system

Abstract

We provide the system. [Solution] A data acquisition means that receives data from a device that collects health data, A data storage means that stores received data and performs cleansing to maintain data integrity, An analytical tool that provides personalized health advice by analyzing stored data, A user interface means that notifies the user of the analysis results and provides health-related information in natural language, An incentive management system that calculates rewards based on healthy behaviors and awards points to a designated account, A data output method for sharing data with external organizations, 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 method for controlling a persona chatbot, which is performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional health management systems have the problem that it is difficult to effectively analyze the collected health data of users and provide personalized health advice. In addition, there is a lack of a mechanism to provide sufficient incentives to promote users' healthy behaviors using the collected data, resulting in the problem that users' participation motivation does not continue. Furthermore, it is required to effectively feedback the collected data to medical institutions and insurance companies for use in promoting more extensive preventive medicine and optimizing insurance products.

Means for Solving the Problems

[0005] This invention proposes a health management system that collects health data in real time and performs advanced data analysis using AI to provide personalized health advice. The system has a data storage means that cleanses the data received from a health data collection device and stores it as accurate and consistent data. Furthermore, it analyzes the data using an analysis means and provides appropriate health advice through a user interface that provides health information in natural language in an easy-to-understand manner for the user. In addition, an incentive management means awards points to the user's electronic account according to their healthy behavior, thereby increasing their motivation to participate. Furthermore, it is equipped with a data output means that allows data to be shared with external organizations, enabling medical institutions and insurance companies to utilize the data and support the promotion of preventive medicine and the optimization of insurance products.

[0006] "Health data" refers to information that indicates the user's physical condition, including physiological measurements such as heart rate, body temperature, and steps taken.

[0007] "Data acquisition means" refers to a function for receiving data from a device that collects health data.

[0008] A "data storage means" is a function that stores received data and performs cleansing to maintain data integrity.

[0009] "Analysis means" refers to a function that analyzes stored data and provides personalized health advice.

[0010] "User interface means" refers to a function that notifies the user of the analysis results and provides health-related information in natural language.

[0011] An "incentive management system" is a function that calculates rewards based on healthy behaviors and awards points to a designated account.

[0012] A "data output method" is a function for sharing data with external organizations. [Brief explanation of the drawing]

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

[0014] 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.

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

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

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

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

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0034] The health management system of this invention is intended to receive data from health data collection devices located in each household or owned by individuals, and to provide personalized health advice using AI. This system works by linking a server and terminals and performing the following processes.

[0035] The server first receives health data sent from the terminal and stores it in a database. The stored data is then cleansed to remove incomplete data and noise. For example, heart rate data sent from a smartwatch is centrally managed, and irregular data points are excluded to build an accurate dataset.

[0036] Next, the server uses an AI algorithm to analyze the user's health data. This analysis generates personalized health advice for each user. For example, if the analysis determines that the user's stress level is high based on recent data, it might recommend "trying deep breathing exercises to relax."

[0037] Furthermore, the server transmits the analysis results to the terminal via the user interface, notifying the user's smartphone or dedicated device with health advice in natural language. Users can then use this information to manage their daily health.

[0038] In incentive management, the server awards rewards (points) based on users' health behaviors. For example, a user who achieves a daily goal of 10,000 steps will receive bonus points in their electronic payment system account to encourage further health maintenance activities.

[0039] Finally, the server shares data with healthcare institutions and insurance companies as needed. This shared data can be used to propose preventive care and design insurance products. Healthcare institutions can use this information to provide personalized health plans for individual patients.

[0040] As described above, this system comprehensively manages users' health and promotes a healthy lifestyle by providing personalized advice and incentives.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The device continuously acquires health data such as heart rate, steps taken, and body temperature from the user's smartwatch or smartphone. The device then prepares to send this data to a server at predetermined time intervals.

[0044] Step 2:

[0045] The server receives health data transmitted from the terminal. To maintain data accuracy, the server performs a cleansing process before storing the data in the database, removing incomplete data points and noise. Normalization is then performed to standardize the data format.

[0046] Step 3:

[0047] The server inputs the cleansed data into an AI analysis algorithm. The server uses this algorithm to analyze patterns in health data and detect anomalies or specific health risks. For example, if it detects high fluctuations in heart rate, it may suspect the effects of stress.

[0048] Step 4:

[0049] Based on the user's health status, the server generates personalized health advice. This advice is then translated into natural language that is easy for the user to understand.

[0050] Step 5:

[0051] The server notifies the user's device of the advice it has generated. The device then displays this information visually to the user, providing health status notifications and daily advice.

[0052] Step 6:

[0053] The server analyzes user behavior data and calculates incentive points when healthy behavior is confirmed. The server then awards the points to the user's account via an electronic payment system.

[0054] Step 7:

[0055] If necessary, the server will share data with healthcare institutions and insurance companies. This data sharing will enable healthcare institutions to provide preventative health management and insurance companies to offer more appropriate insurance products.

[0056] (Example 1)

[0057] 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."

[0058] To effectively collect and utilize health data, it is necessary to maintain data accuracy while providing real-time health assessments and personalized health advice. However, conventional systems fail to adequately remove incomplete data, provide users with timely and useful information, and promote healthy behaviors through incentives.

[0059] 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.

[0060] In this invention, the server includes data acquisition means for receiving data from a device that collects health data, data storage means for storing the received data and removing incomplete data and noise to maintain data integrity, and analysis means for providing personalized health advice by analyzing the stored data and performing analysis using an AI algorithm. This enables users to understand their health status in real time and receive personalized advice.

[0061] "Health data" refers to information about an individual's physical condition and activities, such as heart rate, steps taken, calorie consumption, and sleep patterns.

[0062] "Data acquisition means" refers to a function that receives data from a device that collects health data and makes it available for use within the system.

[0063] "Data storage means" refers to a function that stores received data and keeps it in an organized state, and includes processing to remove incomplete data and noise.

[0064] "Analysis means" refers to the function that analyzes stored data and generates personalized health advice using AI algorithms.

[0065] "User interface means" refers to the function of notifying the user of the analysis results and providing health-related information in natural language.

[0066] An "incentive management system" refers to a function that calculates rewards based on users' health behaviors and awards points to the electronic payment platform.

[0067] "Data output means" refers to functions that allow data to be shared with medical institutions and insurance organizations, and to enable its use externally.

[0068] This health management system works by linking a server and a device to effectively collect and analyze health data from users and provide personalized health advice. The server first receives health data transmitted from the device. For example, devices such as smartwatches and fitness trackers provide data such as heart rate and steps taken.

[0069] The received data is stored in the server's data storage system. This storage system performs a cleansing process to maintain data integrity, removing incomplete data and noise. The software used combines commonly available data cleansing tools and AI algorithms.

[0070] Next, the server uses AI algorithms to analyze the cleansed data in detail. This analysis includes data calculations to assess the user's health status and generate optimized health advice. For example, if recent data indicates that the user's activity level has decreased, it might advise, "You should try taking a few more steps this week."

[0071] Through a user interface, the server sends analysis results to the terminal, and the user receives this advice in natural language via a smartphone or dedicated device. In this way, the user can use it to improve their daily life.

[0072] The system also includes an incentive management function that calculates rewards and awards points based on users' healthy behaviors. For example, users who achieve a goal of 10,000 steps per day are awarded points that can be used on the electronic payment platform, encouraging them to engage in further healthy activities.

[0073] Furthermore, this system includes a data output mechanism for sharing important health data with medical institutions and insurance organizations. This shared information is used to develop personalized preventive healthcare recommendations and design insurance products.

[0074] A specific example of a prompt message would be, "Generate advice for a healthy lifestyle based on the user's recent activity data." In this way, the system enables personalized health management and effectively supports the user in leading a healthy life.

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

[0076] Step 1:

[0077] The server receives health data from the device. This data is transmitted from health data collection devices such as smartwatches and fitness trackers. Specifically, it includes heart rate, steps taken, calories burned, etc. The input is health data from the device, and the output is raw, uncleaned data stored in data storage. The server buffers this received data in a temporary area to prepare for the next processing step.

[0078] Step 2:

[0079] The server uses data storage to cleanse the received raw data. Specifically, it filters out abnormal values ​​and noise, performing processing to maintain data integrity. For example, data cleaning is performed, such as removing heart rate data that represents extreme outliers. The input to this process is the raw data received from step 1, and the output is consistent, cleansed data. The server stores this in a database for long-term storage.

[0080] Step 3:

[0081] The server analyzes the cleansed data using an AI algorithm. This analysis includes a process that individually assesses the user's health status and generates optimized health advice. The input is the cleansed data, and the output is personalized health advice as a result of the analysis. For example, if a decrease in physical activity is observed, advice will be generated notifying the user to increase walking.

[0082] Step 4:

[0083] The server sends health advice obtained through analysis to the terminal using a user interface. Users receive the advice on their smartphones or dedicated devices and use it for health management. The input is health advice generated by AI, and the output is a notification message displayed on the user's terminal. Based on this, users can take specific actions.

[0084] Step 5:

[0085] The server uses incentive management tools to evaluate the user's health behavior and calculate points. Based on the calculation results, points are then awarded to the electronic payment platform. The input is the user's health behavior data, and the output is the awarded points and a notification. As a specific example, bonus points are offered to users who exceed their daily step count goal.

[0086] Step 6:

[0087] The server utilizes data output mechanisms to share data with healthcare institutions and insurance organizations as needed. This shared data is then used for preventative medicine and insurance product design. Input is user health data, and output is information transmitted to external organizations in an appropriate format. This allows healthcare institutions to provide customized health plans.

[0088] (Application Example 1)

[0089] 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."

[0090] A challenge lies in the insufficient integration of personalized health management advice with behavior-based reward systems. Furthermore, there is a need for a means to analyze users' health data in real time and promptly provide appropriate preventative measures. Additionally, a system is needed that effectively utilizes health behavior-based incentives to enhance users' health maintenance.

[0091] 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.

[0092] In this invention, the server includes an acquisition means for receiving information from a data collection device, a storage means for storing the received information and performing cleaning to maintain data integrity, and a means for linking rewards to an electronic transaction service based on user behavior data. This makes it possible to provide users with real-time and effective health advice, as well as to immediately provide incentives based on their health behaviors.

[0093] "Acquisition means" refers to a mechanism for receiving necessary information from a data collection device.

[0094] "Memory means" refers to a function that safely and systematically stores received information and performs cleaning to maintain data integrity.

[0095] "Analysis means" refers to the process of analyzing stored information and generating health advice tailored to each individual user.

[0096] A "user interface means" is a mechanism for notifying users of analysis results and providing health-related information in natural language.

[0097] A "motivation management tool" is a management function that calculates rewards based on healthy behaviors and awards points to a designated electronic trading account.

[0098] "Information output means" refers to system functions for sharing information with external organizations.

[0099] "Electronic trading services" are services that support financial transactions conducted via the internet.

[0100] To implement this invention, the server and terminal must first be equipped with specific functions. The server receives health information from a data collection device as a means of acquiring information and prepares a database to store the received information. The program is built using the Python language and uses the Flask framework to handle communication between the server and terminal. PostgreSQL is used for the database and a cleaning process is performed to maintain the integrity of the information. The cleaning process is performed to remove incomplete data containing noise and to build an accurate dataset.

[0101] Next, the server performs analysis using an AI model based on TENSORFLOW® and generates personalized health advice for each user. The generated advice is sent to the device through a user interface built on the frontend using React Native. This allows the device to notify the user of the health advice based on the analysis results in natural language.

[0102] Furthermore, as a motivational management tool, the user's device records health behaviors and calculates points based on them. The calculated points are linked to an electronic trading service as incentives. For example, points awarded as incentives can be used for discounts at partner stores through an external trading service.

[0103] A concrete example is a case where a user uses a smartwatch to measure their daily step count, and the RI application uses a generated AI model based on that data to provide advice to the user encouraging them to walk more. An example of a prompt to the generated AI model would be: "The user's step count data is as follows: Daily step count data. Based on this data, please provide appropriate health advice and incentives."

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

[0105] Step 1:

[0106] The server receives health data transmitted from the device. For example, heart rate and step count data from a smartwatch are examples. This data is transferred to the server and stored in a database. During this process, the server verifies the validity of the information and issues appropriate alerts if there are any abnormal values.

[0107] Step 2:

[0108] The server performs a cleansing process on the stored health data. Since the input data occasionally contains noise, this is removed to create a consistent dataset. Specifically, extremely high or low heart rate values ​​are treated as invalid. This allows the server to proceed to the next analysis step with reliable data.

[0109] Step 3:

[0110] The server analyzes the cleansed data using TensorFlow. In this process, an AI model identifies patterns in the health data and assesses the user's health status. For example, if the average heart rate over the past week is high, it may indicate a high stress level. This analysis results are then generated as personalized health advice for each user.

[0111] Step 4:

[0112] The server sends the analysis results to the device and displays them in the user interface. The device uses React Native to notify the user of advice in natural language. This notification includes specific relaxation methods and points to be mindful of in daily life. The user can then use the advice to improve their daily activities.

[0113] Step 5:

[0114] The server calculates incentives based on the user's health behavior and awards points through an electronic transaction service. This process inputs data such as the user's daily step count goal achievement status, and points are calculated and awarded as a reward upon achievement. Users can then use these points as discounts at participating stores.

[0115] Step 6:

[0116] When user data should be shared, the server uses the necessary information output means to share information with external organizations. This allows healthcare institutions and insurance organizations to access user health information as needed and provide personalized health plans.

[0117] 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.

[0118] This invention provides a system incorporating an emotion engine to enhance user health management. First, the terminal simultaneously collects the user's health data and emotion data. Health data includes physiological information such as heart rate, body temperature, and steps taken, while emotion data refers to the emotional state collected from the user's facial expressions and tone of voice using the smartphone's camera and voice analysis.

[0119] The server receives this data transmitted from the terminal and stores it in a database. A data storage system performs a cleansing process to maintain data accuracy and consistency. The cleansed data is then analyzed using AI-based analysis tools. Here, the relationship between health data and emotional data is evaluated to make a comprehensive judgment about the user's health status and emotional tendencies.

[0120] For example, if a user's stress level is high and the emotion engine simultaneously recognizes emotions such as "anger" or "anxiety," the server will generate advice recommending stress-management breathing techniques or relaxation music to the user. Furthermore, by continuously evaluating emotional stability, the system constantly monitors whether the user is leading a healthy lifestyle and provides appropriate feedback.

[0121] The analysis results and advice are transmitted to the terminal via a user interface. The terminal provides this information to the user visually and audibly, presenting it in an easily understandable format. By receiving this information, the user can engage in daily improvement measures based on their own health status and emotions.

[0122] Furthermore, this system also includes a reward management mechanism to provide incentives tailored to the user's emotions. Specifically, it awards additional points if positive emotions are maintained for a certain period, encouraging users to maintain and improve their well-being.

[0123] Thus, the present invention realizes a more comprehensive and practical health management solution by integrating and analyzing emotional and health data and providing personalized advice.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The device uses sensors from the smartwatch or smartphone to collect health data such as the user's heart rate, steps taken, and body temperature. Furthermore, the device uses its camera and microphone to analyze the user's facial expressions and voice tone to acquire emotional data. This allows it to identify the user's current emotional state (e.g., joy, anger, sadness, surprise, etc.).

[0127] Step 2:

[0128] The device periodically sends collected health and emotional data to a server. This data transmission is conducted using encrypted communication to ensure security.

[0129] Step 3:

[0130] The server receives data sent from the terminal and performs data cleansing before storing it in data storage. This removes incomplete data and noise, maintaining data integrity.

[0131] Step 4:

[0132] The server passes the cleansed data to an analysis tool, which then analyzes the relationship between health data and emotional data in detail. For example, if the heart rate is high and the emotional data indicates "anxiety," it is analyzed as potentially indicating a stress response.

[0133] Step 5:

[0134] The server generates personalized health advice based on the analysis results. This advice addresses emotional states and provides specific stress relief and relaxation techniques. For example, if the emotion is "anger," it provides guidance on deep breathing.

[0135] Step 6:

[0136] The server converts the generated advice into natural language and sends it to the terminal through the user interface. The terminal notifies the user of the advice and makes the information easy to understand by displaying it visually and audibly.

[0137] Step 7:

[0138] As users continue their daily activities, the server uses a reward management system to calculate incentive points for users who maintain a positive emotional state. The server then awards these points to users' electronic accounts to encourage positive health behaviors.

[0139] (Example 2)

[0140] 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".

[0141] In modern society, the increase in lifestyle-related diseases and stress-related illnesses is a serious problem, but the means to comprehensively manage individual health conditions and emotional fluctuations and provide appropriate feedback are limited. In particular, there is a need to promote healthy behaviors through emotion-based incentives.

[0142] 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.

[0143] In this invention, the server includes data acquisition means for receiving information from terminals that collect physiological information and emotional states; data storage means for storing the received information and performing cleansing to maintain the accuracy and consistency of the information; and analysis means for analyzing health status and emotional tendencies using a generated AI model based on the stored information and providing personalized health advice. This makes it possible to evaluate the relationship between individual health status and emotions, provide appropriate health advice, and promote healthy behaviors through emotion-based incentives.

[0144] "Physiological information" refers to data that indicates the body's condition, such as heart rate, body temperature, and steps taken.

[0145] "Emotional state" refers to the psychological state analyzed from the user's facial expressions and tone of voice.

[0146] A "terminal" is a user interface device used to collect physiological information and emotional states from the user.

[0147] "Data acquisition means" refers to a series of functions for receiving information from a terminal.

[0148] A "data storage method" is a processing method for storing received information and maintaining its accuracy and consistency.

[0149] A "generative AI model" is a system of algorithms used to analyze health status and emotional tendencies using machine learning.

[0150] "Analysis tools" refer to analytical functions that provide personalized health advice based on stored information.

[0151] An "incentive management system" is a method for calculating rewards based on emotional states and providing points to a virtual account.

[0152] This invention is a system that comprehensively manages the user's health and emotional state and provides appropriate feedback and incentives. Specific embodiments are described below.

[0153] Terminal role:

[0154] The terminal is the primary device for collecting the user's physiological information and emotional state. Specifically, this includes smartphones and wearable devices. These terminals are equipped with heart rate monitors, accelerometers, and temperature sensors to acquire the user's daily physiological data. They also use cameras and microphones to analyze the user's facial expressions and voice, and to understand their emotional state in real time.

[0155] Server role:

[0156] The server receives physiological information and emotional states transmitted from the terminal and stores them neatly using data storage means. Crucially, the data undergoes a cleansing process to ensure accuracy and consistency. Using a generative AI model, the server analyzes health status and emotional tendencies and generates personalized health advice.

[0157] User interface:

[0158] The server notifies the user of the analysis results through a user interface. Information is provided in a format easily understandable to the user through visual displays and audio messages. Recommended health advice includes breathing exercises for stress management and a list of relaxation music.

[0159] Granting incentives:

[0160] Furthermore, the server manages emotion-based incentives. If positive emotions are maintained for a certain period, points are awarded to the user's virtual account. These points can later be redeemed for cash and serve as an incentive for users to improve their well-being.

[0161] Examples of specific cases and prompt statements:

[0162] For example, if a user experiences stress at work, the device detects this change, and the server suggests appropriate relaxation methods. This suggestion is sent to the device as an app notification. An example of a prompt message would be, "The user's emotional data has shown a 'Happy' state for a week straight. Do you want to send a prompt to add reward points?"

[0163] This invention allows users to more effectively manage their health and emotions, and enables them to engage in sustainable health improvement activities through individually customized feedback and rewards.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The device collects the user's physiological information and emotional state. Inputs include data from a heart rate monitor, accelerometer, and temperature sensor, as well as facial expressions and voice tone captured through the camera and microphone. Specifically, it converts signals from the sensors into digital data to measure the user's heart rate, steps, and body temperature, and determines their emotional state through image processing and voice analysis. The output is a set of health data and emotional data, which packages this information.

[0167] Step 2:

[0168] The device sends the data obtained in Step 1 to the server. To ensure data security, an encryption protocol is used to securely transfer the data. Collected health data and emotion data are used as input, and they are sent to the server as output, allowing for further processing on the server side.

[0169] Step 3:

[0170] The server receives data and stores it using data storage means. It receives health and sentiment data transmitted from terminals as input. Through a data cleansing process, it detects outliers and imputes missing values ​​to maintain accuracy and consistency. The output is a cleansed and organized dataset.

[0171] Step 4:

[0172] The server uses a generative AI model to analyze the cleansed data. It takes stored health and emotional data as input and runs machine learning algorithms to evaluate their correlations. The output is an analysis based on the user's health status and emotional tendencies. Specifically, it detects abnormal health patterns and emotional changes.

[0173] Step 5:

[0174] The server generates health advice for the user based on the analysis results. It utilizes the output of the generated AI model as input information to create personalized feedback. The output consists of specific health advice and recommended actions for the user. Examples of its operation include suggesting breathing exercises for stress reduction and recommending relaxation music.

[0175] Step 6:

[0176] The server sends the generated advice to the terminal. It receives the advice created in step 5 as input and notifies the user through the user interface. The output is visual or auditory feedback displayed on the terminal. Specific actions include displaying notification messages or pop-ups.

[0177] Step 7:

[0178] The server evaluates emotion-based incentives and awards points accordingly. It uses analyzed emotion data as input, measuring the duration of a positive emotional state. The output is reward points added to the user's virtual account. For example, if positive emotions are observed for a week consecutively, additional points are automatically calculated.

[0179] (Application Example 2)

[0180] 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".

[0181] In recent years, the importance of personal health management has increased, and the demand for personalized health advice is on the rise. However, conventional systems struggle to integrate and analyze health and emotional information to provide precise suggestions tailored to individual circumstances. Furthermore, in physical stores, specific product and service suggestions that take into account the user's real-time emotions and health status are not provided, making it difficult to optimize the customer experience.

[0182] 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.

[0183] In this invention, the server includes information acquisition means for receiving information from a device that collects health information, information storage means for storing the received information and normalizing it to maintain consistency, and user interface means for notifying the user of the analysis results and providing health-related information in natural language. This makes it possible to provide personalized health suggestions and improve the user experience in physical stores.

[0184] "Information acquisition means" refers to devices that have the function of receiving information from equipment that collects health information.

[0185] An "information storage device" is a device that stores received information and has the function of normalizing it to maintain its integrity.

[0186] An "analysis tool" is a system that analyzes stored information to provide personalized health recommendations.

[0187] A "user interface means" is a device that notifies the user of the analysis results and provides health-related information in natural language.

[0188] The "reward management system" is a function that calculates rewards based on health and emotional status and grants rewards to designated accounts.

[0189] An "information output means" is a mechanism for outputting data for sharing information with external organizations.

[0190] A "store assistant device" is a device that has the function of suggesting products and services in a physical store based on the customer's emotions and health information.

[0191] In the system implementing this invention, the server plays a central role. Specifically, it receives health information and emotional information transmitted from terminals. This data is acquired using the camera and voice functions of smartphones and smart glasses. Health information includes heart rate, body temperature, and steps taken, while emotional information is acquired through facial expression analysis and voice analysis.

[0192] The server performs a normalization process to store the received data with integrity. This normalization can be performed using AI libraries such as TensorFlow or PyTorch. This ensures data consistency and enables highly accurate analysis. The analysis results compare the user's emotional tendencies with health indicators to generate personalized health advice. Based on this, a user interface providing natural language suggestions is displayed on the user's device.

[0193] Furthermore, to enhance the in-store user experience, a store assistant function has been implemented. This function analyzes the user's emotions and health status in real time and recommends the most suitable products and services. For example, if the system detects that the user is experiencing high stress levels, it will recommend products with relaxation effects.

[0194] As a concrete example, by passing a prompt to the AI ​​model—"Generate advice to suggest the optimal wellness product using the customer's current emotional state and health data"—appropriate suggestions tailored to individual circumstances can be quickly provided.

[0195] In this way, by coordinating terminals, servers, and physical stores, a system can be realized that aims to improve users' health management and quality of life.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The device collects the user's health and emotional information. Inputs include heart rate, body temperature, steps, facial expressions, and voice tone data acquired by smartphones or smart glasses. Outputs include sending this information to a server as a series of data packages. Specifically, the device utilizes its sensors and cameras to collect data in real time.

[0199] Step 2:

[0200] The server securely stores data received from the terminal. It uses the raw data received as input and employs a data normalization process. The data is stored in cloud storage, and consistency is ensured by removing outliers and duplicates. Cleaned data is generated as output. Specifically, the database system organizes the data structure.

[0201] Step 3:

[0202] The server performs analysis using a generated AI model based on clean data. The input consists of cleansed health and emotional information. The output is personalized advice regarding the user's health status. As a concrete example of data processing, the AI ​​model is used to analyze stress levels and emotional tendencies.

[0203] Step 4:

[0204] The server sends the analysis results to the terminal. The input includes the generated health advice, and the output is the advice displayed on the user interface of the user terminal. Specifically, the information is presented visually and audibly using natural language, providing information in a way that is easy for the user to understand.

[0205] Step 5:

[0206] Users receive real-time product and service suggestions within the store. The input is based on analysis results displayed on a terminal. The output is appropriate product and service suggestions from a store assistant. Specifically, store staff present the user with the best options based on the advice displayed on the terminal.

[0207] Step 6:

[0208] The server calculates rewards based on health and emotional states. Inputs include analysis results and pre-set reward criteria. Output is the addition of reward points to a specific account. For example, the application using the system accesses the points system, calculates rewards, and awards them.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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".

[0225] The health management system of this invention is intended to receive data from health data collection devices located in each household or owned by individuals, and to provide personalized health advice using AI. This system works by linking a server and terminals and performing the following processes.

[0226] The server first receives health data sent from the terminal and stores it in a database. The stored data is then cleansed to remove incomplete data and noise. For example, heart rate data sent from a smartwatch is centrally managed, and irregular data points are excluded to build an accurate dataset.

[0227] Next, the server uses an AI algorithm to analyze the user's health data. This analysis generates personalized health advice for each user. For example, if the analysis determines that the user's stress level is high based on recent data, it might recommend "trying deep breathing exercises to relax."

[0228] Furthermore, the server transmits the analysis results to the terminal via the user interface, notifying the user's smartphone or dedicated device with health advice in natural language. Users can then use this information to manage their daily health.

[0229] In incentive management, the server awards rewards (points) based on users' health behaviors. For example, a user who achieves a daily goal of 10,000 steps will receive bonus points in their electronic payment system account to encourage further health maintenance activities.

[0230] Finally, the server shares data with healthcare institutions and insurance companies as needed. This shared data can be used to propose preventive care and design insurance products. Healthcare institutions can use this information to provide personalized health plans for individual patients.

[0231] As described above, this system comprehensively manages users' health and promotes a healthy lifestyle by providing personalized advice and incentives.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The device continuously acquires health data such as heart rate, steps taken, and body temperature from the user's smartwatch or smartphone. The device then prepares to send this data to a server at predetermined time intervals.

[0235] Step 2:

[0236] The server receives health data transmitted from the terminal. To maintain data accuracy, the server performs a cleansing process before storing the data in the database, removing incomplete data points and noise. Normalization is then performed to standardize the data format.

[0237] Step 3:

[0238] The server inputs the cleansed data into an AI analysis algorithm. The server uses this algorithm to analyze patterns in health data and detect anomalies or specific health risks. For example, if it detects high fluctuations in heart rate, it may suspect the effects of stress.

[0239] Step 4:

[0240] Based on the user's health status, the server generates personalized health advice. This advice is then translated into natural language that is easy for the user to understand.

[0241] Step 5:

[0242] The server notifies the user's device of the advice it has generated. The device then displays this information visually to the user, providing health status notifications and daily advice.

[0243] Step 6:

[0244] The server analyzes user behavior data and calculates incentive points when healthy behavior is confirmed. The server then awards the points to the user's account via an electronic payment system.

[0245] Step 7:

[0246] If necessary, the server will share data with healthcare institutions and insurance companies. This data sharing will enable healthcare institutions to provide preventative health management and insurance companies to offer more appropriate insurance products.

[0247] (Example 1)

[0248] 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."

[0249] To effectively collect and utilize health data, it is necessary to maintain data accuracy while providing real-time health assessments and personalized health advice. However, conventional systems fail to adequately remove incomplete data, provide users with timely and useful information, and promote healthy behaviors through incentives.

[0250] 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.

[0251] In this invention, the server includes data acquisition means for receiving data from a device that collects health data, data storage means for storing the received data and removing incomplete data and noise to maintain data integrity, and analysis means for providing personalized health advice by analyzing the stored data and performing analysis using an AI algorithm. This enables users to understand their health status in real time and receive personalized advice.

[0252] "Health data" refers to information about an individual's physical condition and activities, such as heart rate, steps taken, calorie consumption, and sleep patterns.

[0253] "Data acquisition means" refers to a function that receives data from a device that collects health data and makes it available for use within the system.

[0254] "Data storage means" refers to a function that stores received data and keeps it in an organized state, and includes processing to remove incomplete data and noise.

[0255] "Analysis means" refers to the function that analyzes stored data and generates personalized health advice using AI algorithms.

[0256] "User interface means" refers to the function of notifying the user of the analysis results and providing health-related information in natural language.

[0257] An "incentive management system" refers to a function that calculates rewards based on users' health behaviors and awards points to the electronic payment platform.

[0258] "Data output means" refers to functions that allow data to be shared with medical institutions and insurance organizations, and to enable its use externally.

[0259] This health management system works by linking a server and a device to effectively collect and analyze health data from users and provide personalized health advice. The server first receives health data transmitted from the device. For example, devices such as smartwatches and fitness trackers provide data such as heart rate and steps taken.

[0260] The received data is stored in the server's data storage system. This storage system performs a cleansing process to maintain data integrity, removing incomplete data and noise. The software used combines commonly available data cleansing tools and AI algorithms.

[0261] Next, the server uses AI algorithms to analyze the cleansed data in detail. This analysis includes data calculations to assess the user's health status and generate optimized health advice. For example, if recent data indicates that the user's activity level has decreased, it might advise, "You should try taking a few more steps this week."

[0262] Through a user interface, the server sends analysis results to the terminal, and the user receives this advice in natural language via a smartphone or dedicated device. In this way, the user can use it to improve their daily life.

[0263] The system also includes an incentive management function that calculates rewards and awards points based on users' healthy behaviors. For example, users who achieve a goal of 10,000 steps per day are awarded points that can be used on the electronic payment platform, encouraging them to engage in further healthy activities.

[0264] Furthermore, this system includes a data output mechanism for sharing important health data with medical institutions and insurance organizations. This shared information is used to develop personalized preventive healthcare recommendations and design insurance products.

[0265] A specific example of a prompt message would be, "Generate advice for a healthy lifestyle based on the user's recent activity data." In this way, the system enables personalized health management and effectively supports the user in leading a healthy life.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The server receives health data from the device. This data is transmitted from health data collection devices such as smartwatches and fitness trackers. Specifically, it includes heart rate, steps taken, calories burned, etc. The input is health data from the device, and the output is raw, uncleaned data stored in data storage. The server buffers this received data in a temporary area to prepare for the next processing step.

[0269] Step 2:

[0270] The server uses data storage to cleanse the received raw data. Specifically, it filters out abnormal values ​​and noise, performing processing to maintain data integrity. For example, data cleaning is performed, such as removing heart rate data that represents extreme outliers. The input to this process is the raw data received from step 1, and the output is consistent, cleansed data. The server stores this in a database for long-term storage.

[0271] Step 3:

[0272] The server analyzes the cleansed data using an AI algorithm. This analysis includes a process that individually assesses the user's health status and generates optimized health advice. The input is the cleansed data, and the output is personalized health advice as a result of the analysis. For example, if a decrease in physical activity is observed, advice will be generated notifying the user to increase walking.

[0273] Step 4:

[0274] The server sends health advice obtained through analysis to the terminal using a user interface. Users receive the advice on their smartphones or dedicated devices and use it for health management. The input is health advice generated by AI, and the output is a notification message displayed on the user's terminal. Based on this, users can take specific actions.

[0275] Step 5:

[0276] The server uses incentive management tools to evaluate the user's health behavior and calculate points. Based on the calculation results, points are then awarded to the electronic payment platform. The input is the user's health behavior data, and the output is the awarded points and a notification. As a specific example, bonus points are offered to users who exceed their daily step count goal.

[0277] Step 6:

[0278] The server uses data output means to share data with medical institutions and insurance organizations as needed. As a result, the shared data is used in preventive medicine and the design of insurance products. The input is the user's health data, and the output is information sent to external institutions in an appropriate format. This enables medical institutions to provide customized health plans.

[0279] (Application Example 1)

[0280] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0281] The issues are the insufficient provision of personalized advice in health management and the integration of a behavior-based reward system. Also, there is a need for means to analyze the user's health data in real time and promptly present appropriate preventive measures. Furthermore, it is necessary to provide a system that can effectively utilize incentives based on healthy behaviors to enhance the user's health maintenance.

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

[0283] In this invention, the server includes acquisition means for receiving information from a device that collects data, storage means for storing the received information and performing cleaning to maintain data consistency, and means for linking rewards to an electronic transaction service based on the user's behavior data. This enables the server to provide effective health advice to the user in real time and at the same time immediately give incentives based on healthy behaviors.

[0284] The "acquisition means" is a mechanism for receiving necessary information from a device that collects data.

[0285] The "memory means" refers to a function that stores the received information safely and orderly and performs cleaning to maintain data integrity.

[0286] The "analysis means" is a process of analyzing the stored information and generating health advice suitable for each user.

[0287] The "user interface means" is a mechanism for notifying the user of the analysis results and providing health-related information in natural language.

[0288] The "motivation management means" is a management function that calculates rewards based on health behaviors and awards points to a specified electronic trading account.

[0289] The "information output means" refers to a system function for sharing information with external organizations.

[0290] The "electronic trading service" is a service that supports financial transactions conducted via the Internet.

[0291] To implement the present invention, first, the server and the terminal need to have specific functions. As a means of acquiring information, the server receives health information from a device that collects data and prepares a database to store the received information. A program is constructed using the Python language, and the Flask framework is utilized to handle communication between the server and the terminal. PostgreSQL is used for the database, and cleaning processing is performed to maintain the integrity of the information. The cleaning processing is carried out to remove incomplete data containing noise and construct an accurate data set.

[0292] Next, the server performs analysis using an AI model based on TensorFlow to generate personalized health advice for each user. The generated advice is sent to the device through a user interface built on the frontend using React Native. This allows the device to notify the user of the health advice based on the analysis results in natural language.

[0293] Furthermore, as a motivational management tool, the user's device records health behaviors and calculates points based on them. The calculated points are linked to an electronic trading service as incentives. For example, points awarded as incentives can be used for discounts at partner stores through an external trading service.

[0294] A concrete example is a case where a user uses a smartwatch to measure their daily step count, and the RI application uses a generated AI model based on that data to provide advice to the user encouraging them to walk more. An example of a prompt to the generated AI model would be: "The user's step count data is as follows: Daily step count data. Based on this data, please provide appropriate health advice and incentives."

[0295] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0296] Step 1:

[0297] The server receives health data transmitted from the device. For example, heart rate and step count data from a smartwatch are examples. This data is transferred to the server and stored in a database. During this process, the server verifies the validity of the information and issues appropriate alerts if there are any abnormal values.

[0298] Step 2:

[0299] The server performs a cleansing process on the stored health data. Since the input data occasionally contains noise, this is removed to create a consistent dataset. Specifically, extremely high or low heart rate values ​​are treated as invalid. This allows the server to proceed to the next analysis step with reliable data.

[0300] Step 3:

[0301] The server analyzes the cleansed data using TensorFlow. In this process, an AI model identifies patterns in the health data and assesses the user's health status. For example, if the average heart rate over the past week is high, it may indicate a high stress level. This analysis results are then generated as personalized health advice for each user.

[0302] Step 4:

[0303] The server sends the analysis results to the device and displays them in the user interface. The device uses React Native to notify the user of advice in natural language. This notification includes specific relaxation methods and points to be mindful of in daily life. The user can then use the advice to improve their daily activities.

[0304] Step 5:

[0305] The server calculates incentives based on the user's health behavior and awards points through an electronic transaction service. This process inputs data such as the user's daily step count goal achievement status, and points are calculated and awarded as a reward upon achievement. Users can then use these points as discounts at participating stores.

[0306] Step 6:

[0307] When the user's data needs to be shared, the server uses the information output means necessary to share information with external organizations. As a result, medical institutions and insurance organizations can refer to the user's health information and provide individual health plans as needed.

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

[0309] The present invention provides a system incorporating an emotion engine to enhance user health management. First, the terminal simultaneously collects the user's health data and emotion data. Health data includes physiological information such as heart rate, body temperature, and number of steps, and emotion data refers to the emotional state collected from the user's facial expressions and voice tone using the smartphone's camera and voice analysis.

[0310] The server receives these data transmitted from the terminal and stores them in a database. The data storage means performs a cleansing process to maintain the accuracy and consistency of the data. The data after cleansing is analyzed by analysis means using AI. Here, the relevance between the health data and the emotion data is evaluated, and the user's health status and emotional tendency are comprehensively judged.

[0311] For example, when the user has a high stress level and at the same time the emotion engine recognizes emotions such as "anger" or "anxiety", the server generates advice to recommend stress management breathing methods or relaxation music to the user. Also, by continuously evaluating the emotional stability, it is constantly monitored whether the user is leading a healthy lifestyle and appropriate feedback is provided.

[0312] The analysis results and advice are transmitted to the terminal via a user interface. The terminal provides this information to the user visually and audibly, presenting it in an easily understandable format. By receiving this information, the user can engage in daily improvement measures based on their own health status and emotions.

[0313] Furthermore, this system also includes a reward management mechanism to provide incentives tailored to the user's emotions. Specifically, it awards additional points if positive emotions are maintained for a certain period, encouraging users to maintain and improve their well-being.

[0314] Thus, the present invention realizes a more comprehensive and practical health management solution by integrating and analyzing emotional and health data and providing personalized advice.

[0315] The following describes the processing flow.

[0316] Step 1:

[0317] The device uses sensors from the smartwatch or smartphone to collect health data such as the user's heart rate, steps taken, and body temperature. Furthermore, the device uses its camera and microphone to analyze the user's facial expressions and voice tone to acquire emotional data. This allows it to identify the user's current emotional state (e.g., joy, anger, sadness, surprise, etc.).

[0318] Step 2:

[0319] The device periodically sends collected health and emotional data to a server. This data transmission is conducted using encrypted communication to ensure security.

[0320] Step 3:

[0321] The server receives data sent from the terminal and performs data cleansing before storing it in data storage. This removes incomplete data and noise, maintaining data integrity.

[0322] Step 4:

[0323] The server passes the cleansed data to an analysis tool, which then analyzes the relationship between health data and emotional data in detail. For example, if the heart rate is high and the emotional data indicates "anxiety," it is analyzed as potentially indicating a stress response.

[0324] Step 5:

[0325] The server generates personalized health advice based on the analysis results. This advice addresses emotional states and provides specific stress relief and relaxation techniques. For example, if the emotion is "anger," it provides guidance on deep breathing.

[0326] Step 6:

[0327] The server converts the generated advice into natural language and sends it to the terminal through the user interface. The terminal notifies the user of the advice and makes the information easy to understand by displaying it visually and audibly.

[0328] Step 7:

[0329] As users continue their daily activities, the server uses a reward management system to calculate incentive points for users who maintain a positive emotional state. The server then awards these points to users' electronic accounts to encourage positive health behaviors.

[0330] (Example 2)

[0331] 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".

[0332] In modern society, the increase in lifestyle-related diseases and stress-related illnesses is a serious problem, but the means to comprehensively manage individual health conditions and emotional fluctuations and provide appropriate feedback are limited. In particular, there is a need to promote healthy behaviors through emotion-based incentives.

[0333] 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.

[0334] In this invention, the server includes data acquisition means for receiving information from terminals that collect physiological information and emotional states; data storage means for storing the received information and performing cleansing to maintain the accuracy and consistency of the information; and analysis means for analyzing health status and emotional tendencies using a generated AI model based on the stored information and providing personalized health advice. This makes it possible to evaluate the relationship between individual health status and emotions, provide appropriate health advice, and promote healthy behaviors through emotion-based incentives.

[0335] "Physiological information" refers to data that indicates the body's condition, such as heart rate, body temperature, and steps taken.

[0336] "Emotional state" refers to the psychological state analyzed from the user's facial expressions and tone of voice.

[0337] A "terminal" is a user interface device used to collect physiological information and emotional states from the user.

[0338] "Data acquisition means" refers to a series of functions for receiving information from a terminal.

[0339] A "data storage method" is a processing method for storing received information and maintaining its accuracy and consistency.

[0340] A "generative AI model" is a system of algorithms used to analyze health status and emotional tendencies using machine learning.

[0341] "Analysis tools" refer to analytical functions that provide personalized health advice based on stored information.

[0342] An "incentive management system" is a method for calculating rewards based on emotional states and providing points to a virtual account.

[0343] This invention is a system that comprehensively manages the user's health and emotional state and provides appropriate feedback and incentives. Specific embodiments are described below.

[0344] Terminal role:

[0345] The terminal is the primary device for collecting the user's physiological information and emotional state. Specifically, this includes smartphones and wearable devices. These terminals are equipped with heart rate monitors, accelerometers, and temperature sensors to acquire the user's daily physiological data. They also use cameras and microphones to analyze the user's facial expressions and voice, and to understand their emotional state in real time.

[0346] Server role:

[0347] The server receives physiological information and emotional states transmitted from the terminal and stores them neatly using data storage means. Crucially, the data undergoes a cleansing process to ensure accuracy and consistency. Using a generative AI model, the server analyzes health status and emotional tendencies and generates personalized health advice.

[0348] User interface:

[0349] The server notifies the user of the analysis results through a user interface. Information is provided in a format easily understandable to the user through visual displays and audio messages. Recommended health advice includes breathing exercises for stress management and a list of relaxation music.

[0350] Granting incentives:

[0351] Furthermore, the server manages emotion-based incentives. If positive emotions are maintained for a certain period, points are awarded to the user's virtual account. These points can later be redeemed for cash and serve as an incentive for users to improve their well-being.

[0352] Examples of specific cases and prompt statements:

[0353] For example, if a user experiences stress at work, the device detects this change, and the server suggests appropriate relaxation methods. This suggestion is sent to the device as an app notification. An example of a prompt message would be, "The user's emotional data has shown a 'Happy' state for a week straight. Do you want to send a prompt to add reward points?"

[0354] This invention allows users to more effectively manage their health and emotions, and enables them to engage in sustainable health improvement activities through individually customized feedback and rewards.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] The device collects the user's physiological information and emotional state. Inputs include data from a heart rate monitor, accelerometer, and temperature sensor, as well as facial expressions and voice tone captured through the camera and microphone. Specifically, it converts signals from the sensors into digital data to measure the user's heart rate, steps, and body temperature, and determines their emotional state through image processing and voice analysis. The output is a set of health data and emotional data, which packages this information.

[0358] Step 2:

[0359] The device sends the data obtained in Step 1 to the server. To ensure data security, an encryption protocol is used to securely transfer the data. Collected health data and emotion data are used as input, and they are sent to the server as output, allowing for further processing on the server side.

[0360] Step 3:

[0361] The server receives data and stores it using data storage means. It receives health and sentiment data transmitted from terminals as input. Through a data cleansing process, it detects outliers and imputes missing values ​​to maintain accuracy and consistency. The output is a cleansed and organized dataset.

[0362] Step 4:

[0363] The server uses a generative AI model to analyze the cleansed data. It takes stored health and emotional data as input and runs machine learning algorithms to evaluate their correlations. The output is an analysis based on the user's health status and emotional tendencies. Specifically, it detects abnormal health patterns and emotional changes.

[0364] Step 5:

[0365] The server generates health advice for the user based on the analysis results. It utilizes the output of the generated AI model as input information to create personalized feedback. The output consists of specific health advice and recommended actions for the user. Examples of its operation include suggesting breathing exercises for stress reduction and recommending relaxation music.

[0366] Step 6:

[0367] The server sends the generated advice to the terminal. It receives the advice created in step 5 as input and notifies the user through the user interface. The output is visual or auditory feedback displayed on the terminal. Specific actions include displaying notification messages or pop-ups.

[0368] Step 7:

[0369] The server evaluates emotion-based incentives and awards points accordingly. It uses analyzed emotion data as input, measuring the duration of a positive emotional state. The output is reward points added to the user's virtual account. For example, if positive emotions are observed for a week consecutively, additional points are automatically calculated.

[0370] (Application Example 2)

[0371] 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."

[0372] In recent years, the importance of personal health management has increased, and the demand for personalized health advice is on the rise. However, conventional systems struggle to integrate and analyze health and emotional information to provide precise suggestions tailored to individual circumstances. Furthermore, in physical stores, specific product and service suggestions that take into account the user's real-time emotions and health status are not provided, making it difficult to optimize the customer experience.

[0373] 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.

[0374] In this invention, the server includes information acquisition means for receiving information from a device that collects health information, information storage means for storing the received information and normalizing it to maintain consistency, and user interface means for notifying the user of the analysis results and providing health-related information in natural language. This makes it possible to provide personalized health suggestions and improve the user experience in physical stores.

[0375] "Information acquisition means" refers to devices that have the function of receiving information from equipment that collects health information.

[0376] An "information storage device" is a device that stores received information and has the function of normalizing it to maintain its integrity.

[0377] An "analysis tool" is a system that analyzes stored information to provide personalized health recommendations.

[0378] A "user interface means" is a device that notifies the user of the analysis results and provides health-related information in natural language.

[0379] The "reward management system" is a function that calculates rewards based on health and emotional status and grants rewards to designated accounts.

[0380] An "information output means" is a mechanism for outputting data for sharing information with external organizations.

[0381] A "store assistant device" is a device that has the function of suggesting products and services in a physical store based on the customer's emotions and health information.

[0382] In the system implementing this invention, the server plays a central role. Specifically, it receives health information and emotional information transmitted from terminals. This data is acquired using the camera and voice functions of smartphones and smart glasses. Health information includes heart rate, body temperature, and steps taken, while emotional information is acquired through facial expression analysis and voice analysis.

[0383] The server performs a normalization process to store the received data with integrity. This normalization can be performed using AI libraries such as TensorFlow or PyTorch. This ensures data consistency and enables highly accurate analysis. The analysis results compare the user's emotional tendencies with health indicators to generate personalized health advice. Based on this, a user interface providing natural language suggestions is displayed on the user's device.

[0384] Furthermore, to enhance the in-store user experience, a store assistant function has been implemented. This function analyzes the user's emotions and health status in real time and recommends the most suitable products and services. For example, if the system detects that the user is experiencing high stress levels, it will recommend products with relaxation effects.

[0385] As a concrete example, by passing a prompt to the AI ​​model—"Generate advice to suggest the optimal wellness product using the customer's current emotional state and health data"—appropriate suggestions tailored to individual circumstances can be quickly provided.

[0386] In this way, by coordinating terminals, servers, and physical stores, a system can be realized that aims to improve users' health management and quality of life.

[0387] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0388] Step 1:

[0389] The device collects the user's health and emotional information. Inputs include heart rate, body temperature, steps, facial expressions, and voice tone data acquired by smartphones or smart glasses. Outputs include sending this information to a server as a series of data packages. Specifically, the device utilizes its sensors and cameras to collect data in real time.

[0390] Step 2:

[0391] The server securely stores data received from the terminal. It uses the raw data received as input and employs a data normalization process. The data is stored in cloud storage, and consistency is ensured by removing outliers and duplicates. Cleaned data is generated as output. Specifically, the database system organizes the data structure.

[0392] Step 3:

[0393] The server performs analysis using a generated AI model based on clean data. The input consists of cleansed health and emotional information. The output is personalized advice regarding the user's health status. As a concrete example of data processing, the AI ​​model is used to analyze stress levels and emotional tendencies.

[0394] Step 4:

[0395] The server sends the analysis results to the terminal. The input includes the generated health advice, and the output is the advice displayed on the user interface of the user terminal. Specifically, the information is presented visually and audibly using natural language, providing information in a way that is easy for the user to understand.

[0396] Step 5:

[0397] Users receive real-time product and service suggestions within the store. The input is based on analysis results displayed on a terminal. The output is appropriate product and service suggestions from a store assistant. Specifically, store staff present the user with the best options based on the advice displayed on the terminal.

[0398] Step 6:

[0399] The server calculates rewards based on health and emotional states. Inputs include analysis results and pre-set reward criteria. Output is the addition of reward points to a specific account. For example, the application using the system accesses the points system, calculates rewards, and awards them.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] [Third Embodiment]

[0404] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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".

[0416] The health management system of this invention is intended to receive data from health data collection devices located in each household or owned by individuals, and to provide personalized health advice using AI. This system works by linking a server and terminals and performing the following processes.

[0417] The server first receives health data sent from the terminal and stores it in a database. The stored data is then cleansed to remove incomplete data and noise. For example, heart rate data sent from a smartwatch is centrally managed, and irregular data points are excluded to build an accurate dataset.

[0418] Next, the server uses an AI algorithm to analyze the user's health data. This analysis generates personalized health advice for each user. For example, if the analysis determines that the user's stress level is high based on recent data, it might recommend "trying deep breathing exercises to relax."

[0419] Furthermore, the server transmits the analysis results to the terminal via the user interface, notifying the user's smartphone or dedicated device with health advice in natural language. Users can then use this information to manage their daily health.

[0420] In incentive management, the server awards rewards (points) based on users' health behaviors. For example, a user who achieves a daily goal of 10,000 steps will receive bonus points in their electronic payment system account to encourage further health maintenance activities.

[0421] Finally, the server shares data with healthcare institutions and insurance companies as needed. This shared data can be used to propose preventive care and design insurance products. Healthcare institutions can use this information to provide personalized health plans for individual patients.

[0422] As described above, this system comprehensively manages users' health and promotes a healthy lifestyle by providing personalized advice and incentives.

[0423] The following describes the processing flow.

[0424] Step 1:

[0425] The device continuously acquires health data such as heart rate, steps taken, and body temperature from the user's smartwatch or smartphone. The device then prepares to send this data to a server at predetermined time intervals.

[0426] Step 2:

[0427] The server receives health data transmitted from the terminal. To maintain data accuracy, the server performs a cleansing process before storing the data in the database, removing incomplete data points and noise. Normalization is then performed to standardize the data format.

[0428] Step 3:

[0429] The server inputs the cleansed data into an AI analysis algorithm. The server uses this algorithm to analyze patterns in health data and detect anomalies or specific health risks. For example, if it detects high fluctuations in heart rate, it may suspect the effects of stress.

[0430] Step 4:

[0431] Based on the user's health status, the server generates personalized health advice. This advice is then translated into natural language that is easy for the user to understand.

[0432] Step 5:

[0433] The server notifies the user's device of the advice it has generated. The device then displays this information visually to the user, providing health status notifications and daily advice.

[0434] Step 6:

[0435] The server analyzes user behavior data and calculates incentive points when healthy behavior is confirmed. The server then awards the points to the user's account via an electronic payment system.

[0436] Step 7:

[0437] If necessary, the server will share data with healthcare institutions and insurance companies. This data sharing will enable healthcare institutions to provide preventative health management and insurance companies to offer more appropriate insurance products.

[0438] (Example 1)

[0439] 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."

[0440] To effectively collect and utilize health data, it is necessary to maintain data accuracy while providing real-time health assessments and personalized health advice. However, conventional systems fail to adequately remove incomplete data, provide users with timely and useful information, and promote healthy behaviors through incentives.

[0441] 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.

[0442] In this invention, the server includes data acquisition means for receiving data from a device that collects health data, data storage means for storing the received data and removing incomplete data and noise to maintain data integrity, and analysis means for providing personalized health advice by analyzing the stored data and performing analysis using an AI algorithm. This enables users to understand their health status in real time and receive personalized advice.

[0443] "Health data" refers to information about an individual's physical condition and activities, such as heart rate, steps taken, calorie consumption, and sleep patterns.

[0444] "Data acquisition means" refers to a function that receives data from a device that collects health data and makes it available for use within the system.

[0445] "Data storage means" refers to a function that stores received data and keeps it in an organized state, and includes processing to remove incomplete data and noise.

[0446] "Analysis means" refers to the function that analyzes stored data and generates personalized health advice using AI algorithms.

[0447] "User interface means" refers to the function of notifying the user of the analysis results and providing health-related information in natural language.

[0448] An "incentive management system" refers to a function that calculates rewards based on users' health behaviors and awards points to the electronic payment platform.

[0449] "Data output means" refers to functions that allow data to be shared with medical institutions and insurance organizations, and to enable its use externally.

[0450] This health management system works by linking a server and a device to effectively collect and analyze health data from users and provide personalized health advice. The server first receives health data transmitted from the device. For example, devices such as smartwatches and fitness trackers provide data such as heart rate and steps taken.

[0451] The received data is stored in the server's data storage system. This storage system performs a cleansing process to maintain data integrity, removing incomplete data and noise. The software used combines commonly available data cleansing tools and AI algorithms.

[0452] Next, the server uses AI algorithms to analyze the cleansed data in detail. This analysis includes data calculations to assess the user's health status and generate optimized health advice. For example, if recent data indicates that the user's activity level has decreased, it might advise, "You should try taking a few more steps this week."

[0453] Through a user interface, the server sends analysis results to the terminal, and the user receives this advice in natural language via a smartphone or dedicated device. In this way, the user can use it to improve their daily life.

[0454] The system also includes an incentive management function that calculates rewards and awards points based on users' healthy behaviors. For example, users who achieve a goal of 10,000 steps per day are awarded points that can be used on the electronic payment platform, encouraging them to engage in further healthy activities.

[0455] Furthermore, this system includes a data output mechanism for sharing important health data with medical institutions and insurance organizations. This shared information is used to develop personalized preventive healthcare recommendations and design insurance products.

[0456] A specific example of a prompt message would be, "Generate advice for a healthy lifestyle based on the user's recent activity data." In this way, the system enables personalized health management and effectively supports the user in leading a healthy life.

[0457] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0458] Step 1:

[0459] The server receives health data from the device. This data is transmitted from health data collection devices such as smartwatches and fitness trackers. Specifically, it includes heart rate, steps taken, calories burned, etc. The input is health data from the device, and the output is raw, uncleaned data stored in data storage. The server buffers this received data in a temporary area to prepare for the next processing step.

[0460] Step 2:

[0461] The server uses data storage to cleanse the received raw data. Specifically, it filters out abnormal values ​​and noise, performing processing to maintain data integrity. For example, data cleaning is performed, such as removing heart rate data that represents extreme outliers. The input to this process is the raw data received from step 1, and the output is consistent, cleansed data. The server stores this in a database for long-term storage.

[0462] Step 3:

[0463] The server analyzes the cleansed data using an AI algorithm. This analysis includes a process that individually assesses the user's health status and generates optimized health advice. The input is the cleansed data, and the output is personalized health advice as a result of the analysis. For example, if a decrease in physical activity is observed, advice will be generated notifying the user to increase walking.

[0464] Step 4:

[0465] The server sends health advice obtained through analysis to the terminal using a user interface. Users receive the advice on their smartphones or dedicated devices and use it for health management. The input is health advice generated by AI, and the output is a notification message displayed on the user's terminal. Based on this, users can take specific actions.

[0466] Step 5:

[0467] The server uses incentive management tools to evaluate the user's health behavior and calculate points. Based on the calculation results, points are then awarded to the electronic payment platform. The input is the user's health behavior data, and the output is the awarded points and a notification. As a specific example, bonus points are offered to users who exceed their daily step count goal.

[0468] Step 6:

[0469] The server utilizes data output mechanisms to share data with healthcare institutions and insurance organizations as needed. This shared data is then used for preventative medicine and insurance product design. Input is user health data, and output is information transmitted to external organizations in an appropriate format. This allows healthcare institutions to provide customized health plans.

[0470] (Application Example 1)

[0471] 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."

[0472] A challenge lies in the insufficient integration of personalized health management advice with behavior-based reward systems. Furthermore, there is a need for a means to analyze users' health data in real time and promptly provide appropriate preventative measures. Additionally, a system is needed that effectively utilizes health behavior-based incentives to enhance users' health maintenance.

[0473] 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.

[0474] In this invention, the server includes an acquisition means for receiving information from a data collection device, a storage means for storing the received information and performing cleaning to maintain data integrity, and a means for linking rewards to an electronic transaction service based on user behavior data. This makes it possible to provide users with real-time and effective health advice, as well as to immediately provide incentives based on their health behaviors.

[0475] "Acquisition means" refers to a mechanism for receiving necessary information from a data collection device.

[0476] "Memory means" refers to a function that safely and systematically stores received information and performs cleaning to maintain data integrity.

[0477] "Analysis means" refers to the process of analyzing stored information and generating health advice tailored to each individual user.

[0478] A "user interface means" is a mechanism for notifying users of analysis results and providing health-related information in natural language.

[0479] A "motivation management tool" is a management function that calculates rewards based on healthy behaviors and awards points to a designated electronic trading account.

[0480] "Information output means" refers to system functions for sharing information with external organizations.

[0481] "Electronic trading services" are services that support financial transactions conducted via the internet.

[0482] To implement this invention, the server and terminal must first be equipped with specific functions. The server receives health information from a data collection device as a means of acquiring information and prepares a database to store the received information. The program is built using the Python language and uses the Flask framework to handle communication between the server and terminal. PostgreSQL is used for the database and a cleaning process is performed to maintain the integrity of the information. The cleaning process is performed to remove incomplete data containing noise and to build an accurate dataset.

[0483] Next, the server performs analysis using an AI model based on TensorFlow to generate personalized health advice for each user. The generated advice is sent to the device through a user interface built on the frontend using React Native. This allows the device to notify the user of the health advice based on the analysis results in natural language.

[0484] Furthermore, as a motivational management tool, the user's device records health behaviors and calculates points based on them. The calculated points are linked to an electronic trading service as incentives. For example, points awarded as incentives can be used for discounts at partner stores through an external trading service.

[0485] A concrete example is a case where a user uses a smartwatch to measure their daily step count, and the RI application uses a generated AI model based on that data to provide advice to the user encouraging them to walk more. An example of a prompt to the generated AI model would be: "The user's step count data is as follows: Daily step count data. Based on this data, please provide appropriate health advice and incentives."

[0486] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0487] Step 1:

[0488] The server receives health data transmitted from the device. For example, heart rate and step count data from a smartwatch are examples. This data is transferred to the server and stored in a database. During this process, the server verifies the validity of the information and issues appropriate alerts if there are any abnormal values.

[0489] Step 2:

[0490] The server performs a cleansing process on the stored health data. Since the input data occasionally contains noise, this is removed to create a consistent dataset. Specifically, extremely high or low heart rate values ​​are treated as invalid. This allows the server to proceed to the next analysis step with reliable data.

[0491] Step 3:

[0492] The server analyzes the cleansed data using TensorFlow. In this process, an AI model identifies patterns in the health data and assesses the user's health status. For example, if the average heart rate over the past week is high, it may indicate a high stress level. This analysis results are then generated as personalized health advice for each user.

[0493] Step 4:

[0494] The server sends the analysis results to the device and displays them in the user interface. The device uses React Native to notify the user of advice in natural language. This notification includes specific relaxation methods and points to be mindful of in daily life. The user can then use the advice to improve their daily activities.

[0495] Step 5:

[0496] The server calculates incentives based on the user's health behavior and awards points through an electronic transaction service. This process inputs data such as the user's daily step count goal achievement status, and points are calculated and awarded as a reward upon achievement. Users can then use these points as discounts at participating stores.

[0497] Step 6:

[0498] When user data should be shared, the server uses the necessary information output means to share information with external organizations. This allows healthcare institutions and insurance organizations to access user health information as needed and provide personalized health plans.

[0499] 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.

[0500] This invention provides a system incorporating an emotion engine to enhance user health management. First, the terminal simultaneously collects the user's health data and emotion data. Health data includes physiological information such as heart rate, body temperature, and steps taken, while emotion data refers to the emotional state collected from the user's facial expressions and tone of voice using the smartphone's camera and voice analysis.

[0501] The server receives this data transmitted from the terminal and stores it in a database. A data storage system performs a cleansing process to maintain data accuracy and consistency. The cleansed data is then analyzed using AI-based analysis tools. Here, the relationship between health data and emotional data is evaluated to make a comprehensive judgment about the user's health status and emotional tendencies.

[0502] For example, if a user's stress level is high and the emotion engine simultaneously recognizes emotions such as "anger" or "anxiety," the server will generate advice recommending stress-management breathing techniques or relaxation music to the user. Furthermore, by continuously evaluating emotional stability, the system constantly monitors whether the user is leading a healthy lifestyle and provides appropriate feedback.

[0503] The analysis results and advice are transmitted to the terminal via a user interface. The terminal provides this information to the user visually and audibly, presenting it in an easily understandable format. By receiving this information, the user can engage in daily improvement measures based on their own health status and emotions.

[0504] Furthermore, this system also includes a reward management mechanism to provide incentives tailored to the user's emotions. Specifically, it awards additional points if positive emotions are maintained for a certain period, encouraging users to maintain and improve their well-being.

[0505] Thus, the present invention realizes a more comprehensive and practical health management solution by integrating and analyzing emotional and health data and providing personalized advice.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] The device uses sensors from the smartwatch or smartphone to collect health data such as the user's heart rate, steps taken, and body temperature. Furthermore, the device uses its camera and microphone to analyze the user's facial expressions and voice tone to acquire emotional data. This allows it to identify the user's current emotional state (e.g., joy, anger, sadness, surprise, etc.).

[0509] Step 2:

[0510] The device periodically sends collected health and emotional data to a server. This data transmission is conducted using encrypted communication to ensure security.

[0511] Step 3:

[0512] The server receives data sent from the terminal and performs data cleansing before storing it in data storage. This removes incomplete data and noise, maintaining data integrity.

[0513] Step 4:

[0514] The server passes the cleansed data to an analysis tool, which then analyzes the relationship between health data and emotional data in detail. For example, if the heart rate is high and the emotional data indicates "anxiety," it is analyzed as potentially indicating a stress response.

[0515] Step 5:

[0516] The server generates personalized health advice based on the analysis results. This advice addresses emotional states and provides specific stress relief and relaxation techniques. For example, if the emotion is "anger," it provides guidance on deep breathing.

[0517] Step 6:

[0518] The server converts the generated advice into natural language and sends it to the terminal through the user interface. The terminal notifies the user of the advice and makes the information easy to understand by displaying it visually and audibly.

[0519] Step 7:

[0520] As users continue their daily activities, the server uses a reward management system to calculate incentive points for users who maintain a positive emotional state. The server then awards these points to users' electronic accounts to encourage positive health behaviors.

[0521] (Example 2)

[0522] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0523] In modern society, the increase in lifestyle-related diseases and stress-related illnesses is a serious problem, but the means to comprehensively manage individual health conditions and emotional fluctuations and provide appropriate feedback are limited. In particular, there is a need to promote healthy behaviors through emotion-based incentives.

[0524] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0525] In this invention, the server includes data acquisition means for receiving information from terminals that collect physiological information and emotional states; data storage means for storing the received information and performing cleansing to maintain the accuracy and consistency of the information; and analysis means for analyzing health status and emotional tendencies using a generated AI model based on the stored information and providing personalized health advice. This makes it possible to evaluate the relationship between individual health status and emotions, provide appropriate health advice, and promote healthy behaviors through emotion-based incentives.

[0526] "Physiological information" refers to data that indicates the body's condition, such as heart rate, body temperature, and steps taken.

[0527] "Emotional state" refers to the psychological state analyzed from the user's facial expressions and tone of voice.

[0528] A "terminal" is a user interface device used to collect physiological information and emotional states from the user.

[0529] "Data acquisition means" refers to a series of functions for receiving information from a terminal.

[0530] A "data storage method" is a processing method for storing received information and maintaining its accuracy and consistency.

[0531] A "generative AI model" is a system of algorithms used to analyze health status and emotional tendencies using machine learning.

[0532] "Analysis tools" refer to analytical functions that provide personalized health advice based on stored information.

[0533] An "incentive management system" is a method for calculating rewards based on emotional states and providing points to a virtual account.

[0534] This invention is a system that comprehensively manages the user's health and emotional state and provides appropriate feedback and incentives. Specific embodiments are described below.

[0535] Terminal role:

[0536] The terminal is the primary device for collecting the user's physiological information and emotional state. Specifically, this includes smartphones and wearable devices. These terminals are equipped with heart rate monitors, accelerometers, and temperature sensors to acquire the user's daily physiological data. They also use cameras and microphones to analyze the user's facial expressions and voice, and to understand their emotional state in real time.

[0537] Server role:

[0538] The server receives physiological information and emotional states transmitted from the terminal and stores them neatly using data storage means. Crucially, the data undergoes a cleansing process to ensure accuracy and consistency. Using a generative AI model, the server analyzes health status and emotional tendencies and generates personalized health advice.

[0539] User interface:

[0540] The server notifies the user of the analysis results through a user interface. Information is provided in a format easily understandable to the user through visual displays and audio messages. Recommended health advice includes breathing exercises for stress management and a list of relaxation music.

[0541] Granting incentives:

[0542] Furthermore, the server manages emotion-based incentives. If positive emotions are maintained for a certain period, points are awarded to the user's virtual account. These points can later be redeemed for cash and serve as an incentive for users to improve their well-being.

[0543] Examples of specific cases and prompt statements:

[0544] For example, if a user experiences stress at work, the device detects this change, and the server suggests appropriate relaxation methods. This suggestion is sent to the device as an app notification. An example of a prompt message would be, "The user's emotional data has shown a 'Happy' state for a week straight. Do you want to send a prompt to add reward points?"

[0545] This invention allows users to more effectively manage their health and emotions, and enables them to engage in sustainable health improvement activities through individually customized feedback and rewards.

[0546] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0547] Step 1:

[0548] The device collects the user's physiological information and emotional state. Inputs include data from a heart rate monitor, accelerometer, and temperature sensor, as well as facial expressions and voice tone captured through the camera and microphone. Specifically, it converts signals from the sensors into digital data to measure the user's heart rate, steps, and body temperature, and determines their emotional state through image processing and voice analysis. The output is a set of health data and emotional data, which packages this information.

[0549] Step 2:

[0550] The device sends the data obtained in Step 1 to the server. To ensure data security, an encryption protocol is used to securely transfer the data. Collected health data and emotion data are used as input, and they are sent to the server as output, allowing for further processing on the server side.

[0551] Step 3:

[0552] The server receives data and stores it using data storage means. It receives health and sentiment data transmitted from terminals as input. Through a data cleansing process, it detects outliers and imputes missing values ​​to maintain accuracy and consistency. The output is a cleansed and organized dataset.

[0553] Step 4:

[0554] The server uses a generative AI model to analyze the cleansed data. It takes stored health and emotional data as input and runs machine learning algorithms to evaluate their correlations. The output is an analysis based on the user's health status and emotional tendencies. Specifically, it detects abnormal health patterns and emotional changes.

[0555] Step 5:

[0556] The server generates health advice for the user based on the analysis results. It utilizes the output of the generated AI model as input information to create personalized feedback. The output consists of specific health advice and recommended actions for the user. Examples of its operation include suggesting breathing exercises for stress reduction and recommending relaxation music.

[0557] Step 6:

[0558] The server sends the generated advice to the terminal. It receives the advice created in step 5 as input and notifies the user through the user interface. The output is visual or auditory feedback displayed on the terminal. Specific actions include displaying notification messages or pop-ups.

[0559] Step 7:

[0560] The server evaluates emotion-based incentives and awards points accordingly. It uses analyzed emotion data as input, measuring the duration of a positive emotional state. The output is reward points added to the user's virtual account. For example, if positive emotions are observed for a week consecutively, additional points are automatically calculated.

[0561] (Application Example 2)

[0562] 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."

[0563] In recent years, the importance of personal health management has increased, and the demand for personalized health advice is on the rise. However, conventional systems struggle to integrate and analyze health and emotional information to provide precise suggestions tailored to individual circumstances. Furthermore, in physical stores, specific product and service suggestions that take into account the user's real-time emotions and health status are not provided, making it difficult to optimize the customer experience.

[0564] 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.

[0565] In this invention, the server includes information acquisition means for receiving information from a device that collects health information, information storage means for storing the received information and normalizing it to maintain consistency, and user interface means for notifying the user of the analysis results and providing health-related information in natural language. This makes it possible to provide personalized health suggestions and improve the user experience in physical stores.

[0566] "Information acquisition means" refers to devices that have the function of receiving information from equipment that collects health information.

[0567] An "information storage device" is a device that stores received information and has the function of normalizing it to maintain its integrity.

[0568] An "analysis tool" is a system that analyzes stored information to provide personalized health recommendations.

[0569] A "user interface means" is a device that notifies the user of the analysis results and provides health-related information in natural language.

[0570] The "reward management system" is a function that calculates rewards based on health and emotional status and grants rewards to designated accounts.

[0571] An "information output means" is a mechanism for outputting data for sharing information with external organizations.

[0572] A "store assistant device" is a device that has the function of suggesting products and services in a physical store based on the customer's emotions and health information.

[0573] In the system implementing this invention, the server plays a central role. Specifically, it receives health information and emotional information transmitted from terminals. This data is acquired using the camera and voice functions of smartphones and smart glasses. Health information includes heart rate, body temperature, and steps taken, while emotional information is acquired through facial expression analysis and voice analysis.

[0574] The server performs a normalization process to store the received data with integrity. This normalization can be performed using AI libraries such as TensorFlow or PyTorch. This ensures data consistency and enables highly accurate analysis. The analysis results compare the user's emotional tendencies with health indicators to generate personalized health advice. Based on this, a user interface providing natural language suggestions is displayed on the user's device.

[0575] Furthermore, to enhance the in-store user experience, a store assistant function has been implemented. This function analyzes the user's emotions and health status in real time and recommends the most suitable products and services. For example, if the system detects that the user is experiencing high stress levels, it will recommend products with relaxation effects.

[0576] As a concrete example, by passing a prompt to the AI ​​model—"Generate advice to suggest the optimal wellness product using the customer's current emotional state and health data"—appropriate suggestions tailored to individual circumstances can be quickly provided.

[0577] In this way, by coordinating terminals, servers, and physical stores, a system can be realized that aims to improve users' health management and quality of life.

[0578] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0579] Step 1:

[0580] The device collects the user's health and emotional information. Inputs include heart rate, body temperature, steps, facial expressions, and voice tone data acquired by smartphones or smart glasses. Outputs include sending this information to a server as a series of data packages. Specifically, the device utilizes its sensors and cameras to collect data in real time.

[0581] Step 2:

[0582] The server securely stores data received from the terminal. It uses the raw data received as input and employs a data normalization process. The data is stored in cloud storage, and consistency is ensured by removing outliers and duplicates. Cleaned data is generated as output. Specifically, the database system organizes the data structure.

[0583] Step 3:

[0584] The server performs analysis using a generated AI model based on clean data. The input consists of cleansed health and emotional information. The output is personalized advice regarding the user's health status. As a concrete example of data processing, the AI ​​model is used to analyze stress levels and emotional tendencies.

[0585] Step 4:

[0586] The server sends the analysis results to the terminal. The input includes the generated health advice, and the output is the advice displayed on the user interface of the user terminal. Specifically, the information is presented visually and audibly using natural language, providing information in a way that is easy for the user to understand.

[0587] Step 5:

[0588] Users receive real-time product and service suggestions within the store. The input is based on analysis results displayed on a terminal. The output is appropriate product and service suggestions from a store assistant. Specifically, store staff present the user with the best options based on the advice displayed on the terminal.

[0589] Step 6:

[0590] The server calculates rewards based on health and emotional states. Inputs include analysis results and pre-set reward criteria. Output is the addition of reward points to a specific account. For example, the application using the system accesses the points system, calculates rewards, and awards them.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] [Fourth Embodiment]

[0595] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0596] 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.

[0597] 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).

[0598] 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.

[0599] 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.

[0600] 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).

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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".

[0608] The health management system of this invention is intended to receive data from health data collection devices located in each household or owned by individuals, and to provide personalized health advice using AI. This system works by linking a server and terminals and performing the following processes.

[0609] The server first receives health data sent from the terminal and stores it in a database. The stored data is then cleansed to remove incomplete data and noise. For example, heart rate data sent from a smartwatch is centrally managed, and irregular data points are excluded to build an accurate dataset.

[0610] Next, the server uses an AI algorithm to analyze the user's health data. This analysis generates personalized health advice for each user. For example, if the analysis determines that the user's stress level is high based on recent data, it might recommend "trying deep breathing exercises to relax."

[0611] Furthermore, the server transmits the analysis results to the terminal via the user interface, notifying the user's smartphone or dedicated device with health advice in natural language. Users can then use this information to manage their daily health.

[0612] In incentive management, the server awards rewards (points) based on users' health behaviors. For example, a user who achieves a daily goal of 10,000 steps will receive bonus points in their electronic payment system account to encourage further health maintenance activities.

[0613] Finally, the server shares data with healthcare institutions and insurance companies as needed. This shared data can be used to propose preventive care and design insurance products. Healthcare institutions can use this information to provide personalized health plans for individual patients.

[0614] As described above, this system comprehensively manages users' health and promotes a healthy lifestyle by providing personalized advice and incentives.

[0615] The following describes the processing flow.

[0616] Step 1:

[0617] The device continuously acquires health data such as heart rate, steps taken, and body temperature from the user's smartwatch or smartphone. The device then prepares to send this data to a server at predetermined time intervals.

[0618] Step 2:

[0619] The server receives health data transmitted from the terminal. To maintain data accuracy, the server performs a cleansing process before storing the data in the database, removing incomplete data points and noise. Normalization is then performed to standardize the data format.

[0620] Step 3:

[0621] The server inputs the cleansed data into an AI analysis algorithm. The server uses this algorithm to analyze patterns in health data and detect anomalies or specific health risks. For example, if it detects high fluctuations in heart rate, it may suspect the effects of stress.

[0622] Step 4:

[0623] Based on the user's health status, the server generates personalized health advice. This advice is then translated into natural language that is easy for the user to understand.

[0624] Step 5:

[0625] The server notifies the user's device of the advice it has generated. The device then displays this information visually to the user, providing health status notifications and daily advice.

[0626] Step 6:

[0627] The server analyzes user behavior data and calculates incentive points when healthy behavior is confirmed. The server then awards the points to the user's account via an electronic payment system.

[0628] Step 7:

[0629] If necessary, the server will share data with healthcare institutions and insurance companies. This data sharing will enable healthcare institutions to provide preventative health management and insurance companies to offer more appropriate insurance products.

[0630] (Example 1)

[0631] 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".

[0632] To effectively collect and utilize health data, it is necessary to maintain data accuracy while providing real-time health assessments and personalized health advice. However, conventional systems fail to adequately remove incomplete data, provide users with timely and useful information, and promote healthy behaviors through incentives.

[0633] 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.

[0634] In this invention, the server includes data acquisition means for receiving data from a device that collects health data, data storage means for storing the received data and removing incomplete data and noise to maintain data integrity, and analysis means for providing personalized health advice by analyzing the stored data and performing analysis using an AI algorithm. This enables users to understand their health status in real time and receive personalized advice.

[0635] "Health data" refers to information about an individual's physical condition and activities, such as heart rate, steps taken, calorie consumption, and sleep patterns.

[0636] "Data acquisition means" refers to a function that receives data from a device that collects health data and makes it available for use within the system.

[0637] "Data storage means" refers to a function that stores received data and keeps it in an organized state, and includes processing to remove incomplete data and noise.

[0638] "Analysis means" refers to the function that analyzes stored data and generates personalized health advice using AI algorithms.

[0639] "User interface means" refers to the function of notifying the user of the analysis results and providing health-related information in natural language.

[0640] An "incentive management system" refers to a function that calculates rewards based on users' health behaviors and awards points to the electronic payment platform.

[0641] "Data output means" refers to functions that allow data to be shared with medical institutions and insurance organizations, and to enable its use externally.

[0642] This health management system works by linking a server and a device to effectively collect and analyze health data from users and provide personalized health advice. The server first receives health data transmitted from the device. For example, devices such as smartwatches and fitness trackers provide data such as heart rate and steps taken.

[0643] The received data is stored in the server's data storage system. This storage system performs a cleansing process to maintain data integrity, removing incomplete data and noise. The software used combines commonly available data cleansing tools and AI algorithms.

[0644] Next, the server uses AI algorithms to analyze the cleansed data in detail. This analysis includes data calculations to assess the user's health status and generate optimized health advice. For example, if recent data indicates that the user's activity level has decreased, it might advise, "You should try taking a few more steps this week."

[0645] Through a user interface, the server sends analysis results to the terminal, and the user receives this advice in natural language via a smartphone or dedicated device. In this way, the user can use it to improve their daily life.

[0646] The system also includes an incentive management function that calculates rewards and awards points based on users' healthy behaviors. For example, users who achieve a goal of 10,000 steps per day are awarded points that can be used on the electronic payment platform, encouraging them to engage in further healthy activities.

[0647] Furthermore, this system includes a data output mechanism for sharing important health data with medical institutions and insurance organizations. This shared information is used to develop personalized preventive healthcare recommendations and design insurance products.

[0648] A specific example of a prompt message would be, "Generate advice for a healthy lifestyle based on the user's recent activity data." In this way, the system enables personalized health management and effectively supports the user in leading a healthy life.

[0649] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0650] Step 1:

[0651] The server receives health data from the device. This data is transmitted from health data collection devices such as smartwatches and fitness trackers. Specifically, it includes heart rate, steps taken, calories burned, etc. The input is health data from the device, and the output is raw, uncleaned data stored in data storage. The server buffers this received data in a temporary area to prepare for the next processing step.

[0652] Step 2:

[0653] The server uses data storage to cleanse the received raw data. Specifically, it filters out abnormal values ​​and noise, performing processing to maintain data integrity. For example, data cleaning is performed, such as removing heart rate data that represents extreme outliers. The input to this process is the raw data received from step 1, and the output is consistent, cleansed data. The server stores this in a database for long-term storage.

[0654] Step 3:

[0655] The server analyzes the cleansed data using an AI algorithm. This analysis includes a process that individually assesses the user's health status and generates optimized health advice. The input is the cleansed data, and the output is personalized health advice as a result of the analysis. For example, if a decrease in physical activity is observed, advice will be generated notifying the user to increase walking.

[0656] Step 4:

[0657] The server sends health advice obtained through analysis to the terminal using a user interface. Users receive the advice on their smartphones or dedicated devices and use it for health management. The input is health advice generated by AI, and the output is a notification message displayed on the user's terminal. Based on this, users can take specific actions.

[0658] Step 5:

[0659] The server uses incentive management tools to evaluate the user's health behavior and calculate points. Based on the calculation results, points are then awarded to the electronic payment platform. The input is the user's health behavior data, and the output is the awarded points and a notification. As a specific example, bonus points are offered to users who exceed their daily step count goal.

[0660] Step 6:

[0661] The server utilizes data output mechanisms to share data with healthcare institutions and insurance organizations as needed. This shared data is then used for preventative medicine and insurance product design. Input is user health data, and output is information transmitted to external organizations in an appropriate format. This allows healthcare institutions to provide customized health plans.

[0662] (Application Example 1)

[0663] 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".

[0664] A challenge lies in the insufficient integration of personalized health management advice with behavior-based reward systems. Furthermore, there is a need for a means to analyze users' health data in real time and promptly provide appropriate preventative measures. Additionally, a system is needed that effectively utilizes health behavior-based incentives to enhance users' health maintenance.

[0665] 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.

[0666] In this invention, the server includes an acquisition means for receiving information from a data collection device, a storage means for storing the received information and performing cleaning to maintain data integrity, and a means for linking rewards to an electronic transaction service based on user behavior data. This makes it possible to provide users with real-time and effective health advice, as well as to immediately provide incentives based on their health behaviors.

[0667] "Acquisition means" refers to a mechanism for receiving necessary information from a data collection device.

[0668] "Memory means" refers to a function that safely and systematically stores received information and performs cleaning to maintain data integrity.

[0669] "Analysis means" refers to the process of analyzing stored information and generating health advice tailored to each individual user.

[0670] A "user interface means" is a mechanism for notifying users of analysis results and providing health-related information in natural language.

[0671] A "motivation management tool" is a management function that calculates rewards based on healthy behaviors and awards points to a designated electronic trading account.

[0672] "Information output means" refers to system functions for sharing information with external organizations.

[0673] "Electronic trading services" are services that support financial transactions conducted via the internet.

[0674] To implement this invention, the server and terminal must first be equipped with specific functions. The server receives health information from a data collection device as a means of acquiring information and prepares a database to store the received information. The program is built using the Python language and uses the Flask framework to handle communication between the server and terminal. PostgreSQL is used for the database and a cleaning process is performed to maintain the integrity of the information. The cleaning process is performed to remove incomplete data containing noise and to build an accurate dataset.

[0675] Next, the server performs analysis using an AI model based on TensorFlow to generate personalized health advice for each user. The generated advice is sent to the device through a user interface built on the frontend using React Native. This allows the device to notify the user of the health advice based on the analysis results in natural language.

[0676] Furthermore, as a motivational management tool, the user's device records health behaviors and calculates points based on them. The calculated points are linked to an electronic trading service as incentives. For example, points awarded as incentives can be used for discounts at partner stores through an external trading service.

[0677] A concrete example is a case where a user uses a smartwatch to measure their daily step count, and the RI application uses a generated AI model based on that data to provide advice to the user encouraging them to walk more. An example of a prompt to the generated AI model would be: "The user's step count data is as follows: Daily step count data. Based on this data, please provide appropriate health advice and incentives."

[0678] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0679] Step 1:

[0680] The server receives health data transmitted from the device. For example, heart rate and step count data from a smartwatch are examples. This data is transferred to the server and stored in a database. During this process, the server verifies the validity of the information and issues appropriate alerts if there are any abnormal values.

[0681] Step 2:

[0682] The server performs a cleansing process on the stored health data. Since the input data occasionally contains noise, this is removed to create a consistent dataset. Specifically, extremely high or low heart rate values ​​are treated as invalid. This allows the server to proceed to the next analysis step with reliable data.

[0683] Step 3:

[0684] The server analyzes the cleansed data using TensorFlow. In this process, an AI model identifies patterns in the health data and assesses the user's health status. For example, if the average heart rate over the past week is high, it may indicate a high stress level. This analysis results are then generated as personalized health advice for each user.

[0685] Step 4:

[0686] The server sends the analysis results to the device and displays them in the user interface. The device uses React Native to notify the user of advice in natural language. This notification includes specific relaxation methods and points to be mindful of in daily life. The user can then use the advice to improve their daily activities.

[0687] Step 5:

[0688] The server calculates incentives based on the user's health behavior and awards points through an electronic transaction service. This process inputs data such as the user's daily step count goal achievement status, and points are calculated and awarded as a reward upon achievement. Users can then use these points as discounts at participating stores.

[0689] Step 6:

[0690] When user data should be shared, the server uses the necessary information output means to share information with external organizations. This allows healthcare institutions and insurance organizations to access user health information as needed and provide personalized health plans.

[0691] 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.

[0692] This invention provides a system incorporating an emotion engine to enhance user health management. First, the terminal simultaneously collects the user's health data and emotion data. Health data includes physiological information such as heart rate, body temperature, and steps taken, while emotion data refers to the emotional state collected from the user's facial expressions and tone of voice using the smartphone's camera and voice analysis.

[0693] The server receives this data transmitted from the terminal and stores it in a database. A data storage system performs a cleansing process to maintain data accuracy and consistency. The cleansed data is then analyzed using AI-based analysis tools. Here, the relationship between health data and emotional data is evaluated to make a comprehensive judgment about the user's health status and emotional tendencies.

[0694] For example, if a user's stress level is high and the emotion engine simultaneously recognizes emotions such as "anger" or "anxiety," the server will generate advice recommending stress-management breathing techniques or relaxation music to the user. Furthermore, by continuously evaluating emotional stability, the system constantly monitors whether the user is leading a healthy lifestyle and provides appropriate feedback.

[0695] The analysis results and advice are transmitted to the terminal via a user interface. The terminal provides this information to the user visually and audibly, presenting it in an easily understandable format. By receiving this information, the user can engage in daily improvement measures based on their own health status and emotions.

[0696] Furthermore, this system also includes a reward management mechanism to provide incentives tailored to the user's emotions. Specifically, it awards additional points if positive emotions are maintained for a certain period, encouraging users to maintain and improve their well-being.

[0697] Thus, the present invention realizes a more comprehensive and practical health management solution by integrating and analyzing emotional and health data and providing personalized advice.

[0698] The following describes the processing flow.

[0699] Step 1:

[0700] The device uses sensors from the smartwatch or smartphone to collect health data such as the user's heart rate, steps taken, and body temperature. Furthermore, the device uses its camera and microphone to analyze the user's facial expressions and voice tone to acquire emotional data. This allows it to identify the user's current emotional state (e.g., joy, anger, sadness, surprise, etc.).

[0701] Step 2:

[0702] The device periodically sends collected health and emotional data to a server. This data transmission is conducted using encrypted communication to ensure security.

[0703] Step 3:

[0704] The server receives data sent from the terminal and performs data cleansing before storing it in data storage. This removes incomplete data and noise, maintaining data integrity.

[0705] Step 4:

[0706] The server passes the cleansed data to an analysis tool, which then analyzes the relationship between health data and emotional data in detail. For example, if the heart rate is high and the emotional data indicates "anxiety," it is analyzed as potentially indicating a stress response.

[0707] Step 5:

[0708] The server generates personalized health advice based on the analysis results. This advice addresses emotional states and provides specific stress relief and relaxation techniques. For example, if the emotion is "anger," it provides guidance on deep breathing.

[0709] Step 6:

[0710] The server converts the generated advice into natural language and sends it to the terminal through the user interface. The terminal notifies the user of the advice and makes the information easy to understand by displaying it visually and audibly.

[0711] Step 7:

[0712] As users continue their daily activities, the server uses a reward management system to calculate incentive points for users who maintain a positive emotional state. The server then awards these points to users' electronic accounts to encourage positive health behaviors.

[0713] (Example 2)

[0714] 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".

[0715] In modern society, the increase in lifestyle-related diseases and stress-related illnesses is a serious problem, but the means to comprehensively manage individual health conditions and emotional fluctuations and provide appropriate feedback are limited. In particular, there is a need to promote healthy behaviors through emotion-based incentives.

[0716] 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.

[0717] In this invention, the server includes data acquisition means for receiving information from terminals that collect physiological information and emotional states; data storage means for storing the received information and performing cleansing to maintain the accuracy and consistency of the information; and analysis means for analyzing health status and emotional tendencies using a generated AI model based on the stored information and providing personalized health advice. This makes it possible to evaluate the relationship between individual health status and emotions, provide appropriate health advice, and promote healthy behaviors through emotion-based incentives.

[0718] "Physiological information" refers to data that indicates the body's condition, such as heart rate, body temperature, and steps taken.

[0719] "Emotional state" refers to the psychological state analyzed from the user's facial expressions and tone of voice.

[0720] A "terminal" is a user interface device used to collect physiological information and emotional states from the user.

[0721] "Data acquisition means" refers to a series of functions for receiving information from a terminal.

[0722] A "data storage method" is a processing method for storing received information and maintaining its accuracy and consistency.

[0723] A "generative AI model" is a system of algorithms used to analyze health status and emotional tendencies using machine learning.

[0724] "Analysis tools" refer to analytical functions that provide personalized health advice based on stored information.

[0725] An "incentive management system" is a method for calculating rewards based on emotional states and providing points to a virtual account.

[0726] This invention is a system that comprehensively manages the user's health and emotional state and provides appropriate feedback and incentives. Specific embodiments are described below.

[0727] Terminal role:

[0728] The terminal is the primary device for collecting the user's physiological information and emotional state. Specifically, this includes smartphones and wearable devices. These terminals are equipped with heart rate monitors, accelerometers, and temperature sensors to acquire the user's daily physiological data. They also use cameras and microphones to analyze the user's facial expressions and voice, and to understand their emotional state in real time.

[0729] Server role:

[0730] The server receives physiological information and emotional states transmitted from the terminal and stores them neatly using data storage means. Crucially, the data undergoes a cleansing process to ensure accuracy and consistency. Using a generative AI model, the server analyzes health status and emotional tendencies and generates personalized health advice.

[0731] User interface:

[0732] The server notifies the user of the analysis results through a user interface. Information is provided in a format easily understandable to the user through visual displays and audio messages. Recommended health advice includes breathing exercises for stress management and a list of relaxation music.

[0733] Granting incentives:

[0734] Furthermore, the server manages emotion-based incentives. If positive emotions are maintained for a certain period, points are awarded to the user's virtual account. These points can later be redeemed for cash and serve as an incentive for users to improve their well-being.

[0735] Examples of specific cases and prompt statements:

[0736] For example, if a user experiences stress at work, the device detects this change, and the server suggests appropriate relaxation methods. This suggestion is sent to the device as an app notification. An example of a prompt message would be, "The user's emotional data has shown a 'Happy' state for a week straight. Do you want to send a prompt to add reward points?"

[0737] This invention allows users to more effectively manage their health and emotions, and enables them to engage in sustainable health improvement activities through individually customized feedback and rewards.

[0738] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0739] Step 1:

[0740] The device collects the user's physiological information and emotional state. Inputs include data from a heart rate monitor, accelerometer, and temperature sensor, as well as facial expressions and voice tone captured through the camera and microphone. Specifically, it converts signals from the sensors into digital data to measure the user's heart rate, steps, and body temperature, and determines their emotional state through image processing and voice analysis. The output is a set of health data and emotional data, which packages this information.

[0741] Step 2:

[0742] The device sends the data obtained in Step 1 to the server. To ensure data security, an encryption protocol is used to securely transfer the data. Collected health data and emotion data are used as input, and they are sent to the server as output, allowing for further processing on the server side.

[0743] Step 3:

[0744] The server receives data and stores it using data storage means. It receives health and sentiment data transmitted from terminals as input. Through a data cleansing process, it detects outliers and imputes missing values ​​to maintain accuracy and consistency. The output is a cleansed and organized dataset.

[0745] Step 4:

[0746] The server uses a generative AI model to analyze the cleansed data. It takes stored health and emotional data as input and runs machine learning algorithms to evaluate their correlations. The output is an analysis based on the user's health status and emotional tendencies. Specifically, it detects abnormal health patterns and emotional changes.

[0747] Step 5:

[0748] The server generates health advice for the user based on the analysis results. It utilizes the output of the generated AI model as input information to create personalized feedback. The output consists of specific health advice and recommended actions for the user. Examples of its operation include suggesting breathing exercises for stress reduction and recommending relaxation music.

[0749] Step 6:

[0750] The server sends the generated advice to the terminal. It receives the advice created in step 5 as input and notifies the user through the user interface. The output is visual or auditory feedback displayed on the terminal. Specific actions include displaying notification messages or pop-ups.

[0751] Step 7:

[0752] The server evaluates emotion-based incentives and awards points accordingly. It uses analyzed emotion data as input, measuring the duration of a positive emotional state. The output is reward points added to the user's virtual account. For example, if positive emotions are observed for a week consecutively, additional points are automatically calculated.

[0753] (Application Example 2)

[0754] 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".

[0755] In recent years, the importance of personal health management has increased, and the demand for personalized health advice is on the rise. However, conventional systems struggle to integrate and analyze health and emotional information to provide precise suggestions tailored to individual circumstances. Furthermore, in physical stores, specific product and service suggestions that take into account the user's real-time emotions and health status are not provided, making it difficult to optimize the customer experience.

[0756] 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.

[0757] In this invention, the server includes information acquisition means for receiving information from a device that collects health information, information storage means for storing the received information and normalizing it to maintain consistency, and user interface means for notifying the user of the analysis results and providing health-related information in natural language. This makes it possible to provide personalized health suggestions and improve the user experience in physical stores.

[0758] "Information acquisition means" refers to devices that have the function of receiving information from equipment that collects health information.

[0759] An "information storage device" is a device that stores received information and has the function of normalizing it to maintain its integrity.

[0760] An "analysis tool" is a system that analyzes stored information to provide personalized health recommendations.

[0761] A "user interface means" is a device that notifies the user of the analysis results and provides health-related information in natural language.

[0762] The "reward management system" is a function that calculates rewards based on health and emotional status and grants rewards to designated accounts.

[0763] An "information output means" is a mechanism for outputting data for sharing information with external organizations.

[0764] A "store assistant device" is a device that has the function of suggesting products and services in a physical store based on the customer's emotions and health information.

[0765] In the system implementing this invention, the server plays a central role. Specifically, it receives health information and emotional information transmitted from terminals. This data is acquired using the camera and voice functions of smartphones and smart glasses. Health information includes heart rate, body temperature, and steps taken, while emotional information is acquired through facial expression analysis and voice analysis.

[0766] The server performs a normalization process to store the received data with integrity. This normalization can be performed using AI libraries such as TensorFlow or PyTorch. This ensures data consistency and enables highly accurate analysis. The analysis results compare the user's emotional tendencies with health indicators to generate personalized health advice. Based on this, a user interface providing natural language suggestions is displayed on the user's device.

[0767] Furthermore, to enhance the in-store user experience, a store assistant function has been implemented. This function analyzes the user's emotions and health status in real time and recommends the most suitable products and services. For example, if the system detects that the user is experiencing high stress levels, it will recommend products with relaxation effects.

[0768] As a concrete example, by passing a prompt to the AI ​​model—"Generate advice to suggest the optimal wellness product using the customer's current emotional state and health data"—appropriate suggestions tailored to individual circumstances can be quickly provided.

[0769] In this way, by coordinating terminals, servers, and physical stores, a system can be realized that aims to improve users' health management and quality of life.

[0770] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0771] Step 1:

[0772] The device collects the user's health and emotional information. Inputs include heart rate, body temperature, steps, facial expressions, and voice tone data acquired by smartphones or smart glasses. Outputs include sending this information to a server as a series of data packages. Specifically, the device utilizes its sensors and cameras to collect data in real time.

[0773] Step 2:

[0774] The server securely stores data received from the terminal. It uses the raw data received as input and employs a data normalization process. The data is stored in cloud storage, and consistency is ensured by removing outliers and duplicates. Cleaned data is generated as output. Specifically, the database system organizes the data structure.

[0775] Step 3:

[0776] The server performs analysis using a generated AI model based on clean data. The input consists of cleansed health and emotional information. The output is personalized advice regarding the user's health status. As a concrete example of data processing, the AI ​​model is used to analyze stress levels and emotional tendencies.

[0777] Step 4:

[0778] The server sends the analysis results to the terminal. The input includes the generated health advice, and the output is the advice displayed on the user interface of the user terminal. Specifically, the information is presented visually and audibly using natural language, providing information in a way that is easy for the user to understand.

[0779] Step 5:

[0780] Users receive real-time product and service suggestions within the store. The input is based on analysis results displayed on a terminal. The output is appropriate product and service suggestions from a store assistant. Specifically, store staff present the user with the best options based on the advice displayed on the terminal.

[0781] Step 6:

[0782] The server calculates rewards based on health and emotional states. Inputs include analysis results and pre-set reward criteria. Output is the addition of reward points to a specific account. For example, the application using the system accesses the points system, calculates rewards, and awards them.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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."

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] The following is further disclosed regarding the embodiments described above.

[0805] (Claim 1)

[0806] A data acquisition means that receives data from a device that collects health data,

[0807] A data storage means that stores received data and performs cleansing to maintain data integrity,

[0808] An analytical tool that provides personalized health advice by analyzing stored data,

[0809] A user interface means that notifies the user of the analysis results and provides health-related information in natural language,

[0810] An incentive management system that calculates rewards based on healthy behaviors and awards points to a designated account,

[0811] A data output method for sharing data with external organizations,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system described in paragraph 1 is characterized by analyzing users' health data in real time and proposing preventive measures.

[0815] (Claim 3)

[0816] The system according to claim 1, characterized in that it includes means for calculating rewards and awarding points to an electronic account.

[0817] "Example 1"

[0818] (Claim 1)

[0819] A data acquisition means that receives data from a device that collects health data,

[0820] A data storage means for storing received data and removing incomplete data and noise to maintain data integrity,

[0821] By analyzing stored data, it provides personalized health advice, and the analysis method uses AI algorithms to perform the analysis.

[0822] A user interface means that notifies the user of the analysis results and provides health-related information in natural language,

[0823] An incentive management system that calculates rewards based on healthy behaviors and awards points to an electronic payment platform,

[0824] A data output method for sharing data with medical institutions and insurance organizations,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, characterized in that it analyzes users' health data in real time and proposes personalized preventive measures.

[0828] (Claim 3)

[0829] The system according to claim 1, characterized in that it includes means for calculating rewards in accordance with healthy behaviors and awarding points to an electronic service provider's account.

[0830] "Application Example 1"

[0831] (Claim 1)

[0832] An acquisition means for receiving information from a data collection device,

[0833] A storage means that stores the received information and performs cleaning to maintain data integrity,

[0834] An analytical tool that provides personalized health advice by analyzing stored information,

[0835] A user interface means that notifies the user of the analysis results and provides health-related information in natural language,

[0836] A motivational management system that calculates rewards based on healthy behaviors and awards points to a designated electronic trading account,

[0837] Information output means for sharing information with external organizations,

[0838] A means of linking rewards to electronic trading services based on user behavior data,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, characterized by analyzing the user's health information in real time, proposing preventive measures, and further providing the generated advice to the user in natural language.

[0842] (Claim 3)

[0843] The system according to claim 1, characterized in that it awards points to an electronic trading account as an incentive based on the user's behavior, enabling real-time rewards.

[0844] "Example 2 of combining an emotion engine"

[0845] (Claim 1)

[0846] A data acquisition means that receives information from a terminal that collects physiological information and emotional states,

[0847] A data storage means that stores received information and performs cleansing to maintain the accuracy and consistency of the information,

[0848] An analytical means that uses a generated AI model based on stored information to analyze health status and emotional tendencies, and provides personalized health advice.

[0849] A user interface means that notifies the user of the analysis results visually and audibly, and provides health recommendations in natural language,

[0850] An incentive management system that calculates rewards based on emotional state and grants additional points to a virtual account,

[0851] A means of outputting information for sharing information with external organizations,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, characterized in that it evaluates users' emotional data in real time and proposes stress management measures.

[0855] (Claim 3)

[0856] The system according to claim 1, characterized in that it includes means for awarding points for maintaining a positive emotional state for a certain period of time.

[0857] "Application example 2 when combining with an emotional engine"

[0858] (Claim 1)

[0859] Information acquisition means for receiving information from a device that collects health information,

[0860] Information storage means that stores received information and normalizes it to maintain information integrity,

[0861] An analytical means that provides personalized health recommendations by analyzing the stored information,

[0862] A user interface means that notifies the user of the analysis results and provides health-related information in natural language,

[0863] A reward management system that calculates rewards based on health and emotional status and grants rewards to designated accounts,

[0864] Information output means for sharing information with external organizations,

[0865] A store assistant tool that suggests products and services based on the customer's emotions and health information in physical stores,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system described in item 1, characterized by analyzing users' health information in real time and proposing preventive measures.

[0869] (Claim 3)

[0870] The system according to claim 1, characterized by including means for calculating rewards and awarding rewards to an electronic account. [Explanation of Symbols]

[0871] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data acquisition means that receives data from a device that collects health data, A data storage means that stores received data and performs cleansing to maintain data integrity, An analytical tool that provides personalized health advice by analyzing stored data, A user interface means that notifies the user of the analysis results and provides health-related information in natural language, An incentive management system that calculates rewards based on healthy behaviors and awards points to a designated account, A data output method for sharing data with external organizations, A system that includes this.

2. The system described in item 1, characterized in that it analyzes users' health data in real time and proposes preventive measures.

3. The system according to claim 1, characterized in that it includes means for calculating rewards and awarding points to an electronic account.

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