:

[0735] The device encrypts the collected health and emotional data at regular intervals and securely transmits it to the server. The transmission frequency is adjusted according to the usage scenario.

[0736] Step 3:

[0737] The server stores the received data in an integrated database and performs analysis using statistical models and machine learning algorithms. This analysis includes evaluating the user's health status as well as analyzing emotional patterns.

[0738] Step 4:

[0739] Based on the analysis results, the server predicts the user's future health risks and emotional tendencies. For example, it may determine that the user has high stress levels and is experiencing a prolonged lack of exercise.

[0740] Step 5:

[0741] The server uses an emotion engine to generate preventative measures tailored to the user's emotional state. These include guidance on deep breathing exercises to reduce stress and suggestions for enjoyable exercises.

[0742] Step 6:

[0743] The device notifies the user of the generated preventative measures. Notification methods are diverse, including smartphone pop-up messages and voice alerts, prioritizing user convenience.

[0744] Step 7:

[0745] Users implement recommended preventative measures and provide feedback on their device regarding the results and their reactions to the suggestions. This feedback includes information such as the difficulty of implementation and its impact on their emotions.

[0746] Step 8:

[0747] The device collects user feedback and sends it to the server. The server then performs further analysis based on this feedback and updates preventative measures as needed.

[0748] Step 9:

[0749] By iterating through this process, the system improves individual user health management and helps maintain better health. By providing a detailed assessment of how emotional states affect health management, it offers more appropriate advice.

[0750] (Example 2)

[0751] 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".

[0752] Traditional health management systems are limited to analysis based solely on health information, making it difficult to provide personalized recommendations that take into account the user's emotional state. Furthermore, they lack sufficient functionality to update preventative measures based on user feedback, highlighting the need for more accurate, long-term health and emotional management.

[0753] 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.

[0754] In this invention, the server includes means for acquiring health information and emotional information, means for analyzing the acquired health information and emotional information to evaluate the health and emotional state, and means for generating personalized preventive measures based on the health and emotional state and notifying the user. This enables comprehensive health management that also takes into account the user's emotional state.

[0755] "Health information" refers to data that indicates the user's physical condition, including heart rate, steps taken, and calorie consumption.

[0756] "Emotional information" refers to data that indicates the user's emotional state, and is obtained from things like tone of voice and facial expressions.

[0757] "Analysis" is the process of processing acquired data to evaluate health and emotional states and derive patterns and trends.

[0758] "Preventive measures" refer to specific suggestions for improving health tailored to each individual user, based on their health and emotional state.

[0759] "Feedback" refers to information about users' experiences and opinions, which the system uses to update preventative measures.

[0760] "External devices" refer to devices used to acquire health information and emotional information, and include wearable devices and smartphones.

[0761] An "emotion engine" is a component within a system that analyzes a user's emotional state and generates appropriate preventative measures based on the results.

[0762] This invention is a system for providing more personalized health management by utilizing the user's health and emotional information. First, the user uses external devices such as wearable devices and smartphones to collect health and emotional information through their daily activities. Specifically, data such as heart rate, steps taken, and calorie consumption are recorded using sensors, and emotional information is obtained from facial expressions and tone of voice. OpenCV and speech recognition technology are utilized for this purpose.

[0763] The device periodically encrypts the acquired data and sends it to the server using Bluetooth or Wi-Fi. AES technology is used for this encryption. The server analyzes the received data using analysis platforms such as Python's scikit-learn and TensorFlow. During this analysis process, the system assesses the user's health and emotional state and predicts user tendencies and risks.

[0764] Furthermore, the server uses an emotion engine to generate preventative measures based on the analysis results. The emotion analysis used here employs NLP techniques that leverage generative AI models. For example, if high stress levels are detected, specific measures such as "try meditating for 5 minutes a day" may be suggested.

[0765] The device notifies the user of the generated preventative measures. This utilizes smartphone push notifications and voice guidance. The user implements the suggested preventative measures and inputs feedback into the device, thereby feeding back their health management to the system. The server collects this feedback information and uses it to improve the preventative measures.

[0766] For example, if the user's average stress level is high based on data from the past week, relaxation activities will be recommended. Also, if many positive emotions are detected, suggestions for increasing activity will be made. An example of a prompt for the generating AI model would be: "Based on my health and emotional data, please provide appropriate health management advice for me this week. Please include relaxation measures tailored to my stress level."

[0767] In this way, the system can provide comprehensive health and emotional management optimized for each user.

[0768] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0769] Step 1:

[0770] Users collect daily health and emotional information using wearable devices and smartphones. Inputs include physical information such as heart rate, steps taken, and calorie consumption, as well as emotional information extracted from facial expressions and tone of voice. Outputs are these data, which are used for analysis in the next step. Specifically, this involves wearable devices recording this data in real time using sensors, and smartphones capturing emotional information through cameras and microphones.

[0771] Step 2:

[0772] The device periodically encrypts the collected health and emotional information and sends it to the server. The input is the raw data obtained, which is securely encrypted using AES technology. The output is encrypted data that is sent to the server. The specific actions involved include the transfer of data to the server via Bluetooth or Wi-Fi.

[0773] Step 3:

[0774] The server analyzes the received data. It receives encrypted health and emotional information as input and first decrypts it. Next, it analyzes the data using machine learning algorithms to evaluate the health and emotional state. The output is the analysis result, which includes the user's health tendencies and stress level. The data analysis process uses scikit-learn and TensorFlow to demonstrate its operation.

[0775] Step 4:

[0776] The server generates personalized preventative measures based on the analysis results. The input is the evaluation of health and emotional states, and based on this, an emotional engine using NLP technology devises appropriate preventative measures. The output is specific advice and action plans for the user. The generation of preventative measures using a generative AI model is the concrete operation.

[0777] Step 5:

[0778] The device notifies the user of the generated preventative measures. The input consists of advice and action plans generated on the server, which are communicated to the user via smartphone push notifications and voice guidance. The output consists of specific notifications received by the user, which include visual and auditory actions.

[0779] Step 6:

[0780] The user implements the suggested preventative measures and inputs feedback into the device. The input includes comments on satisfaction with and the effectiveness of the implemented preventative measures. The output is this feedback data, which is sent to the server and used for subsequent analysis. The user's action of inputting feedback into the device is the concrete operation.

[0781] Step 7:

[0782] The server improves preventative measures based on user feedback. The input is user feedback information, which is analyzed and used as new data to inform the next preventative measures. The output is the improved preventative measures, which are then used for subsequent notifications. The concrete operation involves data analysis based on feedback and updating preventative measures.

[0783] (Application Example 2)

[0784] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0785] In modern society, personal health management should ideally focus not only on physical aspects but also on emotional ones. However, conventional health management systems primarily collect and analyze only physical data, making it difficult to achieve multifaceted health management that takes into account an individual's emotional state. This poses a challenge in providing more accurate and effective individual health management, particularly in caregiving settings.

[0786] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0787] In this invention, the server includes means for acquiring biometric information over a long period of time, means for analyzing the acquired biometric and emotional information to predict health and mental states, and means for generating personalized lifestyle improvement measures and mental health maintenance measures based on these prediction results and notifying the information processing device accordingly. This enables the integrated evaluation of individual biometric and emotional states, allowing for individualized responses in care settings. Furthermore, by continuously updating improvement measures based on user feedback, it becomes possible to provide continuous health support optimized for the user.

[0788] "Biometric information" refers to data that indicates an individual's physical condition, and includes heart rate, steps taken, and calorie consumption.

[0789] "Emotional information" refers to data used to evaluate an individual's emotional state, and is acquired using methods such as facial expressions and tone of voice.

[0790] "Health status" refers to the results of an assessment of an individual's physical condition, derived from the analysis of various biological information.

[0791] "Mental state" refers to an individual's emotional and psychological condition and is evaluated by analyzing emotional information.

[0792] "Lifestyle improvement measures" refer to guidelines for actions and activities proposed to improve individual vitality based on acquired biometric information and predicted health status.

[0793] "Mental health maintenance measures" are guidelines for actions and activities proposed to maintain an individual's mental state, and are generated based on emotional information and assessments of their mental state.

[0794] To implement this invention, it is essential to use terminals such as smartphones and wearable devices. These terminals function as hardware for collecting biometric and emotional information. Specifically, they incorporate sensors to collect biometric information such as heart rate, steps taken, and calorie consumption, and use cameras and microphones to acquire emotional information such as facial expressions and tone of voice.

[0795] The terminal temporarily stores the collected data and sends it to the server using encryption technology. On the server, software for analyzing the received data, specifically an analysis system equipped with machine learning algorithms, runs. This analysis system, using platforms such as TensorFlow, can evaluate health status from biometric information and mental state from emotional information.

[0796] Based on these evaluation results, the server generates lifestyle improvement and mental health maintenance strategies tailored to each individual user. The generated suggestions are communicated to the user via an information processing device, and the user is encouraged to adjust their daily actions accordingly. User feedback is entered via a terminal and sent to the server. Based on this feedback, the analysis system further optimizes the generated suggestions.

[0797] As a concrete example, data such as heart rate, steps taken, and emotions are automatically collected as the user goes about their daily life. If the system determines that the user is experiencing stress, it will notify the user's smartphone with a suggestion such as, "Let's try some yoga today to help you relax." An example of a prompt message would be, "Analyze the user's heart rate and emotion data and suggest exercises aimed at reducing stress."

[0798] In this way, the entire system can continuously collect and analyze data, incorporate feedback, and provide users with the most optimal health support.

[0799] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0800] Step 1:

[0801] The device acquires biometric information such as the user's heart rate, steps taken, and calorie consumption using sensors. At the same time, it also collects emotional information from facial expressions and voice tone using a camera and microphone. The input data consists of biometric and emotional information, which is temporarily stored within the device to be used as output.

[0802] Step 2:

[0803] The device periodically transmits stored biometric and emotional information to the server using encryption technology. The input is the data acquired in step 1, which is output to the server as encrypted data based on the transmission protocol.

[0804] Step 3:

[0805] The server decodes the received biometric and emotional information and stores it in a database. During this process, machine learning algorithms are used to analyze the data. The input is encrypted data, and the processing results in an evaluation of health and mental state.

[0806] Step 4:

[0807] The server uses a generative AI model to generate personalized lifestyle improvement plans and mental health maintenance plans based on the assessment results of physical and mental health status. The input is the analysis results from step 3, and the output is the specific suggestions that are generated.

[0808] Step 5:

[0809] The server notifies the information processing device of the generated lifestyle improvement measures and mental health maintenance measures. The input is the formulated proposals, and the output is the notification delivered to the user via the terminal.

[0810] Step 6:

[0811] The user receives a notification via their device and enters feedback into the device regarding the implementation of lifestyle improvement measures. This input is user feedback information, which is then sent to the server as input data for the next analysis.

[0812] Step 7:

[0813] The server receives feedback from users and updates the analysis system. The input is feedback information, and the output is data that will be used to generate the next lifestyle improvement plan, taking into account the results of measuring the effectiveness of the suggestions.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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."

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] The following is further disclosed regarding the embodiments described above.

[0836] (Claim 1)

[0837] Means of obtaining long-term health information,

[0838] A means of analyzing acquired health information and deriving health prediction results,

[0839] A means of generating personalized preventive measures based on health prediction results,

[0840] A means of notifying the terminal of the generated preventive measures,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, comprising means for using an external device for collecting health information.

[0844] (Claim 3)

[0845] The system according to claim 1, further comprising means for obtaining user feedback and updating the generated preventive measures.

[0846] "Example 1"

[0847] (Claim 1)

[0848] A mechanism for acquiring long-term health information,

[0849] A mechanism that analyzes acquired health information and derives health prediction results,

[0850] A mechanism that generates personalized preventive measures based on health prediction results,

[0851] A mechanism to notify the terminal of the generated preventive measures,

[0852] A mechanism that uses encryption technology to protect data,

[0853] A mechanism that uses machine learning algorithms to predict health risks,

[0854] A mechanism that creates preventive measures using generative AI models,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, comprising a mechanism for using an external device for collecting health information.

[0858] (Claim 3)

[0859] The system according to claim 1, further comprising a mechanism for obtaining user feedback and updating the generated preventive measures.

[0860] "Application Example 1"

[0861] (Claim 1)

[0862] Means for acquiring long-term biological information,

[0863] A means for analyzing acquired biometric information and deriving health prediction results,

[0864] A means of generating personalized lifestyle improvement measures based on health prediction results,

[0865] A means of notifying the information processing device of the generated lifestyle improvement measures,

[0866] A means for automatically creating the aforementioned lifestyle improvement measures using a generation AI,

[0867] A means of setting reminders based on biometric data and behavioral goals and notifying users,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, comprising means for using a portable terminal for collecting biometric information.

[0871] (Claim 3)

[0872] The system according to claim 1, further comprising means for obtaining user feedback and dynamically updating the generated lifestyle improvement measures.

[0873] "Example 2 of combining an emotion engine"

[0874] (Claim 1)

[0875] Means for acquiring health information and emotional information,

[0876] A means for analyzing acquired health and emotional information to evaluate health and emotional status,

[0877] A means of generating personalized preventive measures based on health and emotional status,

[0878] A means of notifying the terminal of the generated preventive measures,

[0879] A means of collecting user feedback and updating preventative measures based on that feedback,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, comprising means for collecting health information and emotional information using an external device.

[0883] (Claim 3)

[0884] The system according to claim 1, comprising means for evaluating analysis results based on emotional information and generating preventive measures using an emotional engine.

[0885] "Application example 2 when combining with an emotional engine"

[0886] (Claim 1)

[0887] Means for acquiring long-term biological information,

[0888] A means of analyzing acquired biometric information to predict health status,

[0889] A means of generating personalized lifestyle improvement measures based on predictions of health status,

[0890] A means of notifying the information processing device of the generated lifestyle improvement measures,

[0891] A means for acquiring and analyzing emotional information and evaluating mental state based on the analysis results,

[0892] A means of generating mental health maintenance measures based on an assessment of mental state,

[0893] A system that includes this.

[0894] (Claim 2)

[0895] The system according to claim 1, further comprising means for using an external terminal for collecting biometric and emotional information.

[0896] (Claim 3)

[0897] The system according to claim 1, further comprising means for obtaining user feedback and updating the generated lifestyle improvement measures and mental health maintenance measures. [Explanation of symbols]

[0898] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of obtaining long-term health information, A means of analyzing acquired health information and deriving health prediction results, A means of generating personalized preventive measures based on health prediction results, A means of notifying the terminal of the generated preventive measures, A system that includes this.

2. The system according to claim 1, further comprising means for using an external device for collecting health information.

3. The system according to claim 1, further comprising means for obtaining user feedback and updating the generated preventive measures.

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